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2012/06/12 | 666 | 2,866 | <issue_start>username_0: I am doing my doctorate in global studies in Germany, and would like to apply for a post-doc position in North America, especially in Canada. The topic I work on has considerable policy relevance, and I see that some North American professors are working in those areas. I am a non-EU citizen. Is there any chance that I will be accepted?<issue_comment>username_1: Your institution almost certainly has a policy on this, and an office dedicated to administering it. You should consult them. If you really think this is serious, you should also consult an intellectual property attorney rather than relying on the babblings of some goofball from the Internet (e.g. me).
In the US, *employees* of a university are commonly required to sign an agreement that the university has an interest in any patent or other profitable invention that they develop in the course of their work. The agreement usually specifies that the royalties and other profits are to be split between the university and the inventor.
I have not heard of this being required of students, and if you had signed one, you would presumably know. However, if your advisor had a significant part in the project, he or she may have some rights in it as a matter of law (and the university in turn would get a share of your advisor's, per their agreement).
Note that it may be relevant where the funding for the project came from, and what if any financial support you received from the university.
Upvotes: 5 [selected_answer]<issue_comment>username_2: >
> Would I have all rights for selling this product or since my professor is essentially guiding me and assisting me in my research, does he/she own a part of it as well?
>
>
>
This is a very difficult and touchy question. You need to talk to your supervisor as early as possible. Many students drastically underestimate the supervisor's contribution. Starting your graduate studies with the idea is very different from developing the idea in conjunction with your supervisor.
Upvotes: 2 <issue_comment>username_3: Regardless of how the legal situation is, discuss it with you supervisor in a friendly way to figure out
* if he wants to put stones in your way, even for the things which you legally could sell. (Even if you may win a lawsuit, financing a lawsuit before you actually sell products could be a problem)
* if he is plainly supportive of the idea and helps you without own interest
* if he like to give you recommendation for a company/group working on these topics
* if he would like to create a company with you (in that case you would have continued access to the research)
* if some technology is already patented for some applications or if parts of the materials you used were obtained under an NDA.
* if there is a competitor (e.g. another student of his/people he knows in the field)
Upvotes: 0 |
2012/06/13 | 633 | 2,792 | <issue_start>username_0: Up to now, I've only applied to "public" academic positions (universities or public research centers). I have now an academic CV, that is roughly 7 pages long (2 pages of "classic" CV, and 5 pages of research experience + list of publications).
I would be interested in applying for research positions in industry. I know that if I wanted to apply for a regular position (for instance, as a software developer), only my classic CV is enough (and the long one might actually be counter-productive). However, the kind of position I'm interested in is very similar to a public research position, i.e. people there publish, applying for public funding, might even co-supervise PhD students.
So, what kind of CV should I send for such a position? Should I consider the application process as the standard public one, or rather as the standard industry one?
--
In case it's relevant, it's in Computer Science, and I'm thinking of positions such as those available at Microsoft Research, IBM Research, etc.<issue_comment>username_1: As a general rule of thumb, if the position you are applying for (1) has the term "researcher" or something similar in the position title and (2) requires a PhD (or research masters), definitely send the academic one. The fact they're looking for PhDs means they're interested in research experience, and your academic CV will highlight that much better than your industry one.
That being said, it is an industry position, so I would do my best to keep the resume two pages, and offer to send the full CV on request. Definitely include "Selected Publications" and "Selected Oral Presentations" sections, but keep it short.
---
One extra idea for consideration: I sent my (academic) resume to an industry position, and they sent it back asking for a short (one to three) sentence blurb describing each of my projects (at the time, my resume included work as a research assistant, graduate student, and post-doc). I ended up using that format for all the jobs I applied to, since most people have no idea what "Temporal Dynamics of the Cortico-limbic System" actually means. It's a good way to help them actually get a feel for what you accomplished and what your skills are.
Upvotes: 5 [selected_answer]<issue_comment>username_2: I didn't really change my CV for industrial research positions versus academia, but I did tailor my research statement a fair bit. As I recently explained to a student, industrial research labs are often more interested in your skills, and academic positions are more interested in your contributions to an area (*all these statements are "on average"*). That might indicate ways to modify your CV ? maybe have a blurb highlighting your interests/skills up front for the industry position ?
Upvotes: 3 |
2012/06/13 | 876 | 3,617 | <issue_start>username_0: This question is a follow-up on a comment at a [recent question](https://academia.stackexchange.com/questions/1836/when-does-one-go-for-a-double-doctorate), where <NAME> mentioned that an acquaintance of his has "3 professorships and 2 doctorates."
* How does a faculty member get appointed simultaneously by two or more universities? Are all except one honorary posts?
* Can any faculty member work with a second university with the consent of their present employer? What are the conflicts of interests that come into play usually?<issue_comment>username_1: Here in the Netherlands it is relatively common to appoint somebody as a professor for one day a week. For instance, one of the colleagues is 4 days a week at our university and 1 day a week at a different university. Another colleague is 4 days a week working for a company, and works at the university only on Mondays.
Upvotes: 3 <issue_comment>username_2: The situation I've seen most often is that a professor will hold a primary appointment at school A, where they have a lab and complete their research. Often for reasons related to collaboration, school B will then give the professor an appointment in a related if not identical department. If the professor also teaches a few courses at school C, they will likely be granted an adjunct position there as well.
This was my setup when I was in graduate school; I was a grad student in bioengineering at [U Pitt](http://www.pitt.edu/), and I had a secondary appointment at [CMU](http://www.cmu.edu) due to both lab collaborations and my being registered in a [certificate program](http://www.cnbc.cmu.edu/) there.
Upvotes: 5 [selected_answer]<issue_comment>username_3: Another common scenario happens when a professor leaves university X for university Y, but still has grants and PhD students at university X. In that situation, it's fairly common for university X to keep the faculty member on the books without pay, possibly with the word "adjunct" added to their title, so that the students and the grants that pay them don't have to move from X to Y. (And to simplify the paperwork if the professor changes their mind and moves back to X.)
Upvotes: 4 <issue_comment>username_4: In addition to the excellent answers given above, here are some reasons I've seen for professors to get an appointment at a different university (or to retain an appointment when they moved):
* They're a major part of a center at University A, and University A really doesn't want 'Co-Director of the A Center for Really Important Research' to be listed as a faculty member elsewhere.
* Weird university tradition. For example, my university automatically gives all outside readers on a dissertation committee an appointment at the school. It's utterly meaningless, but technically its a thing.
* Collaboration, grants, etc. are sometimes easier if everyone is technically faculty at the same school, and schools will sometimes bring in a professor from another nearby university to "shore up" a program, allow for easier mentorship if a professor keeps appearing on grad student committees, etc.
Upvotes: 3 <issue_comment>username_5: A common scenario I've observed is where University A offers a job to a professor at University B, a far less prestigious and well-connected institution, but the professor and his/her family wish to remain in the location of University B for personal reasons. If the professor is good enough, University A may agree to a half-time or similar arrangement, where the professor is only on-campus for one semester per year (usually 4 months).
Upvotes: 3 |
2012/06/13 | 869 | 3,431 | <issue_start>username_0: I am expecting to finish my masters degree in computer science by mid October and then I was planning to apply for a PhD program this year.
My question is, how much time will I have after being done with my masters to apply? I know different US universities have different deadlines but a general safe assumption would really be appreciated.
I still haven't decided which universities I would be applying to. I am also aware of the fact that eventually I'll have to look up at the websites for the exact deadlines but for now I just needed an approximate idea of a date by which I should have everything ready for submission including my GRE scores (which I am planning to retake).<issue_comment>username_1: It varies greatly.
* There are the fixed deadline places, for which there are deadlines starting from Nov 15 or thereabouts, all the way upto Jan 15.
* There are also places with rolling deadlines, where it's better to apply early.
In general, you should have your materials ready to go in the beginning/middle of November and that should take care of most deadlines.
Upvotes: 5 [selected_answer]<issue_comment>username_2: It depends on what program it is.
Most top ecology and biology programs have deadlines in early December.
Most top-20 physics and astronomy programs (excluding Columbia) have deadlines on December 15th. Columbia's deadlines are usually on the first few days of January. Mid-tier astronomy programs tend to have deadlines around January 15.
That said, there are weaker programs in Physics (like Montana State and Kansas) that have deadlines that go well into Spring.
======
As for most strong Earth/Atmospheric/Planetary science programs - they primarily have deadlines in late December and early January.
(E.g. for this year for EAPS programs - deadlines were Stanford in mid-December, Berkeley on Dec 19, Harvard/Princeton on Dec 31, Caltech on Jan 1, Yale on Jan 2, Columbia on Jan 4, MIT on Jan 5, Chicago on Jan 9, UWash+Brown on Jan 15, and Cornell on Feb. 1st).
As for my case, I was utterly unprepared until mid-December.
I didn't take my General GREs until mid-December (though I took the Biology GRE in mid-October and the Physics GRE in mid-November - those two dates are the two subject GRE test dates in autumn), and only asked for LORs in mid-December (I managed to get them all in by January 2, but a couple of professors said that they wouldn't be able to write me LORs because they would be traveling during late December - I was fortunate to have many options to choose from though). Then I didn't start on my personal statement until the last two weeks of December (and I went through like 7-8 proofreaders). So the result was that I missed the Harvard/Princeton deadline and barely made the Caltech deadline with a crappy essay. Everything past January 2 was fine though.
Upvotes: 2 <issue_comment>username_3: The situation also depends a bit on if you're looking for financial support in the form of fellowships. If so, then you need to be a bit ahead of the schedule for applying to the graduate departments directly—some fellowship programs, including the NSF, typically have deadlines in November.
However, in general, if the school does not do "rolling admissions," then the deadline will be somewhere in December or early January, with decisions given in February or early March, and a decision expected usually by May 1.
Upvotes: 3 |
2012/06/14 | 850 | 3,316 | <issue_start>username_0: Do universities sponsor a student's conference travel and publication costs if the research on the paper has been done elsewhere? For example, a student may have published a paper during a master's at university 1 and may currently be pursuing PhD at university 2. Similarly will the second university bear the cost of the journal publication? Are there any caveats involved (eg., indication of university's name in the paper, related department, etc)?
On the other hand, will the first university sponsor one of its alumni for the travel? (This looks unlikely to me.)<issue_comment>username_1: It varies greatly.
* There are the fixed deadline places, for which there are deadlines starting from Nov 15 or thereabouts, all the way upto Jan 15.
* There are also places with rolling deadlines, where it's better to apply early.
In general, you should have your materials ready to go in the beginning/middle of November and that should take care of most deadlines.
Upvotes: 5 [selected_answer]<issue_comment>username_2: It depends on what program it is.
Most top ecology and biology programs have deadlines in early December.
Most top-20 physics and astronomy programs (excluding Columbia) have deadlines on December 15th. Columbia's deadlines are usually on the first few days of January. Mid-tier astronomy programs tend to have deadlines around January 15.
That said, there are weaker programs in Physics (like Montana State and Kansas) that have deadlines that go well into Spring.
======
As for most strong Earth/Atmospheric/Planetary science programs - they primarily have deadlines in late December and early January.
(E.g. for this year for EAPS programs - deadlines were Stanford in mid-December, Berkeley on Dec 19, Harvard/Princeton on Dec 31, Caltech on Jan 1, Yale on Jan 2, Columbia on Jan 4, MIT on Jan 5, Chicago on Jan 9, UWash+Brown on Jan 15, and Cornell on Feb. 1st).
As for my case, I was utterly unprepared until mid-December.
I didn't take my General GREs until mid-December (though I took the Biology GRE in mid-October and the Physics GRE in mid-November - those two dates are the two subject GRE test dates in autumn), and only asked for LORs in mid-December (I managed to get them all in by January 2, but a couple of professors said that they wouldn't be able to write me LORs because they would be traveling during late December - I was fortunate to have many options to choose from though). Then I didn't start on my personal statement until the last two weeks of December (and I went through like 7-8 proofreaders). So the result was that I missed the Harvard/Princeton deadline and barely made the Caltech deadline with a crappy essay. Everything past January 2 was fine though.
Upvotes: 2 <issue_comment>username_3: The situation also depends a bit on if you're looking for financial support in the form of fellowships. If so, then you need to be a bit ahead of the schedule for applying to the graduate departments directly—some fellowship programs, including the NSF, typically have deadlines in November.
However, in general, if the school does not do "rolling admissions," then the deadline will be somewhere in December or early January, with decisions given in February or early March, and a decision expected usually by May 1.
Upvotes: 3 |
2012/06/15 | 1,948 | 8,192 | <issue_start>username_0: I find that when I'm working on research in the office, at least with the door open, I often get interrupted and am generally less productive. On the other hand, I often have students ask to meet with me outside of office hours on days when I don't plan to be in my office, and they're unhappy when I mention that I won't be available that day. I suspect this hurts my student evaluation scores (which are the primary basis for the evaluation of our teaching in our tenure process).
Additionally, as junior faculty, I'm aware that getting tenure is partly about "fitting in" with your department. In mine, many (most?) folks are in their offices for something closer to 9-5 five days a week, perhaps leaving early on Friday. When I am in the office, I make a point to stop in and say hi to colleagues, but I'm sure that I'm less visible around the department than many others.
So rather than a specific answer, I guess I'm looking for guidelines in how you approach this type of decision.<issue_comment>username_1: >
> I find that when I'm working on research in the office, at least with the door open, I often get interrupted and am generally less productive.
>
>
>
You need to redefine productive. Having your door open will increase the time it takes to get a manuscript out, but that is not the only thing that defines productivity. Time spent improving your teaching evals (e.g., by meeting with students) and being visible to your colleagues IS time well spent.
Upvotes: 3 <issue_comment>username_2: I think the basic guideline is the perception that you give. The actual time you spend in and out of your office is less important than whether your colleagues feel like you are an active member of the department and your students feel like you are accessible.
* For your colleagues, I would be conscious of the times and events that seem to build cohesion in your department and make sure you are present for those. This can be informal things like having lunch together. Or more formal things like departmental seminars.
* For your students, I would set consistent and clearly defined office hours and always be available during those times (even if you don't have a student scheduled). You will need to make sure that these office hours are distributed such that most of your students can utilize some of them and be prepared to be somewhat flexible for students that have consistent conflicts.
Upvotes: 2 <issue_comment>username_3: You may be able to mollify your students a bit by paying attention to your wording. Saying "I can't meet with you on Tuesday because that's my day for research" makes the student feel like a low priority. Saying "I'm afraid I can't meet on Tuesday, but how about Wednesday?" helps convey that the student is important to you and you would like to meet with them when you can. I think accessibility is as much about perception as reality.
Upvotes: 3 <issue_comment>username_4: There are a few issues here: one is dealing with students, and one is general "visibility".
For the first, I can understand student unhappiness if you give them office hours but wish to meet outside the allotted time. They have constraints as well and probably don't have a lot of free time to meet with you :). Maybe, since you're concerned about distractons, you can pack all "distracting" activities into a single day and do your office hours on that day ? Alternately, do you prefer to work at home or at a cafe ? If the latter, you could always do your office hours and then disappear.
For the second, I think the issue is a little overrated. As dQdM points out, the issue is whether you're perceived as being active and a "good citizen". The former can be achieved by responding to email promptly and the latter by your usual service responsibilities.
A bit of perspective: I came to academia from a research lab. In a lab, people are usually around all the time, and I was shocked when I came to academia to see how few faculty were around in the office at any given time. So I don't think your absence might even be noticed that much :)
Upvotes: 3 <issue_comment>username_5: I think it's important to set boundaries with students, with colleagues, and with yourself about when and where you're available for meetings. It's also important to find environments that most effectively support different types of work, and give yourself permission to use them.
Like you, when I'm in my office, I *expect* to be interrupted; so when I'm working at my desk, I can only productively work on tasks that survive interruption. Put bluntly, the office is where I have meetings; if I need to think, I find a whiteboard in an empty conference room; if I need to write, I go to a coffee shop. (username_4 is correct; I am in a coffee shop *right now*.) As Daniel says, all three places allow for productive work, but of very different types. Even in the computer science building, for small meetings where I don't want to be interrupted, I prefer to go to the *other* person's office. And because my undergraduate office hours are occasionally *very* popular (especially right before exams), I don't hold them in my actual office, but in a larger room down the hall with couches and whiteboards.
You express two points of concern, which I'll exaggerate:
* **My students won't like me if I'm not available on their schedule.** I agree with username_2 and Nate. Spread out your office hours to fit as many students' schedules as possible, be in your office (or "office") for every minute of office hours even if nobody shows up, and be willing to offer *occasional* off-schedule meetings. It might help to announce *in your syllabus* times that you're willing to schedule sporadic meetings. ("I'm also available for occasional meetings Tuesday or Thursday afternoons; send me email to set up an appointment!") Consider moving (not adding) your office hours if student demand doesn't match your announced schedule. But then stick to your guns. Yes, some students will be unhappy, but that's inevitable; don't take it personally. Your availability outside regular office hours will *not* be the most significant bit in your student evaluations.
* **My colleagues won't like me if they don't see me in my office.** I agree with username_2 and username_4 here. Yes, it's important to be visible and active citizen of your department; that's not the same thing as being constantly on call. The amount of time you spend at your desk will *not* be the most significant bit in your tenure evaluation. The danger is not that nobody sees you in your office, but that nobody knows what you're doing. Give regular talks to your colleagues *and their PhD students* showing off the results of your out-of-office effort. Go to faculty meetings, and *occasionally* offer an opinion. (Careful, that gun is loaded.) Attend seminars, *especially* for faculty candidates, *and ask questions*. If there is a regular departmental social event ("Tea" in many math departments), be there. And so on.
Finally, I *strongly* encourage you to raise these concerns with your department chair or your senior faculty mentors. (You *do* have a senior faculty mentor, don't you? If not, find one!) They can help you navigate your department culture far better than Some Guy On The Intertubes.
Upvotes: 5 [selected_answer]<issue_comment>username_6: When you announce your office hours to the class at the beginning of the term display a chart that includes all reasonable hours but no marks. Point to a particular time and ask, "If I choose this hour how many people will not be able to come to this office hour?" If lots of students raise their hands, say "That's not one of my office hours." If a lot of students indicate they can go mark it as an office hour. Repeat this process, perhaps rearranging previously chosen hours until every student can go to at least one office hour. If you are also available by appointment at other times It should solve a lot of problems. Also at the beginning of the the first day of class you will create a favorable impression. Before you do this you should have a very good idea of time when you do not want office hours.
Upvotes: 0 |
2012/06/15 | 762 | 3,251 | <issue_start>username_0: I was reviewing a journal paper recently that is closely related to work a colleague and I are doing. We are currently in the process of writing up our paper, and I plan to cite the paper I reviewed in ours. The paper I reviewed has been accepted after two rounds, but has not appeared in print yet. The author does not have a preprint on their website (or other preprint services). We don't build on the reviewed paper I want to cite directly, but our work is closely related. In the future we will probably start building on the reviewed paper, so I want my co-author to be aware of it as soon as possible.
Can I send the last version of the reviewed paper (that I saw) to my colleague? Can I cite the paper as "(in press)" if I submit our paper before the reviewed paper finally appears in print?<issue_comment>username_1: No, you cannot. Until the paper is publicly available you cannot show it to anyone nor even acknowledge its existence. The only slight exception I would make is that if a colleague was going to pursue similar/identical research, I would tell them they might want to contact the author. I would assume that this would result in my identity as a reviewer being revealed.
Your options are:
1. Submit your manuscript as is, but add the citation as soon as it become public.
2. Reveal your identity as a reviewer and ask the author for a preprint.
Option 1 is reasonable since you believe the existence of the paper is not critical to your paper. Option 2 is okay, but remember the reviewers of you manuscript will not have easy access to the cited material.
Upvotes: 6 [selected_answer]<issue_comment>username_2: You can't cite the paper till it's in print or somewhere publicly available. As for sharing with your colleague, I think there are degrees. If the colleague is sitting in your office and you show them a hard copy, that's less of a problem than emailing a copy to a colleague (remote or local) which I would not do.
Upvotes: 3 <issue_comment>username_3: Since the paper will appear soon (it's been accepted!) and it is not essential for the *present* article, why not follow username_1s' advice, namely submit your article as is, and add the citation in the proofs. Discussing the future with your collaborator can wait a couple of weeks.
Since you sound in a big hurry, I will elaborate in another direction now.
I have always been very adamant about keeping reviewer information confidential. Other people... not: in my discipline (theoretical physics) people have told me that they reviewed my paper or (worse) the paper of somebody else. In short, it is not unheard of to reveal such information, especially when the outcome is acceptance. Is it good for the trade? That's another topic for discussion.
So if you are in a HUGE hurry to discuss with your colleague you may follow username_2 above: if your colleague is not in a different city and you trust each other, you may decide to share the confidential information *confidentially*, which is to say, just show them the article which you in fact judged positively, discuss it, destroy the evidence. (I've seen that too, and without the destruction-of-the-evidence part.)
Though I personally would just wait...
Upvotes: 1 |
2012/06/15 | 570 | 2,344 | <issue_start>username_0: At my institution, I am required to publish four papers in addition to my PhD thesis. Of course my planned papers are related to my dissertation project, but I'm not sure if it could be considered academic dishonesty / plagiarism to use these works to form part of my dissertation. I'm not thinking about copying them word for word, but I was hoping that I can include summarized versions of those works as chapters in my dissertation. Is this a common practice or would this be considered self-plagiarism? Should my dissertation work be completely separate from those other publications?<issue_comment>username_1: Depends probably on the rules at your institution. At many places actually this is the usual custom. Ask your adviser.
If you write out the correct references so that everybody who reads knows that this part can be found in that paper, then it is never plagiarism. See also the ["Sandwich thesis"](https://academia.stackexchange.com/questions/149/what-is-a-sandwich-thesis) question mentioned in the comment by [<NAME>](https://academia.stackexchange.com/users/3/andy-w).
Upvotes: 4 <issue_comment>username_2: Supplementing the answer from username_1:
The rules likely vary by field also. In chemistry, converting previously published papers into a [Sandwich Thesis](https://academia.stackexchange.com/questions/149/what-is-a-sandwich-thesis) is generally accepted and commonplace. My thesis contained a chapter that was prefaced "Portions of this chapter were previously published as (citation), and have been reproduced with permission. Copyright is held by (publisher of the journal)." This was followed by further copyright information and the statement required by the publisher. My institution also required that I submit paperwork in support of my use of published and copyrighted material.
In addition to consulting your adviser and your institution, you should consult the journals in which you publish. Many have policies in place for this. For example, from the American Chemical Society's [copyright faq](http://pubs.acs.org/page/copyright/journals/faqs.html):
>
> The current ACS Journal Publishing Agreement covers several permitted uses by authors... Permitted uses of all versions include:
>
>
> * Use in theses and collections of your own work
>
>
>
Upvotes: 6 [selected_answer] |
2012/06/16 | 2,126 | 8,051 | <issue_start>username_0: Is the impact factor really useful for judging the quality of a journal article?<issue_comment>username_1: "Quality" is very subjective. Impact factor is a good measure of how often articles in a journal are cited, but this doesn't necessarily directly relate to "quality".
Many journals with a narrow focus will have relatively low impact factors, but are still considered very "high quality" (i.e. prestigious to publish in) journals.
As an example, in the geosciences, the Journal of Structural Geology is a much more prestigious (and harder to publish in) journal than, say, Tectonophysics (just to pick another Elseviver journal), but it has a lower impact factor.
Upvotes: 3 <issue_comment>username_2: **No.**
-------
---
To answer Paul's comment:
The title of the post asks about journals. For a well-worn list of criticisms of Impact Factor as an indicator of journal quality, see [the Wikipedia article](http://en.wikipedia.org/wiki/Impact_factor#Criticisms) and the sources it cites. In many disciplines, important papers receive most of their citations [well outside the IF's two-year window](http://www.mathunion.org/fileadmin/IMU/Report/CitationStatistics.pdf). Raw citation data can be manipulated by editorial policies, some more [nefarious](http://en.wikipedia.org/wiki/Coercive_citation) than [others](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1475651/?tool=pmcentrez), or even by [individual papers](http://classic.the-scientist.com/blog/display/57500/). Thomson-Reuters' calculations of Impact Factor are [not reproducible](http://jcb.rupress.org/content/179/6/1091.full), even using their own citation data. Et cetera ad nauseam. But most importantly: **Having lots of citations is not the same thing as quality.**
The body of the post asks about journal articles. Impact Factor is (roughly) the average number of citations to all papers published by a journal in a given time window. Even if it were a reliable measure of *average* quality (which it isn't), it would say nothing at all about the quality of any *individual* paper.
Upvotes: 6 [selected_answer]<issue_comment>username_3: If the other two answers haven't made this obvious. The answer is **no**, but it can be taken even further than what the above answers suggests. [We can study this!](http://arxiv.org/abs/1205.4328)
The above image is the [coefficient of determination](http://en.wikipedia.org/wiki/Coefficient_of_determination) between the impact factor of journals and the 2-year citation rate of their papers from 1902 to 2009, for all natural and medical sciences journals. Basically this means that even if all you care about is the number of citations you get then even then the impact factors of journals is starting to matter less and less.
Of course, what matters is how influential your ideas are and not how much they are cited, [but we don't really a have a good metric for that.](https://academia.stackexchange.com/q/1020/66)
Upvotes: 4 <issue_comment>username_4: The editors of *Epidemiology* (a very good journal), recently had [an editorial](http://journals.lww.com/epidem/Fulltext/2012/07000/We_Are_Number_One_But_Nobody_Cares_That_s_Good.1.aspx) about this:
>
> Most major epidemiology journals, including ours, have seen a steady
> rise in their impact factors during recent years. At the same time,
> the relative rank of these journals changes from year to year. Such
> changes are unlikely to represent true annual changes in these
> journals' relative quality. We think the various epidemiology journals
> are indeed different, and they deserve to be evaluated and compared.
> But we're happier when such assessments are based on matters of
> substance, such as editorial policies, quality of reviews, quality of
> editing, efficiency in the processing of manuscripts, and the (real)
> impact of the journal on the field.
>
>
>
Said editorial also has links to several others in that journal critiquing the notion of the Impact Factor, and its scientific merit (or lack thereof).
Upvotes: 3 <issue_comment>username_5: Clearly, people here don't seem to like the idea that impact factor would be used to evaluate journal quality. I'm not a big fan of impact factor either. It can be gamed (and the degree to which it is gamed will increase as greater importance is placed on it). It also becomes particularly problematic when you compare across disciplines with different citation practices and citation half-lives. Furthermore, there's also the risk that people start using impact factor to evaluate article quality, which is a lot more questionable.
That said, within a discipline, I generally find that better journals have higher impact factors. It's not perfect. But there is a strong correlation. There's also research that shows very high correlations between various indices of journal quality including peer ratings, impact factor, and various other citation based indicators.
So in short, **within a field, impact factor is one of many variables that typically correlates highly with journal quality**. If you know nothing about the quality of a journal, you'll know more about it by looking at its impact factor. However,
That shouldn't stop you from looking at other and most likely better indicators of journal quality (e.g., what is your own evaluation of the content published in the journal in recent years).
It is also really important to ask yourself why you want to evaluate journal quality. Is it to select a journal? to reward, promote, or hire academics? Using impact factor in these cases can be problematic for a wide variety of reasons, but these are separate issues.
Here's one article that I found providing empirical evidence by Saha et al (2003). To quote the abstract:
>
> Objectives: Impact factor, an index based on the frequency with which
> a journal's articles are cited in scientific publications, is a
> putative marker of journal quality. However, empiric studies on impact
> factor's validity as an indicator of quality are lacking. The authors
> assessed the validity of impact factor as a measure of quality for
> general medical journals by testing its association with journal
> quality as rated by clinical practitioners and researchers.
>
>
> Methods: We surveyed physicians specializing in internal medicine in
> the United States, randomly sampled from the American Medical
> Association's Physician Masterfile (practitioner group, n = 113) and
> from a list of graduates from a national postdoctoral training program
> in clinical and health services research (research group, n = 151).
> Respondents rated the quality of nine general medical journals, and we
> assessed the correlation between these ratings and the journals'
> impact factors.
>
>
> Results: The correlation between impact factor and physicians' ratings
> of journal quality was strong (r2 = 0.82, P = 0.001). The correlation
> was higher for the research group (r2 = 0.83, P = 0.001) than for the
> practitioner group (r2 = 0.62, P = 0.01).
>
>
> Conclusions: Impact factor may be a reasonable indicator of quality
> for general medical journals.
>
>
>
### References
* <NAME>., <NAME>. & <NAME>. (2003). Impact factor: a valid measure of journal quality?. Journal of the Medical Library Association, 91, 42. [PDF](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC141186/)
Upvotes: 4 <issue_comment>username_6: I have seen that in term of citation of an article you will notice that in most of the cases that an article has been cited many times by author himself in his other research articles or by his students and sometime by his/her colleagues without attaching any real importance of citation. The geographical location of citation also does matter, suppose for an example, if author from certain country receives 10 citation from 10 different countries then these 10 citations would be more important than 10 citations from single country.
Upvotes: 1 |
2012/06/17 | 953 | 4,104 | <issue_start>username_0: I'm currently doing a master's degree near my home state and have been offered to continue at the same grad school with a PhD. The funding side of things has yet to be confirmed, but is almost secure. I also have another offer from a grad school in Switzerland for which the funding is definitely secured.
1. Regarding the position in Switzerland, (i) living abroad for a few years is an advantage and (ii) the research topic is more closely aligned to what I ultimately would like to focus.
2. My current place has the advantage of (i) continuity and knowing the research group environment and (ii) research collaborations and publications that would probably exceed - in terms of both quantity and quality - that of the position in Switzerland.
What other considerations should I factor in to my decision and any advice as to which option I should choose?<issue_comment>username_1: You should definitely take into account quality of life in either position. Also, you should keep in mind that in the academic world, it is looked upon favorably to move around for your training.
Upvotes: 2 <issue_comment>username_2: One big advantage of changing schools is that you meet new people. Most people have a few things they're really good at. By meeting new people, you get to learn the new things that *they* are really good at. More generally, you get to experience the culture of a different place and group of people (both academically and socially). This helps to give you a more developed sense of what is normal (reasonable to expect), and likely will expose you to new insights. All else being (close to) equal, I suggest that you move.
Upvotes: 3 <issue_comment>username_3: In addition to Dan's comments recommending you move, I would add that if your goal is to obtain an academic job, it will help you to have more people familiar with your work. Aside from a few schools and fields, such as MIT and engineering, it almost always makes sense to move, particularly if the quality of the program is substantively similar.
Moving schools also improves the signal sent by your education. Having more good schools on your CV sends the signal that multiple independent parties deemed you worthy of acceptance. If you stay on at the same school, it may not be clear to someone who first reads your CV whether you were accepted into the PhD to start or whether it involved an independent admissions process.
Upvotes: 2 <issue_comment>username_4: Depending on the country you're from, moving to Switzerland for a PhD might be an immediate improvement in your living standards, and that is certainly a good reason. It might also bring you on the spot to take advantage of the stronger hiring prospects in the industry, especially for engineers, mathematicians, biologists. But, from a strictly academic point of view, there are a few other things to consider.
It's true that there are world-class universities in Switzerland, some regularly appear at the top of international rankings. But there are also several mid-tier, "me too" institutions that surf on the popularity of the big ones.
If you ultimately plan for an academic career, **the strength and international visibility of the group** is likely to be more important than changing school/country. It's true that it's tacitly almost a must to have some sort of international stay in your CV but there are other less risky occasions for that than a PhD (e.g. a postdoc). A PhD is a big investment in efforts, time and money so go where the good science is.
Upvotes: 2 <issue_comment>username_5: Go. Even if it is going to be harder, smaller pay, etc.
Science is about diversity. You need new academic experiences. It will greatly improve both you and your work. Even if it doesn't really work.
Further, every time I see a CV that only lists one institution, bs+ms+phd, I think "meh". And I know that doesn't really help in selection processes for postdoc/professors.
I'm saying all that based on my personal experience. It wasn't easy, I had all types of issues, didn't publish much, but it was worth it.
Upvotes: 2 |
2012/06/18 | 513 | 2,273 | <issue_start>username_0: I am writing my masters thesis now. I saw some theses with the acknowledgement chapter after the abstract, in other cases it's the other way around.
I think the acknowledgement chapter is not related to the scientific work in the thesis, and thus should not be put between the abstract and the introduction. An abstract could be nice in the beginning if we're looking at a paper for example, but for a thesis (usually >80 pages) the reader would have to turn the pages anyway.<issue_comment>username_1: You should check the guidelines from your university. I'd assume that they would have requirements for the order. If not, I always prefer the acknowledgements should come first, that way the non-scientific stuff is out of the way.
Upvotes: 5 [selected_answer]<issue_comment>username_2: Actually, under normal circumstances, I would expect the abstract to be as close to the front of a thesis as possible. The reason for this is to make the job of cataloging and searching easier. Abstracts of theses are indexed by services such as [ProQuest](http://proquest.umi.com), and having to wade through additional pages of material makes their work harder.
That said, username_1 is correct in that you should follow whatever regulations your university has. But in general, in the absence of such guidelines, I would put the abstract *before* the acknowledgments—readers want to know as soon as possible if they should bother to read the rest of your thesis. Burying it after the front matter makes it less likely for them to invest the time.
Upvotes: 3 <issue_comment>username_3: Just adding my two cents: around me, people commonly print out and bind their thesis in such a way that **the one-page abstract is on the back cover**. I think it makes a lot of sense, and allows one to get an idea of what the thesis is about without flipping pages (literally).
Upvotes: 2 <issue_comment>username_4: I would prefer to put acknowledgement before the Abstract so that I will be done with the preliminaries first, so that when one reads Abstract he will have started looking at the actual content. To me acknowledgement coming between the Abstract and the introduction is a disruption. But the guidelines of the University are to be followed.
Upvotes: 1 |
2012/06/18 | 617 | 2,499 | <issue_start>username_0: I started to get emails like this:
>
> From: <NAME> ~
>
>
> Title of paper.
>
>
> I need the article to study.
>
>
> thank you!
>
>
> <NAME>
>
>
>
Title of paper identifying one of my papers.
Is this the beginning of academic spam or phishing? Do you get such stuff regularly?
---
**The first reaction** I had was to answer with an ironic version of how I'd like such an email to look and how I'd have answered it.
**On a second thought** I decided that the email was so rude that I won't answer it.
After a few hours I got the very same email a second time. I noticed that the sender name is not the one of the names in the "signature". Also, the sender is rather unknown to the more relevant part of the internet (including pubmed).<issue_comment>username_1: My guess is it's someone setting up a bot to try and get research articles for free. Once your paper is published, the copyright is owned by the journal. Unless you paid for open access, you are breaking the copyright by sharing the article. In addition, you are probably also breaking the terms of the subscription to the journal your university has.
**In other words, ignore the email!**
Upvotes: -1 <issue_comment>username_2: I don't even think this is for harvesting papers. My guess: it is to validate email addresses so that actual spam campaigns can achieve a better return on investment.
Upvotes: 5 [selected_answer]<issue_comment>username_3: The worst case of responding is a little more spam, so I would respond. I often find myself in a culture slash between what I consider rude and students consider acceptable email behavior. It wouldn't surprise me if a number of students were prepping for an exam and all wanted your paper.
As for the paper phishing bot, it seems like it would be more efficient to use student library access to download papers (automatically) than to collect them via email.
Upvotes: 3 <issue_comment>username_4: The beginning and the end of your account contradict each other, so I do not understand if you got this message many times, from several different addresses, or once or twice, from the same person.
In the first case, it definitely seems a spam-like behavior.
In the second, it could be a honest message from a grad student with poor English knowledge. Check if the names are compatible with this explanation; if so,
I would definitely answer. It could even earn you a citation. :)
Upvotes: 2 |
2012/06/18 | 1,925 | 7,807 | <issue_start>username_0: This question came up from [a discussion](http://meta.math.stackexchange.com/a/4466/468) on [meta.MSE](http://meta.math.stackexchange.com/).
My question is:
>
> Do we need to search [MSE](https://math.stackexchange.com/) (or blogs, math forums, ...) to make sure someone hasn't already proven a result when writing a paper?
>
>
> What if we are already aware of a them (so no need for searching)?
>
>
> Is not citing such a post in these two cases considered plagiarism?
>
>
>
As I understand, the common practice is to check standard reviewed reputable publication venues (journals, conferences, maybe arXiv) and also with experts in the area to make sure a result is not already published nor a well-known folklore result. No one is going to search all over the internet and check every post that Google returns and citing other resources is very uncommon. I think checking [MatheOverflow](http://mathoverflow.com) can be considered similar to the later (checking with experts) (also see this discussion on [MO](https://meta.mathoverflow.net/discussion/951/copyrights-at-mo) but that doesn't seem to apply to a site like MSE. I am not going to cite a discussion with some random person on the street (not a professional mathematician) who claimed to have a solution or an idea for a solution for a problem (which is not passed [peer-review](http://en.wikipedia.org/wiki/Peer_review) process and I might not want even want to spend time understanding or checking the correctness of the solution).
>
> What are the accepted practice for checking originality of a result?
>
>
> What is expected from authors regarding this before making a paper submission?
>
>
>
---
Some clarification since there seems to be a misinterpretation of the question about being academic honesty. The question is **not** about posts that
* you are aware of,
* contain a complete rigorousness solution (not just ideas), and
* you are confident the solution is correct.<issue_comment>username_1: In my experience I think the accepted practice is searching the peer-reviewed literature in your field. I'm not saying results published on websites/MSE, etc. aren't valid, they just aren't part of the expected search. Anyways, I don't think peer-reviewed journals would react well to web citations.
Upvotes: 1 <issue_comment>username_2: This is a question of academic honesty and due diligence.
If you did not arrive at the result yourself, but got it from someone else (either on SE or a homeless man on the street) then it is your responsibility to not claim credit for the result. Of course, in the case of the homeless man you can get away with claiming credit, and in most cases for SE, too. However, it is simply not honest.
If you arrived at the result by yourself, then as a research you should provide due diligence and check if the result is already known. This usually consists of checking the standard sources (i.e. published papers, books) and the communities which you are a part of. "Folklore" in mathematics is vast, and MO, math.SE, and cstheory are all becoming part of it. If you are aware that others arrived at a result before you then you should mention it in your paper (either with a full citation, if applicable, or with an acknowledgement).
However, just like you are not expected to search the back-log of every journal ever published, you don't have to scour the whole internet, either. If you want precedent of this: consider all the results that were published independently in the west and the soviet union during the Cold War. It would have been unreasonable of the scholars on both sides to be fully aware of the work of the others.
Upvotes: 4 <issue_comment>username_3: As best I understand it, the clarified question is this: if you are writing a paper and find a posting on the internet that contains ideas on your problem (which may or may not be correct, may be difficult to understand, and in any case do not seem to constitute a complete solution), then should you cite it? Let's assume you are making no use of the ideas, since if you are then you obviously need to cite the posting (regardless of whether you developed the ideas independently).
In general, you must cite it anyway. Of course, there are exceptions. If it's obviously crackpot work, then you are free to dismiss it as worthless. (<NAME> didn't need to cite thousands of crackpot "proofs" of Fermat's Last Theorem.) If it's really only tangentially related to the problem you are working on, then it may not be relevant enough to cite. However, it absolutely *does not matter at all* whether the work is peer reviewed or formally published, who wrote it or what their credentials are, whether it is complete, how easy it is to find, or whether it is difficult to understand.
You don't have to endorse it, and citing a paper does not in any way indicate that you feel it is correct. If you rely on the paper, then that's an endorsement, but mentioning it is not. For example, you could write "Several authors have studied this problem, including..." and give citations to them. Then readers can decide for themselves what to make of these contributions. They will understand from the form of your citation that you feel these works are closely enough related to be worth citing, but not important enough to your paper to discuss in detail. You can also say something more skeptical if warranted.
One reason you don't see these sorts of citations very often is that this situation doesn't often arise. (I've never seen a post on mathoverflow or math.stackexchange that I felt I should cite in one of my papers.) And even when it does arise, the citation may be as a personal communication rather than giving a URL. (It's much better to give a more detailed citation, so other people can find and learn from or evaluate the posting, but I guess an uninformative citation is better than none at all.)
As for due diligence in searching for prior work, there's no simple rule. You should search everywhere you feel there might plausibly be something to find, and you should consult with experts on anything you feel unsure of. It's certainly impossible to search the entire academic literature, let alone the entire internet, so you'll be forced to make compromises compared with an ideal world. For most purposes, non-academic internet sites will not be relevant enough to be worth searching carefully, but I guess it depends on the situation.
Upvotes: 5 [selected_answer]<issue_comment>username_4: I think due diligence in searching the literature includes:
* Talking to at least one expert in the field
* Looking through the bibliographies of any major papers closely related to your paper to see if any of the titles look relevant.
* Searching on google scholar or something similar for papers which cite any papers closely related to your paper.
* Searching on google for some of the key terms in your paper.
The last of these would pick up math.SE, but also often picks up lecture notes, slides, wikis, and other things which would not come up through more traditional academic sources. If you find something clearly relevant then you should cite it. Furthermore, you should do these things before getting too far into a project.
That said, no matter how much due diligence you do, you're going to miss stuff sometimes. 5 years after [my first paper](https://doi.org/10.1007/s000170050074) was published, it was pointed out to me that Osterle gave the same argument in <NAME> (1987/8:165–186). More recently, one of the 3 main results in [this paper](https://arxiv.org/abs/1007.2240) follows from a 15 year old result of Popa. Searching what's known is incredibly hard even if you try your best. But that's no reason not to try your best.
Upvotes: 3 |
2012/06/19 | 988 | 4,357 | <issue_start>username_0: I have a bad habit of trying to objectively measure myself and my understanding. One of the ways I do this is by saying to myself "I only understand X after I have read the entire chapter in this book on X" or "I can start doing a problem on X after I have read all about X." I do this because it generally guarantees that I haven't missed something important; that I'm not stuck on something that's obvious to everybody else. It's also something external that I can point to to justify my ignorance. Like, if I didn't know something, I could say "Well if that's important, then why didn't this author mention it in this entire chapter?"
A lot of my peers don't do this. I think most of them pick up just enough from lecture to do the assignments. I'm really starting to wish I was like that because this way of learning is seriously failing. It's starting to take a tremendous amount of time and I'm under time constraints. But it bothers the hell out of me if I can't first get all the facts down.
And actually I did this last semester with one class because it was simply unfeasible to follow along with the book. When I look back I feel somewhat bad about it because the subject truly interested me.
* So how should I balance **learning** and **solving assignments** during coursework? On one hand, I spend a lot of time with the books as there's a lot of stuff to learn and it helps to know all the facts and the motivation behind the ideas, as they can be enlightening. On the other hand, it leaves no time and my performance in school could be better if I focused more time on doing the assignments.<issue_comment>username_1: I'm not sure whether this is on topic, but I'll answer anyway.
It looks like you have time management problems. Sure, it is interesting to learn as much as possible about a topic and study it deeply, but ultimately, you have assignments and exams to complete. To a large degree you should be optimizing your time usage to do as well as possible in these.
One way to determine whether you have learned enough is to attempt a lot of exercises. See how far you get with them. If you cannot do them, then this helps identify holes in your knowledge. Read about that particular topic. If there are things you don't understand while learning about the topic, work backwards and try to fill in those gaps.
Upvotes: 2 <issue_comment>username_2: This is what I found worked for me throughout my undergrad career and continues to work in my graduate career:
1. Read the chapter focusing on the main ideas (not the details) before lecture.
2. Take great notes in lecture.
3. After lecture is done on a chapter, go back and re-read in more detail and make an outline combining the information in the book with what was presented in lecture.
4. Do all the assigned homework problems, going back to your outline as necessary.
5. If you have time, do the rest of the problems.
Upvotes: 1 <issue_comment>username_3: You may be interested to know that this problem doesn't go away when you finish classes. It's easy to struggle with the same question when you're doing research. Simply put, **you don't have time to learn everything**. You need to estimate how important a topic will be to your future studies (and future research).
Similar to what you're describing, I feel a sense of security when I can reproduce the details of all the relevant proofs. However, taking this approach to everything I learn is simply infeasible. Perhaps you can take comfort in telling yourself that for the stuff that is most important, you'll reread it later and learn it in more detail. The difficulty is that right now you most likely can't really tell which material will be most important (to you).
As I continue to listen to talks and read more papers (or take more classes, at an earlier stage of my career), I watch for which ideas keep coming up. When I hear about a topic repeatedly, I often become convinced that it's really worth learning in depth. In the time between first seeing a topic and finally sitting down to really understand it, I'm also likely to learn about many connections to other areas, which give me more motivation. In addition, I often grow in intellectual maturity, which makes it much easier to grasp ideas that were quite challenging the first time around.
Upvotes: 4 [selected_answer] |
2012/06/19 | 1,144 | 4,851 | <issue_start>username_0: Is there any reason to get paper reprints of your articles? Some journals charge for reprints others give them for free. Should I save the trees and not ask for the free ones? Are reprints so important that I should be paying for them?
I guess I should add I have only seen reprints offered in bundles of 100 (maybe 50) and getting 10 does not seem to be possible.<issue_comment>username_1: I did it initially so that I would have a nice paper copy of my paper. In principle I could hand these out to my colleagues and other people who were interested in my work.
This is rather out-dated these days. You can just send interested parties a pdf and they can read it on their iPad. Save the trees.
I don't see any reason at all to actually pay for reprints. You have a copy already, right?
Upvotes: 6 [selected_answer]<issue_comment>username_2: In the 20 years since I published my first paper, I have been asked for actual paper reprints exactly three times: Twice by researchers, who each asked for one paper by *sending a postcard*, and once by my tenure committee, who required paper copies of everything I'd ever published. In the latter case, it was much easier to just download and print new copies than to hunt for the official reprints, which are still hding somewhere in the back of some disused filing cabinet. (My university finally stopped requiring tenure applicants to kill forests about two years after I got tenure.)
So I'm gonna go with **NO, there is absolutely no reason whatsoever to get paper reprints.**
Upvotes: 4 <issue_comment>username_3: The answer to this question depends on which timeframe you are concerned with, and how optimistic you are about modern technology.
Finding copies of old, paywalled papers can be a problem. Nowadays, most papers are freely available on the web or at least in preprint form from the author's sites. DOI provides a robust way to link to them, even if the actual storage place should change. As the other answers state, *you* have probably no use for prints *now* (or in the near future).
However, what happens in the remote future? Once you stop caring -- maybe you switch career or (eventually) die -- the situation is similar with old papers today: readers are at the mercy of publishers. Is your paper still paywalled? Is the publisher still there? Has some search engine cached a version? Can PDF still be read on modern devices? In the worst case, your paper is practically inaccessible.
Does an archived paper copy help? Depends. There is no way any one place keeps hard copies of everything published. You can give your students and close colleagues hard copies for their own use, and maybe they keep so that maybe even in 50 years, an interested student who can not access your paper (easily) can get a copy from their professor that has undiluted value.
For example, Flajolet died. I am certain my boss (who works in closely related field) knew whom to ask for his academic remains. My boss has himself inherited all the paper accumulated and written by his late advisor (one of which I actually retrieved from the archive to check out for an answer on cs.SE; because it was impossible to find on the webs). This is stuff that does not exist on the web, but in real-world networks. For such, paper is important. Maybe that model is doomed given our technological advances, but I have the feeling that it will have its place for some time at least.
As far as I know, the issue of how we can keep our rapidly accumulating mass of data and knowledge at all and also accessible and organised over time is unsolved. It may be useful to keep that in mind.
Upvotes: 1 <issue_comment>username_4: In Italy, when you apply for a position, you often have to send together with the paperwork one to three dead-tree copies of your $N$ best papers (or sometimes even of *everything* you have published).
This can easily amount to several hundred pages; multiply it by the number of positions you will apply for. It may be costly and troublesome to print them from a university printer. And, you know, printers are always low on toner the day before the deadline.
So in this case reprints are handy to have. I assume Italy is not the only country where this happens.
Upvotes: 2 <issue_comment>username_5: Many of these answers look rather unimaginative a few years down the track, and are obviously provided mainly by mathmos and scientists. With the proliferation of digItal and documents circulated by email, your pdf is less likely than ever before to be read. In my experience - having ordered and posted (yes posted) paper offprints - I had a greater response from recipients than if I'd sent a pdf, which people now routinely ignore. Offprints are handsome and suggest thoughtfulness. Save the electronic copy for your promotion application.
Upvotes: -1 |
2012/06/19 | 890 | 3,765 | <issue_start>username_0: **What techniques have you found to improve collaboration with a remote colleague, in particular to make it feel more like collaboration in person?**
The majority of my collaborations are with colleagues outside of my state. The simplest model I've used is that we each write up certain proofs, and then eventually one of us organizes the various pieces into a draft of a paper, which gets passed back and forth via email until we agree that we're ready to submit. However, this typically feels quite different from collaborating in person. One technique that I've used with surprising success is to skype with my colleague. He was actually able to write on the chalkboard so that I could read it. What techniques have worked best for you?<issue_comment>username_1: If you have some money to spend, [GoToMeeting](http://www.gotomeeting.com/) will hands down make your collaborations feel as if they are in person. You can have a meeting with video, screen sharing, etc. and I believe they have a 30 day free trial.
If you are looking to go the free route, try out [Dropbox](http://www.dropbox.com) for sharing drafts. It will automatically keep version history and it eliminates all the emails. You could also hold meetings over [Skype](http://www.skype.com), however, it is only free for two video feeds.
Upvotes: 2 <issue_comment>username_2: A good [Revision Control](http://en.wikipedia.org/wiki/Revision_control)!! I use [Git](http://git-scm.com/) coupled with [github](http://github.com) being an efficient way (and free) to share and complete a collaboration, especially for `code` and `latex` files.
Else, google docs is also free, allows to multiple persons to edit the same file at the same time, and you see who is doing what. You can do most "Microsoft Word" formats and things. You have also have as service free video-conference and chat.
I highly **discourage** `Dropbox` to share files edited per **more than one person**.
Upvotes: 4 <issue_comment>username_3: One of the hardest parts of remote collaboration is making sure everyone is on the same page. Agreeing on work flow in advance is critical. To me the most important thing is that everyone has a good understanding of the roles and expectations of the individuals. A good timeline, that is flexible, is also very useful. Agreeing upon software, programming, and writing style issues at the outset is also useful.
Upvotes: 4 [selected_answer]<issue_comment>username_4: We use virtual world Second Life for team meetings. It is fun and we feel like we have traditional F2F meeting. We deal with the research of virtual team management. Some of our findings are available in my [publications](http://www.researchgate.net/profile/Jaroslava_Kubatova2/)
Upvotes: 2 <issue_comment>username_5: I am in a mixed group of wet lab and dry lab (bioinformatics) people, and preferences are very different (Word vs. LaTeX), but the last three papers we have written in Google Docs. It has quite some benefits.
* Use of Google Docs is free, you just need an G+ account
* Every one has immediate access to it
* You can write the manuscript collaboratively in realtime. You see the cursor positions of the other blinking.
* Since June 2014 Google Docs has "track changes".
* You can also easily exchange files (even huge files with Google Drive)
* No need to send multiple MS Word files around the globe and merge them afterwards
* Inserting references and formatting the bibliography works perfectly with Paperpile. Also collaborators that do not have Paperpile installed see all the references and citations properly formatted.
* Google Docs has excellent MS Word and PDF export.
As of writing the current free storage in Google Drive is 15 GB for regular users.
Upvotes: 2 |
2012/06/20 | 1,196 | 4,885 | <issue_start>username_0: In India, a minimum of 3 years of [work experience](http://www.jobalertsindia.com/articles/iiit-allahabad-announces-recruitment-2012-for-faculty-positions-1970.html) is necessary to be appointed as an assistant professor in any of the IITs. (Years during the PhD or master's are not counted, but any year spent outside university after bachelor's will count.)
Is such a work experience a necessity even in US universities? If yes, how much will this weigh towards selection? If the candidate has only a post-bachelor's experience, what should be highlighted from that so as to enhance this application?<issue_comment>username_1: I doubt the minimum is rigid and there is a room for lot of considerations.
I'd like to point you to a specific example (IISc). Although the points are opinions of a single person, my conversations with people at IISc have yielded similar opinions.
[Prof. <NAME>](https://sites.google.com/site/giridharmadras/aboutme2)(Did his PhD from TAMU in <3 years!) has an [excellent blog](http://giridharmadras.blogspot.in/) dedicated to IISc/IITs and some excellent articles for prospective faculty.
He points to [one page that he authored about recruitments in IISc](https://sites.google.com/site/newfacultyiisc/recruitment) from which I'd like to quote a few points:
* Technically, there is no age bar and no limit to the experience a candidate may have, but with increasing years of experience, candidates must be considered for correspondingly higher appointments. Though IISc has recruited faculty from 27 to 37 years old at the assistant professor level, the median age of recruitment would be around 30.
* Begin making your enquiries at least one year before you actually want to move. The time taken to acknowledge an application vary from department to department, depending on the chairperson. If this happens, find someone you may know in the department and request them to check with the chairperson of the department. After an application is acknowledged, try to arrange an informal visit with a seminar. You can indicate that you are planning a trip to India in the month of X and would be happy to give a talk/seminar at that time. In your conversation with the chairperson, state the time frame you want to join the institute. Normally, chairpersons will arrange your talk. Besides the talk, a visit with all faculty will also be arranged. Talk to all faculty and make your research sound interesting to them. Talk about your doctoral and postdoctoral research, your future plans and how you plan to distinguish yourself from your advisors. If possible, explain why you think you will fit into the department. Also, there is nothing wrong in stating that you have applied to more than one place and your preferences depending on the offers.
* The suggested timeline is as follows: Apply to several institutes at month X. Follow up and try to get an acknowledgment by month X+1 or X+2. Then, send an email saying that you are visiting India in X+3 month and would like to give a seminar. Try to schedule as many seminars in many institutes in that month. After the seminar, inform them you are willing to return around X+6 months, which will require them to make a quick decision. You can return after X+12 months because the institutes will give you time to join. Please note that each department in IIT/IISc receives at least around 30 to 40 applications per position. Simply applying by email and expecting a detailed response is foolhardy. Unless the applicant shows repeated interest (followed by a visit to give a seminar), it is unlikely that the application will be considered seriously.
The Civil dept at IISc has an [informal FAQ](http://civil.iisc.ernet.in/facultypositions-faq.php) for it's recruitments.
Upvotes: 1 <issue_comment>username_2: There are no universal rules regarding employment status and being hired at US universities. In some fields, such as engineering, graduate students may be hired before they have completed *any* post-graduate professional experience. In contrast, in departments such as physics, it may be required to complete several post-doctoral appointments before being considered for faculty positions.
There are some general guidelines, however:
* In less "applied" STEM (science, technology, engineering, and mathematics) fields, more time is generally required in academic positions before earning a full-time faculty position.
* In more "applied" fields, the time spent is reduced. Unfortunately, in some cases, although industrial experience is often seen as a good thing, it also has a tendency to make it difficult to jump back into a research field—unless one has been employed in an industrial research capacity.
* In humanities fields, it's very difficult of to be able to continue to a faculty position after leaving academia.
Upvotes: 4 [selected_answer] |
2012/06/21 | 762 | 3,199 | <issue_start>username_0: So I'm pretty much working on my own project with guidance from two supervisors. Since it's my own project (and it's exploratory research), the two supervisors don't need to pressure me to get results. So - I got into a lot of dead ends in my current undergrad research project, and while I've definitely learned *a lot* in my current research, I highly doubt it can lead to a publication by now, especially since my supervisors are unfamiliar with the technical details of my model, and the objective of the research is such that there aren't many people in the nation I can contact who are familiar with what my supervisors want to do. I've already contacted a number of people who might be familiar with it, but most of my emails have gone without reply.
At this point, I only have a month left before I leave for graduate school, and I'm not sure how I should conclude my research with my supervisors. I've already written up a report (which could perhaps be analogized to a senior thesis, and which could be helpful for the future) - I've shown the report to one of my supervisors. At this point, what should I do? Should I show them what I've done? Should I feel guilty? I've actually been out of contact with one of my supervisors for several months.<issue_comment>username_1: Find a different supervisor. Maybe a very bored grad student. If that doesn't work work on a different aspect of the project until you hit another deadend.
However, it sounds like you've tried both so there is always finding a new supervisor and a new project when you do finally go to grad school.
Upvotes: 2 <issue_comment>username_2: You say you have learnt a lot, so you need not feel guilty. You do not think your work is publication-worthy yourself, and you also are leaving for grad school - so the only solution is to move on.
Make a neat report explaining your work. Even if it is not publishable, you could include it in your resume as a "**technical report**." From the tone of your question, this seems to be the only way out.
Upvotes: 5 [selected_answer]<issue_comment>username_3: One potential idea is to submit the work and get your degree and head off to grad school. Keep the work that you have done in a bottom drawer for a few months or a year. Then pull it out again and reassess whether it is any good or not. If it is good, then polish it up and submit it somewhere. If it is not good, then you've lost nothing.
It's not uncommon to do unpublishable work at the undergraduate level. Don't worry about it and don't be hard on yourself.
Upvotes: 4 <issue_comment>username_4: To me it sounds like you don't really communicate with your supervisors — that is key to research, even with an independent project. Especially with undergrad research, your supervisor is there to help guide you on your project. With graduate school so close, make sure you conclude with a good meeting with your supervisors.
Also, take this as a learning experience for two things:
1. Research doesn't always work...in fact, it fails more often than it works. If the answers were apparent and easy, it wouldn't be research.
2. Communication with your supervisor/advisor is KEY!
Upvotes: 2 |
2012/06/20 | 811 | 3,349 | <issue_start>username_0: I'm considering applying to chemistry graduate schools within the next six months and am hoping for some guidance in prioritizing aspects. For instance, among my list of considerations there exists: renown of school, programs of interest, appeal of location, amount of financial aid, duration of program, and success of past alumni.
Do others who have already gone through this process have suggestions about how to prioritize these considerations? Were there things in your experience that you thought would be important and ended up not being so. Or visa-versa, are there aspects that you neglected and wish you hadn't?<issue_comment>username_1: As a second year grad student in chemistry, I would say the two most important factors are if there are multiple professors you could see yourself working for and if you feel like you could spend the next 5+ years of your life at the school/in the area. You are going to be stressed out with work, so you want to enjoy where you're living.
I haven't heard of any US chemistry PhD grad programs that don't pay tuition + insurance + stipend, so I don't think that's much of a worry (I would, however, make sure you get all that).
The renown of the school and success of past alumni is actually less important than you'd think. Grad school is all about your research advisor and your research (i.e. a better professor at a lower tier school is better than the other way around).
Upvotes: 3 <issue_comment>username_2: **Prestige**.
Prestige is basically the currency of the academic world. And in the current, grimmer-than-death job market for physical sciences, the prestige of the institution you got your PhD from **is the main factor** in your post-PhD success, or absence thereof.
It might sound stupid, unfair and cynical. Because **it is**. Your success should be proportional to your own value, hard-work and scientific output. But with 200 to 300 graduates applying for every opening position, even in third-class colleges and small firms, **the prestige of your last-attended institution is the actual cut-off**. I have personally met people sitting in hiring committees who openly admitted that any application from someone who didn't graduate from a top-10 US programme or EU equivalent (Oxford, Cambridge, ETH) will go straight to the bin without further reading. Only after this first filter has been applied will they actually start going through your publications, letters of recommendation and actual personal achievements.
So yes, all in all, working for an awesome group in a second-class university might trump working in a mediocre group from an Ivy League. But **nothing** trumps working in an awesome group in a top-tier university, like dozens, not to say hundreds of graduate students do every year. **Those same students you will be faced against when applying for jobs and post-docs**. So in today's gigantic *battle royale* of a job market, where less than 10% science graduates actually land a tenure-track position, if you don't have the full package, you don't stand a chance. That's just how it is.
Personally, I simply wouldn't do a PhD in physical sciences again. And if I did, I certainly wouldn't even consider doing one out of the top-10 programmes of your field. It simply isn't worth your time in terms of employability.
Upvotes: 0 |
2012/06/21 | 820 | 3,426 | <issue_start>username_0: I have little understanding about invited papers, but as far as I know, I think these are non-peer-reviewed papers presented at a conference.
* Now why and when does one go for an invited paper? Is it only to advertise one's work? Is there any other merit in this?
* Also, are normal papers and invited papers the same as far as cost is concerned?<issue_comment>username_1: Being invited to present a paper or give a keynote is an honour. It shows that the community recognizes your work. Of course, it is also a way to further advertise your work, or to reflect on what you have done. Generally, the audience is larger for invited speakers.
One accepts the invitation if one has time and prestige of the conference matches one's reputation. If you have a small reputation, an invited talk at a workshop is an honour. If you are super-famous, you'll tend to only accept invitations to prestigious events.
The cost may depend on how much money the conference has in its budget, which will often depend on the sponsors. Sometimes the conference will waive the registration fee, often they may provide accommodation also, and sometimes they may even cover airfare. Bigger conferences will be able to cover more of the costs. Small workshops may not be able to cover any of the costs.
Upvotes: 5 [selected_answer]<issue_comment>username_2: In addition to username_1's answer:
* I get quite a lot of "invitations" from [scam journals](https://academia.stackexchange.com/questions/101/how-do-you-judge-the-quality-of-a-journal), which I ignore.
* Most of the conferences I attend now publish their "proceedings" as a special issue of one of the relevant journals, and they undergo the usual peer-review. I'd expect that this is true too for the proceedings paper belonging to an invited talk.
However, many of the invited lectures are actually more lecturing(\*) than presenting the very newest work. So the work presented there usually is already published.
(\*) I like it if invited and keynote lectures are actually teaching. It's like an oral review paper, and I think it is a good idea to have an overview and common starting level for hearing the more specialized talks that follow.
Upvotes: 4 <issue_comment>username_3: You asked whether there is any merit to presenting an invited paper other than advertising your work. **Yes.** What you really want, more than people getting excited about a particular paper you write, is for them to **get excited about you**. You want to be viewed as a valuable member of your community, whatever community you choose that to be.
So partly your talk is an advertisement for your work, but more than that, your talk is an advertisement for you, the helpful guy or gal that you are. It increases your visibility, but it's your job to follow up on the opportunities this visibility creates for you. Talk with people. Listen. Really listen to what they're saying (and don't just wait for your turn to talk again). Remember names. Start building your network. Sometimes the most useful thing you'll do at a conference is to grab a beer with new friends, after the talks finish for the day. One key to building a successful network is really trying to help your contacts as much as trying to help yourself. (If you have more questions on networking, I'll be happy to share my thoughts, but I'll stop now for fear of drifting too far off topic.)
Upvotes: 2 |
2012/06/21 | 840 | 3,555 | <issue_start>username_0: I currently hold a BA in Middle Eastern History, which was a pretty good fit for my last job in the Army. I'm getting ready to move on to the civillian world now, and computer science/programming has always been a hobby for me that I'd like to look into turning into a career.
My question is, **what type of program should I look into in order to deepen my knowledge and verify my abilities?** Should I look at getting a second undergrad, or would a programming education certificate be enough to get my foot in the door for employment/grad school later on? Are online certificates such as Kaplan/Phoenix respected enough, especially if they are in addition to a traditional degree?
I basically want to know where to set my sights, in order to get going in software development. Getting an additional qualification seems necessary, so which kind makes the most sense for getting started?<issue_comment>username_1: I think it's going to depend vastly on what kind of position you want to be headed into, and how deep down the rabbit hole you wish to go.
The one thing I would say you ought to focus on is fundamentals: No matter what kind of work you end up doing, be it programming business processes or pursuing a PhD, getting your fundamentals straight will pay dividends over and over again. Courses in algorithms, software engineering, computer architecture, each will give you a different view into the work you do, no matter where you're doing it. A CS undergraduate degree would be a good choice, especially if grad studies is where you want to go after. I understand they vary in quality though, so that's something to bear in mind.
Upvotes: 3 <issue_comment>username_2: I teach in the [post-bac CS program at Mills College](http://www.mills.edu/academics/graduate/cs/program/), which is aimed at people (like you) who have earned a bachelor's degree in a field other than CS and want to transition into CS, most in order to go into software engineering (although we also [prepare students for PhD programs](https://academia.stackexchange.com/a/46929/269) and teaching).
While not quick or cheap, post-bac programs are usually a better choice than a second bachelor's degree, since students don't have to take anything other than CS and related math courses, and many of your peers will be in a similar position to yours.
Upvotes: 2 <issue_comment>username_3: Depending on what you are aiming for (i.e. working at a tech. company versus being a professor) you may have enough qualifications already.
The programming industry is still an industry that is extremely forgiving to those who do not have "qualifications" but who can do the necessary work. This is likely due to the age of the industry and the cross-disciplinary nature.
If you feel you need more traditional preparation sites like [Coursera](http://coursera.org) and others that provide Massively Open Online Courses (MOOC's) are a great place to get some CS courses and you can take some "Intro" courses and more advanced ones for free (paid certificates are also available). Also working on real open source projects ([Github](http://github.com) is one place to start looking for them) that align with your interests and skills will proved that you are capable and be a great resume item.
From what you have written I think that you are just as well off spending time finding a company that needs the skills you already have (if you are a hobbyist you likely know at lest one language pretty well) and practicing the skills you do have.
Upvotes: 1 |
2012/06/22 | 515 | 2,352 | <issue_start>username_0: I recently applied for a PhD position with a major university in Europe. However, I haven't heard back from them even though the dates for the interviews have passed. This somewhat provoces me since I've put a lot of work into my application (it feels disrespectful to not get back to me) and since it makes me unsure whether I have been rejected or merely forgotten. I've already got a response (possibly automately generated) that my application was received by them.<issue_comment>username_1: No, final decisions take a while. It is not the fault of the program, but rather an issue of funding. Programs tend to initially reject very few applicants. An applicant that seems weak on "paper" might get accepted if their proposal fits into a very narrow funding scheme that no other applicants are eligible for. There is no global rank order of the applicants, but rather the "best" applicant for each funding scheme is identified. Programs generally construct strategies which lead to the most funding. The strategy is constantly revised when the program finds out about successful/unsuccessful funding applications and whether the applicant accepts the acceptance. Basically the whole system is a nightmare for everyone.
Upvotes: 3 <issue_comment>username_2: I think it is perfectly reasonable to expect a timely response, however it seems to be the case that many graduate schools are overwhelmed with applications and underestimate the time it takes to deal with them. A slightly more cynical view would be that many graduate school admin departments are understaffed and don't have the resources to cater for expected number of applicants. In my case I submitted my application on 28 Feb and was informed that interviews would take place "during the week of the 19 March". This date came and went and I assumed I had not been successful, so I called the Faculty Graduate Office the following week and was informed that the process was taking longer than expected but I would be notified of the status in due course. On 5 April I received an email saying I had been selected for interview and "we will contact you shortly with an interview date and time". On 25 April I was given the interview date (for the middle of May).
I would recommend that you just call them and ask to know the status.
Upvotes: 4 [selected_answer] |
2012/06/22 | 1,226 | 4,738 | <issue_start>username_0: I realise that the answer to this question varies greatly between department and universities but I would like to get some estimates from somebody within the field since I myself has absolutely no idea. When I apply for a PhD position, can I expect to compete with a handful of other persons or can there be hundreds of applications sent?<issue_comment>username_1: I'm located in Belgium in a computer science department. Recent positions that I've advertised have had between 3 and 30 applicants, depending on the topic. Most applicants were poor or hard to assess (from countries we have little experience with).
We currently have 8 positions open and are not expecting to get 8 good candidates.
Upvotes: 3 <issue_comment>username_2: There was over 80 for the position I applied for a few years ago, at a Dutch neuroscience institute with a good reputation. I imagine there would be more at world famous universities in the US. I hear that most of the time, most applicants aren't that good, even the top 10% who make it to the interview. In my case the best candidate got the position, and the one ranked second (me) got an offer for a position later on... so in the end it was one out of 40.
Upvotes: 5 [selected_answer]<issue_comment>username_3: It really depends on the school and the program. For example, most big US universities that have chemistry PhD programs take ~50 new graduate students every year, so I can't imagine there are *too* many applications for each spot...
Upvotes: 1 <issue_comment>username_4: I wonder if the OP is asking a more Euro-specific question ? In the US you don't apply for a single position - rather there's a large pool of applications for a small set of "slots". In contrast to the answer by @username_3 above, in CS it's not uncommon to see over 500 applications for around 15-20 slots.
Upvotes: 4 <issue_comment>username_5: For Physics (and some Astronomy) PhD programs, you can find out a lot of the information about acceptance rates and total # of applicants from the [AIP graduate handbook](http://rads.stackoverflow.com/amzn/click/0735409668).
Much of the same information is found at www.gradschoolshopper.com, which is somehow down today...
Upvotes: 0 <issue_comment>username_6: The University of Minnesota is one of few institutions that makes their admissions statistics publicly available <https://apps.grad.umn.edu/programs/select_program.aspx?l=t> (choose a field and press the "Program Statistics" radio button)
I pulled the info from my field (Ecology and Evolutionary Biology) and you can see that it varies from year to year but the mean is around 80. Here is the site for the EEB program: <http://gradpub.grad.umn.edu/data/stats/ad/1124400.html>
In general, at least in the US, more and more people are applying to grad school since it is viewed as a viable alternative to the weak job market.
Upvotes: 2 <issue_comment>username_7: The PhD market in CS in Austria is very much a buyers market. I know from at least two professors (both are very well known in their respective fields; one of them is a real "big name" in algorithms) that they have problems finding (reasonable good and motivated) PhD students. As a well doing Master's student (not a genius, they tend to go to the US or UK), I was offered several PhD positions.
Upvotes: 1 <issue_comment>username_8: I have been part of the selection process of several high profile European research institutes in physics. For recent calls we received somewhere in the 150-200 applications for a handful of PhD positions (say 5).
For concurrent application rounds at different institutes in different countries but in the same field, I saw somewhere between 30% and 50% overlap in the candidate pools.
Upvotes: 2 <issue_comment>username_9: As mentioned before, in some US universities every year there is a cohort of new students entering a given PhD program (cohort size can vary a lot even between departments of the same university). At Cornell University, in the life science departments (e.g. Ecology & Evolutionary Biology, Entomology, Plant Sciences, etc) applications received per year can be ~ 100- 150, and cohorts are usually around 10 students per year.
Although the question of "if things have gotten more competitive" in the last decade might be department/school dependent- here are two websites of universities showing their admission stats over the past years- with overall number of applications seemingly going up:
Cornell University Doctorate programs: <https://tableau.cornell.edu/t/PublicContent/views/TheOneDashboard/TheOneDashboard?%3Aorigin=card_share_link&%3Aembed=y>
University of Chicago: <https://provost.uchicago.edu/initiatives/phd-program-data>
Upvotes: 2 |
2012/06/22 | 519 | 2,152 | <issue_start>username_0: I am a math student in the US preparing for an hour long defense of my thesis. I am assuming that the dissertation committee has already read my dissertation by the time of my defense.
* Any advice for preparing and giving the dissertation defense talk?
* What balance between presenting subtle parts of the proofs and a clear big picture of the results obtained should I aim for?
* Would it be more interesting for the committee to hear me explaining more technical parts of the proofs rather then the statements of the main theorems obtained?<issue_comment>username_1: I don't think there is a meaningful answer to this question that is helpful across all US math departments and advisors. There is too much variation in what is expected. You need to find out what your committee expects.
That said, if I had to answer without knowing your department or committee, I would recommend trying to focus on the big picture but making sure that you spend most of your time explaining your contribution to that big picture. That's certainly what you'll want to do for job talks, which you're presumably also giving around now.
If the committee is concerned about your understanding of the technical parts of the proofs, they can ask you in the closed exam following the public talk.
Upvotes: 3 <issue_comment>username_2: Two general suggestions which can overrule anything else I'm going to say: Ask your advisor about the talk! Practice your presentation with your advisor, if that's alright with them.
In general, you should not give details of proofs unless pressed; even then, you should be careful. Proving real theorems in talks (even thesis defenses) is hard, time consuming, and generally of minimal benefit to the audience. You should instead try to give the general picture of what you've done.
Some other good resources for communicating math are:
<http://www.ams.org/notices/200709/tx070901136p.pdf>
<http://www.ams.org/profession/leaders/workshops/gcoll.pdf>
Neither are specifically about the thesis defense, but are general resources for math talks. I've found them both helpful at various points.
Upvotes: 2 |
2012/06/22 | 1,290 | 5,632 | <issue_start>username_0: My PhD advisor liked me during the exams. Then, when I came with the thesis I wrote, he got very angry, maybe because I did not do what he suggested, maybe because what I did is not quite in his domain. The tension between us did not diminish with time, because I like my thesis and don't want to throw it away and start over. I proposed him that I move to another advisor, and he agreed, maybe relieved. But if I will ask another professor to be my advisor, this will raise suspicions on the reason I move. I don't want to make the current advisor look bad, and certainly I don't want to look bad (this is more likely to happen, because he is very respected), so I am in a delicate position. Possible issues which may trigger suspicions about my thesis (and maybe they are the reason my advisor got angry in the first place):
1. the main claim is considered a lost cause,
2. my solution is developed in a large number of steps which are difficult to understand.
Could you please suggest how should I approach the problem of finding and asking another professor to be my advisor?<issue_comment>username_1: Students change advisors all the time for all sorts of reasons. While it is important that advisors and advisees have to recognize that they are supposed to work on improving their relationships and fight through tension, sometimes it just doesn't work out or will not work out in the long term. I've changed advisors and during that process, I found several other students who have gone through the same experience.
It is important to recognize that this does happen for better or for worse and it is important for you to do what is necessary to do what is best for you. Despite what you would think, people (even academics) don't really put much weight on seeing someone change careers or direction so you shouldn't worry about such things being regarded as suspicious.
The first step would be to find a set of people that you can rely on for rational advice since this will likely be an emotional transition. Parents and close friends come to mind but also finding an older student, administrative staff, and understanding professors will be key. In my case, my co-advisor and one of my committee members were resources in addition to several senior graduate students who had changed advisors.
The second would be to start to find a new advisor. If you get along with your current advisor, he would make good suggestions, if you're switching behind his back, probably not so much. As you a mid-PhD student, you now have a much better understanding of what type of advisor/mentee relationship you want and you can narrow down advisors based on that reputation. During my search, I got in touch with senior students first asking them able advising styles (and availability of funding). Only after that initial conversation did I meet with the advisor themselves. Despite what you would think, advisors actually like picking up/stealing veteran graduate students from other advisors due to the expertise that they may add to the discussion.
Finally is the administrative side. You chair will have to know about this and your committee will likely be restructured. People don't have to know the reason why you've changed (my current advisor still doesn't know). If possible, try not to burn any bridges since you will likely need his (his/her for other people reading this) signature at somepoint on your thesis. Try to keep things professional since this really is a career based move rather than something personal although it really is.
The best news is that everyone I've known who have changed advisors are/were really happy about their new sitatuion. It's just a very stressful transition but it really wasn't as stressful as I was expecting.
(edit) I've noticed this about username_2's answer regarding the timing. Yes, it is inevitably that you will lose some time especially if you're in the thesis writing stage of your career. However, if you're an earlier student in the dissertation/research phase (I was in my 3rd year), I didn't feel like I lost much time. Most of the first years of grad school are "wasted" relearning learning and taking classes. Especially in the sciences, most of the early years is developing various soft-skills and competence which isn't lost when you change advisors/research projects. Furthermore, spending an extra year to reconfigure a PhD is probably better than falling into ABD purgatory.
Upvotes: 5 [selected_answer]<issue_comment>username_2: There are many reasons students may switch advisors during the course of their postdoc, so I would not worry too much about the statement is says about you. Given the situation you described, you could simply state that as you progressed through your research your interests diverged, and that would be both truthful and tactful.
Regarding finding another advisor, I would try the following:
1. Talk to your department chair. He/she may be able to help you find other people in your university whose interests align with yours.
2. Talk directly with other professors. Let them know that you're interested in their research, and are interested in joining their lab.
3. Look to switch universities and start everything from scratch.
Do note that, no matter what route you choose, you will have lost a significant amount of time; your new advisor will almost certainly want you to do things you haven't yet done, and will not fully value some things you already have done.
Lastly, I would definitely recommend meeting with your advisor more frequently to avoid such situations in the future.
Upvotes: 3 |
2012/06/23 | 1,668 | 6,484 | <issue_start>username_0: The number of women in some academia disciplines like computer science remains low despite the continuous efforts to increase it. What is being done to make academic careers in computer science (and related fields) more appealing to women? Are there any studies on the ways of improving the working conditions for women in academia?<issue_comment>username_1: [NIH](http://womeninscience.nih.gov/nihwide/index.asp) and [NSF](http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5383) have efforts to encourage women to enter, and stay in, biomedical sciences (NIH) and science and engineering (NSF) careers. I am not sure to what extent these efforts are evidenced based. I am not aware of to what extent the IEEE, DOD, etc. have formalized their "inclusion" efforts.
Upvotes: 3 <issue_comment>username_2: I imagine actual studies will be few and far between - they'd be difficult to conduct, and I suspect the reasons behind many of the outcomes would be near impossible to establish with a degree of certainty. For example, if a woman leaves academia because she got saddled with tons of committee work, student advising and other activities, and is denied tenure, was she just unproductive? Did she get saddled with those duties *because* she is a woman?
Beyond funding initiatives, which username_1 discussed a bit, I think there are some very serious "quality of life" considerations that impact the retention of women in academia:
* "Mommy Tracking" (A "mommy track" is when a woman is put on a particular career trajectory as a result of having, or planning to have children. While it may involve flexible work arrangements, it is often at the expense of her professional career and may involve limited advancement opportunities, being regarded as "less serious about research", etc.) While now notorious enough that it ought to be a thing of the past, penalizing female academics for having children - or the possibility that they *might* have children - forces women to make a Family vs. Career choice that most male academics never have to make. Faced with this dilemma, some female academics will choose family, resulting in a higher attrition rate among women. Worse, some promotion committees, etc., will effectively make the choice *for* them.[This report](http://workplaceflexibility.org/images/uploads/program_papers/mason_-_keeping_women_in_the_science_pipeline.pdf) goes over some of this.
* For those female academics who do have children, consider providing things like breastfeeding rooms or daycare at major conferences.
* Actively questioning our own implicit biases. If your field has a large number of women in it (like my own), the panels at conferences, the awardees, society officers, etc. should have a fairly large number of women in them. Even for fields with less women in them - if 10% of your field is female, but *none* of your invited speakers for a major conference are, that might indicate some implicit bias in how people construct panels, think about the luminaries in their field, etc. Looking at things like panel composition and asking "Do we have any women? If not, why not?" is a useful exercise. Note this is *not* a quota system. The answer may be that no women applied for an award this year. Or that everyone who published on this aspect of a subfield this year happened to be a man. But it also might not be. This kind of thing will impact both the careers of the women chosen, but also allow women who are junior in the field to see "people like them" as big names in the field, which has been shown to be important.
* Stemming from the above, avoid tokenism. The female faculty you *do* have shouldn't have higher burdens of committee work, etc. because "We need a woman on the X committee and you're the only one" while their male colleagues are left free to do research.
* Make academia *unfriendly* to sexist statements. Actually speak up when you hear them. Don't dismiss it as "Oh, X doesn't have any social skills, but they're a brilliant researcher..." when it's driving away other brilliant researchers who don't want to be treated as if they are sex objects (unwelcome flirting, comments on their appearance, etc), or have their accomplishments and contributions dismissed because of their gender.
Upvotes: 5 <issue_comment>username_3: Studies of the nature you are referring to are often published by The National Academies Press. While the print versions must be purchased, NAP provides Electronic Versions in PDF form or online reading for free.
This study, [Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering](http://www.nap.edu/openbook.php?record_id=11741) is slightly dated as it was published in 2007, but likely is just as relevant today.
[Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty](http://www.nap.edu/catalog/12062/gender-differences-at-critical-transitions-in-the-careers-of-science-engineering-and-mathematics-faculty) is from 2010 and deals with the differences in career tracks between men and women in the sciences
[Seeking Solutions: Maximizing American Talent by Advancing Women of Color in Academia](http://www.nap.edu/catalog/18556/seeking-solutions-maximizing-american-talent-by-advancing-women-of-color) from 2013 explores the specific challenges that women of color face and explores recommendations to promote inclusion in that community.
[To Recruit and Advance: Women Students and Faculty](http://www.nap.edu/catalog/11624/to-recruit-and-advance-women-students-and-faculty-in-science) from 2006 discuss best practices on how to attract women to STEM roles in academia.
Some of these are a few hundred pages, so there is a substantial amount of material to get you started. I hope that you find these helpful.
Upvotes: 4 <issue_comment>username_4: I like this very much: [Why So Few? Women in Science, Technology, Engineering, and Mathematics](http://www.aauw.org/research/why-so-few/), published by the American Association of University Women (AAUW).
There's the full report and some succinct versions, including a power point presentation. On each slide showing a graph, there are several Do's highlighted on the right with animation. Excellent, very helpful.
My pet peeve, the lack of opportunities for girls to play with three-dimensional construction toys, gets prominent mention.
(The implicit bias questionnaire they linked to was *weird!*)
Upvotes: 0 |
2012/06/23 | 1,520 | 6,692 | <issue_start>username_0: I am admittedly an undergraduate so I do not have very much experience yet in finding resources.
When I have an idea of something I would consider worthwhile to research (in my case for a proposal to a supervisor about an undergraduate research opportunity), assuming I do extensive research online to see what has been found out about the subject and end up concluding that what I want to look into has not been investigated yet.
How can I ensure that it actually hasn't, and it isn't just a case of me not finding the information that is already available somewhere?<issue_comment>username_1: This is of course always hard to protect oneself from. However, I would say the best way to ensure that your research proposal hasn't been investigated before is to get in contact with an expert in the field and ask him or her this.
Of course, you should investigate the topic thoroughly before approaching the expert. One tip to do this is by using a [citation index](http://en.wikipedia.org/wiki/Citation_index). This enables you to track how a certain paper has been cited, meaning you can follow the trail and more effectevly find out what has been done. For example, if you have located an old seminal paper in your field, finding out which papers have referenced this one should give you a fair chanse of finding what you're looking for.
Upvotes: 6 [selected_answer]<issue_comment>username_2: As an undergraduate doing research, **your primary goal is to learn** and publishing is secondary. Of course, publishing your results and going through all the associated things for the first time is part of the learning. There is nothing fundamentally wrong with spending your undergraduate years rediscovering something. To bring up a particularly relevant example for today: <NAME> was elected a fellow of King's College in 1935 (at the end of his undergrad) by proving the central limit theorem. He failed to notice that it was already proved in 1922. I am sure he learned a lot from it.
Of course, you have to be prepared to rediscover. If you are not it can be very discouraging. From a *person experience*, when I was an undergrad preparing my first first-author paper for submission I ended up finding previous work that had shown almost the same results 3 years prior. I had spent a lot of time rediscovering the ideas and writing my paper and it was a pretty devastating blow. My supervisor's words of encouragement: "Don't worry, at least we know we're on the right track".
He was right, and I was able to learn a lot from the experience (eventually leading to other publications). The particular paper in question sat in the back of my filesystem for a while, but I returned to it 2 years later with fresh eyes to discover that it was not as derivative as I had originally judged it and even this paper ended up being published (with some modifications) at a conference.
Another adviser takes the idea of rediscovery even further. When we move into new territory on a project, he will suggest some directions knowing that the relevant theorems have been proved already. He will tell me: "this result has been proven, but don't look it up, you will arrive at it on your own and then we will both be able to understand it better". This approach works very well in math-like fields since you have the advantage of knowing what you are going after as true, but still get to experience all the joy of (re-)discovery and the lessons you learn along the way. Something that is often not obvious from just reading somebody else's paper.
As an undergrad, don't worry about accidentally working on something that has been done. Concentrate on learning as much as you can!
Upvotes: 4 <issue_comment>username_3: My answer is probably hidden in the previous ones but I'll give it a go anyways:
1) **never assume that you discovered a completely new field**: if there's something that I learned in my research years is to be humble. Maybe soeone starts very boldly thinking that they're the best researchers in the *world*, but for sure everybody at some point realises that the field is populated by experts, who know a great deal of things in the area. So in the case of finding out others sharing your ideas, humbly try and learn from them; in the (exceptional) case of really finding out a new research avenue, humbly start collaborating with others because you won't make any difference to the field only working by yourself.
2) **similar ideas do not prevent you from engaging on the same topic**: this is very common, and I've seen it happening a lot of times. Researchers become depressed when they see that something similar to their ideas has been published already. I think that what happens there is that you find something related, and even if you did not do the work, tend to believe that you would have done *exactly* the same steps. It's only when you engage directly with the problem that you discover that you would tackle it (slightly or vastly) differently: that gives you a lot of *potency* in my opinion, and it's a very good exercise. Take an existing problem, which has been tackled already, and give it your spin.
Upvotes: 4 <issue_comment>username_4: Several years down the line, I'm now be able to answer this question myself: **you can't exclude it**, and this isn't limited to academic topics. People will disclose what research, products, startups or concepts they are working on at conferences, competitions, social events,... but you can never know whether they're working on something and intentionally not disclosing it. Internet research is the closest you can get, supplemented by asking people who know the field you're interested in.
Upvotes: 1 <issue_comment>username_5: Nowadays, people usually submit the preprints for the papers on arxiv. Hence, if you have an idea you can search the databases(arxiv and different research engines such as inspires in physics, for other topics I believe it would be different) by different combinations of words and see if someone else explored that particular idea. You shouldn't be bothered by the novelty of an idea. Suppose you have an idea and you work on it, the outcome would be in general very personal. Even if someone proved a known result and therefore can not publish this result since it has been published before, what one gained in the precess is more than just an idea. In the process of exploring an idea, one develops the techniques which are necessary of creating/observing/exploring ideas in the field, and in no time a new idea which hasn't been explored before comes in.
In the end, don't bother with this problems, just go ahead with the research.
Upvotes: 0 |
2012/06/23 | 854 | 3,356 | <issue_start>username_0: I know that not all, but many papers (that are accepted and published) are based on ideas that can be patented as well. So, why is that there is no system in which a person can apply for a Publishing a Paper and also getting the Idea patented at the same time?
Is it possible that I publish a paper, do not get it patented (going to patent it at a later point of time - because of not having sufficient money to pay for the patent application or whatever) and during that time someone else patents my work in his name? Or Someone else picks up my theory, develops it further and gets a patent? How would the situation be then? What should I do under such a situation?<issue_comment>username_1: Patents are about owning and protecting an idea to make money from it in the future.
Papers are about advancing science. A paper lays claim to an idea, but anyone else is allowed to build on that idea without having to pay royalties.
Upvotes: 4 <issue_comment>username_2: To add to username_1's answer: Even in the rare cases when a publishable result is also patentable, filing for a patent is a serious amount of work. Filing a patent application costs thousands of dollars; submitting a paper is free. Filling a successful patent requires *very* specific and formal language, which is not the same as the specific and formal language used to communicate with other researchers. Why should we spend all that extra effort for [so little reward](https://academia.stackexchange.com/a/1808/65)?
*Or Someone else picks up my theory, develops it further and gets a patent? How would the situation be then? What should I do under such a situation?*
You should cite the patent in your tenure case as iron-clad evidence that your research has real-world impact. Congratulations!
Upvotes: 3 <issue_comment>username_3: >
> not all, but many papers (that are accepted and published) are based on ideas that can be patented as well
>
>
>
This is a misconception. Even in engineering, only a small minority of papers contain ideas worth patenting. The further you get from engineering, the smaller this fraction gets.
>
> So, why is that there is no system in which a person can apply for a publishing a paper and also getting the idea patented at the same time?
>
>
>
You can indeed do both things in parallel, but not via the same process. Publishing a paper is an academic process, whereas filing a patent application is a legal process, and the two things just don't overlap very much.
It's kind of like asking why you can't write your dissertation and apply for jobs via the same process. You can certainly work on both at the same time, and there is a relationship between them, but fundamentally they aren't the same thing.
>
> Is it possible that I publish a paper, do not get it patented and during that time someone else patents my work in his name?
>
>
>
No, it's not possible. Well, technically someone could try, but they would be wasting their time, since your paper would serve as "prior art" that would invalidate the patent.
>
> Or someone else picks up my theory, develops it further and gets a patent?
>
>
>
That could happen, whether or not you file for a patent. They would own the intellectual property rights to the extension of your work, but not to your work itself.
Upvotes: 5 [selected_answer] |
2012/06/24 | 824 | 3,534 | <issue_start>username_0: For graduate schools in STEM which take students with a Bachelor's degree (BS/BE/BTech etc.) and graduate them with an MS and/or PhD, what do they assume about the student's prerequisite knowledge when designing courses, their difficulty and overall dynamics?
My question is pointed towards **knowledge** and not what is on the student's transcripts. For instance, a student might have "Linear Algebra" on his transcripts but might not *remember* Singular Value Decomposition very well.
Do they:
* assume that student knows all the courses he has taken very well and build from there? (What happens to people who had a BS in allied fields? For instance a student may have a BS in Pure Math but enrol for a MS/PhD in Computational Math with little idea about a "compiler".)
* assume he knows nothing and take him to proficiency?
* anything in between?<issue_comment>username_1: This is a very difficult question to answer, since there are so many disciplines and so many programs at so many universities, all of which have their own rules.
But in general, the standard master's program (or coursework phase of a doctoral program) is designed to take someone who has a bachelor's degree *in the same field* and bring them up to a level of competence sufficient to pursue graduate-level research in that field. Very little allowances, if any, are made for people coming from other disciplines and programs, and they're certainly not designed for someone starting with no knowledge whatsoever.
Upvotes: 1 <issue_comment>username_2: They are coming to *grad* school, not some hand-holding, jolly the kids along, summer program.
It is assumed that if students arrive with a deficiency they will take the necessary remedial classes (often that means getting in with the upper-division undergrads) and if you have forgotten something they will do the necessary boning up.
They will be boning up on things from time to time for the rest of their lives, after all. Might as well get in some practice in school.
Upvotes: 3 <issue_comment>username_3: I agree with @username_1's and @dmckee's answers, but let me add a different spin:
Students are admitted to strong PhD programs not on the basis of how much or what they *know*, but rather on their potential for successful research. Every program admits students from all over the world, who may or may not have undergraduate degrees in exactly the same field. Beyond a few fundamental concepts, it is not reasonable to assume that incoming graduate students have *any* specific prior knowledge.
That said, most courses for PhD students are generally taught *as if* the students have a strong undergraduate background in the same field. The definition of "strong undergraduate background" depends *strongly* on the graduate program; the expectations at the top PhD programs are generally extremely high. More importantly, **PhD students in top departments are expected to have the intellectual maturity to recognize and correct weaknesses in their background**, even if the missing material is *not* normally covered in a strong undergraduate program.
**Also:** Courses are arguably the *least* important part of any PhD program.
Upvotes: 5 [selected_answer]<issue_comment>username_4: In my experience it is not uncommon for first year grad classes to share a syllabus (and lectures) with an undergraduate class. The grad class often has some additional assignments (e.g., an extra or longer paper or additional problems in homework assignments).
Upvotes: 2 |
2012/06/24 | 756 | 3,294 | <issue_start>username_0: 1.What qualities do professors (assume STEM if necessary) look for in students (in their MS) when recruiting them for an RA (Research Assistantship)?
---
2.Consider the follow scenario:
* Person 1 : Has excellent fundamentals, thirst for knowledge and good grasping power but no exposure to the academic research per se.
* Person 2 : Above average fundamentals (few random holes here and there. Not too minor, not too major either) and prior exposure to academic research.
Who would a professor rather pick?
---
The motivation of the question is to find out how a potential grad student can prepare himself to increase the likelihood of getting an RA with a professor he/she likes.<issue_comment>username_1: There are two issues here: what do advisors want, and what can they observe? In principle, I think most would choose Person 1 over Person 2, if they knew all the facts. However, this choice will not typically arise in practice, because they won't be able to verify the "thirst for knowledge and good grasping power".
Classroom performance, no matter how excellent, is generally not sufficient for admission to a strong graduate school. The problem is that lots of students get excellent grades, and these grades are only loosely correlated with research ability. The only way to stand out is to do something that's fundamentally more impressive than doing well in courses; this could be a research project, or a substantial exposition, or coding, or any number of other things. Research has some advantages, since that's what you're aiming to do in grad school, but it's by no means necessary. However, if you don't do any research, then you'd better have some other way of demonstrating your talent.
For example, if Person 1 has done no research, but wrote a beautiful, 80-page undergraduate thesis giving an exposition of forcing and the continuum hypothesis, then that might count for more than most undergraduate research projects. On the other hand, if Person 1 can point to nothing concrete except course grades, then that will likely be a problem.
Upvotes: 3 <issue_comment>username_2: From your question, I'm guessing that you're coming from the American system (or similar) in which it is common for students intending to pursue the Ph.D. to start in the MS program at the same school. While they are completing MS requirements, they try to find an advisor and a research project for the Ph.D.
I think that often the advisor will expect the student to start a project and demonstrate some competence and progress while the student is still TA'ing and taking classes. That way the advisor can measure the student's capability before committing to funding. I typically give a student some material to read and ask them to implement and test some existing algorithm, in order to help the student get started and to test the student's capability for research. The most important thing is the ability to rapidly understand new ideas well enough to implement them from scratch.
Coursework is not sufficient to get you a research position, but it can certainly disqualify you. Usually prospective RAs have taken or are taking my (graduate-level) class, and I won't consider taking them on unless they get at least an A-.
Upvotes: 3 [selected_answer] |
2012/06/24 | 693 | 3,116 | <issue_start>username_0: Doing original research in Theoretical Computer Science requires a quite good understanding of almost all areas of Mathematics.
I think double majoring in Mathematics and Computer science for someone who wants to do research in Theory is very important.(Or at least having a knowledge of Algebra, Analysis, Logic, Topology etc.)
I'm wondering how do graduate admission offices take this as an advantage?( Specifically in comparison with other good applicants which have publications or higher GPAs.)
Thanks.<issue_comment>username_1: There are two issues here: what do advisors want, and what can they observe? In principle, I think most would choose Person 1 over Person 2, if they knew all the facts. However, this choice will not typically arise in practice, because they won't be able to verify the "thirst for knowledge and good grasping power".
Classroom performance, no matter how excellent, is generally not sufficient for admission to a strong graduate school. The problem is that lots of students get excellent grades, and these grades are only loosely correlated with research ability. The only way to stand out is to do something that's fundamentally more impressive than doing well in courses; this could be a research project, or a substantial exposition, or coding, or any number of other things. Research has some advantages, since that's what you're aiming to do in grad school, but it's by no means necessary. However, if you don't do any research, then you'd better have some other way of demonstrating your talent.
For example, if Person 1 has done no research, but wrote a beautiful, 80-page undergraduate thesis giving an exposition of forcing and the continuum hypothesis, then that might count for more than most undergraduate research projects. On the other hand, if Person 1 can point to nothing concrete except course grades, then that will likely be a problem.
Upvotes: 3 <issue_comment>username_2: From your question, I'm guessing that you're coming from the American system (or similar) in which it is common for students intending to pursue the Ph.D. to start in the MS program at the same school. While they are completing MS requirements, they try to find an advisor and a research project for the Ph.D.
I think that often the advisor will expect the student to start a project and demonstrate some competence and progress while the student is still TA'ing and taking classes. That way the advisor can measure the student's capability before committing to funding. I typically give a student some material to read and ask them to implement and test some existing algorithm, in order to help the student get started and to test the student's capability for research. The most important thing is the ability to rapidly understand new ideas well enough to implement them from scratch.
Coursework is not sufficient to get you a research position, but it can certainly disqualify you. Usually prospective RAs have taken or are taking my (graduate-level) class, and I won't consider taking them on unless they get at least an A-.
Upvotes: 3 [selected_answer] |
2012/06/24 | 685 | 2,859 | <issue_start>username_0: As a non-native speaker of English, it can often be hard to spot some of the errors that a native speaker would find. For a long time, I've been thinking about getting a native speaker to help me with proof-reading my texts (articles, theses, application, etc), but I don't quite know where to look. What are some good ways to get proofreading for your texts?
For example, are there any websites on the Internet where one can exchange proofreading services between different languages? (Say, if you help me proofread my text in language *x*, I'll help you with yours in language *y*.)<issue_comment>username_1: Yes, Elsevier publishing group provides professional language services, like editing. You can find about it [here](http://webshop.elsevier.com/languageediting/index.cfm)
I also found [this website](https://wordy.com/) which looks good, and it's cheaper and quicker, I think.
Upvotes: 5 [selected_answer]<issue_comment>username_2: Being a native speaker does not mean they are a good proof-reader. In fact, most are far from it.
Your spelling- and grammar-checkers should pick up most of the horrors, and the common 'tricks' of pushing the text to one side for a few days and reading from the end to the beginning will probably allow you to catch the rest yourself.
If your articles are for publication, the editor or sub-editors will tidy up the details if the text is basically sound. A thesis generally needs only to be clearly readable - nobody is going to pull you up for ending a sentence with a preposition or using 'that' instead of 'which'.
For your CV... try a professional CV writer, or one of the many CV templates available on-line. Go to the the university ones though, to avoid the scammers.
Upvotes: 4 <issue_comment>username_3: Learning all the nuances of a second language can be overwhelming. Fortunately, when writing in a technical field, often the vocabulary needed for your paper is a tiny subset of the whole language. However, I recommend that when you get the chance you ask native speakers (or others who write clear, precise prose) to explain the motivation behind their decisions. One good resource that does this is [The Grammar According to West](http://www.math.uiuc.edu/~west/grammar.html).
Upvotes: 2 <issue_comment>username_4: There are professional translators who offer editing and proofreading of scientific articles.
Just google it (the good thing that they don't need to be nearby).
Once I tried such service and I was happy (and the reviewers as well). And even a bit surprised, as I had some doubts if such service can work for scientific texts, full of jargon and complex ideas.
Of course, to start with, the article needs to be decent enough - readable (even if with some grammatical errors). Otherwise you need someone to write it with you, not only correct.
Upvotes: 2 |
2012/06/25 | 571 | 2,268 | <issue_start>username_0: I'm looking for a site (other than [Scimago](http://www.scimagojr.com/)) that presents statistics of published papers and/or citations per country. However, I need more detail than that presented in Scimago. Ideally, I would like to have a list of authors, or the list of papers per country (having all the statistics that Scimago shows is a plus).
Is there any site that provides such information?<issue_comment>username_1: I think what you are asking can be carried out using Web of Science. I used address == "Canada" for the past 5 years, and immediately had published records for 241, 711 articles! You can ask for a citation report, which provides the information that Scimago has at the top. I think the issue you'll face is the sheer volume of data you're asking for. Even limiting the category to "ecology" (my field) yields over 3000 articles.
Of course, this answer won't be much use if your university doesn't have access to WoS.
The other thought I had was using Harzing's Publish or Perish (http://www.harzing.com/index.htm) which uses Google Scholar data. I tried a direct google scholar search, but can't immediately see how to limit it to articles *published* by people in the country as opposed to being about the country.
I hadn't heard of Microsoft Academic Search (see comment by Gopi) - so I had a look. The short answer is yes, it will show you publications by geographic region, but you have to go in by institution. You can get a google scholar like list of papers - and export those in various formats, but I only found the export option at the level of individual author.
For example, Université du Burundi has 16 publications by 5 authors, with an H-index of 4.
Pretty cool map though.
Upvotes: 3 [selected_answer]<issue_comment>username_2: Adding to Gopi's comment, here's a snapshot of [Academic map](http://academic.research.microsoft.com/AcademicMap) at work. I could only see the cumulative publication count in every field; but from the highlight of a few Indian universities I doubt if any quality measure has been considered at all. Also it lacks the cumulative country statistic and the citation counts you ask for.
Upvotes: 2 |
2012/06/25 | 1,633 | 5,807 | <issue_start>username_0: What are the best strategies for assessing if a journal is a "vanity" or "predatory" journal that should be avoided (both for publishing in and reviewing for)? For example, how would one go about determining if a journal/publisher belongs on [Beall’s List of Predatory Open-Access Publishers](https://scholarlyoa.com/publishers/)?<issue_comment>username_1: First, you should probably publish in the same venues that you read and cite. Presumably those are reputable.
Now to describe low-quality vanity publishers. Two essential characteristics are:
1. The publication of very low quality material. This is usually immediately recognizable to any expert. Sometimes it's obvious to anyone; for example, [read this abstract](http://www.scirp.org/Journal/PaperInformation.aspx?paperID=2210&JournalID=160).
2. A business model in which the author (rather than the reader) pays the publisher. Of course, this by itself isn't necessarily indicative of a low-quality publisher (think PLoS). But low-quality publishers can't make money off of subscriptions, since they provide no content of value.
Additional common characteristics of such publishers are:
* Mass e-mails (spam) to academics, especially when the recipients include researchers in unrelated fields. These e-mails may request submission of conference presentations, papers, or book manuscripts, or may contain invitations to journal editorial boards.
* A high number of prominent typographical errors in text attributable to the publisher. For instance, at the beginning of [this article](http://www.benthamscience.com/open/tonumj/articles/V004/1TONUMJ.pdf) "abstract" is mistakenly spelled "abstarct".
Upvotes: 7 [selected_answer]<issue_comment>username_2: There is an established framework for researchers: [Think. Check. Submit.](https://thinkchecksubmit.org/)
>
> 1. **Think**: Are you submitting your research to a trusted journal? Is it the right journal for your work?
> 2. **Check**: Use our [check list](http://thinkchecksubmit.org/check/) to assess the journal.
> 3. **Submit** Only if you can answer "yes" to the questions on our [check list](http://thinkchecksubmit.org/submit/).
>
>
>
The resource is [available in over 40 languages](https://blog.doaj.org/2018/12/19/survey-reveals-need-for-good-guidance-about-trustworthy-places-to-publish-research/) as of 2018. If you want a more personal summary of such criteria, see [qsp on why you don't need a list](https://academia.stackexchange.com/a/83776/32575).
Note that an analysis of the numbers shows that *[The "problem" of predatory publishing remains a relatively small one and should not be allowed to defame open access](http://blogs.lse.ac.uk/impactofsocialsciences/2018/09/25/the-problem-of-predatory-publishing-remains-a-relatively-small-one-and-should-not-be-allowed-to-defame-open-access/)*. Researchers are generally smart enough to not fall in obvious traps; what's left is mostly problems with peer review which exist anywhere, but mostly in journals with scarce transparency.
Upvotes: -1 <issue_comment>username_3: *In addition to* [the answer already offered](https://academia.stackexchange.com/a/2159/19627), you can use a tool such as the one developed by [ULiège Library](https://explore.lib.uliege.be/discovery/search?vid=32ULG_INST:ULIEGE) to help you in your decision.
Their tool, [*Compass to Publish*](https://app.lib.uliege.be/compass-to-publish/test), "uses a criteria-based evaluation to quantify the degree of authenticity of open access journals requiring or hiding article processing charges."
Even if you don't use their plat-form, their set of criteria / tests can be extremely useful to guide your judgment.
>
> What are the criteria?
> ======================
>
>
> *Compass to Publish* uses an evaluation method based on 26 criteria which take the form of questions. These criteria and questions are the
> result of the critical and analytical work of the [team
> behind](https://app.lib.uliege.be/compass-to-publish/pages/9/About)
> *[Compass to Publish](https://app.lib.uliege.be/compass-to-publish/pages/9/About)*,
> who have\* \*examined the practices of a significant number of predatory
> journals and publishers. This examination was then followed by a
> qualitative survey and selection of criteria developed by trusted
> lists and directories, as well as checklists for the identification of
> predatory journals, including:
>
>
> * the ["Transparency and best practice" checklist of the Directory
> of Open Access Journals](https://doaj.org/apply/transparency/)
> (DOAJ) and the [basic criteria for inclusion in the
> DOAJ](https://doaj.org/apply/guide/)
> * [the list of criteria for the identification of predatory journals
> developed by Eriksson &
> Helgesson (2016)](https://link.springer.com/article/10.1007/s11019-016-9740-3)
> * [the v.1.1 criteria version for the identification of predatory
> journals developed by Cabells (a for-profit
> company)](https://blog.cabells.com/2019/03/20/predatoryreport-criteria-v1-1/)
>
>
> Looking at the full range of these criteria, we only retained those
> that are:
>
>
> * truly incriminating and easy to check to ensure user-friendliness;
> * sufficiently relevant and clear;
> * easy to use and check for users.
>
>
> Some information regarding journal policies and procedures can be very
> hard and/or time-consuming to verify. We deliberately decided not to
> include this type of criteria in the evaluation process in an effort
> to ensure user-friendliness.
>
>
>
Upvotes: 2 <issue_comment>username_4: I suggest another approach: You have already performed a literature review for your introduction. What journals are your references published in? Have you ever cited a paper in the journal you are thinking about submitting to?
Upvotes: 2 |
2012/06/25 | 1,513 | 6,358 | <issue_start>username_0: During coursework if a solution manual is available for the textbook, it is always a huge bonus for the student. While the student is exposed to a variety of relevant applications and tricks in the problems, the solution manual ensures the student's hard work spent trying out the problems does not go waste. The solution manual's availability is akin to the presence of a "Cheat" button in [crossword applets](http://www.guardian.co.uk/crosswords/cryptic/25671) - the earlier you press the less you try, but still the presence of the button is useful as such.
What are some useful tips for a student who wants to utilise the solution manual optimally?<issue_comment>username_1: 1. Never use the solution manual before trying everything else; talk to friends, visit the professor, go to class and listen (!), check the internet. Once you use the solution manual for a problem, the potential gain from that problem is significantly and irrecoverably reduced.
2. Use the solution manual to check your work. (Duh.)
3. For problems you aren't planning on solving, you can use the solutions manual to create flashcards and other learning aides (if the course material is anemable to such a construct).
4. If you have a friend/roommate/spouse/trained monkey who can compare your answers to the manual for you, such that you don't actually read through the manual, that may be useful for certain topics.
5. You can make some good money selling it when the semester is over :)
Upvotes: 5 [selected_answer]<issue_comment>username_2: **Ignore it and write a new one.**
Looking at the solution manual is *not* useful; it only gives you *answers*. The point of homework isn't the answers, but the struggle to find them.
Upvotes: 3 <issue_comment>username_3: >
> 1. Attempt the problem on your own first.
> 2. Use outside resources and your own research to try and answer the question.
>
>
>
If you get stuck and can't do it on your own without a little guidance, use outside resources to read about the problem, look through other examples to get an idea of the general procedure, and get a better grasp of what the final answer should actually look like.
Personally, I don't like asking a professor for help or going to my TA's office hours unless I absolutely cannot figure out the problem on my own. So the next step might deviate from other people's opinions on how to use solutions manuals.
>
> 3. Use the solution manual as a quick hint as to the next step.
>
>
>
If you make it through some of the problem and get stuck somewhere, and you cannot find any kind of answer online or in the textbook, then use the solution manual to give you a quick hint as to how you should proceed. It's sometimes useful to see how the book author approached an integration or some weird algebraic step. If you make it to step three, then step four is the critical part of this method.
>
> 4. Use the hint you just took as an opportunity to further your understanding. Work through similar examples outside of the actual homework problem (in the case of a math problem), use a keyword in the solution to read about that step (in the case of a physics problem), etc. Do further research on that particular step, so that when you encounter a similar problem in the future, you will know exactly what to do.
>
>
>
Don't just write down the solution and move on, learn from it to deepen your understanding of the problem. After using the solutions manual and not making any progress on understanding the problem, or that particular step, then I use the following step.
>
> 5. Go to a professor or your TA's office hours to ask the question and get a one-on-one dialogue going about your misconception, why your attempt failed, and/or why the author of the solution did that particular step.
>
>
>
For a mathematics question, I typically ask them on math.stackexchange.com first, as I usually get a great response within minutes of my post. For a physics or other technical/theoretical questions, I found it best to discuss them with someone.
By going through the steps as I have listed them above, I have found that by the time I go to a professor or TA, I can explain the issue I'm having and convey it without stumbling over myself. It taught me to figure out the issue I'm having exactly and to be able to ask a very specific question, rather than going to a professor and asking some vague question about an assigned homework problem and looking for them to give me the solution.
Apologies for the novel, I hope this helps.
Upvotes: 1 <issue_comment>username_4: Don't just read the solutions. This is counterproductive. Work the problems yourself.
However, after finishing, use the answer (not the worked solution) as a check. It is psychologically reinforcing to have a feedback loop and know that you did it right.
If you were wrong, rework the problem. Often the reason will have been a minor calculational mistake (in STEM). You will usually be able to fix this on your own (without even seeing the detailed solution) just by being more careful. If not, sometimes the format of the answer will suggest a more conceptual thing that you missed or prompt you to relook at the textbook description/examples. This is crucial, that you fully redo even "dumb mistake" attempts. Doing this means you won't make them again. It's like music or sports. If you make a mistake, do it over. Do the entire exercise, not just the part you messed up.
In the few cases where the above is not sufficient, look at the actual worked solution to see how they do it. But STILL. Then put the manual down and rework the missed problem yourself. You need to actually practice the solution process. Not just read it. Even though it seems hokey, it will help your learning, versus reading and saying "OK, that was the trick".
I do disagree with other answers that say you should prioritize outreach to friends, Chegg, instructors, SE, before checking the solution manual. It is a tool for you and is extremely convenient for the disciplined drilling problem solver. I would reserve that sort of personalized outreach for when you are baffled by the written solution itself. (Keep a written list and then see your instructor with them.)
But absolutely do use the disciplined drill, check, repeat (if wrong) method that I espouse above.
Upvotes: 2 |
2012/06/25 | 241 | 1,028 | <issue_start>username_0: Can someone publish a paper on the mathematical model of the open source software i.e. parameter analysis of the model (i.e relevant to specific area) which is the backbone of the software.
1. Is it possible to publish paper on model analysis?
2. Apart from citing the software, what kind of credit I should include in the paper?<issue_comment>username_1: Yes, it is possible to publish a paper on model analysis. I would treat this like any other situation in which I wanted to use someone else's "data". Generally, it would be the owner of the data (in this case the developer of the model) that would do the analysis. In the absence of an analysis that I need (or want, or think would be interesting), I would contact the owner(s)/developer(s) and see if they have plans for a similar analysis or would like to collaborate.
Upvotes: 4 [selected_answer]<issue_comment>username_2: Yes, <https://conference.scipy.org/proceedings/> annually publishes papers related to scientific softwares.
Upvotes: 1 |
2012/06/26 | 1,157 | 4,919 | <issue_start>username_0: I am having trouble articulating a statement of intent because of a few things. But to keep on point for Stack Exchange I'll focus only on one: interdisciplinary coursework.
I have spoken to a Fine Arts Department at my top choice and they not only permit but promote taking interdisciplinary courses. My intention is to take as many courses as I can in Psychology while doing the MFA, as well as any required training to use the Eye-Tracking facilities.
1. Should I specify professors from both departments?
2. Should I mention that after I complete the first degree (MFA in my case) I plan to pursue a PhD in the other field (Visual Cognition in my case)
3. Should I state research goals in both fields or keep it to only the school I am initially applying to? (In my case: In the immediate I want to research neurotypical perception through visual design for clearly communicating complex ideas. Eventually though (as in during the PhD if not Post Doctorate) I want to research atypical perception and how to design visual communications that can be easily understood by people with disabilities. I am not sure which disability but mostly Autism Spectrum Disorders, however I also think there could be a strong use for this research in assisting those with Alzheimer's.)
How much of this cross disciplinary information should a person state in their Purpose Statement / Letter of Intent? My concern is that I don't want it to sound like I only want the Psychology Department and leave the committee wondering why I am going for the MFA in Design first.<issue_comment>username_1: In general, the more concrete your essay is—and the more it shows that you've thought through your plans and potential options at the school to which you are applying—the more strongly it will be considered.
Moreover, if you're pursuing an unconventional path, such as applying for psychology but also going for an MFA in design, then you'll probably want to make that clear from the very outset. Otherwise you run the risk of the faculty—which normally makes admissions decisions at the graduate level—thinking you've hoodwinked them somehow. When that happens, that can make your life very uncomfortable.
So you should mention your full degree plans including, if possible, professors from both departments.
*However*, when it comes to writing the essay for admission, that depends a lot on the specific program you're applying to. If you are applying only for a master's program in psychology, then you should talk primarily about psychology-related topics. Your proposed work in design should amplify your psychology work, but not dominate it. On the other hand, if you're applying directly to the PhD program, and the MFA is an integral part of your plans, then you need to explain that at the outset, and should indicate what goals you'll achieve in the MFA that will help you with your PhD.
Upvotes: 2 <issue_comment>username_2: I disagree with [username_1](https://academia.stackexchange.com/users/53/username_1), but with some qualifications.
Generally, I encourage you to lay out your goals and overall plan, including both MFA and PhD. It's vital that you convey your *thought process and justification* for this plan, not just the plan. Is this an impulse? Or has it been many years in the making? Why does this plan suit *you* with your unique skills, capabilities, and disposition? Why does this plan fit the University and department that you are applying for? What about this plan is well known and what is uncertain and unknown?
For example, you should be able to answer this critical question: Why bother with the MFA first? Why not just enter an interdisciplinary PhD program now? What does the combination give you that neither, alone, would provide?
Have you evaluated the many Design Schools (D-Schools) at major universities? Stanford, MIT, and many others have these. If a PhD from these D-Schools won't meet your needs, explain why.
In all of this, what you are really communicating to the committee is what is *unique about you* for MFA and *how you will be successful* in the MFA program.
---
The qualification is that admissions criteria and process is very different for Masters vs. PhD. In Masters programs, no one cares whether you will be a valuable member of the academic research community. What they care about is: can you succeed in the course work (and thesis or capstone project, if required), and will you be a valuable contributor as member of the community of students. In PhD in Psychology, admissions is nearly always determined by whether you are the most attractive research assistant for one of the professors, given their interests and funding.
Therefore, when you write your Masters application, you need to emphasize how your preparation and previous academic work set you up for success in course work and engagement with other students.
Upvotes: 0 |
2012/06/26 | 2,710 | 8,131 | <issue_start>username_0: Is there a way to get the DOI (Document Object Identifier) of a research paper when its title is available?
I am preparing a reference database I could not get the URL of all the references. So, I tried to search online but could not get to anything. I will appreciate if anyone uses such tool/website or has any idea.
**EDIT**
I am sorry but my problem is little bigger. I need to automatically (not manually) get them from the websites. Of course, I do not want to do it for more than 50 papers if that is legal/allowed.<issue_comment>username_1: Resources like [Web of Knowledge](http://www.isiknowledge.com) should have information on the DOI's for any titles that have them available (which should be "all of them" for anything that's been published in the last few years).
Tools like [Papers](http://www.mekentosj.com) or [Mendeley](http://www.mendeley.com) should also be able to "harvest" DOI's for published papers.
Upvotes: 2 <issue_comment>username_2: So you can do this easily using e.g. Web of Science, and probably other bibliographic databases available through your library.
If that isn't an option, a little google search lead me to the [DOI website FAQ](http://www.doi.org/faq.html), and question 3 is yours. There are 8 registration agencies for DOI, and no single way to search across DOI using document metadata. I tested the free [crossref service](http://www.crossref.org/guestquery/) and it was unable to find one of my articles because that journal doesn't use the crossref agency. One of the other agencies has that information, but you might have to search all of them to find it.
Finally, I tried Google Scholar, which will work if the article in question has reference information available online. That depends on the publisher.
Upvotes: 2 <issue_comment>username_3: Reposted from [StackOverflow](https://stackoverflow.com/questions/9711539/reverse-lookup-digital-object-identifier-given-table-of-citations/9795971#9795971):
>
> Here are three options
> ----------------------
>
>
> ### CSV upload to [crossref.org](http://www.crossref.org)
>
>
> CrossRef allows you to upload the linked csv directly, and then
> performs a text query here: <http://www.crossref.org/stqUpload/>
>
>
> However, only 18 of the 250 queries (~7%) returned a doi.
>
>
> ### XML Query
>
>
> Based on the [answer on SO by Brian
> Diggs](https://stackoverflow.com/a/9711893/199217), here an attempt
> that does 95% of the work - toward writing the xml-based query, it
> still has a few bugs that require some deletion using `sed`. But the
> biggest problem that my "session timed out" when the query was
> submitted.
>
>
> the xml syntax includes an option to use fuzzy matching.
>
>
> the doiquery.xml contains the template text in @Brians answer; the
> citations.csv is linked above. (using R, a sample csv can be found on
> I have posted the first few lines of the table on [google
> docs](https://docs.google.com/spreadsheet/pub?key=<KEY>&output=html),
> or the [csv
> version](https://docs.google.com/spreadsheet/pub?key=<KEY>&output=csv)
> (not all records have a doi))
>
>
>
> ```
> library(XML)
> doiquery.xml <- xmlTreeParse('doiquery.xml')
>
> query <- doiquery.xml$doc$children$query_batch[["body"]]
>
> citations <- read.csv("citations.csv")
>
> new.query <- function(citation, query = query){
> xmlValue(query[["author"]]) <- as.character(citation$author)
> xmlValue(query[["year"]]) <- as.character(citation$year)
> xmlValue(query[["article_title"]][["text"]]) <- citation$title
> xmlValue(query[["journal_title"]]) <- citation$journal
> return(query)
> }
>
>
> for (i in 1:nrow(citations)){
> q <- addChildren(q, add.query(citations[i,]))
> }
> axml <- addChildren(doiquery.xml$doc$children$query_batch, q )
>
> saveXML(axml, file = 'foo.xml')
>
> ```
>
> CSV to XML Converter
> --------------------
>
>
> Creativyst software provides a web based [CSV to
> XML](http://www.creativyst.com/cgi-bin/Prod/15/eg/csv2xml.pl)
> converter.
>
>
> Steps:
>
>
> 1. Enter columnames in ElementID's field,
> 2. "document" in DocID field
> 3. "query" in RowID field
> 4. Copy / paste csv in "Input CSV file".
> 5. Click Convert
>
>
> Also, see this related question:
> <https://stackoverflow.com/questions/9880808/shell-script-to-parse-csv-to-an-xml-query>
>
>
>
Upvotes: 3 <issue_comment>username_4: Easier, code-free:
At this time, on the [CrossRef homepage](http://www.crossref.org/), there is a text-input field "Metadata search". It says:
>
> Search CrossRef's database of 71 million records for authors, titles,
> DOIs, ORCIDs, ISSNs, FundRefs, license URIs, etc. You can even paste
> entire references into the search box and discover their DOIs.
>
>
>
I have tried it out only for one DOI of which I happen to know that a record exists in the crossref database.
You can also use the [CrossRef search site](http://search.crossref.org) and paste the title of the paper there.
Upvotes: 1 <issue_comment>username_5: The `R` package [fulltext](https://github.com/ropensci/fulltext) allows you to search DOI's given a title:
```
library(fulltext)
res1 <- ft_search(query = "Estimating Summer Nutrient Concentrations in
Northeastern Lakes from SPARROW", from = "plos")
res1 <- ft_links(res1)
res1$plos$ids
```
>
> [1] "10.1371/journal.pone.0081457" "10.1371/journal.pone.0030492" "10.1371/journal.pone.0049220"
>
>
>
Upvotes: 2 <issue_comment>username_6: Update in 2022: Easiest for me was using the website, where you can just copy and paste your references: <https://search.crossref.org/references>
I also looked at a few python libraries to interact with the crossref REST API, e.g.,
<https://pypi.org/project/habanero/>
<https://gitlab.com/crossref/crossref_commons_py>
The libraries were easy to use in general, but it was not straight forward how to get the DOI based on a title and there were not really any good examples for this task.
Upvotes: 0 <issue_comment>username_7: As <NAME> [mentions](https://academia.stackexchange.com/a/185526/158250), you can use "works" field query from crossref api. ([take a look at Richard Feynman example](https://api.crossref.org/swagger-ui/index.html#/Works/get_works)). It returns a JSON file. You can extract DOI from it.
Upvotes: 0 <issue_comment>username_8: As the author of [pysotsog](https://github.com/WolfgangFahl/pysotsog) I am recommending that library. It searches wikidata at this time and will use other bibliographic databases such as crossref, dblp, library catalogs and so on in the future it's intended to use general search engines as well. The concept is to be a specific search engine for scientific content see [sotsog search strategy](https://wiki.bitplan.com/index.php/Pysotsog#Search_strategy).
Here is a command line example for the title "We Need a Magna Carta for the Internet" by <NAME> with the DOI doi=10.1111/NPQU.11475.
```
sotsog We Need a Magna Carta for the Internet
We Need a Magna Carta for the Internet(Q55693402):✅
Paper ➞ We Need a Magna Carta for the Internet:
wikiDataId=http://www.wikidata.org/entity/Q55693402
doi=10.1111/NPQU.11475
publication_date=2014-07-01 00:00:00
opening https://scholia.toolforge.org/work/Q55693402 in browser
```
There is also a demo available at <http://sotsog.bitplan.com>
[](https://i.stack.imgur.com/LKUh8.png) which points to the [scholia page](https://scholia.toolforge.org/work/Q55693402) of the relevant article.
In simple cases like the one above you can directly search wikidata and use the [wikibase-cli](https://www.npmjs.com/package/wikibase-cli) command line tools for it by searching for the [DOI](https://www.wikidata.org/wiki/Property:P356) property.
First step - find the paper wikidata entry with a full text search
```
wd search We Need a Magna Carta for the Internet
Q55693402 We Need a Magna Carta for the Internet
```
Query the DOI
```
wd query -s Q55693402 -p P356
10.1111/NPQU.11475
```
Upvotes: 0 |
2012/06/26 | 2,271 | 8,532 | <issue_start>username_0: Many of us often talk about Bachelor's and Master's students, PhD students, researchers, professors, and postdocs.
But, do we know what we actually intend when we say "*Post-Doc*"?
We all know that a Post-Doc is a person who, after finishing his/her PhD, works for one or two years as a scientist in a scientific research group. Beyond this simple definition, I would like to know what you think about Post-Doc **roles** inside a laboratory and a group.
**What is actually a Postdoc fellow?**
Is he a **debutant researcher**? Or is he just a **ultra-super-student?** Or is he **half** a super-student, and **half** a researcher?
What is a Postdoctoral fellow supposed to do?
What do you expect from a Postdoctoral fellow?<issue_comment>username_1: There are many formal roles that generally fall under the category of "post-doc":
* The simplest is as a post-Ph.D researcher working with a faculty mentor and doing their own research
* Some postdocs have a role as "lab manager": they help with advising students.
* In addition, if given an appropriate title, a postdoc (as "visiting/research faculty") can write their own grants or collaborate on grants that might support them.
* The limit of the above is a pure soft-money position that is not "supervised" by a faculty member. Such faculty are also often called 'research faculty', and may be many years away from a Ph.D
* In wetlabs, a postdoc role might also be as a lab technician or lab manager, handling supplies, tech work (making knockout mice for example).
Upvotes: 5 <issue_comment>username_2: In addition to username_1's answer, I'd say that a postdoc is no longer a student. A PhD student is expected to demonstrate that she can do research, and this is sanctioned by the PhD degree. A postdoc is rather expected to demonstrate that she can be trusted with a permanent academic position.
In my field, postdoc positions usually denote fixed-term positions (between 1 and 3 years) with limited "official" administrative responsibilities (i.e. no involvement in the long-term management of the department). Most postdocs are usually funded on some project, which implies some concrete duties w.r.t. to the project (such as taking care of the "deliverables"). Some are more like "fellowships", where the applicant must come up with her own research agenda.
But there is no common basis, and it can varies greatly from one position to another.
Upvotes: 5 <issue_comment>username_3: To quote (jokingly) a rather blunt friend of mine, a post-doctoral fellow is "someone who has a Ph.D. but is still nobody." I don't quite subscribe to so dismal a view, but it usually means someone who hasn't achieved full independence yet (inasmuch as they still have at least a nominal advisor).
I expect a postdoctoral fellow in my group to be a competent researcher who doesn't need much instruction on the basics of *how* to do research, but might need some training on the particular skills needed in my group. She should be capable of taking over virtually any duty in the group, and would be expected to take on some of the duties that would be associated with being a professor (or staff member in a research lab somewhere). That would mean being responsible for supervising undergraduates doing research projects, occasionally covering lectures, and being responsible for supervising the lab (purchasing equipment, and so on).
That said, the post-doctoral fellow would not be left entirely to her own devices: since she is still effectively in a training situation, she would be given help and advanced notice. I would not just surprise her with duties; they would be assigned per mutual agreement, and always with a specific purpose in mind. (In other words, I am not simply "dumping" duties on the post-doc.)
Upvotes: 5 <issue_comment>username_4: This answer is a bit different from the others. The other answers deal with what a postdoc **is**. My answer describes the difference between the roles of grad students and postdocs in two ways.
The first distinction is this: A graduate student is an **apprentice**, while a postdoc is a **journeyman**.
The academic system in which a student earns a doctorate and eventually becomes an academic is based on the apprenticeship system.
The graduate student is the [apprentice](http://en.wikipedia.org/wiki/Apprenticeship) . The graduate student is learning his or her craft from the adviser (master). The graduate student hones his or her research skills performing the research of the adviser. Completion of the PhD defense signals that the student has completed the apprenticeship.
The post-doctoral fellow is the [journeyman](http://en.wikipedia.org/wiki/Journeyman), one who has completed training in the basic skills, but is not yet considered a master. To this end, the postdoc seeks out other masters to learn from. Since the postdoc is not an apprentice, the postdoc is given more freedom to design and implement his or her project. The postdoc is also expected to be able to work with minimal oversight from the adviser. A postdoc journeyman becomes a master upon successfully securing his or her own academic position.
The second distinction is in terms of classification and compensation. At most US institutions, the graduate student is a **student**. The grad student may receive a stipend and benefits, but someone pays tuition for that student. A postdoc is an **employee** who receives a salary and benefits. No money is paid back to the institution by (or on behalf of) the postdoc.
Upvotes: 5 <issue_comment>username_5: A postdoc is also someone that needs to be hired by academia to do a task, that is, a contractor for academia. I am a scientific programmer, but I am hired as a postdoc because that's the only kind of contract they can give in academia. I publish no papers, have no interest in an academic career, and I have no other duties other than coding (or at least that would be the plan).
Upvotes: 2 <issue_comment>username_6: At some universities, a [Post-Doctoral Fellow](http://nexus.od.nih.gov/all/2012/06/29/postdoctoral-researchers%E2%80%94facts-trends-and-gaps/) is a different title than a Post-Doctoral Researcher due to [federal regulations in the USA](http://grants.nih.gov/grants/guide/notice-files/NOT-OD-12-033.html).
The **Post-Doctoral Fellow** is paid through a different funding mechanism (e.g., NRSA training grants) and is not considered an employee (thus is excluded from health insurance and retirement benefits) and is treated more like a student (may take classes or do a clinical residency; receives a non-negotiable stipend which is [not ordinary income](http://www.postdocs.cornell.edu/tax-issues)). There may be a citizenship requirement for this funding mechanism.
The **Post-Doctoral Researcher** is an employee (qualifies for benefits but does not take classes or receive training). It may be easier to deal with visa requirements through this position.
In both cases, postdocs do research. Neither is guaranteed to advance your career to being a professor, but [pretty much everyone has to do it](http://nexus.od.nih.gov/all/2012/06/22/so-what-does-the-biomedical-research-workforce-look-like/).
Upvotes: 3 <issue_comment>username_7: Thanks to the recent PhD Comics, I have a link to a pretty definitive definition
.
The NIH link is: <http://grants.nih.gov/grants/funding/all_personnel_report_faq.htm#774>
This links to a letter to the US National Postdoctoral Association: <http://grants.nih.gov/training/Reed_Letter.pdf>
Upvotes: 4 <issue_comment>username_8: >
> "Whatever else they may be, postdoctorates are one of the greatest bargains in the US economy. Where else can one hire Ph.D.s, whose training and smarts put them among the best and brightest in the world, to work 60 hours a week for $30,000 to $40,000 a year, with limited benefits and little power to influence their working conditions and pay?" -- <NAME>, *Thanks for the Great Postdoc Bargain*
>
>
>
<http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2002_08_30/nodoi.4149859741665864757>
Upvotes: 2 <issue_comment>username_9: In some cases, postdocs are seen as a means to shuffle newly-minted PhD's into "alt-ac" careers. I just came off of one such postdoc myself. Since the postdoc was very programming/data/technology centered, and I actually went back to school to get out of programming, it didn't really take with me.
Upvotes: 0 |
2012/06/27 | 653 | 2,866 | <issue_start>username_0: Assuming that one has begun a PhD program at a university which allows a student to choose an advisor after his/her first year in the program and the student has narrowed down to a couple of potential advisors, what is the etiquette for approaching them for advisor commitment? Specifically,
* How do you get across that you aren't sure yet about him being your advisor and are fishing around before you decide without sounding rude?<issue_comment>username_1: Advisors understand that students are fishing around - that's normal. What's less common (but possible) is students fishing around in wildly different areas. Just keep in mind that either an advisor is going to see something in you that will motivate them to try and convince you to work with them, or they'll view you as someone who needs to make up their mind first, and won't spend too much time thinking about you.
In that respect, matching up with an advisor is more like dating than an job interview, at least in the US :)
Upvotes: 5 [selected_answer]<issue_comment>username_2: The adviser already knows that you are not 100% committed to his or her research group. If the system lets students choose, then the advisers know the rules. The faculty in that department very likely made those rules. They know that you are considering other advisers.
It is never rude to consider other advisers. Some programs even require it. My graduate program required that I interview at least three potential advisers and rank them. The faculty would then be informed of the list of students who chose each faculty member at the first choice *only*. Faculty had to fill open positions from this first.
This system was implemented by the faculty a few years previous, when they became dissatisfied with an approach that left matching entirely up to students and faculty meeting and getting along. Faculty would then choose their favorites from all interested students. Usually, this meant that a small number of students were not chosen at all, which was not acceptable.
Upvotes: 3 <issue_comment>username_3: username_2 is basically correct—as advisors, we know that students have multiple options for choosing advisors, particularly in setups where the students pick advisors after their arrival. Therefore, we won't (or at least shouldn't) take it personally when someone tells us they're considering other advisors.
In general, however, you should also remember that these processes are often *double-sided*: in case of competition, the advisors also have a choice in who they want as their top choice. If you are overly hesitant, the advisor may choose another candidate who is more certain as a top choice. So, you should be honest if you're not immediately "sold" on working for a particular advisor, but don't be so negative as to make the advisor look elsewhere.
Upvotes: 3 |
2012/06/27 | 1,117 | 4,600 | <issue_start>username_0: I am preparing to write and submit a grant proposal to the US National Science Foundation (NSF) through the [Research at Undergraduate Institutions (RUI)](http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5518) program. This will be my first submission to this program.
From my understanding, proposals submitted through RUI are sent to the appropriate program in the appropriate division and directorate. Since my project involves synthetic chemistry in alternative reaction media, my proposal would probably go to the [Chemical Synthesis (SYN)](http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503419&org=CHE&sel_org=CHE&from=fund) program in the [Division of Chemistry](http://www.nsf.gov/funding/pgm_list.jsp?org=CHE) under the [Directorate for Mathematical and Physical Sciences](http://www.nsf.gov/dir/index.jsp?org=MPS).
My colleagues, both in chemistry and in other disciplines, have shared conflicting reports about how RUI proposals are evaluated. Will my proposal be evaluated against the entire pool of proposals in the SYN program from all institutions? Or, are RUI proposals evaluated separately?
I am hoping that RUI proposals are evaluated separately, since I do not have access to the same research infrastructure that someone at a research institution does.<issue_comment>username_1: At least in mathematics (and presumably in other fields, although I have no direct experience with that), they are evaluated in the same pool as all the other proposals. In practice, there seem to be two key differences in how they are treated:
1. Panel members can be a little more forgiving for certain aspects of RUI proposals. For example, if the PI publishes excellent papers but not very many of them, then this low publication rate would probably be considered a bigger drawback in a non-RUI proposal.
2. The NSF may give some degree of preference to a few RUI proposals, as described below.
The review panel classifies proposals into three categories: roughly 10% that are highly recommended for funding, 30-40% that are recommended for funding, and 50-60% that are not. The available funding is never sufficient to cover all the proposals recommended for funding, so further decisions need to be made after the panel.
The ones not recommended for funding are automatically eliminated, and the ones highly recommended for funding are more or less guaranteed to be funded. The ones in the middle are ranked in order by the panel, but the NSF is not required to follow this ranking. They largely follow it, but they adjust it based on their own criteria (for example, balance of fields, geographical diversity, etc.). My understanding is that if no RUI proposal in a given panel would otherwise be funded, then the highest-ranked RUI proposal may be bumped up in the rankings. However, this is not guaranteed, and it depends on having a high enough ranking from the panel. The program officers may also compare how panels in different areas ranked their RUI proposals, to see which ones seem most worthy of funding. However, there is no RUI-only panel.
Upvotes: 5 [selected_answer]<issue_comment>username_2: My experience with Computer Science panels (IIS Division) is similar.
However, in IIS, acceptance rates are typically somewhat lower:
10 % Highly competitive (HC),
15 % Competitive (C),
75 % Not recommended for funding by panel (NRFP)
NRFP proposals are immediately discarded.
Sometimes some of the NRFP proposals are classified as Low Competitive (but still
immediately discarded).
Highly competitive proposals are usually, but not always funded.
Some (approx. one third) of Competitive proposals are funded.
Total funding (acceptance) rates (e.g., for III) are typically around 15 percent, sometimes a bit higher (up to 20%) for Early Career proposals.
Funding rates in Computer Science vary widely across programs, solicitations, size classes,
divisions, year submitted, etc.
ranging from 10% to 50% (depending on budgets, number of proposals
submitted, etc.) In general funding rates decline with increasing size (budget) class
of the proposals.
RUI proposals are considered together with other proposals but
treated somewhat more leniently.
Proposals from all of the review panels for a particular cluster (group), e.g., III in IIS Division of the CISE directorate,
for a particular solicitation (and size class) are considered together when
making funding decisions.
Panel recommendations are only advisory to NSF staff, who also consider geographic
diversity, programmatic priorities and balance, institutional diversity, etc.
Upvotes: 3 |
2012/06/27 | 822 | 3,676 | <issue_start>username_0: Assuming that one has begun a PhD program at a university which allows a student to choose an advisor after his/her first year in the program and the student has narrowed down on a couple of potential advisors, once a student has established contact with his potential advisor,
At what point do you discuss things like authorship, expectations and other such details? First meet, second meet or otherwise?<issue_comment>username_1: Think of your first meeting as a mutual interview. The adviser wants to know if you are worth spending time and money on. You need to ask questions to determine if the adviser is worth spending years of your life on.
Good topics for the first meeting are:
1. The adviser's expectations of you (including hours worked per week, number of papers to publish before graduating, etc.)
2. Your expectations of your adviser (Do you get vacation time? How long does it take your students to graduate? How much grant funding do you have? What earns me authorship on papers? etc.)
3. Your potential project (What are the long-term goals? What are the short-term objectives? Have other students worked on this project before?)
4. What preparations do you need to make? (Should you take a particular course? Should you read certain papers or books?)
You should also try to get candid answers to these questions from the students who already work for your potential adviser.
Upvotes: 6 [selected_answer]<issue_comment>username_2: This question should be asked during your interview, in a polite but directed manner. The question is simply, "How do you assign authorship on papers co-authored with graduate students?" The answer should address who is on the paper, the ordering of the authorship, and what criterion is used to determine who is a co-author.
You should also *definitely* ask his current graduate students the exact same question, preferably students who have gone through the process of writing a paper with the advisor at least once, just to make sure that what the advisor says is mostly mirrored in what the students report.
Upvotes: 4 <issue_comment>username_3: I assume the main issue for you is the question of authorship.
In academia, there are rules about authorship, and they differ between the disciplines slightly. You can expect that supervisor will request to be last author of every paper. I very much advise against trying to bargain around that, this is the only scientifically relevant reward your supervisor will get for supervising you. And since professors talk to each other, you may unknowingly ruin your reputation by trying to negotiate on this.
There are details about authorship of other people who might give you research ideas (like other grad students in the lab, outside collaborators) but I don't believe the general answer can be provided to you by the professor in a way that would protect you in the future if he will be trying to coax you into accepting another author that you don't see as having significant contribution to the paper. Usually can people agree on everything in general, but then it is always down to the question, what amount of work does qualify someone to demand co-authorship.
For example, a person who does some simple physical/electrical measurements is certainly not justified to be a co-author, but if someone performs experiment for you that requires trial of toxicity of the chemical substance including 50 live mice, and you depend on his knowledge and experiences to actually design the experiment properly, then such person will probably be expected to be co-author, especially if you only took the results and ran them through SPSS.
Upvotes: 0 |
2012/06/27 | 736 | 3,156 | <issue_start>username_0: Assume that one has begun a PhD program at a university which allows a student to choose an advisor after his/her first year in the program and the student has narrowed down to a couple of potential advisors and has taken at least one course with each of them.
* How early should one contact potential advisors? Just at the beginning of the program (early bird?) or after 6 months (after taking courses with each one) or right when you need to choose (don't make decisions till you have to?)?<issue_comment>username_1: **As soon as is reasonable.**
Think of it this way. There are possibly other students competing to work with this advisor, and they might have funds only for one new student. There might be other considerations - maybe the advisor is going on sabbatical, or isn't taking new students, and so on.
So as soon as possible, set up a meeting with the potential advisor, indicate your potential interest (and why!), and ask them point blank if they're taking students, and how they prefer to screen students (some do it through course work, some might like to work with you on an independent study project, and so on).
Depending on what they say, you can take further followup action - maybe they want you to take their class, and maybe they have a research group that has open meetings, and so on.
Bottom line: don't wait till your year is over, because it is quite possible that someone has made a decision already.
Caveat: if this year-delay is institutionalized, it might be that all advisors wait till the end of the year to even think about new students. I suspect this is unlikely, but in any case that first meeting will help clarify it.
Upvotes: 4 [selected_answer]<issue_comment>username_2: It is never too early to contact potential advisers. If you have your heart set on a particular individual, you may even want to initiate contact before you start (and perhaps as early as right after you have applied). Early contacts usually come from motivated students.
Waiting a while gives you the ability to get to know the adviser and helps them get to know you. This could be beneficial, but you should not wait too long. Last minute contacts suggest a variety of behaviors that are unappealing in a graduate student, like laziness, forgetfulness, lack of dedication, etc.
If the program lets graduate students choose, then the advisers are well aware of how the system works. A short email requesting more information and an in person interview would be appropriate. If you know the adviser better (you have already taken a class with this person), then an informal stop by their office would be fine, too. Most advisers expect this kind of contact in this situation. An initial contact by email does not have to be anything more than:
>
> Dear Professor {X}, I am (or will be soon) a graduate student in your department. I am interested in joining your research group. I have read about your work in the field of {Y} and found it interesting, and I want to learn more. Are you available to meet with me at one of the following times {suggest three specific times}? Thanks.
>
>
>
Upvotes: 2 |
2012/06/27 | 1,497 | 6,549 | <issue_start>username_0: I am an undergraduate student with no experience at all.
If you are writing a research paper, that you are planning to submit for publication, how do you state that a particular statement/fact is commonly known to all and it's dicoverer/inventor is not known, and that there exists no research paper that introduced the concept but there do exist various books/websites that discuss the very concepts and a few derivatives of the concept?
I am not able to cite a particular statement/bunch of statements as I am not able to find any research paper related to that at all.
The particular thing in question is: <https://math.stackexchange.com/questions/161661/source-of-probably-the-most-simplest-math-trick>
What should I do?<issue_comment>username_1: If you found a book that discusses the statement, and you really can't track down where it comes from (does the book cite anything?), then citing the book (with chapter/page numbers) is acceptable.
Upvotes: 4 <issue_comment>username_2: If the statement/concept/idea that you think you want to cite is covered (without citation) in the introductory undergraduate textbook(s) on the subject, then it is likely common knowledge in the field. This is particularly true in science and social science. For example, you should not need to cite things like the following:
* Fluorine atoms are more electronegative than carbon atoms.
* Asexual reproduction of eukaryotic cells usually occurs by mitosis.
* Force is a vector quantity, while power is a scalar quantity.
* The free market equilibrium price for a commodity is found at the intersection of the supply and demand curves for that commodity.
* Sigmund Freud is commonly considered the founder of psychoanalysis.
Upvotes: 4 <issue_comment>username_3: You don't need to cite common knowledge. If you think it is common knowledge, wait for the reviewer to ask for a reference, before going to all the trouble of sorting out the history. Often if a reviewer doesn't think the statement is common knowledge he/she will suggest a reference or provide a conflicting reference.
Upvotes: 5 [selected_answer]<issue_comment>username_4: One helpful approach can be to ask a few people that you think of as familiar with the field (in your case, this could be professors or certain other students). Even though most schools offer no class in recreational mathematics, you can ask 3 or 4 people who you see as at least "intermediate" level (whatever that means) in recreational math. If all of them immediately recall the information in question, then it's a good bet that you don't have to cite it. Alternatively, if most of them don't know it, then perhaps you should cite it.
Specifically for recreational math, I recommend that you ask around on the website Art of Problem Solving: <http://www.artofproblemsolving.com/> (from the front page, click on "online community"). This site is focused on contest mathematics, which is distinct from recreational math, but a close cousin (and many people interested in one are also interested in the other).
Upvotes: 2 <issue_comment>username_5: I will answer your question in a somewhat sideways manner: I think anyone who is trying to write a math research paper who does not already have a PhD in mathematics or a closely related field should do so in close consultation with an **adviser** who does have such a PhD. It is not practical to learn all the standards of journals and publishing on your own. Virtually every undergraduate is also in the following situation: their own perspective on mathematics is not yet mature, and thus if they submit a paper to a research journal they will be submitting their work to an audience with far more experience and background knowledge. (A few truly brilliant undergraduates do work up to the standards of other mature, professional mathematicians, but even for them the work is probably much less than what they will be capable of later on. I can't think of a single instance of a professional mathematician whose undergraduate work was in the same league as their later research.)
In fact -- and I don't mean to be discouraging by saying this -- most research done by undergraduates is not of a publishable quality. This does not mean that undergraduates should not engage in research -- I think it is very valuable and enjoyable for them to do so (if anyone cares: I did research as an undergraduate, had a blast doing it, and did not try to publish it) -- but only that formal publication should probably be a goal for later in one's career than that, especially nowadays when it is so easy to put your work on the web.
In particular, you write
>
> a particular statement/fact is commonly known to all and its discoverer/inventor is not known, and that there exists no research paper that introduced the concept but there do exist various books/websites that discuss the very concepts and a few derivatives of the concept?
>
>
>
Honestly, this sounds unlikely to me. The vast majority of mathematical topics that are discussed in books are also discussed in research papers (maybe it is somewhat different in recreational mathematics, but even there I imagine it's still mostly true). How do you know that no research paper treats the concept in question? Searching the mathematical literature is itself a skill that takes both general experience and specific expertise in the subfield you're searching in: as a research mathematician it is common enough for me to come across a mathematical concept, try to find it in the literature, and only find it several weeks or months later when I have become more familiar with the local terminology and standard results. Similarly, most mathematical concepts *are* traceable to a specific discoverer/inventor, although admittedly the generic mathematician does not feel as honorbound to track down primary sources as academics in most other fields (in my opinion this is a rare "character flaw" of the generic mathematician!).
Even if you have done all the mathematical research yourself, consulting an experienced adviser on how to write up and submit your work could save you a lot of time and headache. Some journals/editors/referees are relatively supportive of authors writing their first paper (everyone who has published a paper was in that situation at one time!), but if your paper is, or looks to be, below the level of papers they want to publish, it will probably get bounced back to you with little constructive criticism. An adviser can be much more kind...
Upvotes: 2 |
2012/06/27 | 748 | 2,885 | <issue_start>username_0: How do I find research on strategies for recruiting students into undergraduate programs?
I am an assistant professor at a small comprehensive 4-year public institution. My department (chemistry) has implemented a new strategic plan. One of our principle action items involves recruiting more high school students to attend our institution with the intention in majoring in chemistry.
I know that research is conducted, probably at the institution level, on what recruitment strategies are successful in various disciplines. However, since my research background is in chemistry, I am not familiar with the resources to find this research.
I will start with this question: What journals and/or databases should I be reading and using? I know about the *Chronicle of Higher Eduction*. Which others are good resources?
Update: I would like to find relatively recent reports, preferably in peer-reviewed literature or published by not-for-profits. Strategies that use social media would be great. There is a nice study linked in Dan C.'s answer, but it is from April 2004, meaning the data likely predates Facebook.<issue_comment>username_1: Does this webpage have some of the information that you're looking for?
<https://www.noellevitz.com/papers-research-higher-education/student-recruitment-and-financial-aid>
(I recommend the "Factors to enroll" report; it asks for a login, but you can continue as a guest.) Alternatively, you might also find the following pdf useful.
<http://www.edgeip.com/media/edgeip/graphics/Research0404.pdf>
Upvotes: 0 <issue_comment>username_2: There is a nice survey report online: ["What Matters to Student Success"](http://cpe.ky.gov/NR/rdonlyres/AFA304F0-C125-40C2-96E5-7A8C98915797/0/WhatMatterstoStudentSuccessAReviewoftheLiterature.pdf), a report from the Government of Kentucky.
The work analyses the factors most crucial to student success in a college. It also discusses recruitment of students. For example,
>
> Consumerism colors virtually all aspects of the college experience, with many colleges and universities “marketizing” their admissions approach to recruit the right “customers” — those who are best prepared for college and can pay their way (Fallows et al. 2003)
>
>
>
It does not exactly answer your question on strategies for selection, but gives some insight to what makes a successful student pool, for which admission process is one determinant.
I am sure the reference papers cited in the journal (and their citations) will assist you in an extensive literature survey.
For a relevant journal, I found this: [Journal of College Admission](http://www.nacacnet.org/research/PublicationsResources/Journal/Pages/Journal-of-College-Admission.aspx). I do not know about the quality, but I was able to find this discussion (or thereabout) in a few papers herein.
Upvotes: 3 [selected_answer] |
2012/06/28 | 761 | 3,132 | <issue_start>username_0: When it comes to getting a tenure-track position where one has both a lot of resources and academic freedom to do what one wants to do (so having a tenure-track position in a top university might be more useful because one may get more resources at a top university).
Or in short, when one wants to maximize one's chances of becoming a "top scientist".
There are obviously *numerous* factors, and "rank/prestige" should not be considered as a factor in itself. That said, the period after one gets a PhD is an extremely important time for building up connections, and those are easier to get at a top university.<issue_comment>username_1: * Getting experience at a top university is good for your CV and for you as a researcher.
* Getting more research experience under your belt can increase your chance of getting a job in the future, but there are no guarantees.
* A post-doc will give you a chance to write more papers and to find out what topic you really want to do your research on.
* Taking the assistant professorship now would get you immediately on the track to full professor, which may be delayed if you spend years doing a post doc.
* Working at a non-top university might mean that the students you have access to, for example, as RAs or PhD students, to develop your research vision, will not be as good as at the top university. That said, as a post-doc, you will generally not have access to such students anyway, except perhaps as part of a collaboration.
* The pressure to succeed at a top university is much greater than at a non-top university.
Upvotes: 3 <issue_comment>username_2: A post doc is merely a step towards getting a tenure track position. I would always suggest taking the tenure track job. The only time I would even suggest thinking about it is if the tenure track position has a heavy teaching load (e.g., 3-3 or higher in the sciences and 4-4 or higher in the humanities). Even with a ridiculous teaching load I would still suggest the tenure track position.
Not everyone with a postdoc at a top university gets a tenure track job afterwards.
Upvotes: 3 <issue_comment>username_3: A postdoc is not a career goal; it's yet another step in training, with the purpose of preparing the trainee for the eventual faculty role. With that in mind,
### Postdoc is good because:
* You're associating yourself with some top-notch researchers and gaining valuable research experience.
* You're learning new techniques and methodologies from a well-known and well-respected professor.
* You're writing grant proposals that are X times more likely to be funded because 's name is on them.
### Faculty is good because:
* You're running your own lab and getting experience managing teaching, research, mentoring, and administrative duties.
* You're proving that you can do it by yourself.
* You can do research on your own interests, without having to worry about what your PI wants you to do.
Additionally, think about the [opportunity cost](http://en.wikipedia.org/wiki/Opportunity_cost) of both choices... it really depends on where you want your career to go.
Upvotes: 3 |
2012/06/28 | 3,585 | 14,863 | <issue_start>username_0: Most Australian universities have a large number of international students. Common countries of origin include China, Singapore, and Malaysia. When interacting with students, it's much nicer if you can learn their name and pronounce it correctly. To reduce the scope of this question, I thought I'd limit it to Chinese names, but it could be broadened to include other Asian countries.
Several challenges emerge with Chinese names for an Australian (and presumably others from North America, Europe, etc.):
* challenges of pronunciation
* challenges when reading a Chinese name of knowing which part of the full name is the name to address the student by
* challenges in remember the name. In particular, I find it difficult to remember a name that I can't pronounce. Furthermore, the less familiar I am with a name and the fewer associations I have with a name, the harder I find it is to remember.
### Questions
* How can I learn how to pronounce names of Chinese students?
* How can I better recall names of Chinese students? e.g., get acquainted with common names, build some semantic knowledge around Chinese names.
I'd be particularly interested in any good online resources for this purpose.
**UPDATE:** *Following the suggestion of @scaaahu I have asked [this question also on Chinese Stack Exchange.com](https://chinese.stackexchange.com/q/1818/807) . I normally would not cross-post, but I think that this question might be a rare exception to the rule where cross-posting will provide complementary perspectives.*<issue_comment>username_1: First of all, I would like to sincerely thank you for your consideration on behalf of all Chinese (I am a Chinese myself).
Now, to answer your questions. One of the best ways to pronounce the individual Chinese names correctly is to ask them - those Chinese students. They would be glad to tell you.
In its nature, Chinese is hard for English speaking people to pronounce. This site is not the right place to discuss the details. There is a better [Stack Exchange site](https://chinese.stackexchange.com/).
There are many Chinese dialects. Mandarin Chinese is the most common one. I just did a search, [this site](http://mandarin.about.com/od/pronunciation/a/How-To-Pronounce-Mandarin-Chinese.htm) seems to be a good place to use. I tried some of the audio sound clips and they sound fine.
Again, asking them is the best way. My Chinese last name is Hu. The correct pronunciation is like "who". Many non-Chinese speaking people pronounce it wrong. I already got used to it but I am always happy to tell them the right way when they ask me. So, thank you again for asking.
Your second question is how to recall Chinese names. This is even harder for a non-Chinese speaking person. My suggestion is to ask individuals what names they go by. If they have English names, would they prefer you call their English names?! I believe most of them would say yes. If they insist on you calling them by their Chinese names, I am afraid the only way is to ask their names every time you meet them. I am not sure they would like it but if that's what they want, that's the way they get.
Upvotes: 4 <issue_comment>username_2: There's a [specific site](https://www.cs.cmu.edu/~zhuxj/readpinyin.html) that has examples of Chinese names. You might find that quite helpful. There aren't audio files though, and there seems to be a disagreement between the site and the one linked above about the correct pronunciation of 'zh' (as 'dr' or 'j').
Upvotes: 2 <issue_comment>username_3: **Learning how to pronounce**
As has already been mentioned, the best way is probably to ask the person yourself. As a Chinese, I don't expect a non-Chinese to be able to pronounce my Chinese name correctly; pronouncing the [pinyin](https://en.wikipedia.org/wiki/Pinyin) is good enough. Furthermore, I wouldn't expect a non-Chinese to know how to get the [four tones](https://en.wikipedia.org/wiki/Four_tones_(Chinese)) correct.
However, if you're curious, and if you know the exact Chinese characters, one convenient resource is to use Google Translate's audio tool to play back Chinese words:
<https://translate.google.com/#zh-CN/en/>
You'll need to either cut and paste the characters into the text box, or use the handwriting tool to input the characters. Note that the accuracy of the handwriting tool is likely dependent on getting the [stroke order](https://en.wikipedia.org/wiki/Stroke_order#General_guidelines) correct.
**Remembering Chinese names**
Unless the Chinese name is very common, it's likely very hard to remember a person's Chinese name without writing it down. In academic circles, if the person has a publication record, you could get hold of this indirectly by getting a journal reference to a paper authored by the person. Alternatively, with social media, you could add/follow the person via Research Gate, LinkedIn or Facebook. Now that you have the name in writing, you could also check on the web as to how it is pronounced.
Upvotes: 1 <issue_comment>username_4: Well, as I was some sort of a foreigner myself, I can tell you:
Ask them
========
The best way to pronounce someone's one in a foreign language is how they tell you it is pronounce. Notice, that this "first-hand" pronunciation might not correspond with the official one. (For the latter: basically, the way how our X-language tongue reproduces Y-language sounds might not benefit the recognition.)
Also, specifically for Chinese: some of them specifically adapt "easier" names for the communication with Westerners. So, fully expect something like:
"How should I call you?"
"Oh, my actual name is XY, but please call me just Z."
Upvotes: 0 <issue_comment>username_5: This isn't about pronunciation, but one thing I used to find myself confused by was not knowing which name was the family name and which was the given name (since sometimes people use the Chinese convention of family name first, but sometimes they westernize it by putting the family name last). A pattern that really helps for this is that family names are almost always 1-syllable, while given names are usually 2-syllables. This won't always work, it won't help you with Jiang Qing say, but as a quick sample it does work for every president of the PRC.
Upvotes: 2 <issue_comment>username_6: To those commenting "ASK THEM," yes, obviously. However, this doesn't exactly address the OP's issue, because this only provides a one-off for each individual name, but doesn't provide a framework for learning to pronounce new names in the future. It's also endlessly frustrating for a student to have to be like "my name is pronounced X," english speaker: "S?" Chinese student: "**X**", english speaker: "**S**??", etc.
The key issue for Chinese names from the perspective of native English speakers is that **pinyin does not exactly map onto English pronunciation**. I can't count how many times an English speaker has encountered pinyin like "qi" and subconsciously inserted a u, morphing it into "qui" or some other monstrosity.
It can be helpful to learn some basic Mandarin linguistics for name pronunciation. For example, each Chinese character is made up of a final and optional initial phoneme. The allowed phonemes are super small, thus all possible Mandarin pinyin can be written in a compact table:
<https://chinese.yabla.com/chinese-pinyin-chart.php>
Obviously this doesn't cover the tones, but comparing a student's name with this table and practicing the pronunciation (such as by recording yourself and comparing your recording with the audio file for the same pinyin) will get you much further than 99% of native English speakers.
As for remembering the names, this is challenging because as you mention memory is related to how many external connections you have to the word. Since Chinese names typically have no connections inside of a native English speaker's memory, to remember them you need to actively create those connections through simple/stupid mnemonics. As an English example: "His name is Joe, joe is another word for coffee, java is another word for coffee, Java is an island in the Pacific." or something similarly inane. It's remarkable how effective this has been for me personally.
As a conclusion, the important point is that you are making an honest effort and most people will appreciate that you are trying to do the right thing, no matter how far away you are from the "perfect" pronunciation. I once overheard an introduction between a Chinese person and English speaker, in which the English speaker came out with this gem: "Don't bother telling me your name, I'll never remember." If that had happened to me, that would stick in my craw for the rest of my life, and quite possibly beyond!!
Upvotes: 2 <issue_comment>username_7: This answer is going to address not only Chinese names, but all unfamiliar names.
Most answers say "*Just ask them!*", but personally I found that to be insufficient and only marginally helpful. I have the same experience when people ask me about my own name, which many find confusing to pronounce. Repeating it back to them many times at their request does not appear to be the best way to help them remember.
Here's what works for me:
1. Do not try to remember sounds (what you hear). Write it down, and remember the written form instead. We are better at handling structured, abstract information than an unfamiliar amorphous sound blob.
2. Once you wrote it down, *learn* the basics of how to read the language in question, i.e. how to convert written letters to spoken sounds. This is easy with phonetic writing systems, such as Chinese [pinyin](https://en.wikipedia.org/wiki/Pinyin), and shouldn't take more than 20-30 minutes. Once you have the knowledge, you can apply it to all Chinese names: just ask the person to write it down for you in pinyin. If you are a teacher with many Chinese students, it's a worthwhile investment of time.
The key is: (1) Remember a formalized, abstract representation. This helps with memory. (2) Learn the basic rules about how to read out this representation aloud, e.g. from Wikipedia. This provides some certainty and takes away the anxiety about "pronouncing it wrong."
Your own native language may not have all the required sounds. You may struggle with some of them. If you do, simply find a "good enough" approximation and stick to it deliberately. The purpose is to take out the anxiety from pronouncing the names. For example, if you are an English speaker and you can't roll your *r*s, simply substitute an English *r*. If you try to get that *r* right every time you talk to "Carlos", you may eventually find yourself avoiding saying his name.
Mandarin Chinese does not have many sounds that are especially difficult or unfamiliar to English speakers. The big one I can think of is ü, as in pinyin *yu*. Just ask someone to pronounce that sound for you, decide on the best approximation you can produce, and stick to it deliberately, even knowing that it is far from perfect. For *xi* and *qi*, most will naturally and easily substitute the English *sh* and *ch*. Of course, Chinese tones are also difficult, but again: skip them deliberately.
---
I find that this method works well for me in practice, both for remembering *names* or *words in foreign languages*. It also seems to work for helping others remember my own name. Many find its spelling intimidating, but once I explain that *sz* and *cs* are both indivisible units that represent a single sound (*s* from snake and *ch* from child, respectively), people find it much easier.
Unfortunately, in my experience, some English speakers struggle tremendously with the very concept of phonetic writing, or rather putting it into practice. Instead of consistently applying the rules of pronunciation specific to the writing system / language in question, they keep sliding back into trying to read it "the English way". They can't seem to segregate in their head two separate sets of pronunciation rules (for two languages) for the same set of symbols (Roman letters). I could never quite understand why, therefore I could never find an efficient way to help them. If you are one of them, then this might not be the best method for you. However, if you are a native speaker of a language that uses a phonetic writing system, understanding the basics of a different phonetic system will be trivial to you.
---
This method won't work well for a language that is usually written in Roman letters, but does not use a consistent phonetic system, such as French. In that case, you may need to make up your own phonetic representation. Languages that are not written in Roman letters usually have standard romanizations that tend to be consistent and phonetic (such as pinyin for Mandarin Chinese), so the only problem is knowing *which* Romanization you are dealing with, in case there are several.
Upvotes: 3 <issue_comment>username_8: To be honest, unless you learn Chinese, it's always hard for you to remember a Chinese name. Let me give you an example. Apple is apple in English, while in Chinese it is spelled as Ping Guo [苹果]. The apple in Chinese consists of two Chinese characters and seven English letters in Chinese Pinyin (the Romanization of Chinese). Either of the two Chinese characters can be assembled with other Chinese characters to make up a new Chinese name. And unlike western names, Chinese names often do not have a fixed meaning defined by ancestors or convention although each Chinese character has its own meaning solely or multiply. You may hear lots of Chinese boys called Junjie and cannot identify who is who since in Chinese Pinyin their given names are completely the same. But in Chinese per se their given names can be written in different Jun [e.g. 俊, 骏, 隽] and different Jie [e.g. 杰, 洁, 捷] and so the meaning of the names might be wholly different as well. So the best way to remember a Chinese name is to understand the meaning of the names, while to understand the meaning, the best way is to know how they are written in its original language. As each Chinese character may look like a picture to English speakers, it might be still hard for you to link the 'picture' with the romanizations.
However, instead of given names, just remembering the most popular surnames may ease you. Most Chinese people are named by a handful of surnames. Of course, the drawback is that you may be confused again once two or more Chinese you meet up have the same surname. But at least this is a little bit of progress, isn't it? If you still want to have a better understanding, then there is no more suggestion than learning this language.
**1. familiarize yourself with popular Chinese surnames**
**2. ask the meaning of their names and let them write it down in its original language**
**3. learn the language**
Upvotes: 1 |
2012/06/28 | 491 | 1,900 | <issue_start>username_0: I have a paper for review and I would like to include comments (on clarifications/suggestions/errors) over specific paragraphs or statements.
What are some ideal software resources that could help reviewers? I would prefer them to be Linux-based.<issue_comment>username_1: If you have access to the LaTeX source, there are a number of packages that will help you. Some include [todonotes](http://www.tex.ac.uk/tex-archive/help/Catalogue/entries/todonotes.html) and [fixme](http://www.tex.ac.uk/tex-archive/help/Catalogue/entries/fixme.html).
However it's more likely that you have a PDF only. In that case, you need a PDF annotating package. A free cross-platform solution is [Xournal](http://xournal.sourceforge.net/), which runs on windows/linux (and maybe Mac).
If you're on a iOS device, then [Goodreader](http://www.goodiware.com/goodreader.html) is a nice app that does annotations.
There's always [Adobe Acrobat](http://www.adobe.com/products/acrobat.html) as well. Both of these solutions are not free though.
Update: (by @atiretoo)
One issue to be careful with providing comments on a pdf or other document is maintaining anonymity. **Adobe Acrobat (and probably other software), automatically flags your annotations with information about you unless you are careful to remove that from the document before commenting**.
Upvotes: 5 [selected_answer]<issue_comment>username_2: The approach taken by many reviewers (myself included) is to simply reference the page and line number, or to insert a copy of the statement in question in the referee report, e.g.:
>
> On page 7, line 6, the word "mispell" should be "misspell".
>
>
>
This is probably the easiest approach if the number of such comments is not too large, since it doesn't require any extra software and doesn't require the authors to search through the PDF for your annotations.
Upvotes: 4 |
2012/06/29 | 6,009 | 24,385 | <issue_start>username_0: I am a third year (starting fourth year in the fall) PhD student in mathematics. I've passed all qualifying exams and am currently doing research. As far as I can tell, I am not doing poorly. I have the good fortune of having a great advisor, being in a very supportive department, and having friends and family who genuinely care about my success.
The fact is research is hard. It appears to consist primarily of staring at a problem for days and days and days without getting anywhere. Sometimes, rarely, I do figure something out and that feels wonderful, but the overwhelming majority of my time appears to be spent banging my head against a mostly figurative wall. I am not complaining about the material being hard, and I am not averse to putting in hard work, but I get frequently discouraged when I realize the vast volumes of mathematics that I yet know nothing about (and probably never will). It's very hard to quantify progress - in particular, there are too few tangible returns after too many hours worked. I find myself thinking along the lines of "Oh, if only someone actually smart were thinking about this problem they would have solved it in moments" and so on.
I've talked about this to some number of people; here is some advice I have received:
* Take a day off. Putting in hours upon hours of trying things doesn't magically lead to a solution, particularly if the brain is tired and just wants to sleep.
* Have a hobby. Since math research doesn't exactly provide instant gratification, a hobby might provide some instead.
* Talk to other graduate students. Realize that many graduate students go through this.
I'm interested to know how other folks have dealt with being discouraged as a graduate student. Does it get better with time and experience? Is this a sign that research is not for me and that I should seriously consider a life outside academia?<issue_comment>username_1: >
> Does it get better with time and experience?
>
>
>
sort of, in that you become smart enough to realize that there ISN'T anyone smarter who would have figured it out in a couple of minutes :)
>
> Is this a sign that research is not for me and that I should
> seriously consider a life outside academia?
>
>
>
Certainly not !
Research is hard work. You're on the cutting edge, charting territory no one has explored before. It takes courage, persistence, energy and a VERY THICK SKIN for rejection. After all, (and this pertains to CS), probably 95% of your job applications will be turned down, 75% of your papers will be rejected the first time, a grant proposal has a 1 in 10 change of succeeding.
But it's the small sublime moments of joy when you realize that you've discovered something that no one else knows that make it fun. And the feeling, as time goes on, that you're immersed in a wonderful lake of , with beautiful new ideas around you as far as you can see.
p.s the advice you were given is very sound. Take breaks, find fulfilling things to do outside of work, and realize that everyone (even seasoned researchers) feel the same frustrations and highs that you do.
Upvotes: 7 <issue_comment>username_2: >
> The fact is research is hard. It appears to consist primarily of staring at a problem for days and days and days without getting anywhere. Sometimes, rarely, I do figure something out and that feels wonderful, but the overwhelming majority of my time appears to be spent banging my head against a mostly figurative wall.
>
>
>
Yes. This. And it wouldn't be so damn tempting if those bricks didn't wiggle just a little bit every time I slammed my forehead into them. Sometimes I think my eyes must be playing tricks on me, what with the repeated cranial trauma and all. But then I remember how good it felt the last time my head actually went *through* the wall, and so I keep plugging away.
I've found it extremely useful to have two or three walls to bang my head against at any given time. Surprisingly, sometimes banging my head against one wall actually makes one of the other walls weaker. But most walls prove considerably stronger than my head; so it's helpful to have options, so I don't feel so bad about walking away with some scalp intact.
If you're very lucky, one good smack on the bricks will actually bring the *ceiling* crashing around your ears. That takes a long time to clean up, but sometimes the debris will knock down other walls for you. And then you have a whole new set of even bigger walls to bang your head against!
>
> I find myself thinking along the lines of "Oh, if only someone actually smart were thinking about this problem they would have solved it in moments" and so on.
>
>
>
**Do not listen to the [Impostor Syndrome](http://en.wikipedia.org/wiki/Impostor_syndrome).** Everyone "actually smart" is hearing *exactly* the same voice in their head saying "Oh, if only someone who actually knew how to hit walls with their forehead hit this wall, it would come down like a stack of cards." when in fact the wall really is made of brick.
>
> Does it get better with time and experience?
>
>
>
Yes. Eventually, you'll move from hoping that you'll be able to knock down a wall with your head someday, to being surprised at how often the walls you hit with your head actually fall, to finally believing that you really can knock down walls with your head sometimes. (For me, the second transition happened some time after tenure.)
But your head will still hurt.
Upvotes: 10 [selected_answer]<issue_comment>username_3: It definitely does not mean research isn't for you. Research is hard, and it takes some getting used to. Your experiences sound normal, and it will indeed get better.
Part of the problem is that it's tempting to focus too much on the destination: proving theorems, writing papers. These things happen only occasionally, and thinking about them (or their absence) too much is an easy way to become depressed. Instead, you want to reach the point of enjoying the journey itself. This takes some perspective and confidence, but it will come with time.
For example, imagine that one day you mention a difficulty you're having to your office mate, who tells you about a wonderful theorem that's relevant. If you're feeling insecure, this is awful: you think about the time you wasted not knowing about this theorem, and you worry that your office mate knew it and therefore you should have. On the other hand, if you're confident in yourself, then it feels great: you learned something beautiful that will help your research, and how can it be a bad day when you learned something like that? This confidence can take time to develop, but as you feel more relaxed and bolder, everything will become more enjoyable.
Another thing to keep in mind is that progress is difficult to measure when you don't know where you're going (which is what research is!). Grad students sometimes feel bad because they don't think they're en route to solving their thesis problems. Often they're right, but that's not a problem. If you knew in advance that you were going to solve it, then it wouldn't be research. The goal isn't to solve the problem you started with, and indeed you often won't. Instead, the goal is to find something exciting along the way. Once you're used to this, you can say to yourself, "OK, probably I'm not going to solve this problem, but it's worth a try, and in any case I'm sure that if I think hard enough about it, something interesting and worthwhile will come out of that work."
Basically, I think of this as a phase transition that happens in a certain point in one's development as a researcher. Before the transition, you think "Oh no, there's so much to learn. How can I ever learn enough to be a good researcher?" Afterwards, you think "Well, I don't know that much in the overall scheme of things, but I seem to be doing research anyway. And I'm so glad there's a lifetime supply of great mathematics to learn, so I'll never be bored." The key is to relax and trust that everything will work out, even when it feels overwhelming.
I know this is easier said than done, and I struggled with it myself. For years, I felt like I wasn't a real mathematician, and I would tell myself I would be one if only I could accomplish some goal: publishing a paper, learning some difficult topic, publishing a paper I was proud of, getting a job, etc. However, it was never enough. I thought the end game was deciding I was a real mathematician, but it turns out it's developing the confidence not to worry about this, and I've been much happier since that point.
Upvotes: 8 <issue_comment>username_4: I love the answers above, but here's another possible bit of advice: find ways to work with others.
Research on your own can be isolating. Working with other graduate students can make the process much more enjoyable. Staring at a problem on your own is both less fun and generally less productive than trying to work through it with a colleague. Synergy happens. Two brains isn't just better than one; it's better than two brains working separately.
In graduate school there's an artificial sense that you should be working on your own to get "your" PhD for "your" work on "your" problem. This mindset is counterproductive, but it may be unavoidable depending on your field and school. If you have to keep some problems to work on on your own, but then find one or two problems to work on with your officemates or others. Once you're done with graduate school, the artificial "work on your own" constraints will start to disappear.
Upvotes: 7 <issue_comment>username_5: Your situation was once mine. I suffered through the challenge of having a project that just wasn't working, while at the same time *both* of my advisors happened to be on sabbatical. Adding to the indignity, it was a theoretical/modeling project, which meant that the failure wasn't because some experimental device wasn't working, but because *I simply wasn't clever enough.* If I were, of course I'd see exactly what's wrong, and figure out what's going on! And, to make the pressure worse, I found out that the next semester, I'd be responsible for giving the very first departmental seminar among the graduate students in my class. So, yeah, it was a bit of a perfect storm brewing there.
Ultimately, though, the "Eureka!" moment *did* come—I was literally walking around campus when the idea struck. And, the next time my advisors were back (a few weeks later), I had a working prototype simulation to show them!
What am I trying to say? Well, a few things:
* **Don't give up.** The course of true research never did run smooth.
* **Failure is normal—and even to be expected.** Just about nothing works exactly as you predicted it would. More importantly, if something *doesn't* go wrong, then your project has been badly designed, and in fact, I would argue that you're only doing *development*, not research!
* **Don't be afraid to fail!** Failure teaches you lessons that you will never learn from success. I needed a few really abysmal grades in college to get me on the right track—the proverbial kick in the pants that allowed me to realize I couldn't coast through college the way I did through high school.
Upvotes: 6 <issue_comment>username_6: The research experience varies considerably across disciplines. I hold appointments in a computer science department and a business school and also collaborate with biologists and medical researchers. In some disciplines you are expected to work solo but in other domains, collaboration and working with a team is encouraged. The depth of the problem being addressed also varies widely. Plant biologists may spend years working on a specific gene and I believe this is true for mathematicians as well. The pace is much faster in computer science and in business schools breadth and applicability to the real world is often favored over deep thoughts.
It is good that you are asking these questions now. I have seen many faculty asking these questions long after they have received tenure and realizing that research is not for them.
Keep in mind that a doctoral degree does open many doors beyond research and that the skills that you have required (logical thinking, formal reasoning) apply widely.
Upvotes: 5 <issue_comment>username_7: >
> The fact is research is hard. It appears to consist primarily of
> staring at a problem for days and days and days without getting
> anywhere.
>
>
>
Here is what I found helpful in this regard: consider switching to a different *style*
of research. Instead of studying problems, study *techniques.* Avoid focusing on questions
like "Is X true?" Instead, focus on questions like
"No one seems to have observed that object X is as an instance of object of type Y. Does the available theory about objects of type Y say anything useful about X?"
"There seems to be a parallel between techniques used to prove statements of type U and statements of type V. How deep does the parallel go?"
"Objects A and B appear to both satisfy property Z. Can we prove a general theorem about when property Z is satisfied? What are the really important parts in the proofs that A and B satisfy Z? "
I don't mean my advice to apply generally - this is only my personal experience. I found working on questions of the type "Is X true?" to be very frustrating - immensely rewarding if I succeeded in resolving them, but they felt like banging my head against the wall most of the time. When I changed my research style to study techniques, there was a lot less blank staring involved and research became more fun.
Upvotes: 6 <issue_comment>username_8: I love the answers here, and I just want to add that I find the following things helpful.
* Read the literature. You can get a lot of good ideas from seeing how other people have solved similar problems before. (It's also rather frustrating to have your manuscript rejected by a journal because you didn't do enough reading. It's better for you to find these things out on your own.)
* A related point is that learning the vocabulary in another discipline may show you that your problem was actually already solved, but other researchers just called it something else.
* Describe your problems to a colleague. Just restating the problem to a third party can help you to see something new. ("[Confessional debugging](http://en.wikipedia.org/wiki/Rubber_duck_debugging)").
* Find something bigger to procrastinate on. You can fool yourself into working on an unpleasant task A if you feel like you're avoiding a harder task B.
YMMV because my research is engineering, not math. Good luck!
**Edit:** I forgot to say that for a short term boost of morale, consider reading all of the [Phdcomics](http://www.phdcomics.com/comics/archive.php?comicid=1). They're funny, cathartic, and painfully true. If you like posting to a forum to complain about the problems in academia, [The Chronicle of Higher Education](https://chronicle.com/forums/) will show you that you're not alone at all. Finally, if you want proof that your problems have occurred before, [The NIH Catalyst](http://irp.nih.gov/catalyst/archived-issues) goes all the way back to 1994 with hilarious comics about [the types of people in academia](http://dentcartoons.blogspot.com/2008/02/nine-types-series.html).
Upvotes: 5 <issue_comment>username_9: I have a perhaps different view, I guess, from most people here...hopefully my experience can be helpful.
Like Louiqa said, it is better to think about it now than later. And it is not about whether you are good in research, of whether you are smart enough (don’t underestimate yourself!). It could come down to what you see yourself doing in the future, perhaps in the next 5-10 years.
I used to be very sure that I wanted to be a researcher for the rest of my life. I did quite well, and actually went straight into a PhD program after undergrad. But I am a very project/task-oriented person (like to “complete” things) and I really enjoy talking to people about science - the basic science research work I did didn’t give me many of such opportunities (long hours at the bench not talking to anyone else). I also don't see myself becoming a post-doc and do more research work. It took me a while to realize that it is not just another PhD student day (and this happened after I passed my PhD qualifying exam with flying colours). I decided to wrap up my project and apply to graduate with a MSc instead.
After everyone went into shock, I freaked out for a week, and then started to look into my past experiences, trying to combine what I liked doing with what I had the skills for. I now work as a Communications Coordinator at a physics dept and I LOVE my job (despite occasionally hating the fact that I don’t have a PhD and cannot lead my own research project) I cannot be happier that I decided to do something else.
To be honest, everyone is different. Another friend of mine just finished PhD, and became a research scientist for a hospital and loves what she does now (she said she also had some really bad moments). In the end it comes down to you. My advice is to start looking at your plan for the next 5-10 years. Do you want to stay in research in academia (post-doc, faculty position, etc). Do you like teaching and inspiring students (teaching only positions?)? Do you want to go into industry? Perhaps you have other skills (a lot of what I do now depends on the soft skills I acquired during grad school, so still time well spent) that might lead to something that you want to do? What are the qualifications required for what you want to do? These are questions that you can ask yourself now, instead of later.
Good luck and I wish you the best!
(btw, a lot of the other advices you got here are also very good, and were found useful by my other PhD friends)
Upvotes: 6 <issue_comment>username_10: All researchers-in-training must constantly grapple with:
**Uncertainty** – You have no idea whether the hard work you are putting into your project even matters.
**Isolation** – Nobody around you understands or empathizes with what you are doing, since they either lack technical context or are too busy with their own creative struggles.
If you can properly manage these two emotions and make consistent forward progress every single day, get private feedback from a mentor every week or two, and get external feedback from paper submissions a few times per year, then you can successfully finish your Ph.D.
The bad news is that it's impossible to fully eliminate uncertainty and isolation when doing research. But if it's any consolation, recognize that these feelings are completely normal; **all of your fellow grad students are facing them as well.**
Additionally, you have to deal with discouragement. The very fact that there are others like you can be encouraging.
Upvotes: 4 <issue_comment>username_11: Obviously research is difficult but the gratification, though not instant, is worth the effort in the end. Problem solving requires you to bang your head on the proverbial wall. You have to experience failure because working through an obstacle builds resilience. Resiliency stems from your beliefs & attitudes about yourself and your experiences.
>
> **Does it get better with time and experience?**
>
>
>
Yes, it does. You learn how to cope with not finding the answer. You will also stretch your brain to find other ways of arriving to the answer. As you progress in your career & continue to build your knowledge base you will have more information to pull from when you get "stuck."
>
> **Is this a sign that research is not for me and that I should seriously consider a life outside academia?**
>
>
>
I would advise exploring other options not because "research is not for you" but because it is necessary to see what life is like in the industry. Then after having experienced both you can make an educated decision. Furthermore, nothing is set in stone. You could start in academia and then move to industry or the reverse.
Upvotes: 3 <issue_comment>username_12: If at any time you feel
* You are not understanding what supervisor is saying.
* You are not on the right path.
* Your colleagues are performing better than you.
* Only you don't understand a thing and lagging behind in deadlines.
* Your supervisor is not happy with your work.
* Your advisor is rude to you but you saw him laughing with another student.
* Your work seems easy but others' work is very impressive.
* You just want to quit.
* You can't quit because of social pressure.
* You just wanna go with flow.
Any symptoms like above mean you are doing PhD and almost every student get such mood swings any time. I almost gone through all but when I talked with my colleagues they were thinking exactly like me, some even saying my work is very nice and their work is easy etc... So don't worry just work and read.
Upvotes: 4 <issue_comment>username_13: A [duplicate question](https://academia.stackexchange.com/questions/88208/ph-d-work-seems-like-a-drag-sometimes-is-this-normal) directed me here. My answer adds another piece of evidence that doing research (in mathematics) can be a drag and it is normal. I've recently read the [preface](https://books.google.be/books?id=s_l6VK1ZPtMC&lpg=PP1&pg=PR13#v=onepage&q&f=false) to the textbook "*All the Mathematics You Missed: But Need to Know for Graduate School*" by Professor <NAME>. I recommend reading the entire thing, but in particular, these few sentences made it clear to me that struggling is part of the journey for everyone:
>
> Math is Hard. Unfortunately, people are just not that good at
> mathematics. While intensely enjoyable, it also requires hard work and
> self-discipline. I know of no serious mathematician who finds math
> easy. In fact, most, after a few beers, will confess how stupid and
> slow they are. This is one of the personal hurdles that a beginning
> graduate student must face, namely how to deal with the profundity of
> mathematics in stark comparison to our own shallow understandings of
> mathematics.
>
>
> (...)
>
>
> Mathematics is exciting, though. The frustrations should more than be
> compensated for by the thrills of learning and eventually creating
> (or discovering) new mathematics. That is, after all, the main goal
> for attending graduate school, to become a research mathematician. As
> with all creative endeavors, there will be emotional highs and lows.
> Only jobs that are routine and boring will not have these peaks and
> valleys. Part of the difficulty of graduate school is learning how to
> deal with the low times.
>
>
> <NAME> - *All the Mathematics You Missed: But Need to Know for Graduate School*
>
>
>
Upvotes: 2 <issue_comment>username_14: >
> It's very hard to quantify progress - in particular, there are too few tangible returns after too many hours worked.
>
>
>
This comes down to something called [Delayed Gratification](https://en.wikipedia.org/wiki/Delayed_gratification). Certain jobs, schools, activies, or frankly anything else, require different levels of Delayed Gratification. For example, at the lower levels, there is the [Marshmellow Test](https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment). In the middle there are things like studying instead of having fun with your friends. And on top there is college and graduate school. I once heard the following saying, "The Greatest Test Of Delayed Gratification Is Graduate School" or something similar. You clearly have a bunch of it, as you are most likely in a good graduate school and have survived high school and college with good grades. Researching can be one of the things that takes the most. This is a life skill, so try to work on that too. Research will get easier because you'll get better at this - **but it will never be easy**. If you go into mathematical research then easy discoveries are not what's in store for you - **ever**.
As to the quantifying progress - **Don't try**. Also, instead of looking at it as a [step function](https://en.wikipedia.org/wiki/Step_function), **even if it may be**, instead assume you are making progress all the time. And I think you really are. Every path you go down is progress - You've now ruled something out or discovered something. This a tangable return.
Upvotes: 0 |
2012/06/29 | 463 | 1,864 | <issue_start>username_0: I imported many pdf articles into Mendeley but saw that Mendeley did not capture all the fields correctly. So, I needed to change them manually. It would be a tedious job to do so with many documents. Fortunately, many of the documents share common journal names and years. So, I needed a way to edit them together.
Is there a reference manager where we can edit selected references in one go without having to do this for each reference individually?
I have tried Endnote. Mendeley, and JabRef so far but could not find such a way. I will be grateful if someone has any idea/experience in this direction.
**Edit-1:**
After solving the batch editing problem, I also had a problem with getting the syntax for the authors correct.<issue_comment>username_1: Mendeley does it actually. I did not try properly before.
Then I had a problem with getting the authors correct. What you need to do is put them as follows
```
Last Name, First Names
Last Name, First Names
```
and so on.
**After Edit-1:**
Another useful thing in Mendeley is that, you just have to select the authors from the pdf and Mendeley will give suggestions on the syntax based on existing authors in the current database. See the following image.
Upvotes: 4 [selected_answer]<issue_comment>username_2: If you use bibtex, then you can batch edit with things like sed, awk, or simple find/replace in any text editor.
Upvotes: 3 <issue_comment>username_3: In my experience, the author syntax changes if you're using DOI or PMID. In addition to the method that @username_1 suggests, if you filter by authors, you can simply drag and drop author names onto each other and they will merge into your designated syntax.
Upvotes: 1 |
2012/06/30 | 969 | 3,660 | <issue_start>username_0: I am currently researching on Recreational Mathematics - Mathematical Tricks and I have come across this article: [Ten Amazing Mathematical Tricks](http://www.jstor.org/stable/25678174?origin=JSTOR-pdf&).
This article is of free style and does not follows the pattern/format I have seen in all the other articles/research paper so far.
My question is: Is this a research paper? Is it a paper? If not, what is it? Can such a paper be cited as a reference? I want to know more about these freestyle articles.<issue_comment>username_1: I can't see the actual article, but if it's an MAA article by <NAME>, it's probably a list of interesting puzzles. <NAME> also writes a column of this kind.
These might not be "research papers" in the sense of proving new theorems on a topic, but they're interesting communications. You can of course cite such a paper as a reference if you use some material from it (and you should!)
Upvotes: 3 <issue_comment>username_2: This is the description of the Journal:
>
> Math Horizons is intended primarily for undergraduates interested in
> mathematics. Thus, while we especially value and desire to publish
> high-quality exposition of beautiful mathematics, we also wish to
> publish lively articles about the culture of mathematics. We interpret
> this quite broadly—we welcome stories of mathematical people, the
> history of an idea or circle of ideas, applications, fiction, folklore,
> traditions, institutions, humor, puzzles, games, book reviews,
> student math club activities, and career opportunities and advice.
>
>
>
So, it don't contain research papers.
Upvotes: 3 <issue_comment>username_3: As the others have stated, what you refer to is an article in a magazine, and not a research paper in a journal. If your goal is to show that you came upon an idea while reading this article, then you should cite the article. (Research papers do sometimes include magazine articles in their bibliographies.)
However, research papers usually cite sources where ideas were first published, and it is rare for original ideas to be first published in magazines as articles. So there is a good chance that the ideas you found in the article were published earlier. I recommend that you search for earlier sources and cite them also.
I also recommend that you read a few issues of the three publications I mentioned in [this answer](https://academia.stackexchange.com/a/1440/64) to see how they cite work in recreational mathematics. (In my opinion, the most prestigious place to publish work on recreational mathematics is the *American Mathematical Monthly*.)
Upvotes: 1 <issue_comment>username_4: A number of people have correctly told you (I can see the article) that this is a collection of mathematical puzzles, rather than a research paper. So let me tell you a little more about this type of article. <NAME> is considered by many to be the biggest popularizer of recreational mathematics in the 20th century (perhaps ever). For 25 years (1956-1981), he wrote a column for Scientific American called [Mathematical Games](http://en.wikipedia.org/wiki/Martin_Gardner#Recreational_mathematics). Many of his columns have been collected into books (such as [<NAME>'s Mathematical Games](http://rads.stackoverflow.com/amzn/click/0883855453)). Some other well-known examples of similar writing include that of [<NAME>](http://en.wikipedia.org/wiki/Douglas_Hofstadter), [<NAME>](http://en.wikipedia.org/wiki/Ian_Stewart_%28mathematician%29), and [<NAME>](http://www.maa.org/devlin/devangle.html).
Upvotes: 4 [selected_answer] |
2012/06/30 | 1,966 | 8,000 | <issue_start>username_0: I am considering buying either an e-book reader or a full tablet. My main motivation is to read books and scientific papers and I really don't need all the extra stuff tablets can give me.
My only concerns are
* whether e-book reader (that has pdf support) will handle many different layouts used in articles (how well will 2-column A4 page fit in an 800x600 e-ink display)
* whether it will display math properly
Do you have any thoughts and (more preferably) experience about this?<issue_comment>username_1: No. Not sufficient. Kindle DX, an almost full-sized page, was not big enough. Refresh rates are not fast enough for flipping back and forth. Zooming interface is terrible. E readers are good for simple flow text, one page after the next. That is not how I read papers. Tablets do much better.
Upvotes: 6 [selected_answer]<issue_comment>username_2: While I still prefer to have a printed copy, I find that my Kindle Fire (which I bought for this particular purpose) works wonderfully. It's a little pricier than a standard kindle, but the touch screen allows you to zoom and scan on pdf pages with ease. It's far superior to reading the same paper on a laptop or desktop monitor.
It's also nice to be able to carry around so many papers without additional weight or bulk!
Upvotes: 3 <issue_comment>username_3: Although my experience in this field is limited, here are a few points I learnt :
The mode of learning should be active. You should scribble, underline, highlight, gnaw or circle or whatever to make sure you understand each and every point the author makes. One can't read papers like a fiction novel. It's not a spectator sport.
Any device which allows you to do the above is good.
On PC you can use xournal. It works really well on Linux flavours.
On tablets, you have many options for markup.
A somewhat related question regarding the review of papers was asked and gathered some interesting answers:
[Useful software resources for reviewing papers](https://academia.stackexchange.com/q/2209/107)
Either way the point is to get involved.
Personally, my productivity is highest on dead tree version but if that's not possible. I strongly prefer my tablet over the computer since distractions are lower. On my computer, any "instant glorification" distraction is one alt+tab away
Upvotes: 4 <issue_comment>username_4: I started reading scientific papers on my iPad and now I hardly print any papers at all. I use an application called *PDF reader*, which also allows one to annotate the pdfs, which is really useful for commenting on student papers. One can make comments using a pen or type them in.
Apart from saving trees, it is very handy for collecting and carrying around hundreds of papers (that I'll never have time to read).
Upvotes: 3 <issue_comment>username_5: The nook doesn't allow you to highlight things, make notes, or bookmarks. If you just read one paper at a time and leave it on it's fine, otherwise carry a piece of paper to remember the page you are on.
Upvotes: 1 <issue_comment>username_6: I would go with iPad too. My experience with trying to use an ereader was horrible. The page turns are far too slow for flipping backwards and forwards, but mainly the claimed advantage of eInk - it doesn't glow - became a problem when trying to research in the evening.
That, plus the iPad's ability to access Google or JSTOR or whatever on the same device, means that I now use the iPad exclusively. The Kindle might get dragged out to read a novel now and then, which is what it is designed for, but for documents? Never.
Upvotes: 3 <issue_comment>username_7: I use a [PocketBook 912 Pro](http://www.pocketbook-int.com/de/products/pocketbook-912) (which is an ebook reader). The display is almost 10" and that is large enough (even too large for, e.g., SIAM papers, but zooming in persistent). Reading is great, reading outside in the sun is even better. The battery is great; I use it for more than a month now and I did not need to charge it (the small time I connected it by USB to upload paper was enough). The stylus allows you to highlight and scribble on papers. DJVU is supported.
Downsides: Paper turning is indeed slow (for me this is not too bad, it slows me down and helps to read more carefully). Taking notes is sometimes slow, erasing notes is very slow. Hyperlinks do not work in pdf documents as well as the table of contents (this is a software issue and I hope, it will be fixed some day).
My conclusion is that if you need a device for traveling which allows you to read something when you only have a few minutes (at a bus stop or so) but also to read a little longer, than an ebook reader is a good option. I use it both to read papers and books and found is especially convenient to referee papers.
Upvotes: 3 <issue_comment>username_8: To answer the specific questions in the OP for the **Kindle DX**.
* Size: if the original journal article is printed on B5 (which is also about the size of many textbooks in mathematics) or smaller, then the auto-zoomed display on the Kindle DX is more than sufficient. If the original article or book is two-column A4 with small font, it does not look so great. If the journal or magazine includes "navigational tools" in the PDF file, they become extraneous header and footers that make things even worse (*American Scientist*, I'm looking in your direction).
* Maths and Graphs: so far everything displays well. This includes digital scans of old (1950s and 60s) mathematics articles, modern journal articles from Springer and Elsevier journals, arXiv downloads, as well as European Mathematical Society and Cambridge University Press eBooks.
For some caveats:
* Bookmarks: yes. Use them plenty, since internal hyperlinking doesn't work. The reader does remember where you "left off" automatically.
* Notes and highlights: no.
* Page turning: slow, but is something I willingly put up with for the convenience. (It is lighter and cheaper than a netbook, and also lighter and more compact then deadwood format.)
* Battery life: great, one charge lasts me usually several weeks. Of course, the main energy consumption in eInk is the page flips. So if your paper reading habit is lots of back and forth page flipping, you will use up your battery much faster.
Upvotes: 3 <issue_comment>username_9: There is software that will help to better format an academic PDF for a 6-inch reader screen. See the MobileRead.com PDF forum (sticky entries): <http://www.mobileread.com/forums/forumdisplay.php?f=184>
Upvotes: 0 <issue_comment>username_10: If you have many PDF files, then you better find a way to organize them on your device. Thus, the software or digital library which you like to use determines which operating system is better for you and hence which device you should pick.
I personally prefer Zotero to organize my PDF files which is only avaiable on Windows. As a result, I bought a 10 inch touch laptop which can be detached from its keyboard. One example is acer switch 10.
If you like Mendeley, then you have three choices, Android, IOS, and Windows. I did not work with Mendeley on IOS. I think between Android and Windows Mendeley works better on Windows.
Consider that the battery on Android and IOS devices last longer than laptops and you can charge these devices via power banks but you cannot charge a laptop with a power bank.
One advantage of Windows is that you can install full MS office on it. Pay attention that some laptops have Windows RT. They usually have MS Office preinstalled, which might need a license, but you cannot install .exe files.
I tried to read papers on my touch laptop for a year. At the end I prefer to read short articles printed on paper. I only read books and long articles on my device.
I think you should first consider which software you need, then decide which device you should get. With regards to e-book readers such as Kindle,forget them. You cannot install apps or software on them.
Upvotes: 0 |
2012/07/02 | 517 | 2,209 | <issue_start>username_0: Recently I submitted a paper in a journal then after a few days I got following comments:
Your submission of the manuscript has been unsubmitted. This is because your "Author's Novelty File" was not detailed enough. In order to proceed, please briefly describe, in a few lines, the new contributions of your paper to the field. Do not repeat for this purpose the content of your abstract. Instead, provide a brief itemized list of these contributions in a separate file.
I don't understand what I have to write now. What he is asking for? I need help with this.
Thanks for giving me time.<issue_comment>username_1: It sounds like the journal wants you to identify in what manner your paper has expanded the state of knowledge in your field. Have you contributed a new experimental method? Or perhaps you've provided new insights into some sort of phenomenon, or measured data for a new material, or so on. These contributions should be collected in this "novelty file."
Upvotes: 5 [selected_answer]<issue_comment>username_2: I think they're asking for an "explanation for non-experts". Often, the abstract of a paper gives only enough information to be fully understood by an expert in the field (or at least in that problem). Part of your job in explaining your work is **explaining why it's important**. Think about how you would explain your work to a student with roughly the same background as you, but who hasn't thought much about your project. Now think about how you would explain it to a researcher working in a completely different field... in 5 sentences or less. Your novelty file should say something like that. Emphasize **here's what we added**.
Upvotes: 2 <issue_comment>username_3: I recently submitted a paper to IJNME. In the 'File Upload' section, it states the following:
>
> "Author's Novelty File" - All Authors are now required to supply an additional
> file. Please briefly describe, in a bullet pointed list, the new contributions
> of your paper to the field. Do not repeat for this purpose the content of your
> abstract. Instead, provide a brief itemized list of these contributions in a
> separate file.
>
>
>
I hope this helps.
Upvotes: 3 |
2012/07/02 | 1,628 | 6,874 | <issue_start>username_0: I am a third-year PhD student in mathematics from a very reputed institute in my country. I have one problem to share and discuss that is related with my guide. He is a senior professor in our department. The day I joined that institute he gave me one research problem and asked me to study about that and present. I did as much as I can and presented. In return, instead of appreciation I was being scolded for small mistakes. And it has continued for the last two years. Seniors say it is his way of working. I have lost my happiness. Sometime I cry for joining this course. My prof is not impressed with my good academic record and my hard work.
Second thing that worries me is that he is not much aware of my research problem. I feel like I know much more than him. He has no paper on that topic, although he has a good number of papers in other topics. I never got help from him in solving research papers. I have to study papers on my own and then have to present to him. And again for small mistake I have to suffer his taunts. I am scared whether I would be able to finish my degree in such an environment or not? Is this normal? Does every PhD student suffer such mental trauma?
I need advice. Thanks.<issue_comment>username_1: Here are some of the less severe options that you can try out first:
* Research the whereabouts of his former students. How many of them graduated, and if so, what was the quality of their work (publication venues/citations etc) and where are they now? Contact them individually, and talk to them about how they resolved this situation.
* Try talking to him, privately at first, in a pleasant, non-accusatory manner. Tell him that you tend to find his method of working rather discouraging, and ask for ways in which both of you can improve your working relation
If nothing else works, consider changing your adviser. As your institute is a reputed one, its almost sure to have a formal procedure which can be initiated by the student to change one's adviser. Before doing that, look around the department and find out, albeit discretely, if there are openings in other groups doing similar kind of work - after all, if no-one is ready to take you in the same dept., you may have to look at other institutions entirely!
Most importantly, do NOT give up on your research/look to find faults with yourself without any valid reason - a lot of researchers have faced similar issues with their advisers at first, and they have successfully either worked out all issues or simply changed to a more suitable adviser down the line!
Upvotes: 5 <issue_comment>username_2: What you describe, unfortunately, somewhat of a common problem in academia, particularly with senior faculty. My advisor and I had a somewhat similar relationship, but he was far less harsh than what you describe.
The following isn't a solution, but just a few points to think about as you consider your options:
* It is almost certain that you will *not* be able to chance your professor's attitude. He's been managing his lab like this for years, and this is his way of doing things. I would strongly suggest speaking to other students about him before approaching him directly; I would venture a guess that talking to him about this issue and your feelings on the matter have a good likelihood do you more harm than good, as he likely will not care and will only think the worse of you for it.
* You should have a research committee; speak to one of the members of your committee about your concerns. Personally, almost all of the useful advice I received during my PhD work was from a committee member, both in terms of direction and in terms of actual research work.
* You can switch labs whenever you want. Yes, it will set you back a few years, but that may be a necessary cost. Always keep that in mind; you're not bound to him by any means.
* On the other hand, sticking it out may be worthwhile, as you'll have a good name behind your PhD, and this is a personality type you will likely come across again in academia (and elsewhere). It's definitely worth getting to know how to deal with this type of individual.
Do realize that his critique of your research doesn't mean it's bad by any stretch, it just means that he's focusing on your mistakes. To get more positive feedback, try joining other journal clubs and lab meetings around the university, and present your ideas there; most would be happy to have you, and would give you useful feedback.
Upvotes: 4 <issue_comment>username_3: Every PhD student suffers mental trauma. You say your advisor "scolds" and "taunts" you. I think of scolding and taunting as personal attacks, and hence unacceptable. That said, your English makes me unsure if he is attacking you or your work. Attacking someones work is a common occurrence in academia. We tend to get one sentence notifications about our successes and pages and pages of feedback about our failures. Even the best researchers fail more often then they succeed. The key thing to remember is that, in general, it is not personal. You need to identify if your advisor is attacking you or your work.
You say your advisor "is not impressed with my good academic record and my hard work". Successful academics tend not to be impressed by academic records and hard work since it is par for the course. This doesn't mean they do not respect you. The best way to find out what your advisor thinks about you is to ask. Ask what you are doing well and what you can work on. Also say that you are feeling unsure of yourself and looking for reassurance. Some advisors will give you reassurance and a shoulder to cry on, others will simply tell you that you are not failing. Not failing is another word for a major success.
Upvotes: 6 [selected_answer]<issue_comment>username_4: I came across your post as I was looking for information on the same subject - too much pressure from my supervisor.
First of all it's good to know I'm not alone... and the comments from all the others really helped.
One thing that helps me is to think that in the end, it's not my supervisor who's going to evaluate me. I'm going to present my thesis to a jury,not to him. He'll probably won't even have any word to say in my final presentation. He's suppose to be a help to get me through, not the person who will judge me.
So try to keep your eyes on the goal instead of making all this efforth to adapting to your supervisor and ending up writing and researching as if him was the one I have to impress. He's not.
Good luck:)
Upvotes: 3 <issue_comment>username_5: You can either keep him and find other secondary supervisors to help you out with your research. Alternatively you can find a new supervisor but I guess you're nearing the end of your PhD so this has to be taken into consideration. I'm sry to hear about your ordeal though.
Upvotes: 2 |
2012/07/02 | 771 | 3,116 | <issue_start>username_0: My university is switching its Virtual Learning Environment (VLE). Specifically, we are moving from WebCT to Moodle, but I don't think this matters. This means I need to spend some time learning how to use the new software, but it also presents me the opportunity to think about how I use a VLE to supplement my traditional teaching. Are there any references of how to best leverage the use of VLEs?<issue_comment>username_1: WebCT is probably about as user unfriendly as it gets so you'll probably find moodle pretty easy to pick up after that!
Try this <http://moodle.org/course/view.php?id=5>
Upvotes: 2 [selected_answer]<issue_comment>username_2: Moodle
======
I recommend you to try a demo site of Moodle. It would be specially helpful to try one that has content. [**Here you can find one**](http://school.demo.moodle.net/). You can choose which role to use, so you will be able to see what students see and to see some settings for each activity.
[**Moodle's documentation**](http://docs.moodle.org/23/en/Main_page) is the bible for using Moodle. You just have to make sure to review the documentation of the current version of your site. For instance, the capabilities of versions 1.9 or lower are much more limited than those of versions 2.0 or higher. However, if you are beginner I suggest you to visit first the demo site. Moodle's documentation could be more difficult to grasp if you do not know how a site works.
Contents
========
I am not sure if you are going to use Moodle as a support for classes ("blended learning") or if you are going to teach an e-learning class. However, regardless of the kind of use, [**here**](http://www.irrodl.org/index.php/irrodl/article/view/869/1575) you can find interesting ideas and concepts about distance education using Moodle. These ideas will be useful even if you are teaching a blended learning class. This document explains some principles for distance education (e.g., the course interface should be simple and intuitive).
Tips
====
* The module called "**Lesson**" is very flexible to show and assess content. Some creative users have used it to simulate a situation where students have to make choices. Depending on those choices the module shows them different contents or questions.
* The "**Workshop**" module allows you to implement a peer review system in a class.
* The "**Assignment**" module can be used as an "electronic mail box". Students can submit their project reports online.
* In the newer versions of Moodle there is an option to create a "**Rubric**". So you can quickly evaluate projects clicking on the rubric.
* You can set up a "**Wiki**" in the course's page. For instance, you can ask students to use the Wiki to document their progress on a project, so you can directly comment in there.
* You can create **groups** of students in the course's page. One of the advantages of this is that you can implement forums for each group, so students can exchange messages within their groups.
* If you are teaching a class that uses a lot of math, you can write in Moodle using Latex syntaxis.
Upvotes: 2 |
2012/07/02 | 1,051 | 4,440 | <issue_start>username_0: The way a grade is assigned in a course influences the way students study which in turn influences their gain from the course.
I want to focus on ways to assign grades which aim to eventually increase the students' gain from the course (in contrast with grade assignment methods which try to single out the most talented students).
I'll start with the questions:
>
> 1. What are such grade assignment methods?
> 2. Are there texts/papers/other resources discussing this subject?
>
>
>
An answer should address issues such as: memorization vs. creativity, the temptation to get help from external sources while doing homework, spoonfeeding vs. self-learning, etc.
I will now give my own example of grade assignment components:
1. Excercises which are graded regularly throught the semester
2. Exercises for self-learning with a solutions manual provided
3. Quizes during the semester
4. Strictly technical questions in quizes/exams
5. Questions in quizes/exams which mostly require to repeat what was taught in class
6. Questions in quizes/exams which require some creativity
7. Student presentations
8. Practical assignments (programming/labs)
9. Interviews (oral exams)
I believe all of the above may have a place in some courses with the specificed goal in mind and would like to get new ideas for grading components, for how to combine them, and to know whether any written work exists which tries to answer this question.
I am thinking mostly of courses aimed towards math and CS students and mostly about senior undergraduates, but I do not want to limit the question to these subjects and this level of students.<issue_comment>username_1: As a recent graduate in CS I can only provide my experience with grade assignment methods I came across during my studies.
I think the worst method is to let the grade solely depend on one exam at the end of the semester. The students will most likely put no effort in learning for this exam during the semester and will instead invest a couple of days before the exam to repeat the lecture's content.
The most promising method I came across was using (bi-)weekly assignments out of which the student had to reach at least 50% of the points. This way, they have to focus on the topic in order to solve the assignments to be able to take part in the exam.
In addition to this method, some lecturers awarded students with more than 80% of the overall points a bonus in the exam, i.e. they will obtain (let's say) 10 points bonus within the exam. I for myself think this really motivated me to invest time into the assignments and consequently into the lecture content.
Finally, there was one lecture where students could pose questions with possible answers in an online learning platform. The other students could use these questions (after review by an assistant or professor) to learn for the final exam. Students posing very good questions gained a bonus for the exam, similar to the above approach.
Upvotes: 3 <issue_comment>username_2: A good place to begin research on grading methods is under such topics as "formative assessment", "educational assessment/evaluation" and more general headings would be "evaluation" and "educational measurement". From memory, there have been favorable results from formative assessments given periodically throughout a course session. Such tests or quizzes help provide numerous chances for students to demonstrate their learning, as well as giving themselves feedback on how well they are or are not progressing. Finding out early that the subject is beyond them is always helpful, as is giving the more capable opportunities to improve over time. There is little merit in giving single, end of course exams or projects, assuming that you value students' opinions and their mastery of the course objectives.
Note that there are a variety of alternatives to "tests" these days, many of which can provide students with the chance to show their learning way beyond the mastery of the facts.
Upvotes: 1 <issue_comment>username_3: I've used several [Pedagogical Patterns](http://www.pedagogicalpatterns.org/) in my courses, namely [Grade it again, Sam](http://csis.pace.edu/~bergin/PedPat1.3.html#gradeitagainsam) that is part of [Some Pedagogical Patterns](http://csis.pace.edu/~bergin/patterns/fewpedpats.html). They're oriented towards computer-science curriculum, but could be adapted to other areas.
Upvotes: 0 |
2012/07/02 | 1,149 | 4,841 | <issue_start>username_0: This is similar to a previously asked question, but deals specifically with addressing the problem on the CV. My adviser had a bit of a personal meltdown and left the department while I was finishing analysis of field data/writing up. His students were assigned the following year to a non-tenured professor. This new professor was of little help to me my last year of writing up, sat on my dissertation with no comments for over 6 months and only produced comments after I went to the departmental chair. He did not think much of the type work I was doing and went so far as to say so during my defense. I will add here that I have a number of publications (>5) including single authored ones-which for my field is significant- as well as an excellent track record of funding and in presenting my research. I have a strong CV but have had no success thus far in securing a job. I once saw one of the letters he had written for me (it needed to be included in a single PDF so he had to send it to me) and it was terrible. It talked mostly about him and how he really did not know me well and with a few generic sounding "he will no doubt exceed" sentences that just sounded fake. Months later a colleague suggested that I find a different reference than my adviser (but was not clear as to why).
So, my question is this: since committees will no doubt look at my list of referees and wonder at my lack of an adviser-I have taken to including a short "note" in the "reference contact information" section explaining that 1) my original adviser left academia at the end of my time as a student and 2) that my new adviser was up for tenure the year I defended and very busy and was in a different field than mine, so instead "below are three people who are in a better position to judge me on my research, teaching, writing skills".
Is this providing too much information or is this instead ensuring that rather than questioning and then rejecting my application the committees will stop and think "oh okay, I can see why the applicant did not include their adviser"?<issue_comment>username_1: Assuming your field is relatively small, the odds are good that your advisor's fate is known to the community. Unfortunately, such events do happen, for all kinds of reasons—just a few weeks ago I learned that a relatively prominent academic in my field basically had his group wiped out because he was arrested on some rather nasty charges!
While it doesn't seem that your original advisor befell such an awful fate, it is clear that, for whatever reason, he is no longer in academia. However, given that he is probably known to many of the people working in your field, it might still be helpful to try to get a letter from him, even if he's doing something completely different. If you can't, because he has refused to do so, then you are entitled to explain the situation in your CV or cover letter. Be succinct and to the point, and stick to the facts; do not make it into a "sob story," which will not endear you to a hiring committee or postdoc advisor.
And good luck—such situations are always stressful, and are always unfortunate for the students caught up in them. It's one of the pitfalls of academic life, and I don't know of a good way to deal with it.
Upvotes: 3 <issue_comment>username_2: **Your current advisor's letters are killing you. Do not ask him for any more letters. Do not list him as a reference in your CV. Don't walk; run.**
>
> I will add here that I have a number of publications (>5) including single authored ones-which for my field is significant- as well as an excellent track record of funding and in presenting my research.
>
>
>
In that case, you really should have no trouble finding enough other good references to overcome any concerns raised by not having a letter from either of your advisors. Everyone reads advisor letters with a grain of salt anyway; strong letters from well-known senior researchers outside your home department have much bigger impact.
Of course, if anyone asks why your current advisor isn't writing you letters, you should answer honestly, but I think adding an explanatory note in advance is unnecessarily defensive.
Upvotes: 4 <issue_comment>username_3: You should definitely get rid of the letter from your new "advisor." I think it would be reasonable to list only your original advisor as your advisor on your CV (not the new advisor).
I think you should make sure that one of your letter writers includes a short paragraph explaining that your advisor left academia and this is why you do not have a letter from your advisor. I would look into whether there's someone you can ask for a letter who feels some responsibility for your original advisor, for example, your advisor's advisor or a frequent coauthor of your advisor.
Upvotes: 3 |
2012/07/03 | 548 | 2,363 | <issue_start>username_0: I'm currently writing a journal article to be published, and the work from this article is basically the entire work of my Master's thesis.
Clearly a thesis is usually more involved and fleshed out than a journal article, but how much direct copying is usually permitted? Is it okay to directly copy-and-paste an entire chapter (or multiple chapters) of this article into my thesis, given that I wrote it anyway? At what point am I plagiarising myself?<issue_comment>username_1: Generally, many publishers and fields have rules against reuse of one's own text if that use is a (a) substantial; (b) not disclosed/attributed; (c) published.
How these terms are interpreted varies among publishers and disciplines. Most publishers would not consider a thesis or dissertation to be a "publication" for this purpose (hence the still widespread practice of converting dissertations into "book"). However, to avoid misunderstandings and keep definitively within the rules it is good practice to cite one's thesis, and to provide a brief summary of what content was reused and the extent it was updated, in the cover letter and in an introductory footnote.
Upvotes: 3 <issue_comment>username_2: In Computer Science, many publishers (well all of those I published with) have an automated system of requesting re-use permissions. For inclusion in a thesis, these tend to be 'as published' or 'post-review, but not typeset' which allows verbatim copies. Verbatim as in: insert the pdf from the journal into the thesis.
You should check for your field but might be pleasently surprised.
Please check with your advisor what he allows you to do.
Upvotes: 1 <issue_comment>username_3: You are in a unique position if you can literally copy-and-paste from your thesis directly to a journal article. If that is the case, take that opportunity and don't worry about self-plagiarism. The issue is whether your work has been previously published in some fashion. A thesis that has been submitted to a university is not considered a publication and, generally speaking, no copyright agreements have been signed. This is, indeed, your work. You are plagiarizing yourself when you publish an article and then lift text directly from that article. If your thesis has not been 'published,' then shape it into a journal article and send it off ...
Upvotes: 2 |
2012/07/04 | 1,050 | 4,045 | <issue_start>username_0: **Problems with discussion boards**: After being involved with StackExchange for a couple of years, I've really grown to despise standard discussion boards. All those meandering threads, no commenting, no ability to edit questions to improve them, inadequate cross-referencing of questions, no markdown support, no voting for good answers; no rss subscription options, the list goes on...
**Educational context**: I have interacted with a few course management systems that include discussion boards for students to ask questions. These systems also lack most of the great features of the StackExchange model of Q&A.
### Question
* Is there a way of deploying a StackExchange-style Q&A site for a university subject?
* Are there any examples of people doing this?
* Does anyone know whether StackExchange themselves have considered this market?
### Initial thoughts
I realise that there are open source clones of stack exchange in existence. However, my concern is that they take a bit of effort to setup.
* Ideally any system should be very simple to deploy for the lecturer, and easy to learn for the student.<issue_comment>username_1: Although there are not as many features as on SE, one possibility could be to [create a sub-reddit](http://www.reddit.com/reddits/create). You can restrict the access of the sub-reddit only to the concerned students if you want to, you can ask questions, you can comment (recursively), you can upvote/downvote the best questions/answers/comments, there is an RSS feed.
I don't know if it covers all the features you want, but that could be a good start, since it's very easy to setup.
Upvotes: 3 <issue_comment>username_2: Yes it's possible. Yes it's been done. There are plenty of StackExchange clones such as OSQA to use - [there's a question over on meta-stackoverflow that lists them](https://meta.stackexchange.com/q/2267/164022). You can pretty much pick your favourite platform, and there will be a StackExchange clone for it.
But:
You might well find that take-up rates are very very low (maybe one in 20 if you're lucky). And you need a lot of people to give a Q&A site enough critical mass to sustain itself.
They're all reasonably easy to use, for the end-users. As to how hard they are to install and maintain, that's a question of the kit and talent you have available. If you've already got a server serving Django apps, then OSQA is easy. If you've got a \*AMP stack, then any of the PHP clones should be pretty easy. Installation and management is the really easy part of the process. Drumming up, and sustaining, partcipation is the really hard part.
Upvotes: 4 <issue_comment>username_3: I don't know how straightforward it is to deploy, but you can use the open-source SE-like [biostar-central](https://github.com/ialbert/biostar-central) developed at GitHub. It is used to host <http://www.biostars.org>.
Upvotes: 1 <issue_comment>username_4: [Piazza](https://en.wikipedia.org/wiki/Piazza_(Q%26A_platform)) is to my knowledge one of the most popular to Q&A platform used for a university subject. Many courses use it in my university and we are quite happy with it.
Some features:
* free
* take less than a minute to create
* edit questions to improve them
* cross-referencing of questions
* voting for good answers
* notification emails
* students and instructors can write answers
* endorse student's answer
* etc
Missing features:
* allow to comment
* markdown support
* no rss subscription options
Screenshots:
[](https://i.stack.imgur.com/2SP2x.png)
[](https://i.stack.imgur.com/8DeaA.png)
Upvotes: 2 <issue_comment>username_5: I think you should take a look at Discourse. It is by <NAME>wood the other coinventor of Stack Overflow.
In his blog he has mentioned all the design decisions that went into its development.
<http://blog.codinghorror.com/civilized-discourse-construction-kit/>
Upvotes: 1 |
2012/07/04 | 661 | 2,779 | <issue_start>username_0: **Background:** I was reading about [Google's Hybrid approach to research](http://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-approach-to-research/fulltext). This prompted me to think about how in academia I try to get multiple output from the one set of inputs. For example, I might try to
* Present a talk and and then write up a journal article
* Write blog posts and question-and-answer combos on StackExchange to force me to learn something that I need to know in order to write a journal article.
* If I learn a new technique in the process of writing a journal article, then present a tutorial on how to apply the technique.
I've also heard senior academics talk about employing this approach. E.g.,
* publishing an article in a journal as well as reframing the content for a magazine or newspaper
### Question
* What are the most important examples of getting multiple outputs for a given input?
* What is a systematic way of incorporating this into your project planning?<issue_comment>username_1: Your answer to this question will depend somewhat on your long term goals. Everything you mention: talks, journal articles, blog posts, tutorials, magazine articles, etc. can be worthwhile in the right circumstances. Most of what you describe consists of taking variations on the **same theme** to **different audiences**.
In what circles do you most want to be known? More generally, **how will you measure success?** Say for example that your primary goal is to get tenure. If you're at a high research school, then publishing an expository article for undergraduates may well be viewed (by your tenure committee) as a waste of your time. In contrast, if you're at a liberal arts school, this may be considered just as valuable as getting a research article in a top journal.
You asked about incorporating this into project planning. I suggest that you **decide which audiences you are most excited to reach**. This will likely be largely *independent* of the particular project. Then as you start on each project, think about how you can **tailor your message to suit each of these audiences**. You may be surprised by how often you find you have something valuable to say to each of your target groups.
Upvotes: 3 <issue_comment>username_2: I've heard of more than one team who produce for each chunk of research
1. a workshop paper describing what is planned, the challenges, requirements, etc.
2. a conference paper describing the results.
3. a journal version of the conference paper, expanding on the results, providing more detail, etc.
I think this is not a bad approach, as it certainly helps to give better shape to the ideas by the time they reach the journal version. (I don't use this approach, though.)
Upvotes: 3 |
2012/07/04 | 643 | 2,819 | <issue_start>username_0: A "Russian style" seminar, as in the Israel Gelfand form, breaks from the traditional format of talk followed by Q&A and just opens the floor to questions at any time. I've seen a few professors who follow this format, so I definitely see its value.
What is the best way to start something like this, and how do you get the most out of the seminar?<issue_comment>username_1: I have seen this format work well in colloquia.
The speaker generally says at the start that they are happy to answer any questions during the talk.
If needed at various points the speaker can also ask if anyone has any questions. Ideally, after asking the speaker pauses for a couple of seconds to give people a chance to ask before moving on to the next section of the talk.
It can also sometimes be necessary to close off a particular discussion if it is going on for too long and is not of particular interest to the general audience.
In general, I think the format works best when the presenter knows the material well, and the audience is reasonably well informed.
Upvotes: 3 <issue_comment>username_2: I use a version of this in the weekly research seminar that I run.
This format typically works best when
1. **the speaker knows ahead of time that he/she is likely to get questions during the talk** and
2. **at least a few of the audience members feel comfortable asking such questions**.
If the speaker is a regular attendee of the seminar, then **(1)** works itself out naturally; otherwise, I recommend that the organizer mention this ahead of time to the speaker. Generally, more experienced speakers are more comfortable with this model. As I'm asking questions during the talk, I watch the speaker's responses. If the speaker starts to get flustered or is unable to answer well a few questions in a row, then I often will stop asking questions. Actually, I usually talk a little with the speaker ahead of time about what I'm hoping for from the talk, who the typical audience is, how long the talks usually go, etc. I find that a few minutes beforehand can save you from the awkward experience of having a talk that is at too low or too high a level.
For **(2)**, I am usually quite comfortable asking questions, and I generally find that at least one other faculty member in the audience is. As Jeromy mentioned, typically this model works best when the questions are asked by well-informed audience members (since they can more easily discern which questions will and will not be helpful to the rest of the audience). If you find that no one else in the audience is asking questions, I suggest that you talk with a few of the regular attendees and ask if they would be willing to start asking questions. (This conversation probably will work better *outside* of the actual seminar.)
Upvotes: 4 |
2012/07/05 | 606 | 2,746 | <issue_start>username_0: How do you stay in touch with researchers whom you meet at conferences? My intention is to stay in touch for possible collaborations and job opportunities.
Usually I meet people, we talk and I send them a "thank you" mail but later I get confused how to take this forward and *maintain* this contact.
I do send links, my preprints and interesting website questions and stuff once in a while but more often than not, I don't get a reply.<issue_comment>username_1: To build and maintain a network of professional relationships, one should develop, maintain, and express genuine interest in other people and their work. Introducing oneself at conferences is a good start, as is recording and updating contact information, and setting up a system to remind yourself to reach out to people regularly. (There are various tools for this, ranging from a spreadsheet, to social network services, to CRM software. If you use gmail, you may want to try "contactually" for the mechanical part. )
Sending your contacts updates on your work is useful. And these should be targeted... it's ok to use a broadcast medium like twitter to announce new work to the world, but avoid sending out mass-email -- instead target those people who are likely to be most interested, and include a brief personal note that puts the new work in the context of their interests.
Most important, keep track of what others in your network are doing -- new publications, new projects, working papers, promotions, etc. (Social networks can be useful for this in some fields for tracking people directly, tracking working papers in your discipline, and disciplinary news can be quite useful, google scholar can also help alert you to new publications from people in your network. ) And when someone in your network is starting something of interest, re-connect -- with congratulations, a useful comment, and possibly some information on your own work (if it really is of interest with respect to their new project).
Upvotes: 5 [selected_answer]<issue_comment>username_2: Maybe this is obvious, but never forget to wrap your networking cover ops in real social ops. In a conference, go talk to people, than go drink a coffee, beer, discuss with them about anything but work at the lunch, etc.
If you do that, it is really simpler to send a thank you mail and to reactivate a contact afterwards.
Regarding the action of sending your work around, why not, but you must target carefully who you are contacting. Be sure that your work is really of interest for the person, this is what will make the difference between a spammer and an interesting contact.
Another way to ping is to ask genuine questions on new papers authored by your contacts.
Upvotes: 2 |
2012/07/05 | 499 | 2,287 | <issue_start>username_0: I do have little research experience on "Web Security" and I made some inventions in that. I have patented my inventions and now when I was trying to apply for Masters by Research in Information Technology in some X University they are asking for Research Publications. I never came across them in my past and I don't know its importance. May anyone tell me
1. Its role to the admissions committee.
2. How important they are as compared to patents.
3. How and where can I release my Research Publications?<issue_comment>username_1: If you're going to be doing a master's degree *by research*, then admissions committees are going to want to see evidence of any previous research that you've done. That way, you're less of a risk for them to admit you, because you've already learned many of the skills you'll need to be successful.
The "currency" for establishing this are patents and research publications. I assume you're familiar with patents, since you ask for a comparison. Basically, research publications will be considered at least as important as patents, as they generally represent a significant amount of original research. (Patents may be a matter of *development* rather than *research*.)
As for how and where to release research publications, that depends on your field. In CS, my understanding is that generally means that you have to publish your research in conference papers, which means that you'll have to get your research work written up and submitted to a conference. Then you'll need to get your paper accepted, and then you can publish.
If you don't have any publications, don't worry. If you still have research experience, a recommendation letter from a research supervisor can also help to establish your qualifications. (Absent some evidence, though, most graduate programs will remain skeptical.)
Upvotes: 3 <issue_comment>username_2: I think they are asking for your publications in case you have any, but I highly doubt that publications will be necessary to get into a masters program. It just something to add to your application to give a more complete picture of you.
Upvotes: 3 <issue_comment>username_3: List your patents in the place where they ask for publications. It is a good substitute at this point.
Upvotes: 1 |
2012/07/05 | 471 | 1,916 | <issue_start>username_0: I was reading [this](http://leiterreports.typepad.com/blog/2010/03/lying-about-the-academic-job-market.html).
>
> Now here's some advice that isn't silly: don't go to graduate school
> unless you get into a strong program.
>
>
>
What's the meaning of "program" in this case? What would be a strong program?<issue_comment>username_1: According to the thesaurus of my dictionary:
>
> *a program of study*: course, syllabus, curriculum.
>
>
>
Basically, it's just another word for curriculum, although I've heard it mostly associated with graduate studies rather than undergraduate ones.
Upvotes: 2 <issue_comment>username_2: In this context, it is referring to the particular department or school, that is to say, "don't go to graduate school unless you get into a good university, or rather a good department." Don't go to graduate school for the sake of going to graduate school - make sure you're going somewhere which does good research, where faculty members publish frequently, has a history of graduates doing well for themselves, etc.
Upvotes: 3 <issue_comment>username_3: One should interpret "program" broadly in this case to includes *curriculum*, *faculty*, and *placement record*.
More generally, consider what your objective is for entering the program. If the primary goal is to pursue research, the program strength is particularly important. If the primary goal is to obtain a tenure-track position -- pay particularly close attention to the placement record of the department, and to its reputation in the discipline. If the primary goal is to obtain credentials, overall program strength may be somewhat less important, if the school is accredited and has a good placement record.
Finally, as practical considerations, you may also wish to consider the completion rate, time to completion, level of student support offered, and RA/TA workload.
Upvotes: 3 |
2012/07/06 | 627 | 2,593 | <issue_start>username_0: [findaphd.com](http://www.findaphd.com/) is an excellent website to search for PhD studentships (PhD funding on specific projects) in the UK. Also, UK universities often advertise PhD studentship opportunities in their "job vacancies" section. However, after much googling, I have yet to find similar sites or departmental advertisements for PhD studentships in the USA.
**Questions:**
1. I don't suppose there is a similar site for the USA that I missed during my search?
2. Are there even off-cycle PhD studentships for specific projects in the USA, like those in the UK?<issue_comment>username_1: There is no such site because it is unnecessary. In Europe, a Ph.D. student is like an employee and is admitted to work on a specific project. In the USA, a Ph.D. student is admitted in a manner similar to an undergraduate (based on general considerations) and is not necessarily attached to a particular project or even a particular advisor.
Hence, you could say that every US department has a number of studentships available each year, always at the same time (start of fall semester) and you apply for all of them simply by submitting an admission application to the program.
Thus the answers to your numbered questions are
1. No.
2. No.
Disclaimer: there are probably exceptions, but what I've written applies 99% of the time.
---
One additional note: I disagree with the comment below that claims that individual faculty do not decide which students are admitted. In every program I know, a small committee of program faculty makes **all** the decisions. If some faculty member (whether on the committee or not) really wants a particular applicant, that applicant will be admitted. My own admission came on the same day that I contacted a faculty member (and as a direct result). Of course, you should always follow [this good advice](https://academia.stackexchange.com/questions/924/contacting-professors-for-phd-vacancies) when contacting faculty you'd like to work with.
Upvotes: 5 [selected_answer]<issue_comment>username_2: The closest that you can come to this in the US is listings of graduate *fellowships*, which is more closely related to the issue of funding rather than admissions. Pretty much every department has an annual cycle for admitting students, as David mentions.
However, funding models vary widely, so acquisition of an external fellowship can make a significant difference in the kinds of projects one can take (since positions tend to be closely tied to specific projects, as a result of the grant model in the US).
Upvotes: 2 |
2012/07/06 | 1,120 | 4,779 | <issue_start>username_0: Assume that one has a skill (assume, programming) and is one of the very few in the department to have it. The other students need a bit of this skill for their projects and you help out when you can.
Where do you set the limit for how much effort/time you spend on helping others this way?
Pros:
* You get insight into other students' work.
* It's a welcome break from our own research
* Maybe earns you a second/third author for setting up the experiment
* Networking !
Cons:
* Effort/Time spent
* You don't necessarily improve your skills (For instance, coding in Python for someone else doesn't augment my Python skills by much. What I do might be really routine)
* You tend to have a *soft* commitment towards that project. For instance, if I start working on it as a favour, it doesn't really come off well if I leave 'em midway.
**EDIT:** I do enjoy the work so long as it is at least a little challenging. I often get really n00bish questions and that is when I start reconsidering my stance on helping people.<issue_comment>username_1: It depends :)
I would invest effort proportionally to the potential benefit for me (in terms of number/quality of publications) as well as the fun I would have doing it. Where the limit lies depends only on you and I can't really give you any advice on it. You should also consider other factors, such as how your own research is progressing. If you have an important deadline coming up, you shouldn't be doing something unrelated, even if it might lead to a good publication or be a lot of fun.
In any case, you should be very upfront about the level of commitment. Leaving them hanging half way is IMHO a bad idea. Not only in terms of you not being able to participate in any successes after that, but also in terms of letting someone down who asked you to help them. If you don't particularly like the project or don't want to invest any effort in it, tell them.
That said, I've been involved with a few such projects and always found it very rewarding.
Upvotes: 3 <issue_comment>username_2: You forget one consideration: do you enjoy using that skill? To take your own example, I enjoy programming, and even though I might not learn or get anything technical from it, I enjoy solving someone's else problem with some lines of code :) Of course, one should be careful of the time spent and the reward you can get from it, but doing something you enjoy is very different than doing something you can, but don't particularly enjoy doing.
For instance, I can probably install a server and manage a website (with a CMS) for someone, but I don't enjoy it, so I would be very explicit about the benefit (I do that for you if you do that for me). But I could do some cool coding on my free time.
On a completely different aspect, it also depends if you intend to stay in academia: it's a very small world, and the person you're doing a favor to today might be the one sitting on your tenure committee in 20 years (if you work in the same department and all stay in academia, it's quite likely that your paths will cross again at some point in the future).
Upvotes: 5 [selected_answer]<issue_comment>username_3: One idea: If you could ask the student if it's okay to record your sessions helping them (sort of like what <NAME> did with Khan Academy), and then maybe save your sessions, then maybe you can then re-run those sessions for later students (so that you won't have to repeat yourself as much, and so that you can always show yourself when you're explaining the concept at your best). That way, you can do that and spend your time giving more personalized instruction to who-ever you're helping.
Upvotes: -1 <issue_comment>username_4: Usually when I help other students I put a large time investment making sure that they are capable of caring on the rest of the project without my help. I see my job is to help them get the ball rolling while maintaining the illusion that I'm the local "expert". An analogy would be dressing yourself up as a consultant rather than a mercenary.
Coding is a particularly good example of this. While you can just code away and have it do the job, claim victory, and get endless praise and thanks, I think that its worth writing quality well commented code so that your peer can take it and do their own thing mostly without your help. At the sacrifice of a lot of initial effort, you get some valuable practice time with teaching and writing interesting projects and they won't feel totally abandoned when you have to do something else.
An additional plus is that you can establish yourself at a person only to inquire when there is a worthwhile need for you skill and that should filter out the "hey dude, can you code this up real quick".
Upvotes: 1 |
2012/07/06 | 626 | 2,646 | <issue_start>username_0: After submitting a paper for review, I received a letter from the editors containing a negative report and informing me that their (editors') decision was to reject the paper. Although the paper was about six months with the referee, it was clear from the report that she did not read it, just had a quick look and wrote a report, full of typos, mistakes and speculations about what the referee thought was in the paper (as she didn't read it).
I wrote a letter to the editor saying that I agree with their decision to reject the paper and would not dispute it. But I also expressed my opinion of the report, because I think it might help to increase quality of the review process. I had no intention to get the paper reconsidered, and even started to prepare a slightly revised version to submit it to another journal. However, they have responded that they would give it to another reviewer.
Now this situation is quite uncomfortable for me: I imagine how the referee will feel if the paper gets accepted and appears in this journal. On the other hand, the referee should be well aware of the (poor) quality of her work, so maybe she will not care.
The question is: should **I** care?<issue_comment>username_1: Oftentimes, the referee will not find out the editor's final decision unless she checks up on the status of your paper herself. Furthermore, the editor could have initially chosen to ask for many referees, and there is often disagreement, so it is commonplace that some referee recommendations are not followed.
Moreover, and perhaps most importantly, don't worry about a referee getting offended that your paper got in. You could have just as easily gotten offended that your own paper got rejected. We all have to learn to live with not always getting what we want, referees included.
Upvotes: 5 <issue_comment>username_2: No, you should not worry. Referees offer *opinions*. The actual *decision* to accept or reject a paper rests with the editor. The referee may very well be offended by the editor's decision to ignore her opinion, but that's certainly not *your* problem.
Upvotes: 6 <issue_comment>username_3: Most journals solicit half a dozen referees hoping that two or three will respond. (I'd be worried about a journal that only uses one referee.) In any case, referees do not usually know how many or who the other referees are, so the person in your case may assume that the vote was 2:1 against them.
In either case, the editor has absolute discretion. They've been known to override even majority negative reports and go with the .... (drumroll please).... minority report.
Upvotes: 3 |
2012/07/08 | 809 | 3,494 | <issue_start>username_0: How is the person selected? How far in advance is the selection process made? When a department makes an offer to have a speaker come, how often is the offer rejected? And what happens if the offer is rejected?<issue_comment>username_1: It varies tremendously, based on the type of talk.
To a first approximation, anyone can invite a speaker if they have grant funding to pay for the travel costs (or if there are no costs). The limiting factor is getting an audience - it's embarrassing for everyone if few people show up - so this typically happens only if it's clear there will be real interest in a talk.
Departments in research universities typically have a number of ongoing seminar series, and often a departmental colloquium as well. There will be one or more organizers for each of these, who invite speakers and introduce them before the talks. How the organizers are chosen varies: sometimes they are volunteers, sometimes they are appointed by the chair, and sometimes they got a grant that's paying for everything. The organizers can then ask whoever they want to speak, although they often solicit suggestions from other department members.
There may also be special distinguished visiting positions or lecture series. These are quite a bit more prestigious and speaker selections are often made by a special committee.
How far in advance invitations are made can vary: typically many months in advance for a prestigious lecture series, but a seminar invitation may have less advance notice.
As for how often an invitation is turned down, it's rare for someone to express a lack of interest in principle, but it's common for them to be too busy or for it to be impossible to arrange a mutually agreeable date. I often suggest that someone should come speak sometime in a seminar I organize. Sometimes they immediately start arranging a date, sometimes they defer it to another time but eventually come, and sometimes it never works out, but even in that case it's never certain it won't happen someday. If a speaker can't make it, it's no big deal: you just invite someone else.
Upvotes: 5 [selected_answer]<issue_comment>username_2: Let me add to @anonymous-mathematician's [answer](https://academia.stackexchange.com/a/2327/6924). If you're interested in having a researcher come speak at your department, the seminar organizers are often open to suggestions, especially if the potential speaker isn't looking for much in the way of travel costs. Conversely, if you're interested in speaking in a seminar, it can be completely appropriate to invite yourself. This often works better when you've at least met the organizer of the seminar where you'd like to speak; however, you definitely don't need to know him or her well. On more than one occasion I invited myself to speak somewhere. The two or three times I can think of off the top of my head, the organizer was happy to have me. (I think it helped that I didn't ask for any travel reimbursement.) And in each case, I enjoyed my trip and I think my audience enjoyed my talk.
Upvotes: 2 <issue_comment>username_3: At my department, we often have talks by speakers who are already visiting anyway for example to serve on a PhD committee or as part of a collaboration with one of our researchers. This is usually a good opportunity to invite them to speak as they are already here anyway, their travel paid for by the PhD defence or by a research project of someone they are visiting.
Upvotes: 2 |
2012/07/08 | 762 | 3,334 | <issue_start>username_0: In papers or books, citation to another book usually doesn't contain the specific page, section, chapter of where a result is borrowed. If the book is really thick, and the readers may have different knowledge levels and familiarity with the book, some readers may find it not easy to locate the borrowed result within the reference book. So why don't people specify the source of a citation as detailed as possible in books?
BTW, it is good to specify as detail as possible for citation to a paper. But since a paper is usually much shorter than a book and it is usually in a searchable electronic form, it may be much easier to find the source in a paper than in a book.<issue_comment>username_1: >
> So why don't people specify the source of a citation as detailed as possible in books?
>
>
>
Well, sometimes they haven't thought about this issue. Sometimes they know something is in a certain book but don't have a copy handy to figure out exactly where. Sometimes they do have a copy handy, and they know they should look it up, but they are too lazy.
In many cases one can easily locate the right section using the table of contents or index, but when this fails it's really annoying.
Upvotes: 4 <issue_comment>username_2: It's always good practice to cite as closely as possible, especially in a book. LaTeX makes it easy to do this with the `\cite[]{}` form. For a paper, it's often not necessary since one typically cites the main result of a paper. But even with a paper, if what you're citing is a lemma buried inside (and that is not obvious from the abstract) it's good to say `[23, Lemma 3.1]` or something like that.
As to why people don't do it, AnonymousMathematician already answered that above.
Upvotes: 5 [selected_answer]<issue_comment>username_3: There is also often a good positive reason to cite a book without reference to specific chapters or subsections, and that is if one is pointing the reader to a source of review or introductory material. I personally often find it much better to point to a comprehensive survey (which is equally often a book rather than a journal paper), rather than a giant and certainly incomplete list of individual references, especially in formats where the number of pages or reference counts is limited.
This is especially the case when doing cross-disciplinary research. For example, I recently had a reviewer query how our paper could assert something that is such common knowledge in my field that I wouldn't have even thought to cite it. Thus, in the revised paper we cite an appropriate undergraduate textbook. Pointing to a specific element inside the book wouldn't have made sense, since you really need the whole foundation. It would be absolutely inappropriate, however, for us to attempt to reproduce an undergraduate class in the text of our paper.
Upvotes: 2 <issue_comment>username_4: One reason to not cite a specific page is if the information isn't on *a specific page*. Books, far more than papers, can communicate not only facts or single points of information, but ideas presented as a coherent whole.
For example, a paper I wrote cited <NAME>'s *The Logic of Scientific Discovery* when talking about the process of scientific reasoning. There's not a page where that takes place - it's the whole book.
Upvotes: 2 |
2012/07/08 | 1,710 | 7,112 | <issue_start>username_0: Should one create slides similar to those that one uses in a good powerpoint presentation? Or are there things that a poster should include that a powerpoint should not include? (and vice versa)<issue_comment>username_1: * More images, and less text. A poster is a highly visual, two dimensional medium, and you should use the real estate as such. Since poster viewers will be skimming it while you explain something to another viewer, it's best to have lots of pictures so they can get a gist of the ideas.
* PPT slides are the poor man's way of making slides on the quick. But they're not ideal. Again, it's best to use the entire real estate as a continuous medium, rather than as a set of tiles (unless you can do some creative flowing with the set of tiles).
* Having said all of that, readers still like some sense of progression through the poster, so it helps to have visual cues (arrows, arcs, etc) that help the reader understand the order in which to read things.
Upvotes: 4 <issue_comment>username_2: 1. The title is your bait, the first paragraph is your hook. Make the bait big and tasty, make the first paragraph catchy.
2. A poster is primarily an advert for you. Secondarily, it's an advert for your research. Thirdly, it's an advert for your department. And it will succeed at those things best, if it gives the casual reader an easily accessible introduction into what's novel about your research.
3. Know the flow: it should be clear to anyone reading, what they should read first, then second, and so on.
4. Make it clear that it's **your** work. Get your name and affiliation in big letters, with a photo of you. Include your contact details, and make sure you can be reached on them during the conference.
5. Don't use powerpoint. A poster is graphic design, so use a graphic design package such as InDesign. If you don't have an eye for graphic design, design the poster with someone who does. Most departments have at least one natural talent. It's often the person who keeps winning the "best poster at conference" prize. It's orthogonal to (i.e. independent of) quality of research.
6. Use a big, clear typeface. Not everyone you need to reach has the healthy, clear vision that most students have.
7. Briefly state the problem, the method, the results, and the implications.
8. Pretty pictures are crucial. The words are there just to supplement the explanation of your work given in the pictures.
9. Test, test, test. Print it out life-size (tiled sheets & sticky tape are your friend), show it to a couple of colleagues (one hot on your area of research, and one hot on design), watch the order in which they read things, ask for feedback. Implement any advice on content that comes from the one who is hot in your area of work. Implement any advice on design that comes from the one hot on design.
Upvotes: 6 [selected_answer]<issue_comment>username_3: Apart from the excellent advice you already received, here are some additional points:
* Bulleted lists instead of flowing text helps make your points more concise. They are also easier to read.
* Don't be *too* concise. Although text should be minimal and the poster is not a stand-alone thing (i.e. you're there to present it), take into account that people who are interested in your work will ask you to mail them a pdf of it after the conference. They should be able to reconstruct your work from it (after having heard you present it once).
* Make the research question and conclusion stand out. Circle them, put them in bold, give them a different color, anything. A person looking at your poster should be able to read the title, the question and the answer at first glance.
* Make the data as easy as possible to read: circle the important parts in the graphs, point arrows to them, write in words what they tell you.
* Avoid putting tables, unless they are really small. Tables are difficult to read. Wherever possible, replace them with graphs.
* Avoid trying to tell your audience everything. Choose one key point to present.
* Aesthetics have a slightly different logic than usually. An ugly background color might work in your favor for example, as long as it makes your poster stand out. Symmetry, however, is highly valued. Also, I wouldn't go for more than 2 colors (apart from the background color and letters).
* I've heard that sans serif fonts are considered better for posters, though I'm not sure why.
Upvotes: 3 <issue_comment>username_4: I broadly support most of the tips given in the previous answers, but I'd like to add some more general concerns which I feel that you should think of before you even draw the first line:
* **Take-home message:** Think about this long and hard. If you had to describe what was exciting about your work in one single sentence, what would it be? What is the one thing you want people to remember about your work? About your presentation? Make sure you're 100% clear about this before you start writing your poster.
* **Clarity:** Once you have your take-home message, make your entire poster subservient to it. Place it prominently in your title and make it crystal clear in your first paragraph, as [EnergyNumbers](https://academia.stackexchange.com/users/96/energynumbers) and [Ana](https://academia.stackexchange.com/users/322/ana) suggest. Anything that's on your poster that does not contribute directly to the take-home message shouldn't be there.
* **Lead your readers/viewers**: If your main argument requires a chain of explanations, display these prominently and mark them as such. Make the text flow follow the flow of your argument or reasoning. Place figures where they nail a point home, and nowhere else. Clip arts and colour can be cool, but don't use them if it will distract your readers from your take-home message or otherwise make their eyes wander.
* **Preparation:** Try to think of the three questions people will ask you when you present your results, and try to answer them pre-emptively in the poster. Also try to be as prepared as possible to explain stuff while standing next to it, e.g. make sure it still has all the data you need to point to when making an argument.
This may all sound a bit reductionist, but remember that apart from the space restrictions, you're also dealing with *time* restrictions. People usually just browse posters while on their coffee break. Your job is to captivate them and make the most of that short break.
This might also all sound a bit too much like leading the viewer/reader like mindless cattle. Don't worry, though, they won't care. I've yet to hear anybody complain about an argument being presented *too* clearly. If anybody wants details beyond the take-home argument, you'll be standing there to give them, which is why you should be prepared and ready for questions.
Upvotes: 3 <issue_comment>username_5: Here you have a great article for the *Ten Simple Rules* series, by <NAME> and <NAME>:
[Ten Simple Rules for a Good Poster Presentation](http://www.ploscompbiol.org/article/info%3adoi/10.1371/journal.pcbi.0030102)
Take a look!
Upvotes: 3 |
2012/07/08 | 1,998 | 8,391 | <issue_start>username_0: I'm a second year PhD student. In general I love my advisor, but recently we've run into some small bumps in the road while working on a paper together. He's controlling the "master" copy and I send most of my contributions and comments through email for him to incorporate.
A few days into the process, we had a conversation that went something like this...
>
> Me: "Hey, so I cited a few papers you might not have seen. How do you
> keep track of your references? I use JabRef to keep up with BibTex
> entries, but I can export those citations to a bunch of different
> formats..."
>
>
> Advisor: "What are you talking about?"
>
>
> Me: "Like, when you need to
> make your references section... how do you keep track of all the
> papers you've cited in the text? Refworks? Endnote? Zotero?"
>
>
> Advisor: "What? ...I use
> the 'copy/paste' method."
>
>
>
I was baffled by that answer. I know he's been doing this for a long time with good success, but I cannot fathom someone who has been collaborating with so many people for so many years is still at the level of manually formatting each entry in a Microsoft Word document and then copying/pasting over whenever that reference is needed elsewhere.
**Any suggestions on how I can help bring this faculty member into the 21st century without seeming presumptuous?**<issue_comment>username_1: Maybe you should get your advisor to use something like svn or cvs with the ability for anyone to edit the "master copy". Then you can slip in your refs as you see fit.
Upvotes: 3 <issue_comment>username_2: This probably sounds defeatist, but you might just have to accept the fact that some people would rather do things the hard way because they fear that the technology will eat up more time than it's worth. Maybe after building more rapport with him, you will have an opportunity to demonstrate the value of your method...for example, if a paper gets rejected from one journal and you can reformat the references in a few minutes, whereas it would have taken him much longer.
Upvotes: 4 <issue_comment>username_3: **Turn the tables.** For your next paper, **you** maintain the master copy. Make sure all the infrastructure is in place, including a fairly solid draft of the paper, before you involve your advisor in the writing process at all. Use whatever version control and reference system you find most useful.
Your advisor may simply refuse to use your tools; fine, you can still incorporate their emailed inputs. Or they may just need someone else to figure out the infrastructure and teach them how to use the tools, instead of figuring it out themselves from the manuals. Either way, you'll have some extra work to bring your inexperienced (and possibly resistant) coauthor up to speed, but that's a standard part of the student-advisor relationship.
In the worst case, your advisor may simply refuse to give up control of the master copy. (Never mind that it's already too late.) In that case, you may need to encourage them to look for another student.
Upvotes: 6 [selected_answer]<issue_comment>username_4: I'd like to give the opposite answer than JaffE's.
**You can't**, give up.
unfortunately, the relationship between you and your advisor is not symmetrical. You can try to make him/her change his/her ways, but if s/he is not willing to change (and habits are difficult to change), you'll end up just fighting windmills
Upvotes: 2 <issue_comment>username_5: Endnote doesn't work on linux. Refworks came into existence in 2001 and is a website, it could disappear any time. Zotero is only 6 years old.
Programs come and go, websites come and go. Text files keep working. They are easy to search, easy to maintain, easy to move between operating systems.
Your supervisor has been around a lot longer than you. Perhaps they have discovered that fancy databases are not worth the extra work they involve, particularly if (like me), they've had a couple of products they love get discontinued over the years.
Upvotes: 3 <issue_comment>username_6: >
> Any suggestions on how I can help bring this faculty member into the 21st century without seeming presumptuous?
>
>
>
You can't. Your attitude *is* presumptuous.
I, for one, still live in the 20th century. I personally prefer the copy-paste method to BibTeX, despite having used BibTeX for multiple different projects. This is on the grounds that it doesn't give me additional files to juggle, and if I have weird references I can easily put them into the bibliography as I think they should look without having to look up the BibTeX documentation.
I'm happy to use BibTeX in my joint papers whenever my collaborators prefer. Typically, when it was `their turn' to handle the document they separated out the bibliography into a BibTeX file, and then I followed suit by adding to it whenever I had additional references to cite. Sometimes my collaborators stopped to ask my permission, which I've always cheerfully given.
I also used a more sophisticated online collaboration tool once when another one of my collaborators set up our document using it. Once again, I prefer to do things the old-fashioned (i.e. ten years ago) way but I was happy to adapt to my collaborator's preference.
Can you "fathom" my preference? Whether you do or not, I concur with username_3 -- if you want things done your way, ask to be in charge, set things up yourself, and then let your advisor know what you've done. Maybe your advisor will adopt your tools permanently if he/she ends up liking them, maybe not.
Upvotes: 3 <issue_comment>username_7: I agree with anonymous.
I'm a young academic but I actually prefer the old copy and paste/formatting by hand over a program that will do it automatically. For me, I've found that generally, the copy and paste format helps in catching errors and is easier to use than an embedded software program that has the potential to crash/become buggy.
I've found that with a variety of those bibliographic programs, they are great for data-basing your entries but not very intuitive or easy to use when it comes to actually writing papers in programs like Word or OpenOffice. They don't always do well in crossing other computers depending on the programs various individuals use (i.e. some might have a mac and use OpenOffice, while the other author has windows and uses Microsoft). The compatibility can cause issues, and many academics use different programs (for example, you use BibTex, my university uses Endnote, my undergraduate uni uses Refworks).
I've also found that while programs such as Endnote can 'technically' adopt styles for a specific journal, the output is never 100% to what the journal requires. It's just easier to type it in right the first time for me, than to try and use a finicky program that can actually cause more, rather than less problems.
You should not rely on these programs to provide you the EXACT formatting required for a journal, in many cases you need to go back and fine-tune. For example, I recently submitted a paper to a journal that wanted a particular style, my referencing program was able to 'output' to this style for the journal, but it wasn't exact even though it had the journal listed as a type of output format. I had to go back and manually format each entry to ensure consistency with the latest printed issues. As I was hand-typing my in-text citations in the exact way as the journal wanted, it wasn't much formatting that needed to be done for the end.
I don't have any issues in keeping track since I use a bibliography program for a database, but tend to add the entries in after the journal article is submitted for review. I just keep track using a plain old word doc, and part of my revision strategy is to go through the paper and find every reference and cross match to make sure I haven't missed any. This also allows me another chance to see any language etc issues that might need fixing up.
I think you need to take a step back and consider that not everyone works in the same way and what you might consider as 'easy' could actually be more difficult for someone else who may have a different way of thinking. Some academics use a multitude of programs, some still prefer more traditional methods and some might jump on board with innovation, and this isn't necessarily a generational gap either.
Upvotes: 2 |
2012/07/08 | 681 | 3,028 | <issue_start>username_0: Can I include in my academic CV that I have attended and completed the [Coursera](http://www.coursera.org) and [Udacity](http://www.udacity.com/udacity) classes I have been taking? I understand that these are not anything major, but the courses definitely gives a good overview and starting formal study on the subject becomes less cryptic. If I can include them without any negative impacts, then under what section should I include them?<issue_comment>username_1: No. Specific coursework (whether formal or informal, online or in-person) does not belong in an academic CV.
Upvotes: 6 [selected_answer]<issue_comment>username_2: If the online course is relevant to your current field of work/study it would help. For example, a graduate student of science could mention a course on scientific computing but a course taken on criminal law would be irrelevant. Also online courses taken can only be shown under professional development and not under academic qualifications since these are non-credit courses.
Upvotes: 3 <issue_comment>username_3: **Yes**. The *certified* courses that connect to skills that doesn't directly apply to your mainstream degree *deserves* to be in your academic CV. It will show, to an extent, an interdisciplinary qualification that you possess.
There are many real life instances where this has helped a lot, especially in industrial placements, career change, and interdisciplinary research.
There are some courses from MOOC you ought not to add in your CV. These include those that overlaps the courses you've already completed as per your educational degrees, minor introductory courses, and those that wouldn't prove a significant impact in your career point of view.
Upvotes: 2 <issue_comment>username_4: Interesting to see how things have changed since 2012 when I asked this question. I started with some artificial intelligence and machine learning coursework in my masters, and afterward attended the first machine learning and probabilistic graphical model courses, which started Coursers (the first ones before Coursera was founded).
Although I did not need to list these courses in my CV, I think if it is certified, it is definitely worth having it in the CV depending on what position one is targeting to apply for. For example, if someone wants to apply for a position where statistical analysis and modeling skills with R are needed, then it is worth listing relevant courses (preferred certified) in the CV. I believe that it will show that the candidate has some skills with R and statistical analysis. It is good to have this is a CV for the industry that may add real value.
Although for an academic CV, I somewhat agree with @username_1 . It may be good to have this in an undergraduate or master's CV if the course is certified, and you want to showcase that specific knowledge that probably is not covered in the curriculum and you want to highlight it. Afterward, when things become more specific, these definitely become pointless.
Upvotes: 1 |
2012/07/08 | 239 | 1,023 | <issue_start>username_0: I am about to reach a stage in my masters where I'll have to make a choice of doing either a regular thesis or a practicum. A practicum is simply an alternative to thesis in which a student has to submit a practical project employing the concepts developed throughout the graduate program and concludes with a paper and presentation of the crafted project.Since I am planning to apply for a PhD soon after graduation will it affect my application if I opt for the practicum instead of the thesis considering the fact that I already have published a technical paper ?<issue_comment>username_1: Personally, I think the fact that you've published a technical paper would carry more weight for a CS Ph.D admissions committee than whether you did a thesis or practicum, since that's the demonstration of your ability to do research.
Upvotes: 3 <issue_comment>username_2: **Thesis.** Thesis, thesis, thesis.
(But I do agree with username_1. The fact that you've published is more important.)
Upvotes: 3 |
2012/07/09 | 3,299 | 12,328 | <issue_start>username_0: It's easy for me to find out salaries for tech-jobs but it seems Professor salaries are quite hush-hush. I really love to teach and would be more than willing to join academia. However, I may choose to work for a few years in the industry before doing so. But even for information sake it's really *really* hard to figure out a tenure and tenure track professor's salary. It's rude to just ask my professors/colleagues directly :P
I'm interested in knowing an 'expected' range for the following countries - preferably both state and private universities (Computer Science). I'm not sure if there is a difference between the MS/PhD faculty and BS/B.Tech/BE though, but it'd be great to highlight the same.
* United States
* Europe (Switzerland, Germany more preferable)
* Australia/New Zealand
* India/China
Intent of information - awareness to take a better decision on the 'money dimension'. Please don't get me wrong, I am not intending to take a job with the most money but if a faculty position pays USD $50,000 per annum after 5 years of intensive effort, I'd like to hold off for a while. If *"it depends"* then on *what* does it depend and after I satisfy those dependencies, what can/should I expect?
**UPDATE**: Just for clarification, I am on the verge of completing my PhD thus piquing my curiosity about the remuneration since it's difficult to ask your advisor or other faculty members. I have and mostly been asked to wait for infinity for the response, hence the question :) I just wanted to know so that I can take an informed decision when I'm at the crossroads of *applying for academia* vs *industry*. Please don't get me wrong. I DO NOT wish to undermine the value of a PhD. I'm genuinely curious and I personally enjoy every bit of my work and it's NOT ABOUT THE MONEY :)<issue_comment>username_1: There are many factors that go into figuring out the answer.
* location - different countries/systems have different ways of paying
* area: salaries vary HUGELY across areas. You didn't mention your area, but you can expect that salaries in the humanities are less than those in engineering which might in turn be less than those in law/business/medicine
* private/public: in the US salaries of profs at public universities are public knowledge - if you look up the university of Utah you can get my salary and that of all my colleagues. Public universities usually have public scales - private universities are - well - private.
* level: I assume you're starting at the lowest level, but based on experience/demand things can vary a lot.
To get information, best to lookup surveys that are usually run by professional organizations in your area - they'll give you good ballpark estimates.
Ultimately you have to remember that a faculty salary, like any other salary, is a market-driven quantity with value set by the market. So it's very important to understand the local economy that drives the numbers - the above factors are some of the main drivers.
Upvotes: 4 <issue_comment>username_2: For the United State, see <http://cra.org/resources/taulbee/> for salary survey data in computer science. Of course, [as username_1 points out](https://academia.stackexchange.com/a/2347/2740) there's enormous variation. The median salary for a tenure-track assistant professor in computer science at a US research university is about $90k, but some make quite a bit less.
Upvotes: 6 [selected_answer]<issue_comment>username_3: There's enormous variation in both field and university. I would however suggest that professorial salaries are not entirely "hush-hush". For example, the University of North Carolina system has all their salary information available to the public:
<http://www.newsobserver.com/2011/02/24/1011452/university-employee-salaries.html>
Look up the department you're interested in, get some names of their lower ranked faculty, and look them up to give yourself a ballpark estimate.
They also break down State and Non-State funding so you can get an idea of how much of the salary is hard money and how much of it is based on grant support.
Upvotes: 4 <issue_comment>username_4: While it is a good question which people naturally would like to know before committing for a PhD, a definitive answer is next to impossible.
Your question on **Indian public sector** is best answered by Prof <NAME>'s blog. In [this post](http://giridharmadras.blogspot.in/2009/09/one-more-post.html), for example, he talks about a new professor getting Rs 52000 per month plus accommodation. Then there are scholarships, consulting work for many small companies (whose numbers are bound to be high in a rising economy), vacation period of 3 months when you "get paid in international currency" (ref:GM's blog), paid conference trips, travel allowances within India, etc.
You cannot directly compare this with any private sector company: you are obviously going to get paid more, but you are stuck inside those cubicles with monotonous work and you are likely to lack intellectual freedom.
Regarding **Indian private sector**, things are hush-hush, and depend on your personality, education, job offers, negotiations, etc. But I would say deemed universities in India are particularly wealthy given India's population and the general affordability to pay high fees. On the flip-side, your colleagues and students are unlikely to be intelligent or sharp, as most of India's intellectual wealth generally lie within the IITs (and at times, the NITs, CEG, etc).
Upvotes: 4 <issue_comment>username_5: Australian starting salary with a PhD is around 70-80K USD. Up to 100K after 5 or so years, ++ if you are a super academic/head the faculty etc.
Cost of living is quite a bit higher than the US.
Tenure doesnt exist in Aus, you will be an academic as long as you can bring in research dollars/you teach important subjects that bring in students. There are no private universities here.
Upvotes: 3 <issue_comment>username_6: Following this question: [What's the net income of a W1/W2 german professor?](https://academia.stackexchange.com/q/221/102), and according to [this link](http://www.myscience.de/en/jobs/salary), in Germany and in 2008, the monthly gross salary could go from 3500 euros per month up to 7000 euros per month.
Upvotes: 3 <issue_comment>username_7: **Australian discussion**
Pay scales are available on most university web sites:
* [Deakin](http://www.deakin.edu.au/careers-at-deakin/assets/resources/why-deakin/benefits/salary-rates-academic.pdf)
* [Monash](http://adm.monash.edu.au/enterprise-agreements/academic-professional-2009/s1-academic-salary-rates.html)
* [UNSW](http://www.hr.unsw.edu.au/services/salaries/acadsal.html)
The first challenge is to get a job. You may need a year or two post doc experience before you can get a level B lecturer position. For reasonable performance you'll typically go up one increment each year (i.e., B1, B2, etc. to B6). To go to C (Senior Lecturer), D (Associate Professor), or E (Professor), it is not automatic. You need to meet more criteria.
Upvotes: 4 <issue_comment>username_8: in Spain you can max out some 35k€ / year
Some details to complete the answer:
I am "<NAME>" (senior lecturer with a tenure position, like a second class Professorship). I have 15 years experience
My total income last year was EUR 45ooo. That included 2ooo for an extra course, 27oo for a positive evaluation of research and 86oo for the 15 years. That gives a basic salary of about EUR 31ooo when you start. But when you start you have a lower category, so the first salary is lower. There are also variations depending on the region.
I pay about 12ooo for taxes and insurance. That leaved some 33ooo as true income.
With the cutbacks, I expect some 30ooo this year.
Upvotes: 2 <issue_comment>username_9: First of all, you assume that after finishing a PhD, your chances for a professor position are good. As far as I can say (a CS post-doc@EU university), this is generally not straightforward unless you deliver a star PhD. Otherwise, expect at least one post-doc appointment. After a return from industry, unless you were *very* active in the research community during your time with a company, again post-doc is what you should expect.
Now to the answers, a European perspective. Generally in the EU, probably except for the UK, the academic salaries are governed by tables subject to annual change. In many countries these would be fixed without a variable component based on performance.
**Germany:**
============
The positions of research assistants underlie [TVoeD regulation](http://de.wikipedia.org/wiki/TV%C3%B6D) (BAT in the past). There is no special category for a post-doc, all research assistants are treated equally. The salary scale reflects the individual's experience, that is, officially years of employment. Generally that should include also academic experience abroad too. Find the current tables also [here](http://oeffentlicher-dienst.info/tv-l/west/) - note the scales differ for West Germany, East Germany, Berlin and Hessen. You are interested in the class E13.
Professorships are remunerated according to the [W scale](http://de.wikipedia.org/wiki/Besoldungsordnung_W). Again the salary differs from state to state, but according to [this](http://www.w-besoldung.net/informationen/wie-hoch-ist-das-grundgehalt/), we speak a baseline of about *EUR 47k*, *EUR 53k* and *EUR 65k* a year for W1, W2 and W3 professorship positions. W1 is for a [Juniorprofessor](http://de.wikipedia.org/wiki/Juniorprofessur), roughly equivalent to an assistant professor. W2 and W3 are two different levels of full professorship, the particular difference is mainly an experience/salary issue. The salaries are again graded in steps according to the number of years of experience at the particular position. You start at 0 and from there your grades increase.
Note however, at least in CS and generally in STEM, Germany does not fare very well in terms of foreigners on senior academic positions. It's relatively rare to encounter a non-German (or Swiss/Austrian) holding a professorship at a German public university.
**Netherlands:**
================
The system is slightly simpler than in Germany, the salaries are fixed according to CAO (Collective Labour Agreement) regulation and subject to annual/bi-annual negotiation and modification. You can find the information on CAO [here](http://www.vsnu.nl/cao-universiteiten.html). Salary-wise you are interested in the [salary table](http://www.vsnu.nl/files/documenten/CAO/Salarisschalen_2013IenII.pdf), columns H1 and H2 (Professor 1 and Professor 2). These are full professorships. Here we speak about *EUR 65k* and *EUR 58k* respectively as a baseline from which the annual grading increase starts. For a assistant professor, the columns of interest are 11-13, so the variance is big. E.g., 11 is also for post-docs, though sometimes assistant professors get that as well. Depends on the particular position.
Now considering positions in the Netherlands taken by foreigners, you are eligible for a so called [30% rule](http://en.wikipedia.org/wiki/Income_tax_in_the_Netherlands#The_30_Percent_Rule) which basically states that you do not have to pay taxes for 30% of your income. That leads to a significant salary increase for foreigners in the first years of their employment in the Netherlands so even the assistant professor salary grades might not look extremely interesting, considering the 30% rule, they turn out to be fine.
All the quotes should be understood brutto before taxation and social system/healthcare/++ deductions.
Upvotes: 5 <issue_comment>username_10: Your expectation should depend on both the potential salary (which the other answers are provided reasonable estimates of) and the probably of the outcome (which most of the other answers have not touched on). If you have not started a PhD program yet, your expectation about how much you will make as a professor should be $0 since the probability of becoming a professor is essentially zero. Even using generous numbers 0.5 get into PhD programs, of those 0.5 finish PhD (0.25 of those who apply). Of those only 0.5 will get a post doc position (0.125 of those who apply for a PhD). Then you have TT position (0.06) and tenure (0.03).
Upvotes: 3 |
2012/07/09 | 387 | 1,629 | <issue_start>username_0: I hold a bachelor's in computer applications. I have got my bachelor's degree from India. Now I want to get a job in research institutes or labs at Europe, USA or India. Will I need higher education like master's or PhD? Money does not matter to me.<issue_comment>username_1: It is highly unlikely that you'd get a permanent research position in CS with a bachelor's degree. It's also unlikely (though not impossible) that you'd get one with an MS degree (for example, it might be possible to enter an organization after an MS, and then move internally into their research wing). The most common path to getting a research job at an institute is after a Ph.D.
Upvotes: 3 <issue_comment>username_2: You could aim at **technical universities** (in Germany and Switzerland, they are called "*Fachhochschulen*", Universities of Applied Sciences). Their focus is more on applied research and teaching. I worked 2 years there as a researcher without a MS in CS.
Upvotes: 4 [selected_answer]<issue_comment>username_3: I would propose to target a PhD student position. As a rule, this requires master degree.
PhD is not required and will actually decrease your chances significantly, as (following unwritten or written rules) you cannot become a PhD student second time, if you already have one degree. You must then aim to the post doctoral position that has much more requirements.
It is not very difficult to get a PhD student position in Europe, and it is not complex to get a few post doctoral positions afterwards, but getting a permanent position in science later is much more problematic.
Upvotes: 1 |
2012/07/09 | 1,198 | 4,726 | <issue_start>username_0: A follow-up question to [this](https://academia.stackexchange.com/questions/2346/after-my-phd-how-much-salary-should-i-expect-as-a-professor) as I feel it is very broad. For example, consider [this link](http://www.collegiatetimes.com/databases/salaries/university-of-texas-at-austin?dept=Infomatn%2Frisk%2Fops+Mgt) displaying the salaries at a particular department in a public school in US.
I see very wide variations within assistant professors. Some assoc. profs earn lesser than asst. profs; people of the same age earn differently and so on. So, my question is, which of the following factors influence salaries at public schools and how?
* Age
* Experience as a faculty member
* PhD at a top school
* Number of years after undergrad/PhD
* Experience at another school (Does a Stanford faculty with 5 years exp. moving to a public school earn more than a faculty member at the same school for 5 years?)
* Number/Impact of publications
* Any other factor
Given all data is in public, I assume there can be no pay negotiations, so is it possible to determine one's salary in advance before the interview process itself?<issue_comment>username_1: In the US, everything you list above—and more—can affect salary. (Do note that, in the US, age discrimination is illegal. Not that it doesn't take place anyway...) The only thing I'm not sure of is "number of years as a PhD", that's more of a proxy for "work done during PhD tenure", which is included in the impact factor.
A few factors I thought of, not likely exhaustive:
* Masters/PhD/postdoc alma mater
* Masters/PhD/postdoc advisor
* Number and quality of publications
* Grant history
* Existing grants
* Collaboration history
* Teaching experience
* Field of research (psych vs history vs engineering vs etc)
* Type of institution (public/private)
+ Economic environment (budget cuts in state funding, etc)
* Teaching load
* Location (Dallas, TX vs Palo Alto, CA vs etc)
* ...?
Upvotes: 3 <issue_comment>username_2: In addition to @username_1's list, there are several factors that can lead to salary variance even within a single department:
* **Negotiating ability** — Some profs are simply better at negotiating for better salaries than others, even with comparable publications, funding, students, teaching evaluations, etc. Conversely, some people have lower salaries simply because they don't realize they *can* ask for more.
* **Time in the department/time since PhD** — All else being equal, the longer you've been here, the higher your salary. But as usual, all else is never equal. In particular, faculty hired with more post-PhD experience are generally paid more.
* **Performance** — In most departments, faculty are reviewed annually, if only very lightly, to make sure we're doing our jobs. Salary is one of the few levers that department chairs have to reward faculty who are doing exceptionally well, or motivating faculty (especially with tenure) who aren't (seen to be) pulling their weight.
* **Offers from other institutions** — This is one of the biggest sources of salary jumps. If a valuable prof in your department starts getting offers from other places, you department is very likely to raise their salary to keep them.
* **Administrative bonuses** — Faculty who hold significant administrative positions (like associate head, or chair of the undergraduate program) often get a salary boost.
* **Variance in the job market** — New assistant profs are generally hired at the prevailing salary rate for new assistant profs. Departments do not collaborate explicitly, but information does flow through applicants who get multiple offers. For example, about 10 years ago, a top-rated US CS department (not mine) decided to significantly increase its salary offers to new profs, to gain a strategic advantage over other departments. It didn't work; other departments (including mine) just raised their offers to compensate. It took several years to correct the resulting salary inversions.
* **Variance in university budgets** — When times are good, faculty get raises. When times are not so good, faculty don't get raises. These times do not necessarily align with fluctuations in market rates.
* **Intramural politics** — Academics are human, and subject to human failings. Everyone who reaches a position of power arrives with their own agenda; sometimes that agenda favors certain people or groups over others, for reasons that are more personal than objectively fair. In some departments, fights over limited resources can be ugly and brutal ("[because the stakes are so low](http://en.wikipedia.org/wiki/Sayre%27s_law)"); sometimes that ugliness is reflected in salary differences.
Upvotes: 5 [selected_answer] |
2012/07/09 | 1,968 | 8,247 | <issue_start>username_0: Engineers and Scientists are probably not the kind of people management consulting companies look for. But I think many of such would be highly qualified and a good fit for management consulting positions, especially where vision and strategy are needed.
What would you suggest for a scientist or an engineer to succeed in getting a job, considering that the hardest part is to even get a phone call?<issue_comment>username_1: I'm afraid I've got to say that many of the things that one might consider an asset, are in reality going to be a hindrance.
Rigour, consistency, reason, domain knowledge, ability to describe uncertainty, always prepared to put appropriate caveats on findings: these are all encumbrances in almost all management consulting.
What you need to cultivate is the ability to form strategy on the basis of a fairly superficial understanding of a business; and the ability to persuade people to pay you lots for you to borrow their watch and then tell them the time.
Oh, and do watch [House of Lies](http://www.imdb.com/title/tt1797404/) through, end to end, a couple of times, to get a feel for it.
Having said that, there is a tiny niche in management consulting available for the skilled technician who's prepared to say no to clients who want "regular" management consulting, and only take on clients who want a thorough, rational, informed job done. There aren't many of those clients, whch is why it's a tiny niche. And it's more likely to be a boutique subject-specific consultancy. But if you find one (or found one, i.e. you are the founder), and it gets the right kind of clients, you'll be very happy (and completely exhausted much of the time), and financially very comfortable while the clients are there.
Upvotes: 4 [selected_answer]<issue_comment>username_2: A management consulting company once tried to recruit me (I have a PhD in computer science). They said they wanted me for my brain and they didn't care so much about my background. That said, I didn't have a PhD at the time, so maybe my brain is now spoiled.
Upvotes: 1 <issue_comment>username_3: Your most likely bet is to sell yourself as an operations management specialist. There's an entire field called [Industrial Engineering](http://en.wikipedia.org/wiki/Industrial_engineering), which is essentially business optimization techniques. There are many aspects of this field that you've likely covered in your coursework, such as optimization methods and probability theory. Emphasizing this in your portfolio can help land you positions in a vast array of fields, ranging from broadly-defined operations management to process engineering to personnel management.
Upvotes: 2 <issue_comment>username_4: Looking at the current set of responses, they seem to be heavily biased. As someone having spoken to several PhDs in management consulting, let me try to clarify some of the nuances.
First, it is not true that you have to let go of rigor, but rather need to be time-sensitive. This is because the management consulting model works on an hourly-basis and therefore, while consulting on a project, your time determines your payment. Most companies, have several reasons to hire external consultants. At times for the expertise, sometimes for an external perspective, and at other times for helping them communicate capabilities and constraints to their superiors. Regardless, one thing is sure that the time-sensitive nature of the field leads to shorter project life-cycles and at times lack of implementation, which is a stark contrast from a PhD experience where the candidate is the sole worker and implementor.
Second, it is completely false that consulting firms consider PhDs as overqualified. In fact, in top-tier management consulting, advanced degree holders (PhDs) enter at the 'associate' level rather than the 'analyst' level, and generally speaking, move up faster.
Third, and logically following the last point, it is a misconception that consulting firms do not entertain advanced degree holders. In fact, the last I checked, ~10%+ of Mckinsey & Co's total employees were PhDs (as listed on their website). Further, most top management consulting firms have a special set of recruiters meant to be hiring advanced degree holders. You may find them on the company websites.
Finally, I admit that as a field, management consulting has its lows such as those mentioned in the answers thus far, but it would be naive to think that other fields (specially academia) do not have their own shortcomings. Overall, I think that the world needs more PhDs and the management consulting world needs more PhDs working to make corporations better, especially since corporations affect several aspects of our lives.
Upvotes: 2 <issue_comment>username_5: Other than Sheth's answer, all the responses (including the green checkmarked one) are misleading. 25+ years out of date and written by people hypothesizing versus having experience. I recommend to any students looking into this avenue to talk to your college career center and to just read some recruiting websites (start with McKinsey).
1. In the late 90s, McKinsey was bringing in 1/3 (real number) of US associates as APDs (alternate professional degree, non MBA). And the global percent was higher. They had a well established recruiting and training process. Other firms were slower but starting to do the same (more the top firms than the lower tiers).
2. Even for top tier MBA students (HBS, etc.) the odds are hard to get consulting jobs, especially at premier firms. Same applies to Ph.D.s, perhaps made more difficult that they have less option to go to lower rung firms (that just bring in MBAs). Note, there's a 2012 comment about "I had a Ph.D. and no phone interview". So? Just having the union card itself is not distinctive. Most ballplayers don't get a combine invite either. It's just odds, just numbers. If you don't get it, you don't have the case interview solving analytical instinct they are looking for
3. They're just looking for willing, trainable brains and the ability to do the work. Figure something out from a blank slate. Problem-solve, etc. Specific technical or field training is irrelevant. They will put the English major into a fab or the material scientist into a film studio in a heartbeat. After all most of the MBAs are generalists without specific industry focus also.
4. Sameness: Job is the exact same gig as what an HBS MBA gets. Same salary and bonus. Same assignments. Same offices. Learning curve is fast so after a gig or two, there's no difference. Plus the case interview selection process has already screened for candidates with qualitative analytical ability within the Ph.D. pool.
5. Slight differences: They do/did have a 3-week training program to give the MBAs the basic toolkit (learn some micro, learn how to do DCF and what goes into the WACC). Good deal. There's maybe a slight disadvantage in not having the MBA for some post consulting interviewing (just dependent on HR departments), but still plenty of options where they just look at people post consulting, not worrying about the MBA. Also, maybe the MBA students sort of "want it more" as their dream job. That was <NAME>'s observation. One other small difference is that most MBAs have pre-MBA working experience (often at the sort of client you serve) so at least they've had a "real job". Plus, you can consider the argument that it's a waste of a Ph.D. (the actual subject matter technical training). And that you ought to be "curing cancer", not analyzing merger synergies.
6. Note that consulting has its good/bad aspects, including for the MBAs. Sure, read House of Lies book. Author was a Booz associate so he'd seen the sausage get made. (He was also the popup video show producer for MTV, which explains the zany writing style.) It's an OK expose and anyone who has been there will read parts that ring true to them. But I also wouldn't assume you know what it's like from a book versus from doing it. Like all the 23 year olds who think they know what an I-bank is like because they read some Michael Lewis. If anything we need a trenchant expose of academia. It's not like it's all daisies there either...
Upvotes: 1 |
2012/07/09 | 1,444 | 6,108 | <issue_start>username_0: My question is : What is a **time effective** way to *learn* new material/ *reviewing* old material you don't completely remember?
Motivation:
For my research in Computer Science, I need **lots of elements** of hypothesis testing that I learnt in Statistics 101 (Roughly 10 years ago). I haven't taken a single stats class ever since. I vaguely remember the terms involved in the subject and am somewhat conversant with the basics of statistics (so I don't need to retake the course). But all in all, it's as good as a blank slate right now.
I have read [How to efficiently read mathematically and theoretically dense books in STEM fields?](https://academia.stackexchange.com/questions/631/how-to-efficiently-read-mathematically-and-theoretically-dense-books-in-stem-fie) but that targets a specific topic that the OP wants to learn, not an entire book. The top 3 answers talk about reading only those sections which you are interesting in. I, on the other hand, need to understand the whole of hypothesis testing which by itself is a book.
My options are (As far as I can see):
1. Take out the book I used / "Best" book of the subject (i.e. the one best suited for my background) and read it from page 1 till I am comfortable. **Too time consuming**
2. Read the chapter I need and then go back to the terms I don't understand. **Time spent in confusion and direction-less reading exceeds the time actually spent adding value**.
3. Take course again/Read cover to cover. **God No.**
4. Any other?
EDIT : I wish to add that I can sacrifice depth for familiarity and savings in time. (Is this a good idea but? Assuming that it's not a central part of my research)<issue_comment>username_1: First of all, you will spend virtually your entire career doing this (trying to remember stuff you forgot), so this is a question that's worth really working on. You'll hopefully receive a good few answers; try them all over the course of a year or two in different situations and choose the one that works.
---
My personal method is very similar to your (2); review the specific parts of the material that you need to know for that specific situation. In most cases, you'll find this to be sufficient for solving a particular problem. If you don't understand your material, then either Google the topic and try to read someone else's overview, or simply take a step back and go over the more fundamental material, working your way back up to the topic at hand.
Note that this will not work for situations where you need everything, such as interviews. For those, you'll want to read outlines and try to summarize contents for yourself. I've also gone back and looked at homework problems, which (assuming they were well-written assignments) can provide you with a good overview of the material that you need to know about the topic.
Upvotes: 4 [selected_answer]<issue_comment>username_2: You didn't really mention WHY you need the material for your research.
One case is when you have this vague feeling that there's a concept from statistics (in your case, hypothesis testing) that you need for your work. In that case, username_1's approach and strategy (2) is best. Focus on what you need, and do deep search, taking detours when needed to understand ancillary concepts.
Another case is when you're moving into a new area and expect that you'll need a firmer base in the material before you can continue (to develop the right intuitions and so on). In that case, strategy (2) is still ok, but it helps to do some background work on the side. I find that working through problems is a good way of keeping you focused on learning by doing, and also tests your fundamentals.
Upvotes: 2 <issue_comment>username_3: Let me answer a different (but related) question. How can you learn material now so that you'll be better able to recall it years from now? Here I recommend that you explore to discover your own [learning style](http://www.learning-styles-online.com/overview/), which has a couple key advantages.
1. Using the right style can help you to absorb information more quickly and retain it longer.
2. Perhaps more importantly, by recreating the techniques you used to learn the material, you can more easily recall it.
A simple example of (2) is that if you learn certain material while listening to classical music, then later you'll be better able to recall it while listening to similar music. This may sound obvious, but far too few people make use of it. Let me give a personal example. Like many people, I retain information far better if I interact with it: take notes, draw diagrams etc. This process can be time intensive, so you have to find a balance. What was terrible though, was that I would often lose my notes, so that when trying to recall the information later I'd be stuck.
Only after I was well into grad school did I settle on marking extensively in my books. I underline, draw arrows, mark key ideas with boxes and stars, etc. The magic is that when I reread later, my markup helps me to quickly remember what I was thinking when I first processed the concepts. So whatever epiphanies I had the first time around are more readily accessible. My point isn't to tell you to mark in books (though I find it invaluable). My point is to tell you to **recall** information in a way similar to how you **learn information**.
Upvotes: 3 <issue_comment>username_4: Tablets make it a lot easier to go through old materials. If you can find ways to download your textbooks and lecture notes as PDFs and then use a tablet to heavily annotate your textbook (you can even insert extra pages too), you can come up with an easily-searchable annotated version of your textbook that you can re-use again for years to come.
I use a Lenovo ThinkPad because of its large screen size and stylus pen, which makes it very easy to annotate and highlight textbooks and lecture notes. I also put my textbooks in my Dropbox folder so that my annotations are auto-saved in my Dropbox (and in case I ever lose my tablet, I won't lose my textbooks along with them).
Upvotes: 0 |
2012/07/09 | 3,828 | 16,498 | <issue_start>username_0: I was recently reading some papers regarding bridging the gap between academia and industry, specifically in undergraduate computer science and software engineering programs. The papers I read were published between the late 1970s and the mid 2010s. I noticed a stark contrast in papers published in the 1990s as compared to the 1970s with regards to education, often in reference to mathematics and science.
A small passage from [Essential Elements of Software Engineering Education](http://dl.acm.org/citation.cfm?id=807660), published in 1976:
>
> It is clear from the above discussion that the education of a software
> engineer will involve the study of a variety of subjects combined with
> a considerable amount of practical experience which must be
> accumulated over a number of years. From a university standpoint, the
> subject matter not only cuts across a number of traditional
> disciplines and boundaries, but also covers topics that historically
> have not been part of academic curricula.
>
>
>
This passage follows a discussion of problems that arise from teaching software engineering, specifically the difficulties in mapping certain aspects of industry into the classroom. This discussion is a significant portion of the paper, and similar discussions are prevalent in other papers written around the same period of time. Generally, there's an understanding that a wide knowledge base is needed in the fields of computer science and software engineering, but the trend is to focus on maximizing the skills and knowledge needed in industry.
Throughout everything published in the mid-to-late 1970s, I get the idea that the people designing the computer science and software engineering curricula understand the industry. This includes technical and non-technical topics, but always serving the needs of the profession. It's well summarized in this statement, also from Essential Elements of Software Engineering Education:
>
> A curriculum in software engineering must be multi-form and in fact be
> a collection of curricula to meet the diverse needs of existing
> professional groups.
>
>
>
Fast-forward to the 1990s. A passage from the [The SEI undergraduate curriculum in software engineering](http://dl.acm.org/citation.cfm?id=107088) reads:
>
> The mathematics and science content of the curriculum should help
> achieve two fundamental objectives. First, it should prepare students
> to participate competently in an increasingly technological society.
> This includes the ability to understand science and technology issues
> well enough to make informed political decisions. Second, the science
> and mathematics content should provide the students with an
> appropriate foundation for subsequent software engineering courses.
>
>
> ...
>
>
> While the physical and life sciences are fundamental to traditional
> engineering disciplines, they provide virtually no basis for software
> engineering. The only significant exception is that electricity and
> magnetism, common topics in introductory physics courses, support the
> study of the computer itself, and software engineers need a basic
> understanding of the machine for which they are developing software.
> To achieve the first objective stated above, however, it is probably
> the case that basic knowledge of physics, chemistry, and biology are
> essential in almost any undergraduate curriculum. Chemistry and
> biology, in particular, are likely to be increasingly important in
> understanding society’s health care, environmental, and genetic
> engineering issues in the next century.
>
>
>
This is really the first time that I saw society come up in a discussion about the content of a curriculum. There were mentions of law and legal topics being relevant for software engineers and computer scientists in previous papers, but always from a professional standpoint. In the above quoted passage, the first objective (and I'm assuming the most important in the eyes of the author) of mathematics and science education is not to prepare the students for future course work or their first job in industry or for future research, but for functioning fully in modern society.
Beyond that, the authors even identify that these topics have limited utility in many industrial settings for software engineers. However, they continue to encourage that basic knowledge should be part of the curriculum for society's benefit.
**My questions:**
1. What happened in the late 1970s into the 1990s that caused a shift from focusing on the profession and entering the workforce (both industrial and academia/research) to the general needs of the society in computer science and software engineering education?
2. Was this phenomenon localized to computer science and software engineering education or was it a widespread event?
3. What were the triggering events?<issue_comment>username_1: This answer is from a retired software engineer’s point of view. In 60’s and 70’s, not too many people had a close look at real computers. I wrote my first few FORTRAN programs on punched cards without actually seeing what a computer looked like.To me, a computer was like a black box. There was a magician living in the computer room, could understand the DO loop instructions in my program and do the job for me. There were a lot of students thinking the same way like me. The professors in CS department were facing tough choices. There was limited time for the students to take the courses. You teach them math and science first, or programming first? What would the industry think if your computer science graduates could not even write programs? Naturally, the curriculum had to be focused on computer science itself.
In late 70’s, micro computers came out. In 90’s, PC became house furniture. A lot of software packages were available. Programming became everybody’s skill like driving a car. In the mean time, the industry found out CS graduates are hard to use because they don’t have enough application domain knowledge. I, for example, had to borrow books from the library to learn how radar operates. In the last few years before I retired, I didn’t need the programming language manual because I knew them. But I had to get on Wikipedia web site many times everyday because I was not familiar with the application I was working on. And I know I was not an exception. Many of my colleagues were doing the same thing. Naturally, the industry and academia have to train the undergraduate student math and science so that they would be more useful.
The above is my observation and my experience. I would not say who is wrong or right. The OP was wondering what happened. I believe it was a gradual process and a basic economics rule, demand and supply.
Upvotes: 2 <issue_comment>username_2: Since I asked this question, I studied a number of factors. I believe that the two primary factors are the age of the disciplines and the education reform in the United States in the 1980s. I also believe that the growth of multi-disciplinary education and the prevalance of computers have spurred these changes more drastically in the computing related fields (although I don't have sufficient knowledge at this time to be as confident in this assessment).
---
The first thing to consider is the age of the disciplines. At the time of the publication of the first articles, in the 1970s, the fields of computer science and software engineering were relatively new on the scene.
The first computer science degree program was started in 1953 at the University of Cambridge. In the US, the first computer science program wasn't founded until 1962. However, some of the papers noted that computer science education didn't made significant advances until 1969, with the publication of the ACM Curriculum 68 and ACM Information Systems Curriculum, which established the central topics to computer science. That's over 15 years between the first CS program and significant advances in CS education.
Software engineering as a separate discipline wasn't even a thought until the NATO conferences in 1968 (Garmisch, Germany) and 1969 (Brussels), and it took another 10 years (1979) before the first graduate program and it wasn't until 1996 that an undergraduate software engineering program existed. If SE as a discipline follows similar trends as computer science, I wouldn't expect significant advances in the education techniques before the late 1980s or early 1990s, after the central topics have been identified and disseminated. As a point of reference, the IEEE's [Guide to the Software Engineering Body of Knowledge (SWEBOK)](http://www.computer.org/portal/web/swebok), which outlines the core knowledge areas and related disciplines of software engineering, wasn't even started until 1993, which puts it at roughly the same time-to-development as computer science.
I'm not an academic, but I would suspect that it's rather difficult to design a curriculum that's relevant to students seeking careers in industry without a solid framework, especially when the goals are to produce a solid, reliable curriculum that stands up to the rigor of engineering. In addition, there is the additional work related to validation and accreditation of the programs. The papers from the 1970s were typically laying the groundwork for the work to come over the next 15-20 years by proposing the key topics and content. Upon further examination, nearly all of the topics presented in the papers were identified as essential knowledge areas of software engineering or as a related discipline in the Guide to the SWEBOK.
---
I believe that educational reform also plays a role in the changes. According to Wikipedia, [education reform](http://en.wikipedia.org/wiki/Education_reform) was occurring around the world, starting in the early 1900s. Considering that the majority (all but one or two) of the papers that I read were written by someone in the United States, I focused my research on the educational reform that started in the 1980s and still continues.
In 1983, a report titled [A Nation at Risk: The Imperative For Educational Reform](http://en.wikipedia.org/wiki/A_Nation_at_Risk) ([PDF](http://datacenter.spps.org/sites/2259653e-ffb3-45ba-8fd6-04a024ecf7a4/uploads/SOTW_A_Nation_at_Risk_1983.pdf)) was published. Although the bulk of the paper is centered on primary and secondary education (in the US, kindergarten through 12th grade), it also mentions a decline in SAT scores, a decline in College Board (AP) test scores, an increase in the teaching of remedial mathematics courses in public 4 year universities, and millions of dollars being spent by businesses and the military for remedial education and training programs. The report found that the average graduate (of secondary schools as well as higher education) is not as well-educated as the average graduate of the previous generation and smaller proportions are completing high school and college.
This report presents the "Learning Society". A learning society has "a basic foundation the idea that education is important not only because of what it contributes to one's career goals but also because of the value it adds to the general quality of one's life." The focus become on life-long learning, well beyond the end of schooling. In contrast to this "learning society", they find that the American education system is expressing standards in "minimum requirements" and students who do the minimum amount of work to get by.
To report also finds problems:
* Students are taking "general track" courses instead of vocational or college preparation courses in secondary education, but only small percentages complete courses like Algebra II, French I, and Calculus
* Large numbers of credits are gained in physical/health education, remedial courses, and courses for training for adulthood
* Grades rose as amount of required effort to complete work rose
* Science-oriented students (4 years of science/math in secondary school) in the US are spending significantly (1/3) less time than any counterpart in many other industrialized nations
* A significant number of public colleges must accept all high school graduates from their state
* Textbooks aren't being written by experienced teachers or scholars
* Many textbooks don't challenge their readers.
* School years are significantly shorter (in length and total days) than many other industrialized countries.
* Teacher preparation curricula are focused on "educational methods" instead of subject matter
* Shortages of teachers, especially in mathematics and science, leading to under-qualified teachers teaching these subjects.
Although many of the recommendations point to changes in secondary education, it only makes sense that ripples flow throughout higher education. Vocational schools, colleges, and universities most likely adjusted their curricula in two directions. The first would be to meet the needs of the students who might have been underprepared by their secondary education by adding courses for subject matter that might have previously been expected by a high school graduate. The second direction would be to create the environment of the Learning Society by creating courses to expand the mind and enable students to "learn to learn" in the future.
---
I believe that [scaaahu might be onto something in his answer](https://academia.stackexchange.com/a/2379/1229), as well. As mentioned in several of the papers in the 1970s, industry can drive academia. This is even evident today with things such as Industrial Advisory Boards, which allow representatives from industry to meet with departments at universities and provide feedback on the quality of the graduates and suggest curricula improvements to allow graduates to ensure they have the skills needed in the workplace.
Specifically, I'm looking at domain knowledge. After reading some work regarding how in the early days of the computing profession, computing professionals were expected to know computing. However, the modern workplace is often cross-functional. Having domain knowledge seems to be more important in these cross-functional teams to facilitate communication.
However, going back to the idea of a Learning Society, even if the education isn't necessarily in the domain of work, the ability to learn to learn along with critical thinking, problem solving, and collaboration (themes that are cross-cutting across nearly every discipline and things that are difficult to teach outside of practice) is critically important to success.
Upvotes: 2 <issue_comment>username_3: In the late 1970s when Freeman, Wasserman and Fairley wrote about software engineering curricula they were talking about graduate education. They assumed that most students would be working professionals returning to school to learn those things that they should have learned earlier, but which were not being taught when they had been in college. Those ideas were implemented in the early MSE programs at places like Wang Institute (where <NAME> and I met). We felt at the time that undergraduates could not appreciate some of the problems and techniques we taught, so it was not worth trying. I remember having arguments with <NAME> at the SEI about exactly this point.
By the 1990s people were beginning to believe that software engineering *should* be taught to undergraduates, so that they wouldn't have to unlearn bad habits when they went to work. There was also an increasing interest in professional licensing of software engineering. Licensing would require ABET-accredited undergraduate programs, among other things. The focus on society is related to the interest in developing a discipline of software engineering aligned with ABET accreditation and professional boards of engineers.
This is not to say that we weren't interested in serving the needs of society in the 1970s and 1980s. We even had a version of a code of ethics that we taught Wang Institute students during orientation. But we assumed that students already knew their role in society. They had come back to school primarily to learn new methods and tools.
By the way, SWEBOK was actually started in 1998 as part of an effort to professionalize software engineering. The software engineering code of ethics was published at about the same time. Both of these projects were meant (by some of us) to support the eventual licensing of software engineering.
Upvotes: 3 |
2012/07/09 | 477 | 2,065 | <issue_start>username_0: The paper I want to publish is just a conjecture.<issue_comment>username_1: There are several ways in which conjectures propagate, and each has its own method for publishing.
1. As future work/further direction on a set of theorems you proved. In this case, you submit the paper based on the set of theorems, and the conjecture is just a bonus. Your primary contribution is the **original theorems**.
2. As a reasonable technical lemma to use to prove a difficult theorem. One again, submit as you would a paper proving any other theorem. Your primary contributions is the **interesting/non-obvious consequence**.
3. As a consequence or expected result based on a novel model. In this case the empirical model is usualy motivated by some sort of science, attempt to publish in the relevant scientific or applied math journals. Your primary contribution is **evidence in the reasonable model**.
4. As a synthesis of connections between many different areas of math that your conjecture brings together. Publish this as a survey that unifies the areas of interest. Your primary contribution is **the connections between fields**.
If your paper does not have any results apart from the conjecture, then such are usually disseminated informally. Tell your friends, colleagues, publish a blog post, ask for a proof on MO. This is a great way to find someone to work with in helping you turn your conjecture into a theorem.
Upvotes: 4 <issue_comment>username_2: If your conjecture is supported by partial result, then as pointed out by username_1, a journal suited to this results is the good choice.
If it is supported by numerical computation, you can aim at "Experimental Mathematics".
If it is not supported, or supported only by previous results, then it would probably be difficult to publish. Sometimes conference proceedings can be a suitable place, but informal dissemination would probably be the most common practice.
Upvotes: 3 <issue_comment>username_3: Try [The Journal of Brief Ideas](https://beta.briefideas.org/).
Upvotes: 1 |
2012/07/10 | 4,104 | 18,063 | <issue_start>username_0: For a long time, I had been using open source software for my work to boost "reproducible research". I believed that if I made my codes open source and the softwares to run those codes in were open source too (or at least free), my research would be utmost reproducible. However, recently, in a discussion, it came through that research is more reproducible if one uses "popular" softwares instead of "unpopular" free ones.
For instance:
I had been using Scilab (Free) for a lot of my work and distributed my files to others. But I was surprised that more people had MATLAB ($$) and preferred if I sent them MATLAB files instead (little modifications).
My question is :
Assuming I'm starting a new project and I wish to make it as reproducible as possible. Should I be using relatively unpopular free software or extremely popular proprietary ones?<issue_comment>username_1: I think there are two kinds of reproducibility:
1. The ability of someone else to run your code and obtain the same output.
2. The ability of someone else to write their own code that does the same thing as yours based on your description and on examination of your code (reproduction from scratch).
The second kind of reproducibility is much more convincing, since **the main point of scientific reproducibility is to verify correctness** of the result. For science that relies on code, it is usually impossible to include *every* detail of the code in the paper, so verification requires examination of the code.
**If you use proprietary software, your code probably makes use of closed source code, and therefore it cannot be verified or reproduced from scratch.** If you use open source software, then all of the code that your code calls is probably open source, so it can all be verified or reproduced by someone else from scratch.
At present, it is probably true that the first kind of reproducibility is more achievable with proprietary, widely-used software. I am optimistic that the current trend will lead to open-source software catching up in terms of wide use (consider [SAGE](http://www.sagemath.org/), for example).
---
Addendum, in light of Epigrad's answer below, which I mainly agree with:
The problem with relying on closed-source code *isn't* that someone else won't know what that closed-source code *is expected to do*.
The problem is that if you have two closed-source implementations of *the same algorithm* and they give *different results* (trust me, they usually will), then you have *no way of determining which (if either) is correct*.
In other words, closed-source code would be fine for reproducibility if it were bug-free. But it's not.
Upvotes: 7 [selected_answer]<issue_comment>username_2: Listen to your colleagues & peers.
They've already told you what is most suitable for them, in order for them to be able to reproduce your results.
That answer, in your particular case, is Matlab.
There will be some others who want to port it to Octave, SciLab, Excel, Fortran or whatever. That's fine too - but if you're flexible about which platform you code in, and coding in Matlab won't make you less productive (or the small reduction in productivity is a price worth paying for the increased reproducibility), then code in Matlab. Because your colleagues have already told you that that's what enables them to reproduce your work, easiest.
There are plenty of good reasons (and maybe some bad ones) why many of your colleagues prefer Matlab. Sometimes the cheapest things can cost you most.
For anyone else reading this, with a similar problem, the gist of the answer is the same: listen to your peers.
Upvotes: 2 <issue_comment>username_3: Agreeing with most that has been said by EnergyNumbers and username_1, I'd like to add some slightly different points:
* the fact that code is written in a particular language (Matlab) does not make it closed source per se.
Just as using Scilab on closed source Windows (or using a closed source BLAS) doesn't make Scilab closed source.
There are journals that require reproducible research and open code and accept Matlab code.
* likewise, popular and free do not exclude each other, nor does proprietary imply that it is popular
* neither of the two imply that the respective software is suited for the reproducible data analysis.
* The choice on the language should be based on several factors
+ how suitable is it for the problem at hand
+ here also: how suitable is it for reproducible data analysis (I'm experimental scientist, so reproducing a data analysis is just a part of reproducible research for me)
+ particularly if talking about sharing code: infrastructure considerations (can code be packaged into libraries? How can data and code and the text be bundled into a reproducible paper?)
+ popularity = size of peer group using this software (which somehow includes the cost of a license)
+ you may also want to consider what the peer group that is interested in reproducible research is using (In my field(s), the reproducible research crowd coincides much more with the open source (R) crowd, while of the peer group working on the same kind of problem probably the majority uses Matlab)
* My old supervisor used to say that the cost of a license is no scientific argument.
But of course you may need to consider it.
* Likewise, popularity is not a scientific argument, but you should critically examine whether the propularity does actually indicate that lots of good reasons exist to use that software.
Examples:
* In my field, R is increasingly popular (by now popular enough to publish in R) and to a certain extent replacing Matlab.
+ So R is both free and popular (yet most people from my field don't make use of the fact that you can look into R's source if they discuss reproducible data analysis)
* Reasons IMHO include
+ most importantly: R is well suited for our kind of problems (I think it is better suited than Matlab, others differ slightly in their opinion and use Matlab. Even others do think it may actually be better suited but not as much as to outweigh learning R right now)
+ Being well suited includes the availability of methods we use at CRAN vs. commercial Matlab toolboxes and Matlab file repository
+ Being better suited includes the fact that I cannot adapt the code of proprietary Matlab toolboxes (p files) to particular needs.
+ Being well suited includes the ease of report generation
+ infrastructure: e.g. R's package concept enforcing a minimal standard and allowing to rely on examples and tests actually being runable vs. a folder full of .m files (I heard that recently a more package like concept was introduced). This helps a lot with sharing the code (whether to reproduce your findings or to use it on their own data)
+ license costs come in indirectly: it is more that you don't need to worry if you install it on your private computer as well and you can tell students to install it on their laptops than worrying about the cost of a few licenses for computers at work.
+ probably far more costly than license fees themselves are the time to get the license manager working or to transfer licenses between computers, and if you just want to give a toolbox a try before deciding whether to buy it or not, the hassle of a) asking the vendor for a demo version and later on b) of filling out order forms and writing justifications why you need to spend money on that license.
Note how many of these arguments are "soft" and in fact have more to do with being used to one system or the other or using a feature is easier, more obvious, or more common among the peer group of that software's users: noweb *can* work with Matlab, documentation of m-files is possible and encouraged (though not really enforced), unit tests are possible in Matlab, Matlab Central has lots of free code, etc. Just R users always know of CRAN, whereas not all Matlab users know of Matlab Central, there's a good culture of citing scientific papers about the implemented method in R help files, shipping code together with example data sets and/or giving actually running example code.
Examples of invalid arguments:
* if my peers did not use version control for coding, I wouldn't consider that an argument *against* using version control (as there are lots of good reasons for using it)
* Or, if the ones refusing to use version control were programming in Matlab, neither would that be an argument against Matlab - but I'd check whether there is any reason that prohibits using version control for Matlab code.
Upvotes: 3 <issue_comment>username_4: To supplement @username_1's answer with a "Yes, but..."
I agree that there are two types of reproducibility - CrossValidated discusses them with some degree of frequency. There is, as has been mentioned, "Can I click 'Run' and get the same answer you did" reproducibility, which I generally don't find very compelling.
There's also "Could I repeat your analysis from what you have provided from Step 1 to Step End, and get the same or a similar answer?" I think this is the one we should be aiming for.
That is *often* helped by using accessible, non-proprietary code, but not always. Consider the following example of an infectious disease dynamics model, expressed as a system of ODEs:
Here, in order to replicate (or fail to replicate) my findings, the software I used doesn't matter. What matters is the equations and parameter values I chose. If I provide those, then the only reason for code being needed is because someone *doesn't* want to implement the study from scratch, and does want to just run the code and see if the results match, tinker with the assumptions a bit, etc. In that case, everyone benefits from the code being in a form people use.
I think the same is often true for statistical analysis that doesn't use novel methods. At this point, what matters is that the *data* is available, and that the code is implemented in a language people understand and use. If 95% of people use SAS, even if it is proprietary, then the way to make your results most accessible, and most easy to replicate, is to have an implementation in SAS. Because if you pick an obscure but free language, what you've done is replaced the "Money" barrier with a "Time to understand" barrier - which for most people equates to the same thing.
The summary is this: I don't think "Free/Open" vs. "Proprietary/Closed" is necessarily the deciding distinction. I think that distinction is accessibility, and trying to maximize that. If there is both an open, free and popular software package that's used (R for example) then great! - use that. But if the field uses primarily one commercial package, picking an obscure alternative just because its free doesn't fix accessibility, it just shifts the burden.
Upvotes: 4 <issue_comment>username_5: My two cents:
I often compare code with a scientific paper. The purpose of a paper is to describe your results and approach to the problem in such a way that your peers can validate/refute your findings, *in whatever way they see fit*, so that a collaborative effort can take place to make progress in the field.
Who cares whether the paper's author has used LaTeX to write his paper, or MS Word? Who cares if the data was processed with MATLAB, or Excel, or Pascal? It is the truth(s) in the paper that count(s), *not* the tools used to get there (although many would gladly jump in here and start a fierce discussion on this point...but in my experience, the arguments used in such discussions tend to be more like religion than anything else).
What is **very important** however, is the means of getting through to your peers. For example, if you write and publish a paper in Esperanto (supposing for the moment that would get accepted), simply because you think it's beautiful and elegant and everyone should speak it. Plus there are many books about how to convey meaning in Esperanto right?
Not many of your peers will be able to understand the paper, let alone get the message and reproduce your findings. You'll have to wait until someone comes along in your field who shares your views on Esperanto, which might take half a lifetime. Altogether this is a very poor way of making progress in your field.
This I think is the crux of your predicament -- if your peers mostly use MATLAB, you'd best stick to MATLAB, because you'll reach the most peers the quickest. Leave it to (other) engineers to sort out whether MATLAB actually produces good (enough) results for your case, and leave it to one of your peers half a world away to translate your code to C++ and verify your findings.
It's not the code that counts -- it's the knowledge that is contained in it.
Upvotes: 2 <issue_comment>username_6: Let me start with a disclaimer. I generally subscribe to the free software community perspective that proprietary software is questionable ethically, and best avoided if possible. I realise this perspective is not commonly held in scientific circles. Having said that, sometimes proprietary software is a necessary, or at least not easily avoided evil, and I'm generally pragmatic about using proprietary software when no good alternatives exist. I've used proprietary software in the past, though currently the only proprietary software I'm currently (sporadically) using is Skype, for which no good free alternatives exist.
However, special considerations apply in a scientific context. One of thse has already been covered by @username_7, namely that in general you can't "see inside" proprietary software to see how something is implemented. Having said that, sometimes proprietary software is written in an interpreted language, as in Splus, and one may be able to see part or all of an algorithm implementation. Regardless, the point holds generally.
A separate and obvious issue, which I don't think anyone has raised, is that using proprietary software forces others who want to use your software to buy the proprietary product you use. These products can be quite expensive, especially for people from poor countries. For example, Matlab, which has been mentioned in this thread, runs to thousands of dollars if one has to pay for a license oneself. Western academic institutions often have site licenses for such popular software, so researchers don't have to pay for it themselves. I personally am quite unhappy when I am expected to use a piece of software written using some proprietary language or package that has to be purchased.
A related issue is that much, if not most research, is done using public funding, i.e. taxpayer money. It seems undesirable to me to use such funds to buy proprietary software, thus adding to the profit of some corporation. In general, there is some movement to make academic work that is done using public funding free. And one can easily make the argument that the usage of proprietary software makes ones scientific product less free. For example, I believe the NIH now has some such policies in place. Similar arguments could be applied to the usage of software tools.
A tangential technical issue is that it is often difficult to get proprietary software to play nice on free software platforms such as the free Unix-like systems currently popular in scientific circles, e.g. the Linux based systems, and the BSD systems. These difficulties include, but are not restricted to
a) ABI problems. If one wants to compile a C/C++ extension for Matlab, for example, one has to use exactly the version of the compiler that the Matlab program has been compiled with
b) The program requires obsolete libraries or requires libraries to be in non-standard places.
I mention this issue in part because my understanding of the question is that it is asking about proprietary vs free in the context of pragmatic usage.
So, to respond to the question directly:
>
> Assuming I'm starting a new project and I wish to make it as
> reproducible as possible. Should I be using relatively unpopular free
> software or extremely popular proprietary ones?
>
>
>
I don't think there is a clear answer. If there is no viable alternative, then one would have to use the proprietary software, as I do with Skype. If there a viable free version, I would use it. Bear in mind that if more people start using the "relatively unpopular free software" it will become more popular. :-)
Upvotes: 4 <issue_comment>username_7: You can do whatever you damn well please, but there are a few considerations:
1) You might have an obligation to disseminate your work. If you are supported by an external funding agency such as the NSF or NIH then dissemination is an obligation. Many private foundations and other funding sources also provide support with the intent of dissemination, whether it's stated explicitly or not.
If you have such an obligation, then absent any other consideration this would suggest you use the most accessible software possible, regardless of whether that is proprietary or free.
2) Open source-ness is important for some kinds of research, but for most research it's irrelevant. Unless you're doing computer systems research, where it really matters how the computer arrives at an answer, the method does not matter so much as correctness. Sometimes it can matter (e.g. if your work is heavily dependent on numerical methods), but it probably doesn't.
3) Communities establish standards of validity and integrity. If everyone else uses MATLAB, then the community has deemed it accurate. Absent evidence to the contrary, using open-source software does not make your results seem more correct or more verifiable in anyone's eyes.
As as side note, Mathworks has a strong reputation for working with researchers. If you really felt that MATLAB was giving you incorrect results, and you had examples to show it, they would be knocking down your door to fix the issue. I've had Mathworks issue me a custom support patch the very same day I called about an obscure hardware incompatibility that was causing incorrect behavior.
Upvotes: 1 |
2012/02/01 | 1,228 | 5,116 | <issue_start>username_0: Many of my computational scientist colleagues used to use Google Reader to share and discuss new journal articles. The loss of Reader's social features killed that, and we have subsequently tried Google+ and Reddit, but neither seems to work nearly as well as Reader did for holding this kind of discussion. Does anyone have experience using a site they like for this purpose?
I'm aware of a few options, like <http://annotatr.appspot.com/>, that seem promising but appear not to be actually used much.<issue_comment>username_1: Might I suggest <http://scicomp.stackexchange.com> ?
While the StackExchange system isn't the best - and indeed isn't designed - for "discussion", I've found that many "What did you think of this paper" type questions can be phrased in SE-compatible formats. CrossValidated has a semi-periodic "Journal Club" bit, and questions and musings about scientific papers come up a fair amount there.
I think if framed correctly, they might find a useful home here.
Generally though, I think the online discussion of scientific papers suffers from a few problems. Generally, the two I find the most problematic:
1. Lack of a clear community to talk about papers in. Essentially, the problem your question is looking for an answer to. I haven't found a really good general purpose one, though I would love to if I did find it. There's blogs and the like, but even the ones talking about peer-reviewed papers are somewhat one sided in terms of their communication, and not great for anything but transient chatter.
2. A hesitation to talk about that online. Among colleagues, it seems somewhat easier to summarize things like "Bad paper is bad", or slice apart someone's methodology. I'd be somewhat more hesitant to do that anywhere where my identity is both traceable and the conversation is saved for eternity (the internet).
Upvotes: 4 <issue_comment>username_2: I haven't used it personally so I can't vouch for its quality, but I know several people in another research group using a site called **[Journal Fire](http://journalfire.com/)** for this purpose. Might want to check it out.
Also, I think the citation manager and social network **[Mendeley](http://www.mendeley.com/)** has some limited discussion capabilities, but I prefer to manage my references with BibDesk so I haven't use it much.
Upvotes: 3 <issue_comment>username_3: The [Selected Papers Network](https://selectedpapers.net/) is a new effort in this direction that intends to federate content from all over the web. To post something to it, just write a Google+ post with #spnetwork and the paper's arxiv ID or DOI in the body. You can also post things directly at the site. The developers are working on interfacing with other social tools like Twitter.
You can read more about the thinking behind it here: <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264905/>.
Edit: The site was shut down several years ago.
Upvotes: 2 <issue_comment>username_4: We have just extended BibBase.org to allow papers to be discussed in a fashion heavily inspired by StackExchange: <http://bibbase.org/blog/stackoverflow-inspired-scientific-discourse>
What's different about BibBase compared to several other sites is that authors integrate bibbase directly into their own homepage. It keeps links to collaborators up to date, and it links to pages on bibbase.org for keywords, and now also for discussion. We want it to be the *unobtrusive* research network that just helps scientists show their publications online on their own pages as they normally would, but with additional features that make it more than that. We think that it is important to facilitate discussion about one's own papers, and therefore this is now a feature provided by bibbase.
[This is an old question, but it seems that most listed solutions have been shutdown by now one way or another.]
Upvotes: 3 <issue_comment>username_5: For public discussions on arXiv preprints there is [SciRate](https://scirate.com/).
For general discussions, also private, there is [PeerLibrary](https://peerlibrary.org/).
Upvotes: 3 <issue_comment>username_6: I use [I, Librarian](http://i-librarian.net/). It is a reference manager, kind of like Mendeley but with a free option.
They have both a paid service option and a free self host option (like wordpress).
It can be private and they have per paper discussion capability.
For a private/semi-private group of collaborators I think it is quite decent.
Upvotes: 2 <issue_comment>username_7: For theoretical physics (or closely related) papers, one possibility is to use the Reviews section of [PhysicsOverflow](https://physicsoverflow.org/) (note that it is possible for the registered PO users to [submit](https://physicsoverflow.org/submit-paper) there papers for review).
Upvotes: 0 <issue_comment>username_8: [Wikiversity](https://en.wikiversity.org/wiki/Research) has useful technical features (the same as Wikipedia): collaborative editing, referencing tools, discussion pages, version control, email alerts, etc. It can very well be used for discussing scientific papers.
Upvotes: 0 |
2012/07/10 | 880 | 3,668 | <issue_start>username_0: Assuming [STEM](https://en.wikipedia.org/wiki/STEM_fields), At what age do tenured professors stop taking new students?
I am a first year PhD student and have my eye on one professor who seems to be really interesting and I feel he finds me as a good candidate too but *for some reason that he refuses to discuss* he does not wish to take me in. Most of his current students are 1-2 years from graduating.
I have a strong reason to believe that it's his age (60-65). That's when I thought about the more general question I am asking.<issue_comment>username_1: Sometimes people avoid taking students if they expect to retire or move before the student would graduate.
Upvotes: 2 <issue_comment>username_2: In general, I strongly believe that this depends on the professor and department in question. One imagines that there is a time that professors *would like to* stop taking students and a time that they *actually* stop taking students, and these do not necessarily coincide. In a small department where there are many graduate students and a shortage of advisors, presumably there is "peer pressure" from other faculty members to continue to advise students.
Upvotes: 3 <issue_comment>username_3: There are no hard and fast rules for this. However, in general, the question is not so much time-after-PhD, but rather time *before retirement.* Most faculty advisors in the final stages of their careers stop taking students, so that they can wind their research groups down gracefully. Frequently, the last years before retirement will be spent writing and teaching, and mentoring a few additional students. The amount of time depends on the average career of a graduate student in the professor's particular discipline, but somewhere between three and seven years before retirement, the "wind down" will begin.
Upvotes: 5 [selected_answer]<issue_comment>username_4: Whether or not one comes up with a number of years since PhD, since tenure, etc., for faculty to stop taking students, that is not the determining mechanism... except in a few cases where the cause *is* simply fatigue or disillusionment with the whole enterprise. But the latter seems uncommon.
Rather, the absolutely dominant cause is senior faculty' estimated time to retirement. Not only would it be bad to retire while one has a PhD student still in progress, but, further, it would be bad to retire while one has a former PhD student pre-tenure. Thus, taking on a student is approximately a 12-year commitment, at least, I think.
And, then, it becomes hard to clearly picture one's own energy level and frame of mind 12 years into the future...
Edit: as to why it might be bad to retire when one has a not-yet-tenured former student: very often, unless the student has taken a sharp turn away from the general enterprise of their advisor relatively quickly, the advisor will still be a leading expert concerning the topic. Even with the presumption that the advisor will be positive rather than negative, that opinion is important. If the advisor is retired, or is operationally retired, the expert-ness of their opinion, e.g., toward the future and future developments, is weakened, and their credibility in appraising *future* contributions of their former student is weakened. One wants to be visibly sufficiently engaged so that one's opinions are connected to current and future events, not only archival or historical or nostalgic stuff!
... and to have this presumably-positive, presumably-helpful letter simply due to retirement or disengagement is a loss that many could not afford. Nothing overtly bad happens, but one has lost a great deal.
Upvotes: 3 |
2012/07/11 | 1,008 | 4,124 | <issue_start>username_0: Imagine you're a PhD student, and you found a research group, in another university, that you'd like to join for a PostDoc period.
You want to join that group because you really like their subjects, and their projects, and/or for other reasons. You just dediced you would like to join them.
Now, what could you do during your PhD to augment the probabilities to reach this objective?
You might say: just relax, **make an awesome work**, and then, when you're ending your doctoral period, contact that group PI and ask to be hired [(we already discussed how to cope with this phase)](https://academia.stackexchange.com/questions/1604/how-to-prepare-for-a-phd-or-postdoc-admission-procedure).
Okay, but, **is there something you could do during your PhD before that moment?**
What could you do?
Keep up-to-date on their scientific papers?
Email them? About what?
Visit their lab?
Thanks a lot<issue_comment>username_1: I would strongly recommend speaking with your advisor as a first step. He may be able to initiate collaboration between you and the researcher running the other lab while you're still performing your PhD work, allowing you to kill two birds with one stone.
If this is impractical for any reason, I would recommend reaching that, as soon as you think you have a strong enough knowledge base to be able to demonstrate expertise in your field, you should reach out to the professor at the second lab and express your interest. Research grants often take many months, and demonstrating your interest in his work at an early stage may give the professor more interest in writing a grant in which you could participate. Note that I would definitely recommend waiting until you can impress the professor with your knowledge. Postdocs are hired to get stuff done. While it's true you still are a PhD student, you're essentially applying for a position as a postdoc, and if you're not an expert (or close to one) in your field, he will likely be wary about bringing you in to your lab.
This last reason is why people typically wait until they're pretty far along, if not outright finished, with their graduate work before looking for postdoc positions.
Upvotes: 3 <issue_comment>username_2: You want to establish substantial professional contact with the head of your target group *long* before your doctoral period is about to end. They need to know who you are already when your postdoc application crosses their desk. It is never too early to start. Here are a few suggestions.
* Ask your advisor to invite the head of the group to give a talk in your department. Meet with them one-on-one. Ask about the possibility of a short visit to their lab to give a reciprocal talk. *(Prerequisite: Have something compelling to talk about; be a good speaker.)*
* Ask about the possibility of summer internships. *(Prerequisite: Be a good candidate for a summer internship.)*
* Ask your advisor to suggest a one-semester student swap. *(Prerequisite: Be someone that the other person would want to hire as an RA.)* More generally: Convince your advisor to collaborate with the other group.
* Ask the head of the group to be an external member of your dissertation committee. Ask at least two years before your defense. *(Prerequisite: Have a thesis topic that they will care about.)*
* Talk to the head of the group and/or his students at conferences. Join them for lunch, or dinner, or coffee, or beer, or whatever. *(Prerequisite: Be an interesting human being. Know a few good places to get lunch/dinner/coffee/beer/whatever.)*
* Don't put all your eggs in one basket. Cultivate *multiple* colleagues. Some may develop into future employers, others into future research collaborators, still others into letter-writers, still others into mentors, perhaps a few into friends, and most into nothing. *(Prerequisite: Know more than one person.)*
* Most importantly, *don't* think of this process primarily as "cultivating a postdoc position". Think of it as **cultivating a research community**. People *will* notice if your motivations are mercenary, if only subconsciously.
Upvotes: 5 |
2012/07/11 | 523 | 2,329 | <issue_start>username_0: I have finished all of my manuscript revisions except for some minor details in the figure.
As I understand it, once the manuscript is accepted, a final draft of high resolution figures will be requested. In the past, I have also had requests to relabel and rearrange subplots.
Does this mean that I can wait until the final draft figures are requested to make minor tweaks, like aligning and sizing fonts and graphical elements? The content will be the same, but I still want to finalize elements such as the font sizes, line widths, title placement and legends.
In the interest of speeding up the process, is it acceptable for to submit revisions with the figures still in draft form?<issue_comment>username_1: I think this will depend highly from the publisher himself, but any problem will probably result in delay before publication.
If the publisher sees for the first time at the final draft slightly new figures, he might still ask for a few modifications. As such, I prefer to do before the next revision as much as possible to make sure that the publisher is happy and everything is done as soon as possible.
Upvotes: 4 [selected_answer]<issue_comment>username_2: As a referee, if figures are in a rough shape, I will absolutely call the paper out on that, and *demand* improvements. This will potentially set the acceptance back by weeks. Unless the figures will take weeks to get right—and in this day, that would usually imply that more data needs to be collected—then it's better to just do it right the first time.
Upvotes: 3 <issue_comment>username_3: I agree with [username_2](https://academia.stackexchange.com/users/53/username_2). Your figures should be as polished as possible before submission. As a reviewer, I am dedicating my time, for free, to improve your paper (if possible) so that it is appropriate for publication. Every problem that you leave unfixed is something that I feel like I have to mention, and it makes me angry/frustrated that the authors didn't take the time to correct obvious problems.
I ask my own lab members to spend time making figures polished and clear even for internal discussions. It is a good exercise in visual communication for them and saves everyone else (other lab members and myself) time since we don't have to waste time.
Upvotes: 1 |
2012/07/11 | 897 | 3,903 | <issue_start>username_0: During my undergrad, we had a reading assignment (for most courses) which would nearly cover up the entire textbook including introductions, summaries, "did you know" and other such *fluff* material.
In graduate school as pointed out in most answers here on SE, one does not read the whole book cover to cover but just read the parts you need and backtrack if doubts. But is this also true if I'm starting out in a new field?
I had my BS in Engineering and I'm pursuing my grad studies in Math, certain topics like Topology are completely new to me. I don't need the whole of Topology but just certain bits and pieces.
Should I attain some familiarity with the topic by reading a good introductory book cover to cover or just dive in (into a completely alien field) and understand only the parts I need?<issue_comment>username_1: I think this has been covered here in prior questions, but to continue on anyway ...
You should read material with a purpose or a goal. If the material is helping to achieve that goal (learning new or foreign material that you have a desire to learn) then reading all of the material is fine. There is no point in reading material though if it has no purpose or a goal (e.g. topics you are not interested in, material you already know sufficiently).
Personally for me it is a mix, some material I skim in large parts, some material I read only portions that I'm directly interested in, and if I'm really engrossed in some material I will read it front to back. Currently I'm reading an introduction cartography text book and really enjoying it and reading every chapter! It would be torture for me though to read though an intro textbook for criminal justice, criminology or sociology.
Same goes for journal articles, posts on Q/A forums, etc.
Upvotes: 2 <issue_comment>username_2: **What do you want to gain from reading that book?**
I typically find that I only read books (or articles) **cover to cover when I hope to work on a very closely related topic**. For example, if I'm trying to improve the result in a paper, I will often read most or all of the details. However, the vast **majority of the time all I need is the big picture**. In that case, I often read the introduction in detail, then skim the remainder to understand the structure of the actual proofs.
In your case as a math grad student, it may be important to understand the types of questions and answers common in topology. So I would probably recommend reading the book well enough that you can at least solve the easier exercises. However, if you don't plan to work in that area, and you aren't preparing for a qualifying exam or something similar, then likely the time you'd take to read the book cover to cover could be spent better elsewhere.
Upvotes: 2 <issue_comment>username_3: You stop reading books cover to cover when you don't need all the information between the covers of the books.
If you take a textbook on topology and flip to the middle, it will probably be largely uninterpretable because you don't know the terminology, previously proved theorems, and so on. You can still glance through the chapter on the material that you really need to know to get an idea of what you're completely missing. If you can select a subset of earlier chapters that let you understand the later one, great! If not, read the whole thing. If that's still not enough, get another textbook or talk to someone who knows the subject well.
You'll have to make the call regarding when it's better to proceed in this way and when it's more efficient to simply start at the beginning and go through it all. If you need to know something well, this is often the approach to take not because the other wouldn't work as well *in principle*, but because in practice the temptation to be less thorough than one really needs to be is often too great.
Upvotes: 5 [selected_answer] |
2012/07/12 | 989 | 4,032 | <issue_start>username_0: When I was 23, I quit my computer science undergraduate studies at USC with only one semester worth of units left and a low (less than 3.0) GPA due to personal reasons. I just turned 30 and have rekindled a desire to pursue research in computational neuroscience. Is there any realistic hope of eventually getting into a PhD program and pursuing a career in academia, or is it too much of a long shot given my personal circumstances and the job market?
Basically, what I want to know is, if I were to finish my BS, kick ass in a master's program (applied mathematics), and have great GRE scores, would that overcome my abysmal undergraduate record and gap in years away from school?
The biggest obstacle in my view is getting into a terminal master's program with my undergraduate record. If I were to get into a 1-year terminal master's program in mathematics at a sub-par university and do great, would I have a chance at a higher ranked university for my PhD?<issue_comment>username_1: My expectation would be yes. Of course, you will have to explain in your application letter that your undergraduate grades do not really represent you any more, etc.
Upvotes: 0 <issue_comment>username_2: The odds of a career in academia are long in general, and certainly your situation is going to make the odds even longer.
That said, people tend to only look at your most recent degree. It seems pretty likely that if you got into a masters program and did very well there, you'd have a good shot at getting into a solid Ph.D. program. But you'd actually have to do very well in the masters program (probably "best in several years" level). Furthermore, even once you get into a solid Ph.D. program the odds are against your getting a job in academia.
Upvotes: 2 <issue_comment>username_3: I flunked out twice as a undergrad engineering student at a major mid-western university. The second time it was a "go away and don't bother to reapply" situation.
I was determined to be a graduate engineer, my dream since age 10. I went to work as an adult technician at the university to save a few $$. Six months later I started night school as an "adult special", determined to learn everything I had missed during the first two years. For the next year, I basically did every example and every problem over in a special set of notebooks I kept, using the open tutoring office in the old department whenever I had a question. I bought or checked out similar math textbooks to get extra problems to work where I was not perfectly confidant of my ability.
I never got less that an A or A+ (95+) in any class, repeating all the classes that I had received C's or worse in, and met informally with each member of the admissions committee to let them know of my progress. They were encouraging but non-committal. I then re-petitioned for admission to a bachelors upper division degree program and requested that my night school grades by substituted for failing ones. I was readmitted on probation but with the old grades.
I continued on that same track for my junior year - all high A's, all homework triple checked before submission, lots of all-nights, no parties, few weekends.
At the end of that year I was taken off of academic probation. A few months later the department head and the chair of the graduate admissions committee/vice department head separately approached me with an invite visit their labs and discuss their research. I had previously made the acquaintance of some of their other grad students to get an idea of what was happening. Both visits resulted in offers of an RA in a PhD program. So no teaching, just $$ support for my Masters research (optical physics/lasers).
After the Masters thesis I was recruited by a local startup - I made a choice not to go the academic route for a number of reasons. the chance to be in on the ground floor in a new technology, money, a new wife, and a desire to start a family among them.
I was lucky to get a third chance. Good luck to you.
Upvotes: 1 |
2012/07/12 | 661 | 2,772 | <issue_start>username_0: This question is somewhat broad, but this place seems like the best place to ask. I have been accepted for an information assurance program, but **I am a computer science major and my main interest lies in software development. Should that deter me from deciding to study information assurance?**
Another factor in the decision is that the program is through scholarships for service, so it is a 2 year program with 2 years of work in a government position for information assurance. Ultimately, I recognize it is a subjective decision. **The core question I am trying to ask is, should you do graduate study even if you are only partially interested in the subject matter?** In other words: how committed do you need to be to the subject matter for graduate study to be worthwhile?
On another note, we continually see news about huge networks with cybersecurity issues. Sony's Playstation network, LinkedIn, and I think I read today that Yahoo! accounts may have been compromised. With the growth of businesses and services online, it seems only natural that an adept skill-set in information assurance would be beneficial.<issue_comment>username_1: Some people do further study because they are interested, indeed passionate about the topic matter. Others do it to improve their job prospects/career outlook/ultimate pay packet.
Ask yourself which category you fall into. If you are not interested, then you may not enjoy it and may not succeed unless your motivation is more financial.
Upvotes: 3 <issue_comment>username_2: An unmotivated graduate student will usually be a lackluster student at best. These students will be more likely to be distracted by whatever their true interests are.
Moreover, in a program like the one you are describing, you will be making a *very* significant career detour. You would be advised not to make such a move unless you are *absolutely* sure that it's something you'll want to do for the next four years, since you will have a payback requirement. If you're not sure about it, this is the kind of move that can wreak havoc on your career—particularly if you (re-)discover your dissatisfaction after the classwork is complete, and the service period begins.
Upvotes: 5 [selected_answer]<issue_comment>username_3: Some students (especially graduate students)
might take a few courses for credit to see how things
go. Invariably, at a later time they can usually be
used toward a degree or transferred.
The standard to be admitted as a special student varies.
Within the Ivy League, applying as a graduate level
special student, say at Harvard University,
is competitive.
See my answer regarding this as stated on this forum
below:
<https://academia.stackexchange.com/a/61120/123306>
Upvotes: 0 |
2012/07/12 | 1,475 | 6,258 | <issue_start>username_0: I'm asking this anonymously. Our department of computer science just received a donation to buy laptops for students. Major activity will be programming and languages used will be C/C++/Python/Perl/Java/R. We will be instituting a lending scheme for students who can't afford them.
As a committee, we now need to decide whether we should be buying Apple Macbook Pro(13") or Thinkpad (T420) both with more or less similar specifications and price. The Apple will run it's OS and Thinkpad will run Debian Linux.
My question is : We are training the students for research and we wish to provide them with an environment close to how it is in the real world. Should we choosing one over the other considering all students have an inclination for research/academia?
This is not meant to be a flame war but rather a question of whether Academia has **hidden liking towards Apple and if yes, why?**<issue_comment>username_1: I think that in most conferences/project meetings I attend, there isn't an overwhelming preference on one side or the other (I'm working in computer science, in case that matters). In other words, there are many academics happily working with linux, and many academics happily working with mac (some are even working with both), so in your case, there is no real wrong choice.
From what I've experienced, in some places, there could be a preference for apple computers because of the way some universities order their hardware: if you ask for a PC, you might not be able to choose which one exactly, and you might end up with a low quality, cheap product, whereas if you ask for a mac, the quality is better (at least, that was true a while ago). But I don't think it can explain why the proportion of apple laptops in the academic world is higher than that of the general population.
In any case, I don't think that working within on environment will prevent the students to switch to another one later one if they need to.
Upvotes: 3 <issue_comment>username_2: I do not think there's any appreciable difference. Both types of machines are able to perform the tasks you require more than adequately, and neither has a significant learning curve for students unfamiliar with the platform. I would choose the one that your IT staff has more experience supporting and the one that has the fewest associated costs.
If all costs really are equal, ask the students; they'll probably have a strong opinion one way or the other.
Upvotes: 2 <issue_comment>username_3: I think that neither apple nor Thinkpad T420 have a reasonable advantage for a programmer. A 13" screen is too small for an activity that it is often done with, much bigger displays (at least in my limited experience). I think that a standard programmer doesn't see any difference between these laptop and a good 750$ 15" laptop; but you can buy two of them at the same price of the T420 or the macbook pro. And you can lend them at a lower rate. If you need more computational power, then probably they are not enough. An OpenCL or OpenGL programmer, for example, cannot seriously use them for his job; expecially without a ssh connection to a high performance desktop or a connection with some cloud computing server.
So, my opinion is that you can find a cheaper laptop, with adequate performance, and increase the number of laptop that you buy. Obviously, if the number of laptops is fixed, there is no advantage in a cheaper laptop, even if I think a bigger display can be useful.
Upvotes: 3 <issue_comment>username_4: Does your donor have a preference? Neither will look like good value for money, but Macs are sexy looking. What about buying Macs and dual booting them? This way your students can get experience in both environments.
Upvotes: 1 <issue_comment>username_5: I don't think that there is technically a real difference between the two alternatives that you mention.
Now, you have to find other ways to choose. I guess that buying macs can seduce prospective students easily (at least more than T420): "Hey Bob, do you now CS dpt XYZ? Sure Alice, this is the coolest on earth, *they even give macbooks to their students*...".
Upvotes: 0 <issue_comment>username_6: Couple of reasons I would go with thinkpads are the following. Please keep in mind I have no CS background.
* Longevity: thinkpads (particularly T series) have a good track record. They can be repaired, reused and upgraded. Apple computers are almost the opposite. A failure in a component often warants for an entire motherboard (since everything is soldered).
* Thermals: Apple has terrible termals. Thinkpads are not particularly superb but they are still significantly better than apple computers. If any of your students needs to do something computation heavy for their research it will be benefical to have some thermal performance.
* Rigidity: I don't know much about apple chasis materials but there is a cult around thinkpads' (reddit r/thinkpad) admiring their rigid, reliable construction. If you go in the afforementioned subreddit, you will see people ressurecting very old thinkpads. On the other hand a significant portion of Apple laptops are now under an [extensive keyboard replacement program](https://support.apple.com/keyboard-service-program-for-mac-notebooks) because their keyboards fail. They say it is a small portion of their keyboards but people around me seem to have this issue more often than they make it sound. And when their keyboards fail, it is absurdly long, expensive and hard to replace them. You do not want your students to go through keyboard issues in their busy periods (final weeks etc.).
* Upgradability: As mentioned, Apple solders every component to their motherboards. Upgrades are near impossible. T series thinkpads usually have really upgradable motherboards. It is not uncommon to see thinkpad lovers using a 10 year old IBM thinkpad with the new CPU, rams, IPS screen and SSD they installed.
* Repertuar: You have mentioned that you would like to give your students "environment close to how it is in the real world" but it is likely the case that they will need to be familiar with a good range of hardware and software. Certainly, thinkpads with linux is the less common choice between the two.
Upvotes: 0 |
2012/07/13 | 723 | 3,012 | <issue_start>username_0: I am currently pursuing a Masters degree in Literature, and I see potential in further research on the current topic of my dissertation for a PhD in the future. I have been told that an Honours thesis cannot be used as a foundation for a Masters dissertation, so i am wondering if a Masters dissertation can be used as a base for a PhD dissertation later on.<issue_comment>username_1: What defines a PhD dissertation can change from one field to another, from one university to another. However, in my case, one chapter of my PhD thesis corresponds to a condensed version of my Master thesis, and as far as I can tell, it's not an unusual practice from where I come from (computer science, france).
It makes sense when the PhD is somehow an extension of the Master thesis, that is, the Master thesis is used to study a particular aspect of the PhD research problem. That being said, the better for you is probably to check some PhD dissertations coming from your department, and check the intersection with the corresponding Master thesis.
Upvotes: 3 <issue_comment>username_2: Seeing your question, I guess this can vary from field to field, but the practice I've seen (engineering, neuroscience, and psychology) is that the PhD work *often* is an extension of the masters research. The masters work explores one facet of the research problem, and the PhD thesis explores two or three more. This is often a practical matter, as sometimes the masters is one step towards the eventual granting of a PhD.
Upvotes: 3 <issue_comment>username_3: In general the master's thesis and the doctoral thesis should not be on exactly the same topic. On the other hand, it is entirely rational for earlier theses to provide the inspiration for later work in one's career.
What you can't really do is retread the same ground—you will need to develop a different topic, with different literature citations and original research. But the move doesn't have to be radical—in literature, for instance, you don't have to go from Sophocles to Virginia Woolf. But you probably shouldn't do *The Winter's Tale* for your master's thesis and then *Cymbeline* for your PhD thesis, either.
Upvotes: 1 <issue_comment>username_4: I have never heard of any institution imposing a restriction such that your doctoral research is in a different field to your Master's research.
As long as your PhD research is a significant body of original research that greatly extends your Master's experience and appropriately cites any previous findings, there should not be a problem.
Indeed, in some countries it is possible to *convert* your Master's research into a PhD. For instance, if you are 6 months into a project and decide you want to greatly extend the scope of your research, you may be allowed a conversion. Bear in mind that where I live, a Master's degree is not a requirement to begin a PhD (you only need an honours degree, which is 1 year of postgraduate lectures and a small research project).
Upvotes: 0 |
2012/07/13 | 1,176 | 5,160 | <issue_start>username_0: I am an international student from an unknown school with a sub 3.0 gpa. I am a CS major but I certainly have a deep interest for statistics grad programs. In the future I want to work on algoritmic trading and want to involve with topics such as time series, machine learning and other statistical techniques used in finance.
Some people I have talked with about this issue recommended me to get a PhD from a top 20 statistics program. But I don't have much math classes on my transcript, actually numerical analysis and ODE's are the most advanced math classes which I had. Also I had a traditional probability and statistics for engineers class.
Currently I am self studying through Walters Rudin's principles of mathematical analysis book and plan to involve further with math. I plan to self study undergrad level topology from munkres, abstract algebra from artin, and some advanced linear algebra and functional analysis may be some measure theory based probability etc. I can attend a university for these classes but it would be extremely difficult for me to manage it just because money constraints which I have. I have to admit that I learn better when I self study and I usually try to attempt most of the exercises in the books.
The problem is that I can't prove that I studied these topics except a good score in math subject gre test. But this test is not a good indicator of abstract math knowledge and most of the test is about calculus.
Does a good score from Math GRE carry a value in MS level admissions to a decent thesis based statistics program with some funding which will help me to get a PhD from a top 20 school later. I don't have any intention of applying to CS grad school because I do not want to get any systems related course as a requirement and majority of the classes I am interested in are mostly offered by math or statistics departments except some machine learning classes offered by CS departments.
So what is your point of view for GRE Math subject test results ? What score you love ? May be I can pay to a college some money for attending to one or two advanced courses such as differential geometry and a grad level real analysis course. If I do well on these courses in what level will they help for admission with some aid ?<issue_comment>username_1: You should check with the statistics departments in question about their admission requirements. In some cases, it may be required; in others, it may not be.
As for recommended scores, that's even harder to say. Different schools will expect different results, depending on the caliber of students they attract. In any case, though, you should aim to get the highest score you can, rather than aiming for a particular target. But at a minimum, you won't want to show a score that results in a below-average score; that probably won't help you at any competitive program.
Upvotes: 1 <issue_comment>username_2: I would say that a good score on the Math GRE subject test is always a good signal. That being said, test scores in general have relatively little importance in PhD admissions -- they can disqualify you, but barring that, won't do much to get you in the door. Its something to optimize only after you have optimized the other, more important parts of your application. In your case, it might be helpful to alleviate any concerns someone might have about your math background.
By the way, you might want to check out CMU's PhD program in Machine Learning: <http://www.ml.cmu.edu/> -- none of those pesky systems course requirements!
Upvotes: 2 <issue_comment>username_3: 1) Most stats programs do not require math GREs.
2) The majority of math GRE is not from advanced mathematics. I strongly suggest you read up on it.
3) Stats programs generally do not require a lot of math background. A solid background in linear algebra, calculus, and multivariate analysis, along an upper level stats sequence is more than sufficient.
4) Your references and any research you've done are going to be the important factors. Did you distinguish yourself in your math classes? Were you the top student? If not, applying to a top 20 school may be an unrealistic goal for you.
Upvotes: 0 <issue_comment>username_4: A GPA of less than 3.0/4.0 will make you ineligible for any top 20 grad school in the US (and Canada), no matter if you apply for a Master's program or a PhD. The lack of math is not as big of a problem as your GPA. If a school accepts you but doesn't think you have enough math in your background, they will put you in a qualifying year instead of the grad program directly.
A good GRE score is good to have, however the cut-off for considering your application is 3.0. If you don't have a minimum 3.0, chances are your application will be disqualified no matter what your GRE score is. These cutoffs are stated clearly at each school's admission requirements page.
For admission consideration, your only option is to talk to the supervisor at your current school and ask if he/she knows anybody at a US school and could put in a good word for you. Otherwise, I am afraid that you are wasting your time.
Upvotes: 0 |
2012/07/14 | 502 | 1,918 | <issue_start>username_0: Should one give gifts to those who one's close to, or those who one generally doesn't interact with very much?
If the professors who wrote my LORs seem reluctant to meet me again for some reason (not sure why - maybe they're just busy), should I simply forget about it?
Also, do people usually give gifts to their advisers for PhD programs, or not really?<issue_comment>username_1: This may vary from country to country, but I think a simple "thank you" is sufficient. Writing letters is part of their job.
I'm actually *forbidden* from accepting gifts from students, on the grounds that an outside observer might interpret the gift as undue influence. But then, I work for a state whose [last two governors are in prison](http://en.wikipedia.org/wiki/Governor_of_Illinois#Corruption) for various flavors of corruption.
Upvotes: 4 <issue_comment>username_2: Here's how I would feel about various expressions of thanks, for something like writing a letter of recommendation:
1. No response at all: I might be a little disappointed but if I am busy I might not even notice. Some people might be more offended.
2. Email saying thank you: Feels perfectly sufficient to me.
3. Handwritten note: A very thoughtful gesture.
4. Token gift (small box of candy, desk trinket, etc): Appreciative but slightly embarrassed.
5. Gift of non-negligible monetary value: Quite embarrassed. I would probably gracefully try to decline it.
6. Gift of money, in any amount: Very uncomfortable. Feels like a bribe. I'd refuse.
Of course this could vary by culture (I'm in the US).
Regarding gifts for an advisor: A common tradition is to give your advisor a nicely bound copy of your dissertation, for their reference and as a memento. (Also be sure to thank them *in* the dissertation!) You could accompany it with a nice personal note. But again, I wouldn't suggest anything of significant value.
Upvotes: 5 |
2012/07/14 | 913 | 3,773 | <issue_start>username_0: I am writing my thesis (in Computer Science, if that's relevant) and I am thinking about the style, especially about using contractions.
I realize that thesis is a formal text and contractions like “*we're*” are quite informal, so they shouldn't be used. But does that apply to all contractions (like “*can't*” or “*don't*”)?
English is not my first language, so I'm not sure how much informal the various contractions are.<issue_comment>username_1: Many people have different opinions, even among those who are native English speakers and/or think a lot about what makes for good exposition. That said, here are a few rules of thumb:
1. **Your thesis is possibly the most formal writing you will ever do.** Survey articles and expository articles (especially for undergrads or other non-experts) are often written less formally. Even many conference proceedings and some journal articles omit some details, and thus can feel less formal than your thesis.
2. **No one will fault you for avoiding contractions altogether.** If in doubt, leave it out (the contraction, that is). I hate some techniques common in formal writing, such as overuse of the passive voice, or nearly any use of the pronoun *one*. But lack of contractions doesn't bother me.
As a more general resource for non-native English speakers, consider Doug West's [*The Grammar*](http://www.math.uiuc.edu/~west/grammar.html). West has written two textbooks and over two hundred papers, as well as having served as a problem editor for the Math Monthly for the last 20-something years. Surely many people will disagree with at least one piece of advice he gives, but what I find helpful is that West *explains his motivation* for each piece of advice he offers.
Upvotes: 4 <issue_comment>username_2: The best advice depends upon the style of your thesis. If you are working in a field where you might be expected to publish your thesis, then something aimed at experts should be written in a formal tone (avoiding contractions whenever possible, outside of direct quotations). However, if you're aiming at a wider audience, then a more conversational tone might be completely appropriate. The same logic applies if the chapters of your thesis are planned for publication.
However, you should also check with your advisor about his or her expectations when writing. It would be better to get a sense of what will be allowed *before* you get too far along; major structural changes are always a pain afterwards.
Upvotes: 3 <issue_comment>username_3: If you are [Lieutenant Commander Data](http://en.wikipedia.org/wiki/Data_%28Star_Trek%29): [your brother can but you can't](http://en.wikipedia.org/wiki/Datalore) (or, as you would say, "cannot"). Otherwise: I see that you can use contractions in your informal writing, so it would be very strange if you were not able to use them when writing your thesis. If you can't, maybe contact [<NAME>](http://en.wikipedia.org/wiki/Oliver_Sacks): he should get at least an article about this, and maybe part of a book.
More seriously: obviously you can. Should you? Speaking as someone who has both written and carefully read theses in a STEM field (mathematics): one generally needs to worry first that the content is complete and correct and second that the writing is good enough so as not to detract (or distract) from the content. The use of contractions would be at least a level below anything I would worry about when reading a thesis...provided they are used correctly.
It's best to stick to language within your comfort zone (especially as a non-native speaker). But your post above indicates to me that you have more than enough facility with English to pull off contractions if you want to. You'll be fine.
Upvotes: 2 |
2012/07/14 | 2,552 | 10,182 | <issue_start>username_0: I am flying to a conference, in which I am presenting a poster.
In principle there might be a problem, as the poster tube:
* is a second piece of hand luggage,
* it is longer than the limit allows.
However, it seems that it works (once I tried with no problem, my colleagues usually have no problem).
**Does it happen that one is not allowed to take a poster tube in the hand luggage?**
If so, how to avoid this problem? (Advice, tips and tricks are welcome.)
If the question is place-specific, I'm interested mostly in EU.<issue_comment>username_1: Sometimes flight attendant staff will be willing to hold a poster for you, since generally they can't safely go as checked luggage, and may not fit in the overhead compartment. You should inquire as to what will be allowed.
One useful alternative I've taken advantage of is to use a printing service in the city where you will be presenting your poster, and picking up the poster there. Nowadays, many will accept things sent by email, file transfer service, or web site. This makes the transfer process easier than before, and avoids the problem of last-minute delays (provided you send it ahead of time!).
Upvotes: 4 <issue_comment>username_2: I've seen many people checking a poster in, since the size limits for checked-in luggage are larger than those for carry-on luggages. Another solution would be to send the poster by mail (preferably with a tracking system) a few days before to the hotel where you're going to stay, or even directly to the conference organizers, who can check that everything went fine.
Upvotes: 3 <issue_comment>username_3: There are a couple solutions to the "transporting a poster tube on a plane" problem:
1. Most flights allow a second piece of hand-luggage, so that bit is fine. And while it is larger in the length dimension than is usually allowed, I've never had this be a problem. In not-very-full flights, it can be stored on top or behind other bags. On fuller flights, I usually just ask a flight attendant for help. So far the tubes have ended up behind the seats in the last row against the bulk head, and in the coat closet usually reserved for first class. Generally, I find they give you "credit" for trying not to be a problem.
2. As has been mentioned, you can get your poster printed or shipped to the conference location.
3. **Cloth posters**. These are becoming more easily accessible. While more expensive than paper posters, if carrying a poster on forces you to check a bag, and your airline charges fees for those, the cost difference vanishes swiftly. These can be folded and stored flat in a shipping envelop or bag. Just make sure to take them out and iron them on a *very low* setting to get the creases out before you hang them.
Upvotes: 6 <issue_comment>username_4: I recently flew with a poster to a conference. The dimensions for hand-luggage usually are Height+width+length <= 42" (inches) (maybe 45", don't quite remember accurately). If the 'tube' sums up to less than this you should be good.
As is mentioned in another answer, most airlines allow two pieces of hand-luggage e.g. A laptop bag and *maybe your tube*, if you don't have anything else.
You are free to carry it with you in the hand unless the tube is arbitrarily long i.e. it exceeds 42 inches in length. If you are below that by length, I doubt the other 2 dimensions would be all that big.
At times they may take it from you right at the boarding gate and ask you to check it in there and then. That's fine. It'll be safe and given to you the instant you exit the aircraft rather than at the carousel.
Some aircrafts are small and may have a separate luggage are for hand bags towards the tail. You can put it there.
If it's a small flight and the tube is not too big (diameter and length wise) just prop it by your leg if you are in a window seat or put it below your seat.
As long as you are within the dimensions and allowable limits of hand luggage (count = 2 weight <= 7kgs) you should be fine
Upvotes: 2 <issue_comment>username_5: Almost every airline allows "a personal item" in addition to your carry-on luggage; otherwise people with purses wouldn't get any carry-on. Your poster tube is probably not "a personal item", but your carry-on could be if it's small enough to fit under your seat. (That's the real reason: there's not enough overhead bin space, but there's plenty of under-seat space. Many people hate having stuff at their feet, though, so the airline can't count on that space being used; saying that you can take a personal item that fits under the seat works for you.) In any case, the way this all works out in practice is that if you're lugging a huge amount of stuff that doesn't look like it'll fit, you'll be asked to check some at the door. Otherwise, it's all okay as long as it will either fit overhead or fit under your seat without sticking out inordinately. I've never seen problems even with backpack (personal item) + carry-on (overhead, fitting strictly within size limits and without requiring lots of shoving to get it to pretend to fit) + poster tube, except on really small regional airplanes with desperately tiny overhead bin space.
Also, if the poster tube doesn't fit in the overhead luggage--it nearly always does--they'll be able to check it at the door to the airplane, just like they do with other overly large and slightly fragile items like strollers.
And, finally, almost everywhere you will present has its own poster printing facilities within a few miles. Any institution of respectable size will have their own, and any city of respectable size--and you're rarely far from one in the EU--will have a copy store of some sort at which you can print at poster size.
So just take the item with you as carry on, and in the extraordinarily unlikely event that something goes wrong and the poster is destroyed, get it printed at your destination. (If you are extra-paranoid, you can get a somewhat tougher plastic poster tube instead of the cardboard ones--bearing in mind that I have yet to see a cardboard one get so crushed that the poster was significantly damaged.)
Upvotes: 2 <issue_comment>username_6: * I only encountered problems once (with Ryanair - I think they wanted to make me pay for late checked luggage). Fortunately it was going back home. I had to make the poster roll small (it then fit into their hand luggage frame) and I loosely folded the poster. As soon as I was in the cabin I unfolded it again and put it into the roll. Someone frome the cabin staff even asked why the poster was folded and said that this was crazy. Fortunately the creases were not that bad.
* I'd always take the poster with the hand luggage: I once attended a conference where at the first 2 days about 1/3 of the posters had an A4 sheet saying: The poster can currently be found at Alitalia's lost luggage department...
* You could print the poster at the place of the conference. I once presented a poster that was printed by the poster printing service of the conference hosting university (who actually allowed orders from the outside).
Upvotes: 4 <issue_comment>username_7: Format your poster so it can be printed as three strips, either vertical or horizontal. Roll them up in a small poster tube that is short enought to fit in your luggage.
Upvotes: 2 <issue_comment>username_8: It depends on the airline's policies, but I have never had any problems on any of AA/AF/AZ/BA/DL/JL/KL/EK/QR/US, and I travelled extensively on all of them as a student, often with posters. The hand luggage restrictions are principally about overhead bin space: but a rolled up poster is small and can probably be squeezed in somewhere on the plane or in the coat cupboard if needs be. Therefore the fact that the tube exceeds one of the dimensions is not really of great interest to most airlines.
If you are travelling on a low cost carrier like RyanAir or EasyJet, that may be a different matter. I know that EasyJet is trying to be "business friendly" now so they probably will not be so interested in a small item like a poster tube. The one time I travelled on Ryanair with a poster I voluntarily checked it in at a very large fee rather than have an argument about it.
Now the warning I do have is: if you do check your poster tube, expect it to go missing. I lost three poster tubes (one permanently) by checking them. Somehow the baggage systems in many airports are not designed to transport tubes (especially if they have straps on them) and so they tend to get jammed somewhere and torn to pieces.
Upvotes: 3 <issue_comment>username_9: I'm returning to this old question because I now know the **best** solution! It's actually username_3's third solution: **fabric posters**.
How to do it:
AstroBetter has an excellent blog post on this (<http://www.astrobetter.com/blog/2015/03/25/fabric-conference-posters-ftw/>) which in turn takes you to the how-to from the company Spoonflower (<https://support.spoonflower.com/hc/en-us/articles/204266984-How-to-Create-a-Fabric-Poster-from-a-PowerPoint-or-PDF>).
This is an even better option than it was a few years ago when username_3 mentioned it, because I've found the fabric printing to be slightly **cheaper than traditional poster printing** (this may depend on your campus printer) and on the fabric recommended by <NAME> on AstroBetter, **no ironing is needed**. It is incredibly fun to be able to stow the poster in the corner of a suitcase and bring it out to show to friends or relatives I'm staying with on the way home from the conference.
---
Historical note: I came across this post several years ago and followed the advice, when creating my first printed conference poster! It ended up being awkward: I asked the flight attendant politely about putting it in a closet or overhead, and she said to put the tube on the floor at my feet (and the feet of the two passengers next to me). As it turned out, I was sitting next to (and inconveniencing) a professor headed to my conference (though he was nice about it). For the return trip, I decided to fold it up and mail it home and avoid having to deal with a poster tube ever again.
Upvotes: 2 |
2012/07/15 | 1,022 | 4,158 | <issue_start>username_0: This question is an off-shoot from [this one](https://academia.stackexchange.com/questions/2346/after-my-phd-how-much-salary-should-i-expect-as-a-professor-of-computer-science), where it has been agreed by most that securing faculty positions is difficult in general. I would like to know what exactly makes this so.
Though in theory university rankings may be pointless, there is a broad quality-based classification of institutions in any country which many will agree on - for example, the crème de la crème, top tier, middle tier and decent universities, of which there could be a few hundreds. We shall assume the student has passed out with a good thesis and impactful publications.
* Is it tough for a student graduating from a higher rung to gain a position in the lower rungs?
* What factors dictate the difficulty in securing a position in a university in the same league?
PS: In India, the answer to Q 1 is "not at all", as there is a heavy crunch for faculty positions even in top institutes. Instead the difficulty arises only when students from low rung colleges seek top positions: in most cases, such students are found wanting in skills.<issue_comment>username_1: It is difficult because there are more graduating PhDs than there are faculty positions. This is because by and large, academic departments are not growing very fast.
Consider a department that has 40 faculty positions, and is not growing. Suppose each faculty member has a career that spans 40 years (Say, ages 28-68). Then in a steady state, this department will hire 1 new faculty member every year. On the other hand, say each faculty member graduates 1 student on average every three years. (This is conservative: say each professor has only 2 students at a time, and each one takes 6 years). So this department graduates 13-14 students every year.
This is what happens in general: each department produces many more PhDs than it consumes, so there must be many who leave the system.
Upvotes: 3 <issue_comment>username_2: The short answer is that there are many more people that *want faculty positions* than there are *positions*. Any time *demand* is higher than *supply*, cost tends to go up. So why is demand so high? Partly it's because being a professor has lots of attractive qualities. However, partly it's because that's what our professors tell us we should be (some do this very explicitly, and some more implicitly). As a result, many students (especially stronger students) decide that the only way to succeed is to become a professor.
Another aspect that makes becoming a professor hard is that most professor jobs require a combination of skills: teaching, research, article writing, grant writing, advising and mentoring, networking, etc. However, grad school generally fails to teach us many of these skills. Most grad schools focus almost exclusively on research, and possibly teaching.
Upvotes: 4 <issue_comment>username_3: One of the strange effects of faculty hiring (and graduate admissions) is that offers do not necessarily go to the strongest candidates. Departments have limited resources to interview, recruit, and hire faculty. Interviews are expensive; startup packages are *really* expensive; faculty job offers burn political capital *even when they aren't accepted*.
So hiring committees make strategic decisions based on the perceived probability that candidates will accept the position. The University of Southeast North Dakota at Hoople would most likely *not* interview superstar applicants, because they don't want to waste their time interviewing someone who's "obviously" going to get offers from stronger schools. As with any self-selection process, this assumption is partly justified and partly Institutional Impostor Syndrome.
So no, selecting an MIT grad is **absolutely not** a no-brainer for U-Cal-XYZ.
And yes, sometimes reasonable PhD students from very strong schools fail to get faculty jobs, or even interviews, because they don't quite have the research record to get an interview at the best departments, but their pedigree scares off weaker departments.
Upvotes: 6 [selected_answer] |
2012/07/15 | 597 | 2,550 | <issue_start>username_0: How does a faculty member seek administrative responsibilities in a Department? Do such positions come in a cycle only? Or should the faculty member make his/her willingness known in advance to the Chair? Are there any politics/wrangling involved?
Moreover, for an assistant professor, is there anything that could be done during PhD so that the profile comes out as someone willing to take admin responsibilities?<issue_comment>username_1: Don't worry, administrative responsibilities will find you unless you flee them, and even then you probably won't avoid them. At most universities, young faculty are expressly spared administrative responsibilities so that they can focus on research and get tenure.
If you're really looking for them, generally all you need to do is vocally speak your mind on every issue that comes up. Most of your colleagues will feign apathy in order to avoid being assigned to a committee.
Upvotes: 5 [selected_answer]<issue_comment>username_2: Administrative duties are considered "service": that is, something that everybody is expected to do. However, that also means that, if you're a junior faculty member under consideration for tenure, having extensive administrative duties won't help advance your case. In many ways, it can, as David says, get in the way of productivity.
That said, you can probably get a sense from your colleagues about the amount of administrative duties you are required to take on. And there is one potential advantage to making your preferences known to the chair (provided he or she is friendly with you, and is working in your best interests): if you have a particular preference for committee duty, then announcing that may make it easier to actually get it!
Upvotes: 3 <issue_comment>username_3: Contrary to JeffE's comment and David's answer, at four-year institutions, opportunities to engage in administrative duties within and outside your department will usually come up long before tenure. And, at four-year institutions, *not engaging* in governance activities will usually hurt your tenure application.
The particulars, of course, vary by disciple, department, and institution, but username_2's advice about following your colleagues' leads is a good way to gauge what you should be doing. If you feel like your department is not the best example, look at others on campus.
Also, as is likely true everywhere in academia, if you voice your "good idea," said idea and the committee developed to implement it will be your for some time.
Upvotes: 2 |
2012/07/16 | 6,624 | 27,829 | <issue_start>username_0: In my field (theoretical computer science), authors of any paper are always listed alphabetically; our papers [don't have "first authors"](http://www.ams.org/profession/leaders/culture/CultureStatement04.pdf). (Well... hardly ever.) In most other disciplines, at least within science and engineering, the ordering of authors is a signal about their relative contributions to the paper, with the first author indicating the most significant contributor. Hiring and promotion committees do give extra weight to "first-author papers" (and sometimes have to be reminded that not all areas have them). As an outsider, I find this practice confusing.
**What does first authorship actually mean in your discipline?** I understand vaguely that the first author is supposed to be the one who "[did the most work](http://en.wikipedia.org/wiki/First_author)", but what counts as "work" in this comparison? Does "most" mean "more than all the other coauthors together" or just "more than any other coauthor"? What happens when the comparison is unclear? How often is "did the most work" the actual truth, versus a cover story for a more complex political decision?
I realize that the precise answer is different for every paper. **I'm looking for general guidelines** for how an outsider (like me) should interpret first authorship *in your field*. Pointers to guidelines from journals or professional societies would be especially helpful.
Please give only one answer per discipline.
Answer list:
------------
* **Math and related fields** with alphabetical ordering:
+ [Pure Math](https://academia.stackexchange.com/a/2468)
+ [Applied Math](https://academia.stackexchange.com/a/2470)
+ Computer Science [(1)](https://academia.stackexchange.com/a/2473) and [(2)](https://academia.stackexchange.com/a/2472), see also [human-computer interaction](https://academia.stackexchange.com/a/8575)
+ See also [physics](https://academia.stackexchange.com/a/56713)
* [Physics](https://academia.stackexchange.com/a/56713), [engineering](https://academia.stackexchange.com/a/2474), and [chemical engineering](https://academia.stackexchange.com/a/2481)
* **Biology, medicine and related fields**, where order matters
+ [Biology](https://academia.stackexchange.com/a/11120)
+ [Chemistry](https://academia.stackexchange.com/a/2489)
+ [Cognitive neuroscience](https://academia.stackexchange.com/a/2468)
+ [Epidemiology](https://academia.stackexchange.com/a/2515)
+ [Medicine](https://academia.stackexchange.com/a/2487)\*
+ [Microbiology](https://academia.stackexchange.com/a/2475)
+ [Psychology](https://academia.stackexchange.com/a/177431/37441)
* [Earth Sciences](https://academia.stackexchange.com/a/8590)
* [Art History](https://academia.stackexchange.com/a/178341/75368)<issue_comment>username_1: **Pure Mathematics**: All authors are assumed to have contributed equally and are listed alphabetically. The American Mathematical Society has put out a [statement](http://www.ams.org/profession/leaders/culture/CultureStatement04.pdf) to this effect.
Upvotes: 7 <issue_comment>username_2: **Cognitive neuroscience.** The first author (most of the time a PhD student or a post-doc) is typically involved in designing the experiment, running it, analyzing data and writing up. The other authors are mostly involved in some but not all of these steps. They will usually help out but not do all of the work (e.g. they might show the first author how to do some analyses, or they might make many useful comments on a draft of the manuscript). In the institute where I'm studying (in The Netherlands), all the papers where I am first author will also be chapters in my thesis, and all the unpublished chapters in my thesis could potentially become papers where I am first author. Those where I am only partly involved will be a chapter in someone else's thesis, and I will not be first author.
The *last author* is as important as the first one. It is typically the supervisor, and ideally the supervisor is heavily involved. In labs that grow too big, a post-doc (once trained by the supervisor) might take over this role, but the last authorship still goes to the supervisor. It's like a brand name, it tells you whose lab the work is coming from. If you know a bit about the field, you will know the general ideas the paper will revolve around. If two supervisors are involved, they have to work out whose name will be last. I know of one situation where the two believed in different outcomes of the experiment, and decided beforehand that the person who turns out to be right will get last authorship. Most of the time, though, the decision is based on who did more supervision, which is ideally agreed on beforehand.
Upvotes: 6 <issue_comment>username_3: **Applied mathematics.** The first author is *usually* the one who contributed the most. However, sometimes the pure mathematics convention of alphabetical ordering is used; this may be expressly declared in a footnote. There are no official guidelines from SIAM.
There is no significance to being the last author, and only those who contribute substantially are listed as authors. If the supervisor is not directly involved in performing the research and writing the paper, he is typically only listed in the acknowledgments.
**opposing view:** The above statement as completely misleading. The authorship order in applied mathematics is usually alphabetical, irrespective of the quality of contribution. This seems to be the common practice, for example, in the SIAM journals.
Upvotes: 6 <issue_comment>username_4: **Computer Science** This really depends on the institution and the group. In one group I've been working in the ordering was *always* alphabetical and doing otherwise would have been considered impolite. In another group, the PhD-first-boss-last principle was used.
Upvotes: 4 <issue_comment>username_5: **Computer Science.** Computer science varies by field:
* **Theoretical computer science** generally follows the same conventions as mathematics: the ordering of authors is alphabetical. Cryptography follows the same conventions.
* In **programming languages**, **computer systems** (e.g., operating systems, databases, computer security, etc.) and other applied fields, the order of the authors is significant. The authors are often listed in order of decreasing contribution; faculty or senior folks are typically listed last. The first author often has led the design, implementation, and experiments presented in the paper or has contributed the most to these elements. Other authors may have contributed more in total, or even individually, to these components, but sometimes at the direction of the lead author. The lead author may also have been considered to be primarily responsible for the writing of the paper, though not always.
In cases where the lead is shared between several people, papers can have multiple "first" authors, listed alphabetically, followed by an alphabetical listing of the other junior authors, followed by the senior authors. I have seen this fact listed explicitly on a CV. Usually, a PI comes last even if he or she provided the bulk of the leadership of the project; a PI coming first is indicative of an unusually high level of contribution from the PI / low level of contribution from the junior authors.
Overall, the meaning of first authorship ends up being vague enough that usually you have to explain the level of contribution explicitly in reference letters as such.
* **HCI** (human-computer interaction) follows similar conventions to those in computer systems. The authors are listed in decreasing order of contribution. The first author is generally the person who both had the "main idea" and led the effort to ensure that the efforts to carry out the research and write the paper occurred properly. The authors are generally then decreasing in order of their contribution.
Upvotes: 6 <issue_comment>username_6: **Engineering**: A first author is usually the lead student or worker on the particular project from which the paper originates. If there are multiple people working on a common project, then the authorship goes to the person whose results are most prominently featured, and who has done the most work in preparing the manuscript for publication.
A significant exception might be in multi-part papers, in which the first authorships may be shared among different people to recognize equality of contributions throughout the combined work.
The *last author* is often a professor, who advised or directed the lead author, but may have done little work on the project themselves.
Upvotes: 5 <issue_comment>username_7: **Microbiology**: Similar to cognitive neuroscience: Ph.D student is first author by virtue of having done most of the work, and the PI is the last author. If it is agreed upon that more than one person did "first-author-level" work, then the authors are listed alphabetically with a footnote noting this fact on the title page.
Upvotes: 4 <issue_comment>username_8: **Chemical Engineering:** The first author is generally considered as the main contributor. In case there are multiple students who made equal contributions, then this is specified as such in the list of authors. (Mostly by adding an asterisk on the names and a footnote explaining the asterisk). Certain groups follow a policy of Adviser first and then rest, though it is considered as arrogant (This is prevalent in mostly Chemistry related sub fields).
In Machine Learning/Applied Computer Science the policy is again similar as Chemical Engineering with Student first and Adviser last, if there are multiple advisers then the advisers tend to rotate between different papers from the same project.
Finally in Medicine especially in General Medical journals, There is a detailed statement of contributions.
>
> E.g. Author Contributions: Dr <NAME> had full access to all of the
> data in the study and takes responsibility for the integrity of the
> data and the accuracy of the data analysis.
>
>
> Study concept and design:
> <NAME>, Deceuninck, Toth, Boulianne, Landry, <NAME>. Acquisition
> of data: Deceuninck, Toth, Boulianne.
>
>
> username_2lysis and interpretation of
> data: <NAME>, Deceuninck, Brunet, Boucher, De Serres.
>
>
> Drafting of the manuscript: <NAME>, Deceuninck.
>
>
> Critical revision of the manuscript for important intellectual content: <NAME>,
> Deceuninck, Toth, Boulianne, Brunet, Boucher, Landry, <NAME>.
>
>
> Statistical analysis:
> Deceuninck. Obtained funding: <NAME>, Boulianne, <NAME>.
> Administrative, technical, or material support: <NAME>, Deceuninck,
> Toth, Boulianne, Landry.
>
>
> Study supervision: <NAME>, De Serres.
>
>
>
Another important point is how are paper cited, from what I remember esp. in chemical engineering. A paper is generally mentioned as Last\_name et al. and if there are only two authors or two equally contributing authors then it is mentioned as Last\_name\_1 & Last\_name\_2 et. al or just Last\_name\_1 & Last\_name\_2.
Upvotes: 4 <issue_comment>username_9: **Medicine:** The first author is *the* author. He or she is credited with the bulk of the work, and some even consider first authorship to be the only authorship of value. This may be partly due to the fact that a medicinal paper often has many authors, with some having done next to nothing for the paper (maybe read it). Although journals would like to discourage this, people write their colleagues' names on their papers, so maybe their colleagues will do the same for them and both get a more impressive publication record.
Sometimes you see asterisks above the first two authors' names, indicating that "both authors contributed equally", although it seems to me that this is in general not well recognized. I've been told journals want one main author. Also, for many academic positions a given number of publications is required, with some minimum number **first authorships**.
Upvotes: 6 <issue_comment>username_10: **Chemistry**: similar to cognitive neuroscience and microbiology and many other fields - the first author is usually the individual who put most of the labor into the work. The PI, usually the last author, may have come up with the idea, but the first author usually does most of the following work: designing the experiments, synthesizing and purifying the compounds, collecting and analyzing the data, and writing the paper. The other authors might be: A student in a collaborating group that conducts an important, specialized experiment for the first author, a junior student in group who prepared some of the intermediates and collected routine data to help the first author and to learn the workings of the group, or a consulting professor offering expertise in an area that the first author and PI are weak in. The last author is usually the PI.
Some journals are beginning to ask for specific descriptions of the contributions of each author to combat vanity authorships. You also occasionally see the note that two or more authors may have contributed equally, but some journals discourage this practice also. As an example of this sort of declaration, the following statement was attached to the final article from my thesis:
>
> Author Contributions
>
>
> B.N.N. and S.Z. were coequal in their contributions and should both be considered first authors. B.N.N., T.Y.M., and G.R.H. proposed the project and designed the experiments. B.N.N., S.Z., J.T.A., and P.C.M. performed the synthesis and characterization. C.M.C. and G.R.H carried out the calculations. B.N.N., S.Z., T.Y.M., and G.R.H. assembled the data and wrote the manuscript.
>
>
>
Historically, the practice of putting the PI last is relatively recent. The PI used to be listed **first**, so that the PI was easier to identify and the collective works of the PI were easier to find in printed catalogs systems (where articles wee often indexed by the first listed author only). The historic order would have thus been: PI, first author, second author, etc. The advent of electronic databases removes the need for the PI to go first, though there are [some who still do it that way](http://pubs.acs.org/doi/abs/10.1021/jo901459t).
Upvotes: 5 <issue_comment>username_11: **Epidemiology**: First author generally means the author who did the bulk of the writing, and is likely directly responsible for the analysis of the data. The last author is (often but not always) the project's PI, a senior member if its a multi-site collaboration, or a place where someone who contributed heavily in some aspect, but not as much as the first author, goes.
Generally speaking, first authorship is considered the most important, last authorship has some benefit in terms of establishing a mentor role or the concept of the author as a senior researcher.
The exception for this is a small number of "pairs" of methodologists who tend to write papers together, which end up getting seen as a kind of equal contributors.
Upvotes: 4 <issue_comment>username_12: In the **computer science/software engineering and human-computer interaction**, the first author is generally the person who both had the "main idea" and led the effort to ensure that the efforts to carry out the research and write the paper occurred properly. The authors are generally then decreasing in order of their contribution. There is generally no consistent policy of putting the PI last in SE or HCI.
Thus, the policy is very similar to the Applied Mathematics answer posted above and quite different from the theoretical Computer Science areas.
Upvotes: 3 <issue_comment>username_13: **Earth Sciences** (Physical Geography, Geology etc.). Authors are listed according to their intellectual contribution to a paper. The first author named on the paper is thus the person who has contributed intelectually the most to the paper. The second, third etc, names have decreasing importance (contribution). If more than one person can be considered first author, those names are listed alphabetically and a note to the fact is made in the acknowledgement.
Only persons who have contributed intellectually to the paper are included. Lab assistants, techncians etc, are thus not included (although it still happens).
If a leading scientist, project leader etc. is not first author the lead role may be indicated by refering to that person as "Corresponding Author". This is common when first authors are junior contributors (students).
Upvotes: 4 <issue_comment>username_14: **Biology:** I felt the answers for medicine, microbiology, and epidemiology may not give the complete picture. Of course this is my own opinion, as there are no real formal rules.
**The unofficial rules:** In biology, the first author is the person whose contribution is larger than that of any other author. It is cannot be the author that contributed more than the combined contribution of all other authors - this definition doesn't even work mathematically (a 25%/35%/40% contribution paper would have no first author).
However, the situation is more complicated. Usually, the subsequent order of authors is according to decreasing contribution. Also, at the end of the authors list the scenario is mirrored: The last author is the senior author (i.e. the PI) that contributed the most, with the order of senior authors again reflecting their contribution (mirrored).
Then, it gets even more complicated. In some cases, you can have co-first authors. This is usually marked by the journal, indicating that these authors had equal contribution. Then, there is the "corresponding author" mark. Some (but this is less widely accepted) use this is to signify equal contribution of the senior authors, so for example you would mark both last 2 authors as "corresponding authors".
**Practical issues:** While it may seem to be silly to people not used to this method, the order of authors is actually quite important. For graduate students and postdocs, fellowships and prizes will often only consider your first-author papers as your "real" papers - this is usually written in the rules (you may asked to list only first-author papers). Furthermore, if you are co-first (equal contribution), you will often be required to detail your exact contribution (sometimes your supervisor needs to detail it as well in these cases). For PIs, the situation is similar - funding agencies will often only consider your last author papers.
Another less important issue is association with the paper. A paper will be generally referred to by the first author's name, e.g. "Smith et al.". If you are the first author you will be immediately associated with the paper. If your paper is high-impact, there can be benefits to this in terms of establishing your name in the field. This is one reason why even "equal contribution" may not be considered really equal by some.
**Biology vs. other disciplines:** Finally, I want to explain why this practice might be useful in biology and how it is different than math or CS, for example. First, any graduate student or postdoc is always under supervision. It is customary that regardless of the actual amount of involvement of the supervisor in a project, the supervisor is always listed as the senior author. You have to remember that it is quite rare for PIs in experimental labs to actually do any actual work themselves (this is different from theorists). This is not to say they cannot be highly involved. Then, many projects are collaborations between multiple research groups. It is very common to see 15-20 authors on a paper, and recently there have been many papers published by research consortiums, having hundreds of authors (although in that case the order of the author list is slightly different). One author could really be doing much more work that some other author, which is on the paper just because he/she contributed some biological sample or ran some program.
**Is it good?** I don't think this system is optimal. It can lead to personal conflicts and affect people's careers. Some journals try to bypass this system by adding a section detailing the individual contribution of each author, but this isn't widely recognized. Some funding agencies ask you to quantitatively mark the contribution in percentage of each author - but how do you do that? It is extremely difficult to quantify. One author spent a lot of time doing experiments, and another spent a lot of time analyzing the results - who should be first? It is very subjective and in the end is often settled by politics.
Upvotes: 4 <issue_comment>username_15: In **physics**, different sub-fields treat this differently.
* In the **sensor physics** sub-field, the order of the authors are typically listed in a similar manner to Earth Sciences, (as described earlier by username_13). The first author is often the corresponding author. The first author is usually the scientist who not only initiated the project, but also performed much of the experimental practice and analysis. Then, the order is based on the intellectual contributions made - usually of the same research or collaborative group. Oother people involved, such as technicians, lab assistants are mentioned prominently in the Acknowledgements.
* In **experimental particle physics**, all results are published "by the collaboration", and the entire collaboration is published in alphabetical or otherwise arbitrary order. For some collaborations, the author list can have thousands of names, most of which have not read the paper (and may even be unaware of its existence). Further, there is a lengthy internal review process for all published papers. Thus, letters of recommendation are crucial to determine quality of research.
* In **Atomic, Molecular and Optical (AMO) physics**, the trend is that the grant holder/advisor will put their name last, and the grad student who did the bulk of the work will be first author, and other contributors go in between. For notable groups, when you see Author X's name last, you know this is stuff they've been working on for decades, that it's their idea, etc. Putting the students name first is a small gesture of recognition for their hard work and a way to help them start to establish their own reputation.
Upvotes: 3 <issue_comment>username_16: **Psychology:** There are two distinct traditions to determining author-order in psychology, which we might label the APA and the biomedical traditions. The APA tradition orders authors by level of contribution. The biomedical approach places special importance on the last author, who is often the supervisor of the work. Finally, given the clash of these two traditions, a hybrid model is also emerging where first authors are most important, second and last authors are equal-next-most important, and then third through second last are in descending contribution.
* The **first author** is usually the person that led the project, analyzed the data, and led drafting of the manuscript. They may or may not have collected the data personally.
* The **second author**: The meaning depends on the author-order tradition applied. Under the APA tradition, they would typically be the second-most important contributor, and where the project is a student or post doc paper, they are often the supervisor. Alternatively, in the biomedical tradition, their contribution is typically not considered to be as important as the last author.
* **Other middle authors** are usually involved in one part or another, lending statistical expertise, or subject-matter knowledge. They may also be junior people that collected the data (e.g. interviews, operating technology like [EEG nets](https://www.egi.com/research-division/geodesic-sensor-net), etc.).
* The role of **last author(s)** depends on the author order tradition implied. They may be the least important contributor. Or they may be the supervisor or PI whose lab collected the data, obtained funding, etc. Data collection in psychology can be expensive and difficult, and take a long time. There can be multiple.
* **Corresponding author** is determined by various rules and is given minimal significance with regards to contribution. Corresponding author is most commonly the first author. If it a student-led work, it is also common for it to be the supervisor. Finally, it might simply be someone with a permanent position who might be able to answer questions years later after the first author has moved on.
* **Shared first authorship** is rare, but it is becoming more common. Alternating first authorship in close collaborations also does occur.
**How do you determine whether last or second authorship implies a greater contribution in a psychology manuscript?**
In general, there are a number of indicators that may suggest which author ordering principle was applied on a given paper. Last authors are more commonly important (a) in fields aligned with medicine, neuroscience, and health, and (b) in papers with many authors (e.g., 6+). Second authors are more commonly important (a) in more prototypical psychology subdisciplines like social psychology, personality, experimental psychology, and industrial-organisational psychology, and (b) in papers with fewer authors. In general, this seems to reflect patterns of collaboration. For instance, if academics are collaborating with the biomedical field, they absorb that tradition.
More generally, second and last authorship tend to be more important roles where the first author is a student or post-doc. In those cases, it is often the case that either the second or last author supervised the work.
Given the ambiguity in psychology about the relative importance of second and last authorship, some academics in psychology will highlight the number of times they have been a lead author, which is often specified as first, second, or last authorship.
**APA Style Manual 7th Edition**
The Style Manual has the following to say in Section 1.22
With regards to professional authors (e.g., where it is not the work of a doctoral student):
>
> Principal authorship and the order of authorship credit should accurately reflect the relative contribution of persons involved... The general rule is that ... principal contributor appears first, with subsequent names appearing in order of decreasing contribution. In some cases, another principal contributor appears last. These conventions can vary from field to field and from journal to journal... If authors played equal roles in the research and publication of their study, they may wish to note this in the author note.
>
>
>
Upvotes: 1 <issue_comment>username_17: **Astronomy:** Generally alphabetical author lists are reserved for collaboration papers. The largest ones may even have the first author as the collaboration's name (See for instance this [LIGO paper](https://ui.adsabs.harvard.edu/abs/2015CQGra..32g4001L/abstract) or this [arxiv version](https://arxiv.org/abs/1710.05832) though the journal version ended up with [Abbott et al](https://ui.adsabs.harvard.edu/abs/2017PhRvL.119p1101A/abstract) just to confuse things).
For papers with smaller author lists, the order would be expected to be in order of contribution.
* The **First author** will have done most of the work, written most the paper, and built the idea to start with. They are also usually the corresponding author.
* **Middle authors** will usually be in order of contribution (sometimes its alphabetical depending on the size of the author list).
* **Last author** Does not convey any explicit meaning.
Upvotes: 2 <issue_comment>username_18: **Art history:** The humanities are still heavily invested in solo authorship. The first author is generally considered to be the one who researched and wrote the vast majority of the paper.
Upvotes: 2 |
2012/07/16 | 624 | 2,611 | <issue_start>username_0: Assuming I am reading a textbook/paper or any kind of rich material and then there is a point where I feel that the chapter I am reading is unnecessary or insignificant (I can get back to it later if I am wrong) or at times unmotivated or boring, what are good practices to skip such chapters?
(Assume Math/Engineering books)
How do you make sure that the content you skipped doesn't get in the way of the subsequent chapters? (i.e. Skipping does not carry any drawbacks)<issue_comment>username_1: I'm usually backtracking, i.e., I try to read a subsequent chapter, then I usually discover that there are some definitions/concepts that I do not understand, so I go backwards to look these up, and, once I've understood these notions I can resume the reading of the subsequent chapter.
Upvotes: 4 [selected_answer]<issue_comment>username_2: How do you judge that the chapter is unnecessary or insignificant without reading it?
I know it is tempting to skip the boring stuff, but I don't see how you can decide that a section is not needed except in hindsight.
Upvotes: 0 <issue_comment>username_3: Often **the authors will tell you** which chapters or sections are prerequisites for others. The most common place to find this is in the preface or forward. If you don't find it there, you may also find this information throughout the book. One book that I'm working through right now has 25 chapters, grouped into 9 sections. At the start of each section, the authors spend about a page outlining the goals of each chapter. **Nearly any time I plan to read much of a book, I first read or skim the introductory material.** These sections usually don't take much time, and they often give me a decent sense of what to expect from the rest of the book.
Upvotes: 3 <issue_comment>username_4: There is absolutely no compulsion to force yourself to read every section ... on a first pass, or second, or on any particular pass through a book. Often, seemingly obscure choices become clearer only later, and, even then, often only on the fifth or tenth time through. That is, genuine reading of sources is essentially never linear, and takes many passes through, both to get an idea of the content, and to absorb it in varying degrees. Certainly we should not feel bound by artificial rules, although the physical linearity of books and papers perpetually misleads us.
Upvotes: 2 <issue_comment>username_5: In addition to the existing suggestions, if you know someone else who has read the book they might have very good advice on which sections are essential or can be skipped.
Upvotes: 1 |
2012/07/16 | 2,468 | 9,798 | <issue_start>username_0: I am a senior maths major (computer science minor) who is pretty worried about the next step in my academic career. First, let me state that I'm about as sure as I can be that I want to get a PhD in mathematics. Unfortunately, I didn't realize what the field entailed, or my passion for it, until I had made many really poor decisions - mostly in the form of bad attendance. For example, I basically just showed up for tests in calc 2, 3, linear algebra, and differential equations, and consequently, my field of potential letters of recommendation is quite small. To make things worse, I come from a party school - I need letters!
I've had one professor (abstract algebra) offer to write me a letter, and I've taken my advanced calc sequence under a professor who I think could write me a good recommendation (adv calc 2 was a graduate course; had [i think] the highest grade out of about 15 students). I'm also taking topology (graduate level) this semester, and am hoping to impress my way to a third letter.
My GPA is okay - cumulative about 3.61; math is all A's and one C in linear algebra. I've also been working through a few books (Spivak's "Calculus" and "Calculus on Manifolds," and am about to start Birkhoff and Maclane's "Survey of Modern Algebra." Although I love the material, and enjoy learning it, the independent studying probably stems from some feeling of inadequacy due to my past immaturity.
I got a 169/170 Q, 165/170 V on the general GRE. Also, I think I can crack 80% on the subject test, but am not overly confident about this. One glaring hole is that I have done zero research, and have done nothing extracurricular - I literally have nothing "extra" going for me.
My concern is that I've seen the resumes of many people accepted to top universities (PhD track), and I just don't stack up. But if my goal is to become a professor one day, it seems that where I go to school is extremely important. So should I just hope that I can get accepted into a top 30-50 school, or would it be beneficial to consider improving my resume in a solid Masters program so that better schools become available?
And if a masters is a viable option, what caliber of school would I need to excel at in order to be a competitive applicant for a top 10 PhD program?<issue_comment>username_1: I've always thought that one good indicator of whether or not you even have the motivation to complete an entire PHD is whether or not you do the practice problems in text books. If you enthusiastically do those practice problems, like you solve them in the shower, then I would say that, barring talent, you at least have the requisite level of enthusiasm for the subject. In other words if your not a fan of practice problems, your prolly not gunna like the 300 page writing part of the PHD, nor the fact that not all 100% of the work you do will make it into that writeup (there's alot of tangential calculation and verification). In this way, personal interest and commitment to mathematical activity is absolutely requisite.
You should wait until you finish that course in topology. Math takes on a different character when you get into analysis, manifolds, algebra and beyond. For me, smooth manifolds was as far as I needed to go in the analysis route to satisfy my curiosity. Then I became interested in other things. If I had had that shift of interest midPHD then I don't think I would have been able to finish.
You should also just sit down, learn LaTex if you haven't already, and write about something that interests you, exploring it to the absolute highest level of detail while always leaving an obvious path for generalization and application. Make it lucid and interesting. Convince the reader you have an idea and entertain them with it. Put it on the Internets, have a proff edit 1 or 2 pages, show it to a friend or classmate, stick it in a library book, whatever. This is one defining characteristic of a mathematician, communicating your thoughts to paper so that they may survive.
You should read this [The Best Writing on Mathematics 2011](http://www.amazon.ca/Best-Writing-Mathematics-2011/dp/0691153159/ref=sr_1_1?ie=UTF8&qid=1342529450&sr=8-1), it will give you a good idea of what doing math as a profession is like. Regardless, you may have an excellent academic record, but what makes a good mathematician is a commitment to doing math and that should be your primary focus, grades second. Although there's nothing wrong with being competitive academically, if that's your thing then go for it.
Personally I didn't like Spivak's manifold calculus text. I went Munkres' Topology, to Lee's Topological Manifolds, and have yet to finish Lee's Smooth Manifolds. If your looking for a reliable publisher, just stick with the yellow covers.
Upvotes: 3 <issue_comment>username_2: If you have a thin B.S. background in math, it is definitely very helpful to get a M.S. in math before (re-) applying to Ph.D. programs. (I say this having been on the Grad Admissions cte, and having been Dir Grad Studies in two different incarnations...) In the U.S., *many* undergrad programs are really very thin, due to the requirement (otherwise wholesome) of "breadth".
The "undergrad research" episodes in summers, and during the academic year, are good for generating enthusiasm and camaraderie, and especially for getting outside the rigid classroom/textbook atmosphere, but (apart from very broad features) are not at all good indicators of what serious research is or will be like. Those programs are *designed* to be fun (pizza parties, etc), so "doing research" in that sense is fun for nearly everyone.
So, apply to MS/PhD programs at the top 30-50 schools, and do the best job you can in the standard/required PhD curriculum (usually, there is no distinction between PhD curriculum and MS, except that the latter is designed to accommodate, if necessary, weaker students, perhaps weak enough so that the MS will be their last degree in Mathematics... don't be misled into taking an "easy route"). And, in the course of doing the coursework, don't be a stranger to the instructors of those courses, who will be your letter-writers for either re-application to "better" schools in a year or two, or will be your letter writers if you need to re-apply to that institution itself for the PhD program, for bureaucratic reasons.
Grades in non-math courses, and grades in calculus and lower-division courses don't matter much, although obviously good grades are a not a bad thing. Admissions committees are well-acquainted with the changes people go through around age 20 and so on. The question is not so much what silly things one has done a few years back, but where one is heading *now*, and what documentable evidence there is for this.
In particular, although self-learning is the most significant long-term way to develop scholarship, it is obviously hard to document. Perhaps the best way is to sign up for courses that appear to re-iterate (serious) content you've already studied. Presumably, you absolutely ace the material and draw the instructor's attention... since self-study beyond "requirements" is, strangely-or-not, extremely rare. Evidence of non-passivity is excellent, if it really proves to be what you feel it is (rather than, say, mere obsessiveness).
So, again, yes, think about "proving yourself" during a year or two of "MS work". No downside, really.
Upvotes: 4 <issue_comment>username_3: I was in a similar situation 1.5 years ago: made a series of bad decisions, have 0 publications or research experience, nothing outstanding about my academic CV. I was in fact wavering between pure and applied mathematics, not really knowing much about research in either area.
After doing a 1 year mathematics MSc at a top-tier university, would I say that in situations similar to yours and mine, doing an MSc is a **very** wise choice. Doing well in a good MSc will **certainly** overshadow what you did in your undergrad.
**The primary goals of the MSc are:**
1. Obtain good grades and recommendations letters.
2. Do Research! It is absolutely vital that the MSc has a significant research component.
**Things I wished I knew back then:**
1. Take courses to maximize your grades. This could mean taking courses you have already taken before. Its not strictly a waste of time: you can see this as a test to see if you can perform in the topic at a graduate level. Also, if you're interested to do research in it, this would seriously help reinforce your knowledge in that area and you can take the opportunity to know the professor teaching it, even doing your dissertation in it.
2. DO RESEARCH. Grab any chance you have to do research. In fact, be prepared to stay behind for a few months after graduation to turn your dissertation into publishable material or for an internship in the department.
3. Pin down your interests ASAP. Do your MSc dissertation in that area if you can. It is such an advantage to have a dissertation project in and a letter of recommendation from someone in the area you're applying to. Contact relevant professors about graduate applications asap.
**Advice for applications:**
IMHO you should aim as high as possible when applying for MSc. Top tier self funded MScs are a lot easier to get into if you don't have terrible grades.
Its not about the prestige of the department. Rather, more competitive places tend to attract highly motivated and competitive people. Being in that environment would seriously inspire you to push harder and accomplish more. Also, they tend to have more "intellectual resources" - brilliant professors, brilliant classmates etc for you to learn from.
**P.S.**
I am a student still in the process of applying for a PhD in Applied Mathematics.
Upvotes: 3 |
2012/07/17 | 1,026 | 4,426 | <issue_start>username_0: On occasion during talks, presenters will say, "as you know from reading my papers" or during a Q&A someone will say "you should know from my papers".
The above clearly seems inappropriate for most settings outside of a group meeting. However, to what extent should I expect my audience to know what I am talking about vs. completely assuming that my audience know nothing?<issue_comment>username_1: I guess that this would strongly depend on the culture within your scientific domain. In my case (software engineering) making this assumption would be inappropriate. You can suggest to read your paper if, e.g., there are some technical details you are not going to present since they might not be of general interest, but you cannot assume that this already happened.
Upvotes: 2 <issue_comment>username_2: In general, the broader the audience, the less you're able to assume about the audience's knowledge of your work. For example, at a general meeting for your profession or a department-wide seminar, you'd have to be more careful to provide background and context than at a smaller, more specialized event like a [Gordon Research Conference](http://www.grc.org).
However, time limitations will also be a major consideration—and in some cases, can outweigh the specialization issue. If you only have ten or twelve minutes to give your talk, then you don't have time for more than a minute or two of background information, regardless of how complex or novel your topic may be.
Upvotes: 2 <issue_comment>username_3: This really depends on your audience.
* At a department colloquium, I assume very little background, probably an undergrad degree in math, but not even an undergrad class in my research area. Here the audience will often be *smart*, but ignorant of the relevant background.
* At a research seminar with lots of undergrad and/or masters students, I still don't assume much background, maybe an undergrad class in the area, but even then I "remind" the audience of important information they "should" know.
* At a research seminar with mainly PhD students and active researchers I assume more, but even here I usually don't assume that they're familiar with the problem or the relevant literature or techniques.
* A conference special session (or minisymposium) is similar to a research seminar with mainly PhD students and active researchers. Here I often expect that much of the audience is familiar with the background and the techniques, but I still usually review them at least briefly. **I've never gotten complaints about giving too much background and context.**
I **never assume the audience has read any of my papers**.
I typically view my talks (at least in part) as advertisements for my papers. The audience will never want to read my paper if they don't see why it's important (so that's my job during the talk). For those few that already want to read the paper, I hope they leave my talk with an outline of the paper, which will make it easier to read. Any time you're talking to an audience with varied background, it's good to briefly describe the key definitions. If you can do this quickly, perhaps only verbally, you don't bore those who already know, and you give the rest a fighting chance to keep up.
Upvotes: 6 [selected_answer]<issue_comment>username_4: In general, when giving a talk, my only assumption about the audience is that they found the title (or me) compelling enough that they decided they didn't have anything better to do with that hour (or 15 minutes).
Or at a conference, that they can't be bothered to get up and leave before the session ends, and decided to stay.
While I will give references to papers I've written, it's always a "For more information..." rather than a "As you all know..."
Upvotes: 3 <issue_comment>username_5: I think the causality is probably reversed: a talk at any level is a chance to "advertise" or "promote" ideas (whether "your own" or due to a larger enterprise) far more dynamically than in a paper. Thus, a good talk will *motivate* people to read your (or others') papers. Papers and talks are not in the same currency.
Further, if you have any reason to believe that most people in the audience have read your paper that you intend to talk about... you'll surely be boring them, and making them sorry they attended, no? Certainly if the talk is just an abbreviated form of a formal paper.
Upvotes: 3 |
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