Update README.md

integrating comments from Dan Morris

README.md

@@ -22,7 +22,7 @@ tags:
 pretty_name: LILA Camera Traps
 ---
 
-# Dataset Card for LILA 
+# Dataset Card for LILA
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
@@ -51,7 +51,7 @@ pretty_name: LILA Camera Traps
 
 ## Dataset Description
 
-- **Homepage:** https://lila.science/ 
+- **Homepage:** https://lila.science/
 - **Repository:** N/A
 - **Paper:** N/A
 - **Leaderboard:** N/A
@@ -79,22 +79,22 @@ For questions about this data set, contact [email protected].
 
 If you use this data set, please cite the associated manuscript:
 ```bibtex
-@inproceedings{DBLP:conf/eccv/BeeryHP18,
-  author    = {Sara Beery and
-               Grant Van Horn and
-               Pietro Perona},
-  title     = {Recognition in Terra Incognita},
-  booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
-               Germany, September 8-14, 2018, Proceedings, Part {XVI}},
-  pages     = {472--489},
-  year      = {2018},
-  crossref  = {DBLP:conf/eccv/2018-16},
-  url       = {https://doi.org/10.1007/978-3-030-01270-0\_28},
-  doi       = {10.1007/978-3-030-01270-0\_28},
-  timestamp = {Mon, 08 Oct 2018 17:08:07 +0200},
-  biburl    = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
+    @inproceedings{DBLP:conf/eccv/BeeryHP18,
+      author    = {Sara Beery and
+                   Grant Van Horn and
+                   Pietro Perona},
+      title     = {Recognition in Terra Incognita},
+      booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
+                   Germany, September 8-14, 2018, Proceedings, Part {XVI}},
+      pages     = {472--489},
+      year      = {2018},
+      crossref  = {DBLP:conf/eccv/2018-16},
+      url       = {https://doi.org/10.1007/978-3-030-01270-0\_28},
+      doi       = {10.1007/978-3-030-01270-0\_28},
+      timestamp = {Mon, 08 Oct 2018 17:08:07 +0200},
+      biburl    = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18},
+      bibsource = {dblp computer science bibliography, https://dblp.org}
+    }
 ```
 </details>
 
@@ -108,13 +108,13 @@ This data set is released under the [Community Data License Agreement (permissiv
 
 Please cite this manuscript if you use this data set:
 ```bibtex
-@article{yousif2019dynamic,
-title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild},
-author={Yousif, Hayder and Kays, Roland and He, Zhihai},
-journal={IEEE Transactions on Circuits and Systems for Video Technology},
-year={2019},
-publisher={IEEE}
-}
+    @article{yousif2019dynamic,
+    title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild},
+    author={Yousif, Hayder and Kays, Roland and He, Zhihai},
+    journal={IEEE Transactions on Circuits and Systems for Video Technology},
+    year={2019},
+    publisher={IEEE}
+    }
 ```
 For questions about this data set, contact [Hayder Yousif]([email protected]).
 
@@ -122,7 +122,7 @@ For questions about this data set, contact [Hayder Yousif]([email protected].
 
 <details>
 <summary> Missouri Camera Traps </summary>
-This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames. 
+This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames.
 
 This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 
@@ -149,30 +149,28 @@ This data set is released under the [Community Data License Agreement (permissiv
 
 Please cite this manuscript if you use this data set:
 ```bibtex
-@article{tabak2019machine,
-  title={Machine learning to classify animal species in camera trap images: Applications in ecology},
-  author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others},
-  journal={Methods in Ecology and Evolution},
-  volume={10},
-  number={4},
-  pages={585--590},
-  year={2019},
-  publisher={Wiley Online Library}
-}
+    @article{tabak2019machine,
+      title={Machine learning to classify animal species in camera trap images: Applications in ecology},
+      author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others},
+      journal={Methods in Ecology and Evolution},
+      volume={10},
+      number={4},
+      pages={585--590},
+      year={2019},
+      publisher={Wiley Online Library}
+    }
 ```
-This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 
-For questions about this data set, contact [email protected].
+For questions about this data set, contact [[email protected]](northamericancameratrapimages@gmail.com).
 
 </details>
 
 <details>
 <summary> WCS Camera Traps </summary>
-This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.
 
-Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files.
+This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.
 
-You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps).
+Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps).
 
 This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
@@ -183,16 +181,16 @@ This data set contains 270,450 images from 187 camera locations in Wellington, N
 
 If you use this data set, please cite the associated manuscript:
 ```bibtex
-@article{anton2018monitoring,
-  title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science},
-  author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U},
-  journal={Journal of Urban Ecology},
-  volume={4},
-  number={1},
-  pages={juy002},
-  year={2018},
-  publisher={Oxford University Press}
-}
+    @article{anton2018monitoring,
+      title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science},
+      author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U},
+      journal={Journal of Urban Ecology},
+      volume={4},
+      number={1},
+      pages={juy002},
+      year={2018},
+      publisher={Oxford University Press}
+    }
 ```
 
 This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
@@ -237,27 +235,27 @@ This data set contains approximately 1.5 million camera trap images from Idaho.
 
 The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).
 
-Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication. 
+Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.
 </details>
 
 <details>
 <summary> Snapshot Serengeti </summary>
-This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/), the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.
+This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/) -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.
 
 Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.
 
 The images and species-level labels are described in more detail in the associated manuscript:
 
 ```bibtex
-@misc{dryad_5pt92,
-  title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},
-  author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C},
-  year = {2015},
-  journal = {Scientific Data},
-  URL = {https://doi.org/10.5061/dryad.5pt92},
-  doi = {doi:10.5061/dryad.5pt92},
-  publisher = {Dryad Digital Repository}
-}
+  @misc{dryad_5pt92,
+    title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},
+    author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C},
+    year = {2015},
+    journal = {Scientific Data},
+    URL = {https://doi.org/10.5061/dryad.5pt92},
+    doi = {doi:10.5061/dryad.5pt92},
+    publisher = {Dryad Digital Repository}
+  }
 ```
 
 For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
@@ -269,7 +267,7 @@ This data set is released under the [Community Data License Agreement (permissiv
 <summary> Snapshot Karoo </summary>
 This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.
 
-Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv). 
+Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv).
 
 For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
 
@@ -369,13 +367,13 @@ This data set contains 51 classes, predominantly mammals such as the collared pe
 The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI).
 
 If you use these data in a publication or report, please use the following citation:
-```bibtext
-@article{velez2022choosing,
-  title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence},
-  author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John},
-  journal={arXiv preprint arXiv:2202.02283},
-  year={2022}
-}
+```bibtex
+    @article{velez2022choosing,
+      title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence},
+      author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John},
+      journal={arXiv preprint arXiv:2202.02283},
+      year={2022}
+    }
 ```
 For questions about this data set, contact [Juliana Velez Gomez]([email protected]).
 
@@ -388,80 +386,13 @@ No leaderboards exist for LILA.
 
 ### Languages
 
-The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English. 
+The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English.
 
 ## Dataset Structure
 
 ### Data Instances
 
-
-Some datasets (e.g. ENA24) have bounding boxes, in which case annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format.
-
-```
-  {'id': '1',
-  'file_name': '1.jpg',
-  'width': 1920,
-  'height': 1080,
-  'annotations': {'id': ['d8e94bd2-1df9-11ea-8572-5cf370671a19'],
-   'category_id': [0],
-   'bbox': [[5.47008, 974.41704, 162.279168, 72.973008]],
-   'taxonomy': [{'kingdom': 0,
-     'phylum': 0,
-     'subphylum': 0,
-     'superclass': None,
-     'class': 1,
-     'subclass': None,
-     'infraclass': None,
-     'superorder': None,
-     'order': None,
-     'suborder': None,
-     'infraorder': None,
-     'superfamily': None,
-     'family': None,
-     'subfamily': None,
-     'tribe': None,
-     'genus': None,
-     'species': None,
-     'subspecies': None,
-     'variety': None}]},
-  'image': {'path': 'https://lilablobssc.blob.core.windows.net/ena24/images/1.jpg',
-   'bytes': None}},
-```
-
-Whereas others (e.g. NACTI) do not have bounding boxes:
-
-```
-{'id': '2010_Unit150_Ivan097_img0001.jpg',
-  'file_name': 'part0/sub000/2010_Unit150_Ivan097_img0001.jpg',
-  'width': 2048,
-  'height': 1536,
-  'study': 'CPW',
-  'location': 'San Juan Mntns, Colorado',
-  'annotations': {'id': ['78731496-3aee-11e9-9e0a-0cc47a9dc1ac'],
-   'category_id': [10],
-   'taxonomy': [{'kingdom': 0,
-     'phylum': 0,
-     'subphylum': 0,
-     'superclass': None,
-     'class': 0,
-     'subclass': 0,
-     'infraclass': 0,
-     'superorder': 0,
-     'order': 2,
-     'suborder': 0,
-     'infraorder': None,
-     'superfamily': None,
-     'family': 4,
-     'subfamily': 12,
-     'tribe': 8,
-     'genus': 26,
-     'species': 65,
-     'subspecies': None,
-     'variety': None}]},
-  'bboxes': None,
-  'image': {'path': 'https://lilablobssc.blob.core.windows.net/nacti-unzipped/part0/sub000/2010_Unit150_Ivan097_img0001.jpg',
-   'bytes': None}},
-```
+The data annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format.
 
 All of the datasets share a common category taxonomy, which is defined on the [LILA website](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).
 
@@ -472,10 +403,10 @@ Different datasets may have slightly varying fields, which include:
 `id`: image ID \
 `file_name`: the file name \
 `width` and `height`: the dimensions of the image \
-`study`: which research study the image was collected as part of \ 
+`study`: which research study the image was collected as part of \
 `location` : the name of the location at which the image was taken \
- `annotations`: information about image annotation, which includes `category_id` (the reference to the [ingLILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/), the bounding box/boxes (`bbox`/`bboxes`) if any,  as well as any other annotation information. \
- `image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`. 
+ `annotations`: information about image annotation, which includes `category_id` (the reference to the [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/), the bounding box/boxes (`bbox`/`bboxes`) if any,  as well as any other annotation information. \
+ `image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`.
 
 
 ### Data Splits
@@ -490,7 +421,7 @@ The datasets that constitute LILA have been provided by the organizations, proje
 
 ### Source Data
 
-#### Initial Data Collection and Normalization
+#### Initial data collection and normalization
 
 N/A