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--- |
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language: |
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- en |
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license: apache-2.0 |
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--- |
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# PubMed HMPV Articles |
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_Current as of January 7, 2025_ |
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This dataset is metadata (id, publication date, title, link) from PubMed articles related to HMPV. It was created using [paperetl](https://github.com/neuml/paperetl) and the [PubMed Baseline](https://pubmed.ncbi.nlm.nih.gov/download/). |
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The 37 million articles were filtered to match either of the following criteria. |
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- MeSH code = [D029121](https://meshb-prev.nlm.nih.gov/record/ui?ui=D029121) |
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- Keyword of `HMPV` in either the `title` or `abstract` |
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## Retrieve article abstracts |
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The full article abstracts can be retrieved via the [PubMed API](https://www.nlm.nih.gov/dataguide/eutilities/utilities.html#efetch). This method accepts batches of PubMed IDs. |
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Alternatively, the dataset can be recreated using the following steps and loading the abstracts into the dataset (see step 5). |
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## Download and build |
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The following steps recreate this dataset. |
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1. Create the following directories and files |
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```bash |
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mkdir -p pubmed/config pubmed/data |
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echo "D029121" > pubmed/config/codes |
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echo "HMPV" > pubmed/config/keywords |
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``` |
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2. Install `paperetl` and download `PubMed Baseline + Updates` into `pubmed/data`. |
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```bash |
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pip install paperetl datasets |
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``` |
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3. Parse the PubMed dataset into article metadata |
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```bash |
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python -m paperetl.file pubmed/data pubmed/articles pubmed/config |
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``` |
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4. Export to dataset |
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```python |
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from datasets import Dataset |
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ds = Dataset.from_sql( |
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("SELECT id id, published published, title title, reference reference FROM articles " |
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"ORDER BY published DESC"), |
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f"sqlite:///pubmed/articles/articles.sqlite" |
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) |
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ds.to_csv(f"pubmed-hmpv/articles.csv") |
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``` |
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5. _Optional_ Export to dataset with all fields |
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paperetl parses all metadata and article abstracts. If you'd like to create a local dataset with the abstracts, run the following instead of step 4. |
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```python |
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import sqlite3 |
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import uuid |
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from datasets import Dataset |
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class Export: |
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def __init__(self, dbfile): |
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# Load database |
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self.connection = sqlite3.connect(dbfile) |
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self.connection.row_factory = sqlite3.Row |
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def __call__(self): |
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# Create cursors |
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cursor1 = self.connection.cursor() |
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cursor2 = self.connection.cursor() |
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# Get article metadata |
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cursor1.execute("SELECT * FROM articles ORDER BY id") |
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for row in cursor1: |
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# Get abstract text |
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cursor2.execute( |
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"SELECT text FROM sections WHERE article = ? and name != 'TITLE' ORDER BY id", |
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[row[0]] |
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) |
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abstract = " ".join(r["text"] for r in cursor2) |
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# Combine into single record and yield |
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row = {**row, **{"abstract": abstract}} |
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yield {k.lower(): v for k, v in row.items()} |
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def __reduce__(self): |
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return (pickle, (str(uuid.uuid4()),)) |
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def pickle(self, *args, **kwargs): |
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raise AssertionError("Generator pickling workaround") |
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# Path to database |
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export = Export("pubmed/articles/articles.sqlite") |
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ds = Dataset.from_generator(export) |
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ds = ds.sort("published", reverse=True) |
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ds.to_csv("pubmed-hmpv-full/articles.csv") |
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``` |
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