ETL processes for medical and scientific papers
paperetl is an ETL library for processing medical and scientific papers.
paperetl supports the following sources:
- File formats:
- XML (arXiv, PubMed, TEI)
- CSV
- COVID-19 Research Dataset (CORD-19)
paperetl supports the following output options for storing articles:
- SQLite
- Elasticsearch
- JSON files
- YAML files
The easiest way to install is via pip and PyPI
pip install paperetl
Python 3.9+ is supported. Using a Python virtual environment is recommended.
paperetl can also be installed directly from GitHub to access the latest, unreleased features.
pip install git+https://github.com/neuml/paperetl
PDF parsing relies on an existing GROBID instance to be up and running. It is assumed that this is running locally on the ETL server. This is only necessary for PDF files.
Note: In some cases, the GROBID engine pool can be exhausted, resulting in a 503 error. This can be fixed by increasing concurrency
and/or poolMaxWait
in the GROBID configuration file.
A Dockerfile with commands to install paperetl, all dependencies and scripts is available in this repository.
wget https://raw.githubusercontent.com/neuml/paperetl/master/docker/Dockerfile
docker build -t paperetl -f Dockerfile .
docker run --name paperetl --rm -it paperetl
This will bring up a paperetl command shell. Standard Docker commands can be used to copy files over or commands can be run directly in the shell to retrieve input content.
Notebook | Description | |
---|---|---|
Introducing paperetl | Overview of the functionality provided by paperetl |
The following example shows how to use paperetl to load a set of medical/scientific articles into a SQLite database.
-
Download the desired medical/scientific articles in a local directory. For this example, it is assumed the articles are in a directory named
paperetl/data
-
Build the database
python -m paperetl.file paperetl/data paperetl/models
Once complete, there will be an articles.sqlite file in paperetl/models
Elasticsearch is also a supported datastore as shown below. This example assumes Elasticsearch is running locally, change the URL to a remote server as appropriate.
python -m paperetl.file paperetl/data http://localhost:9200
Once complete, there will be an articles index in Elasticsearch with the metadata and full text stored.
paperetl can also be used to convert articles into JSON or YAML files. This is useful if the data is to be fed into another system or for manual inspection/debugging of a single file.
JSON:
python -m paperetl.file paperetl/data json://paperetl/json
YAML:
python -m paperetl.file paperetl/data yaml://paperetl/yaml
Converted files will be stored in paperetl/(json|yaml)
Note: The final version of CORD-19 was released on 2022-06-22. But this is still a large, valuable set of medical documents.
The following example shows how to use paperetl to load the CORD-19 dataset into a SQLite database.
-
Download and extract the dataset from Allen Institute for AI CORD-19 Release Page.
scripts/getcord19.sh cord19/data
The script above retrieves and unpacks the latest copy of CORD-19 into a directory named
cord19/data
. An optional second argument sets a specific date of the dataset in the format YYYY-MM-DD (ex. 2021-01-01) which defaults to the latest date. -
Generate entry-dates.csv for current version of the dataset
python -m paperetl.cord19.entry cord19/data
An optional second argument sets a specific date of the dataset in the format YYYY-MM-DD (ex. 2021-01-01) which defaults of the latest date. This should match the date used in Step 1.
-
Build database
python -m paperetl.cord19 cord19/data cord19/models
Once complete, there will be an articles.sqlite file in cord19/models. As with earlier examples, the data can also be loaded into Elasticsearch.
python -m paperetl.cord19 cord19/data http://localhost:9200