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ETL processes for medical and scientific papers

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paperetl is an ETL library for processing medical and scientific papers. It supports the following sources:

  • File formats:
    • PDF
    • 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

Installation

The easiest way to install is via pip and PyPI

pip install paperetl

Python 3.7+ 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

Additional dependencies

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.

Docker

A Dockerfile with commands to install paperetl, all dependencies and scripts is available in this repository.

Clone this git repository and run the following to build and run the Docker image.

docker build -t paperetl -f docker/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. All scripts in the following examples are available in this environment.

Examples

Notebooks

Notebook Description
CORD-19 Article Entry Dates Generates CORD-19 entry-dates.csv file
CORD-19 ETL Builds an article.sqlite database for CORD-19 data

Load Articles into SQLite

The following example shows how to use paperetl to load a set of medical/scientific articles into a SQLite database.

  1. 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

  2. Build the database

    python -m paperetl.file paperetl/data paperetl/models paperetl/models

Once complete, there will be an articles.sqlite file in paperetl/models

Load CORD-19 into SQLite

The following example shows how to use paperetl to load the CORD-19 dataset into a SQLite database.

  1. 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.

  2. 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.

  3. Build database

    python -m paperetl.cord19 cord19/data cord19/models

Once complete, there will be an articles.sqlite file in cord19/models

Load into Elasticsearch

Both of the examples above also support storing data in Elasticsearch with the following changes. These examples assume Elasticsearch is running locally, change the URL to a remote server as appropriate.

Articles:

python -m paperetl.file paperetl/data http://localhost:9200 paperetl/models

CORD-19:

python -m paperetl.cord19 cord19/data http://localhost:9200

Once complete, there will be an articles index in elasticsearch with the metadata and full text stored.

Convert articles to JSON/YAML

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 paperetl/models

YAML:

python -m paperetl.file paperetl/data yaml://paperetl/yaml paperetl/models

Converted files will be stored in paperetl/(json|yaml)

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