doiget-tdm 0.1.0

A command-line application and Python library for obtaining the metadata and full-text of published journal articles for text data mining (TDM) purposes.

doiget-tdm

doiget-tdm is a command-line application and Python library for obtaining the metadata and full-text of published journal articles.

[!WARNING] This package is primarily intended for use in text data mining projects where the user has subscriptions to full-text content and has organised data exchange agreements. Acquisition for most publishers will not work without configuration - see Available publishers.

Features

• Acquire full-text of published articles, with built-in support for multiple publishers and their acquisition methods (e.g., network or local files).
• Currently supported publishers (given appropriate access and configuration):
  • American Medical Association (AMA)
  • American Psychological Association (APA)
  • Elsevier
  • Frontiers
  • IOP
  • PeerJ
  • PLoS
  • PNAS
  • Royal Society
  • Sage
  • Springer-Nature
  • Taylor & Francis
  • Wiley
• Customise acquisition and add additional publishers.
• Retrieve article metadata from Crossref, optionally using a Lightning key:value (DOI:metadata) database formed from a Crossref public data export via crossref-lmdb.

Installation

The package can be installed using pip:

pip install doiget-tdm

Quickstart

Show the default configuration settings:

doiget-tdm show-config

Download the full-text (XML) of the journal article with DOI 10.1371/journal.pbio.1002611 to the default directory:

doiget-tdm acquire '10.1371/journal.pbio.1002611'

Next, you can read through the Workflow document to understand how to use the package in a text data mining project and the Concepts document to learn more about the approach taken by doiget-tdm.

Documentation

See the documentation for detailed information about how to use doiget-tdm.