richcontext-scholapi 1.1.1

Rich Context API integrations for federating discovery services and metadata exchange across multiple scholarly infrastructure providers

richcontext.scholapi

Rich Context API integrations for federating discovery services and metadata exchange across multiple scholarly infrastructure providers.

Development of the Rich Context knowledge graph uses this library to:

• identify dataset links to research publications
• locate open access publications
• reconcile journal references
• reconcile author profiles
• reconcile keyword taxonomy

This library has been guided by collaborative work on community building and metadata exchange to improve Scholarly Infrastructure, held at the 2019 Rich Context Workshop.

Installation

Prerequisites:

• Python 3.x
• Beautiful Soup
• Biopython.Entrez
• Crossref Commons
• Dimensions CLI
• Requests
• Requests-Cache
• Selenium
• xmltodict

To install from PyPi:

pip install richcontext.scholapi

If you install directly from this Git repo, be sure to install the dependencies as well:

pip install -r requirements.txt

Then copy the configuration file template rc_template.cfg to rc.cfg and populate it with your credentials.

NB: be careful not to commit the rc.cfg file in Git since by definition it will contain sensitive data, e.g., your passwords.

Parameters used in the configuration file include:

parameter	value
chrome_exe_path	path/to/chrome.exe
core_api_key	CORE API key
dimensions_password	Dimensions API password
elsevier_api_key	Elsvier API key
email	personal email address
orcid_secret	ORCID API key
repec_token	RePEc API token

Download the Chrome webdriver to enable use of Selenium (SSRN only).

For a good (although slightly dated) tutorial for installing and testing Selenium on Ubuntu Linux, see: https://christopher.su/2015/selenium-chromedriver-ubuntu/

Usage

from richcontext import scholapi as rc_scholapi

# initialize the federated API access
schol = rc_scholapi.ScholInfraAPI(config_file="rc.cfg", logger=None)
source = schol.openaire

# search parameters for example publications
title = "Deal or no deal? The prevalence and nutritional quality of price promotions among U.S. food and beverage purchases."

# run it...
meta, timing, message = source.title_search(title)

# report results
if message:
    # error case
    print(message)
else:
    print(meta)
    source.report_perf(timing)

API Integrations

APIs used to retrieve metadata:

• PubMed family

  • PubMed
  • EuropePMC

• Scholix family

  • OpenAIRE
  • Crossref
  • DataCite

• OA family

  • Unpaywall
  • dissemin
  • Semantic Scholar

• Misc.

  • RePEc
  • Dimensions
  • SSRN

See the coding examples in the test.py unit test for usage patterns per supported API.

Literature

For more background about open access publications see:

Piwowar H, Priem J, Larivière V, Alperin JP, Matthias L, Norlander B, Farley A, West J, Haustein S. 2017.
The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles
PeerJ Preprints 5:e3119v1
https://doi.org/10.7287/peerj.preprints.3119v1

Testing

First, be sure that you're testing the source and not from an installed library.

Then run unit tests on the APIs for which you have credentials:

python test.py

Contributions

If you'd like to contribute, please see our listings of good first issues.

For info about joining the AI team working on Rich Context, see https://github.com/Coleridge-Initiative/RCGraph/blob/master/SKILLS.md

Kudos

Contributors: @ceteri, @IanMulvany, @srand525, @ernestogimeno, @lobodemonte, plus many thanks for the inspiring 2019 Rich Context Workshop notes by @metasj, and guidance from @claytonrsh, @Juliaingridlane.