pybliometrics 3.6 on PyPI

pybliometrics

Enables large-scale access to Elsevier's Scopus API from Python.

Documentation: https://pybliometrics.readthedocs.io

Development: https://github.com/pybliometrics-dev/pybliometrics

https://readthedocs.org/projects/pybliometrics/badge/?version=stable

https://api.codeclimate.com/v1/badges/a4d7edd206a1252dfcfe/maintainability

Example

>>> # Document-specific information
>>> from pybliometrics.scopus import AbstractRetrieval
>>> ab = AbstractRetrieval("10.1016/j.softx.2019.100263")
>>> ab.title
'pybliometrics: Scriptable bibliometrics using a Python interface to Scopus'
>>> ab.publicationName
'SoftwareX'
>>> ab.authors
[Author(auid=57209617104, indexed_name='Rose M.E.', surname='Rose',
 given_name='Michael E.', affiliation='60105007'),
 Author(auid=7004212771, indexed_name='Kitchin J.R.', surname='Kitchin',
 given_name='John R.', affiliation='60027950')]
>>>
>>> # Author-specific information
>>> from pybliometrics.scopus import AuthorRetrieval
>>> au2 = AuthorRetrieval(ab.authors[1].auid)
>>> au2.h_index
34
>>> au1 = AuthorRetrieval(ab.authors[0].auid)
>>> au1.affiliation_current
[Affiliation(id=60105007, parent=None, type='parent', relationship='author',
 afdispname=None, preferred_name='Max Planck Institute for Innovation and Competition',
 parent_preferred_name=None, country_code='deu', country='Germany',
 address_part='Marstallplatz 1', city='Munich', state='Bayern',
 postal_code='80539', org_domain='ip.mpg.de', org_URL='http://www.ip.mpg.de/')]
>>>
>>> # Affiliation information
>>> from pybliometrics.scopus import AffiliationRetrieval
>>> aff1 = AffiliationRetrieval(au1.affiliation_current[0].id)
>>> aff1.author_count
98

Installation

Install the stable version from PyPI:

pip install pybliometrics

or the development version from the GitHub repository (requires git on your system):

pip install git+https://github.com/pybliometrics-dev/pybliometrics

Citation

If pybliometrics helped you getting data for research, please cite our corresponding paper:

Rose, Michael E. and John R. Kitchin: "pybliometrics: Scriptable bibliometrics using a Python interface to Scopus", SoftwareX 10 (2019) 100263.

Citing the paper helps the development of pybliometrics, because it justifies funneling resources into the development. It also signals that you obtained data from Scopus in a transparent and replicable way.