Patents citations from Pubmed Scientific publications

A recently released dataset from Marx, Matt; Aaron Fuegi contains citations from USPTO patents granted 1947-2018 to articles captured by the MS Academic Graph (ID) from 1800-2018.

Files, tab-separated, are available at link : https://zenodo.org/record/3338601

The main file, pcs.tsv, contains the resolved citations matching patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct.

There is also a PubMed-specific match in pcs-pubmed.tsv.

Authors also made availabel source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif.

Scripts and programs are mainly for stata & linux (DO files and sh scripts).

 

Using Orcid for migration studies

ORCID provides a persistent digital identifier that distinguishes every  researcher and, through integration in key research workflows such as manuscript and grant submission, supports automated linkages between professional activities ensuring that one's work is recognized.

ORCID wasn't intended as a massive longitudinal survey of the global population of scientists, but with 3 million profiles and growing, it is becoming just that. So far a quarter of those researchers have voluntarily added personal information to their public ORCID profiles including the years, locations, and descriptions of their education and employment histories. As this voluntary sampling grows, the demographic and migration patterns of the scientific workforce is coming into focus. The biases are also apparent: ORCID users skew young, and certain countries are over- and underrepresented.

Bohannon J and Doran K prepared a set of tools and datasets allowing to use more easily such data:

A file contains 2.8 million public profiles from ORCID in both XML and JSON format. You will need 300 GB of free space to decompress the data and work with it.

A IPython Notebook provides code for processing the data from public_profiles.tar into manageable data files for analysis.

http://datadryad.org/resource/doi:10.5061/dryad.48s16

Another examples of usage of such data in migration studies:

https://towardsdatascience.com/analyse-the-migration-of-scientific-researchers-5184a9500615