https://github.com/dspinellis/alexandria3k

Local relational access to openly-available publication data sets
https://github.com/dspinellis/alexandria3k

bibliometric-analysis crossref data-science orcid scientometrics

Last synced: 12 days ago
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Local relational access to openly-available publication data sets

• Host: GitHub
• URL: https://github.com/dspinellis/alexandria3k
• Owner: dspinellis
• License: gpl-3.0
• Created: 2022-10-31T11:47:38.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-09T16:33:08.000Z (about 1 month ago)
• Last Synced: 2024-10-11T08:47:30.061Z (about 1 month ago)
• Topics: bibliometric-analysis, crossref, data-science, orcid, scientometrics
• Language: Python
• Homepage:
• Size: 2.35 MB
• Stars: 81
• Watchers: 4
• Forks: 14
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
[![Alexandria3k CI](https://github.com/dspinellis/alexandria3k/actions/workflows/ci.yml/badge.svg)](https://github.com/dspinellis/alexandria3k/actions/workflows/ci.yml)

## Alexandria3k

The _alexandria3k_ package supplies a library and a command-line tool

providing efficient relational query access to the following large scientific publication

open data sets.

Data are decompressed on the fly, thus allowing the package's use even on

storage-restricted laptops.

* [Crossref](https://www.nature.com/articles/d41586-022-02926-y)

  (157 GB compressed, 1 TB uncompressed).

  This contains publication metadata from about 134 million publications from

  all major international publishers with full citation data for 60 million

  of them.

* [PubMed](https://pubmed.ncbi.nlm.nih.gov/)

  (43 GB compressed, 327 GB uncompressed).

  This comprises more than 36 million citations

  for biomedical literature from

  [MEDLINE](https://www.nlm.nih.gov/medline/medline_overview.html),

  life science journals, and online books,

  with rich domain-specific metadata,

  such as [MeSH](https://www.nlm.nih.gov/mesh/meshhome.html) indexing,

  funding, genetic, and chemical details.

* [ORCID summary data set](https://support.orcid.org/hc/en-us/articles/360006897394-How-do-I-get-the-public-data-file-)

  (25 GB compressed, 435 GB uncompressed).

  This contains about 78 million author details records.

* [DataCite](https://datacite.org/)

  (22 GB compressed, 197 GB uncompressed).

  This comprises research outputs and resources,

  such as data, pre-prints, images, and samples,

  containing about 50 million work entries.

* [United States Patent Office issued patents](https://bulkdata.uspto.gov/)

  (11 GB compressed, 115 GB uncompressed).

  This  containins about 5.4 million records.

Further supported data sets include

funder bodies,

journal names,

open access journals,

and research organizations.

The _alexandria3k_ package installation contains all elements required

to run it.

It does not require the installation, configuration, and maintenance

of a third party relational or graph database.

It can therefore be used out-of-the-box for performing reproducible

publication research on the desktop.

## Installation and documentation

The _alexandria3k_ is available on [PyPI](https://pypi.org/project/alexandria3k/).

The complete reference and use documentation for _alexandria3k_  can be found [here](https://dspinellis.github.io/alexandria3k/).

## Major contributors

* [Aggelos Margkas](https://github.com/AggelosMargkas): US patents

* [Bas Verlooy](https://github.com/BasVerlooy): PubMed

* [Evgenia Pampidi](https://github.com/evgepab): DataCite

## Publication

Details about the rationale, design, implementation, and use of this software

can be found in the following paper.

Diomidis Spinellis. Open reproducible scientometric research with Alexandria3k. _PLoS ONE_ 18(11): e0294946. November 2023. [doi: 10.1371/journal.pone.0294946](https://doi.org/10.1371/journal.pone.0294946)