Alexandria3k

The alexandria3k Python package supplies a command-line tool and an API providing fast and space-efficient relational query access to several large scientific publication open data sets. Data are decompressed on the fly, thus allowing the package's use even on storage-restricted laptops. The alexandria3k package supports the following large data sets.

• Crossref (184 GiB compressed, 1.9 TiB uncompressed — as of March 2025). This contains publication metadata from all major international publishers. The Crossref data set is split into about 33 thousand files. Each file contains JSON data for 5000 publications (works). In total, Crossref contains data for 167 million works, 35 million abstracts, 465 million associated work authors, and 2.5 billion references.

• PubMed (47 GiB compressed, 707 GiB uncompressed — as of April 2025). This comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details.

• ORCID summary data set (37 GiB compressed, 651 GiB uncompressed — as of October 2024). This contains about 22 million author details records.

• DataCite (24 GiB compressed, 347 GiB uncompressed — as of 2024). 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 (12 GiB compressed, 128 GiB uncompressed — as of January 2025). This contains 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.

Databases populated with alexandria3k can be used by generative AI applications through the Model Context Protocol and its SQLite reference server. Application examples include topic modeling, snowballing, trend analysis, author disambiguation, citation graph generation, research trend analysis, patent similarity detection, grant and funding prediction, co-authorship network mapping, institutional collaboration analysis, knowledge graph augmentation, research impact prediction, academic fraud detection, technology transfer mapping, interdisciplinary research discovery, and research paper recommendations.

Installation and documentation

• 📦 The alexandria3k is available on PyPI.
• 📄 Full reference and use documentation for alexandria3k is available here.

Major contributors

• Aggelos Margkas: US patents
• Bas Verlooy: PubMed
• Evgenia Pampidi: 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