
The fully open, persistent identifier-linked global index of scholarly research.
OpenAlex is a massive, open-source bibliographic index of the world’s scholarly research system, launched by the non-profit OurResearch as a direct successor to the Microsoft Academic Graph (MAG). By 2026, it has become the gold standard for 'Open Science' infrastructure, indexing over 250 million works, 90 million authors, and 100,000 institutions. Its technical architecture is built on a persistent identifier (PID) graph, linking DOIs, ORCIDs, ROR IDs, and PubMed IDs into a unified schema. OpenAlex uses advanced machine learning models for author disambiguation and automated concept tagging, allowing researchers and developers to perform complex bibliometric analysis without the restrictive licensing costs of legacy systems like Scopus or Web of Science. It operates on a 'linked data' philosophy, providing a REST API and complete data snapshots in JSON-LD format. This allows for massive-scale data mining, institutional benchmarking, and the creation of custom discovery tools. As a critical node in the AI research stack, it serves as a primary data source for training Large Language Models (LLMs) on high-quality, peer-reviewed scientific literature.
Uses a neural network-based model to group works by the same author even when names are identical or inconsistently formatted.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Applies a hierarchy of 65,000+ concepts to every work using a transformer-based classifier trained on the Wikidata taxonomy.
Native integration with the Research Organization Registry (ROR) for precise institutional affiliation tracking.
Real-time calculation of citation counts and h-index across the entire database.
Provides the entire database (~1TB+) as a series of compressed JSON-LD files on S3.
The entire metadata schema is open and public, allowing for easy ETL pipeline integration.
Operated by a non-profit (OurResearch) funded by grants and premium users.
Universities struggle to track all publications and citations of their faculty across different departments.
Registry Updated:2/7/2026
AI researchers need high-quality, structured scientific text and metadata for fine-tuning LLMs.
Funding agencies need to verify that research they funded is published in Open Access journals.