The OpenAIRE Research Graph

The OpenAIRE Research Graph is one of the largest open scholarly record collections worldwide, key in fostering Open Science and establishing its practices in the daily research activities.
Conceived as a public and transparent good, populated out of data sources trusted by scientists, the Graph aims at bringing discovery, monitoring, and assessment of science back in the hands of the scientific community.

Imagine a vast collection of research products all linked together, contextualised and openly available. For the past ten years OpenAIRE has been working to gather this valuable record. It is a massive collection of metadata and links between scientific products such as articles, datasets, software, and other research products, entities like organisations, funders, funding streams, projects, communities, and data sources.

As of today, the OpenAIRE Research Graph aggregates around 450Mi metadata records with links collecting from 10K data sources trusted by scientists, including:

After cleaning, deduplication, enrichment and full-text mining processes, the graph is analysed to produce statistics for the OpenAIRE MONITOR (, the Open Science Observatory (, made discoverable via the OpenAIRE EXPLORE ( and programmatically accessible as described at
Json dumps are also published on Zenodo.

TODO: image of high-level data model (entities and semantic relationships, we can draw here:

Graph Data Dumps

In order to facilitate users, different dumps are available. All are available under the Zenodo community called OpenAIRE Research Graph.
Here we provide detailed documentation about the full dump:

Json schema


Graph provision processes

3. Processes
  • harvesting NOT SURE WHAT IOANNA WANTS: is what we have on OK?
  • transformation NOT SURE WHAT IOANNA WANTS: is what we have on OK?
  • doiboost (not in processing, in #Aggregation business logic by major sources)
  • direct zenodo updates
  • deduplication STARTED
  • inference
  • Funder ingestion ("Harry is speaking with funders, gets the list of projects, inference rules, ...")
    For the processes we need a description of what it does, what's the input, which part of the graph it affects, and anything of importance ("if there is no input value for X, then we assume Y and assign the value Z to A")

OpenAIRE entity identifier and PID mapping policy

Aggregation business logic by major sources

2. input sources
  • repositories
  • journals
  • DOIBoost
  • * MAG
  • * Crossref
  • * Unpaywall
  • projects
  • organizations
  • openorgs
  • open citations
  • openAPC
  • etc etc

For each input source class, we need to know what's in there, the format, approximate numbers, peculiarities, important aspects of the data (e.g. "Crossref provides us with the authoritative list of publishers!"). Anything else of importance?

DOIBoost is the intersection among Crossref, Unpaywall, Microsoft Academic Graph and ORCID




The strategy for the resolution of links between publications and datasets is defined by Scholexplorer




  • OpenAIRE entity identifier & PID mapping policy (started, to be completed by Claudio and/or Michele DB)
  • Aggregation business logic by major sources:
    • Unpaywall integration
    • Crossref integration
    • ORCID integration
    • Cross cleaning actions: hostedBy patch
    • Scholexplorer business logic (relationship resolution)
    • DataCite
    • EuropePMC
    • more….
  • Deduplication business logic (started, to be completed by Michele DB)
    • For research outputs ( publications , datasets, software, orp)
    • For research organizations
  • Enrichment
    • Mining business logic
    • Deduction-based inference
    • Propagation business logic
  • Post-cleaning business logic
  • FAQ

Updated by Paolo Manghi 6 months ago · 36 revisions