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Inclusion, transparency,
quality, state of the art
technology.

Our methodological approach is based on the following operational quality criteria:

  • Openness and transparency: Methodological assumptions are openly and clearly presented.
  • Coverage and accuracy: As detailed in graph.openaire.eu multiple data sources are ingested in the OpenAIRE research graph for coverage to the fullest extent possible, in order to provide meaningful indicators.
  • Clarity and replicability: We describe our construction methodology in detail, so that it can be verified and used by the scholarly communication community to create ongoing updates to our proposed statistics and indicators.
  • Readiness and timeliness: The methodology is built around well-established open databases and already tested knowledge extraction technologies - natural language processing (NLP)/machine-learning (ML) - using operational workflows in OpenAIRE to warrant timely results.
  • Trust and robustness: Our methodology also strives to be reliable, robust, and aligned to other assessment methods so that it can be operationalized, used and reused, in conjunction with other assessment methods.
The text above is modified from this report (DOI: 10.2777/268348).

Step-by-step

Starting from existing
research-related data sources
build an open, global
and trusted Research graph
we perform Statistical
Analysis
and produce
Open Science Indicators
furthermore Network
Analysis
producing
Collaboration Indicators
Often combine with external data
(patents, social, company) and perform
Impact Analysis to produce
Innovation Indicators
OpenAIRE
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OpenAIRE has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No. 777541 and 101017452
  license  Unless otherwise indicated, all materials created by OpenAIRE are licenced under CC ATTRIBUTION 4.0 INTERNATIONAL LICENSE.
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