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 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.