The SCIANCE Mapping Methodology provides a structured, scalable, and evidence-based approach to analysing the European landscape of Artificial Intelligence (AI) in Science.
The SCIANCE Mapping Methodology provides a structured, scalable, and evidence-based approach to analysing the European landscape of Artificial Intelligence (AI) in Science.
The mapping approach combines semi-automated data analytics with expert validation and focuses on three interrelated dimensions: (i) key actors involved in AI-enabled scientific research, (ii) relevant research and innovation infrastructures, and (iii) emerging trends in AI adoption across scientific domains. The analysis thematically covers five scientific pilot domains—Astronomy & Physics, Materials Science, Earth Sciences, Life Sciences, and Social Sciences & Humanities—alongside cross-cutting AI research and innovations that support the scientific process.
At the core of the methodology is the OpenAIRE Graph, which serves as a trusted and interoperable backbone linking publications, datasets, software, projects, organisations, and services. A dedicated data pipeline enables systematic data collection, stratification, and enrichment, complemented by the automated detection of AI technologies in scientific texts, technology–application pairing, Technology Readiness Level estimation, and multi-dimensional trend and ecosystem analyses.
In addition, the deliverable defines the methodology for establishing the SCIANCE Good Practices Registry. Using a two-stage design—case identification and in-depth qualitative analysis based on a Context–Mechanism–Outcome framework—the registry documents empirically grounded, transferable examples of effective and responsible AI-enabled scientific practices.
Overall, the methodology supports SCIANCE’s strategic objective of strengthening Europe’s leadership in AI-enabled scientific research by underpinning the project’s Strategic Research and Innovation Agenda, infrastructure roadmap, and policy recommendations.
Disclaimer: This project deliverable has been submitted to the European Commission and is pending formal review and approval by HaDEA.
The Terms of Reference for the RAISE Secretariat pilot, hosted by the European Science Foundation, outlines the scope, roles, responsibilities and processes governing Secretariat activities in the context of the SCIANCE Coordination and Support Action.
The Strategic Advisory Board (SAB) of the SCIANCE project provides high-level guidance to ensure the scientific and strategic quality of project outputs.
The Data Management Plan for the SCIANCE project focuses on data collection, usage and sharing activities within the project. It also describes how data is protected, made accessible and available according to existing regulations and the project’s Grant Agreement.
The SCIANCE Mapping Methodology provides a structured, scalable, and evidence-based approach to analysing the European landscape of Artificial Intelligence (AI) in Science.
The Project Handbook supports the consortium partners in the effective and efficient management of the activities performed in the framework of SCIANCE project.
The SCIANCE Communication, Dissemination, and Engagement framework defines an integrated approach to building visibility, fostering stakeholder engagement, and ensuring the uptake, exploitation, and long-term impact of project results within the emerging European AI in Science ecosystem.
The Terms of Reference for the AI in Science Working Groups (AISWGs) define the objectives, structure, roles and responsibilities, and operational modalities of the working groups covering five scientific pilot areas and five cross-cutting AI-related topics.
The Commission has published an update of the ERA Living Guidelines on the responsible use of Generative AI in research – a set of recommendations offering simple and operational guidance to the research community to foster the adoption and responsible use of generative artificial intelligence (AI).