The sessions will explore the evolving landscape of FAIR data, addressing current limitations and highlighting opportunities created by emerging standards and improved data quality to enhance reuse and preservation.
These sessions will focus on FAIR assessment, metrics, data quality and the uptake of AI-ready FAIR research data within the EOSC Federation, aligning efforts across projects to improve FAIRness and streamline practices for digital objects.
Approach
The sessions focus on advancing the application of FAIR principles through the exploration of assessment tools, methods, and their role in ensuring the quality and preservation of digital objects. Participants will engage in hands-on activities and collaborative discussions to gain practical guidance on implementing FAIR assessments. Interactive discussions will provide practical guidance, technical insights, and avenues for collaboration, supporting the advancement of research data management practices within the EOSC Federation.
Objectives
Develop a feedback matrix to enhance EOSC resources by aligning diverse approaches, sharing knowledge, and addressing common challenges.
Support consistent FAIR assessment across projects and communities by fostering a unified and transparent framework.
Provide guidance for the implementation of FAIR assessments across projects and communities within and outside of the EOSC community through hands-on activities, and collaborative discussions.
❄︎ Contributing to the web of FAIR data and the uptake of AI
Room: TRIANA
Timeslot
Title
Format
Moderator
Rapporteur
Content
Project
9:30 -10:00
Recap of outcomes of preceding session
Talk
Mark Wilkinson
Chris Schubert
10:00-11:00
Opportunities FAIR for AI
Round Table
Daniel Garijo
Chris Schubert
FAIR data to be ready for AI-Productivity, is there a data quality benchmark for training data, can FAIR Metrics support the production chain. Are there approaches of AI for the implementation of FAIR or to check data quality? How incentives looks like to make you capture metadata to support AI, FAIR4ML, the croissant standard, model cards, etc.and the role of EOSC in it.
AI4EOSC, FAIR Impact, OSCARS, OSTrails, FAIR EASE, FAIR2ADAPT, DataGems, etc.
11:00 – 11:15
Coffee Break
11:15-12:00
Opportunities FAIR for AI
Round Table
Daniel Garijo
Chris Schubert
Can EOSC take the opportunity about standards for reporting data quality, to represent provenance and further models to improve Trust in Data Productivity?
AI4EOSC, FAIR Impact, OSCARS, OSTrails, FAIR EASE, FAIR2ADAPT, DataGems, etc.
Sufficient metadata to support discovery for AI generated data.
12:00 – 13:00
Reporting Plenary
WEDNESDAY AFTERNOON
❄︎ Ensuring research security and sovereignty ❄︎ Linking with other Common European Data Spaces and beyond
Room: TRIANA
Timeslot
Title
Format
Moderator
Rapporteur
Content
Project
14:00 -14:10
Recap of outcomes of preceding session
Daniel Garijo
Chris Schubert
14:10 – 15:30
Interoperability with other spaces, FAIR governance
Round Table
Mark Wilkinson
Chris Schubert
OS Trails and interoperability
OSTrails, Skils4EOSC (EOSC Data Spaces), etc.
Interpretations of FAIR Assesments
Governance of FAIR? Governance of assessments?
15:30 – 16:00
Coffee Break
16:00 – 17:00
Towards a network of European Data Spaces
Open Pitch Talks
Matthias Loebe
Chris Schubert
Insight of the European Health Data Space (EHDS) and other research domains
OSTrails, Skils4EOSC (EOSC Data Spaces), SciLake, Lumen, etc.
Open Discussion & Cross Domain Progress
Matthias Loebe
Chris Schubert
Data Spaces and domain specific appoaches: categories of data and data bodies. Access, provenance and quality. Data holder and execution enviroments (SP4).
17:00 – 18:00
Reporting Plenary
Sub-committee members and support team
Sub-committee members
OAEG3: Chris Schubert, Daniel Garijo Verdejo, Munazah Andrabi, Yusnelkis Guisado, Anca Hienola, Francesca Spataro, Cinzia Cappiello
TF2: Mark Wilkinson, Elli Papadopoulou, Richard Dennis
TF4: Jacques Flores, Herve L’Hours
EOSC-A Board Liaison: Klaus Tochtermann, Sara Garavelli
