Arbeitsgruppe

30.09.2026 - Workshop "AI supported Data Retrieval and Data Management in the Health Domain" ISCB GMDS Konferenz 2026

On Wednesday, September 30th, 2026 from 11:00 a.m. to 12:30 p.m., a workshop of the GMDS project group "FAIRe Dateninfrastrukturen" will take place as part of the ISCB GMDS 2026.

Abstract: This workshop is organized by the GMDS project group "FAIRe Dateninfrastrukturen für die Biomedizinische Informatik".  The workshop will introduce and discuss different approaches that develop workflows and tools which use Artifical Intelligence (AI), Machine Learning (ML) and Large Language Models (LLM) for supporting data retrieval, structuring, storing as well as sharing of health-related data and corresponding metadata. The aim is to showcase possiblities to integrate AI, ML and LLM-based methods in health-data management infrastructures and workflows.

Attendees will be provided with insights into how such methods could be applied on what kind of data with which kind of questions. Impulse lectures will highlight the creation of an AI-generated digital twin infrastructure for healthcare in Europe and how AI methods can support the enrichment of health metadata or the usage of biosignals for clinical decision support. Also data quality aspects as well as difficulties and pitfalls of these modern technologies in the context of the health domain will be addressed. An interactive panel discussion with all speakers concludes the workshop, allowing the audience to discuss the topics with the panelists.

Ziel der Arbeitsgruppe ist, die verschiedenen Aktivitäten in Projekten sowie an Forschungsstandorten im Hinblick auf FAIRe Dateninfrastrukturen zu bündeln. Hierzu soll ein gemeinsamer Erfahrungsaustausch erfolgen, sowie gemeinsam an Konzepten (z.B. Verbreitung von FAIR, Koordinierung von Entwicklungen und Guidelines, etc.) im Bereich der Biomedizinischen Informatik und der zugrunde liegenden Daten zu arbeiten.

Folgende Aspekte sind Ausgangspunkte für zu bearbeitende Handlungsfelder dieser inter­disziplinären Arbeitsgruppe:

• Gegenseitiger Erfahrungsaustausch und Austausch von Best Practices zu “FAIRen” Dateninfrastrukturen
  • Austausch bezüglich Standardisierung von Daten, Metadaten und Datenmodellen national, EU-weit und international
  • Sammeln und Bewerten der Passförmigkeit community-konsentierter Vokabularien zur Beschreibung von FAIRen Daten
  • Diskussion spezieller Aspekte (z.B. Persistent Identifier) beim Aufbau und Betrieb von “FAIRen” Dateninfrastrukturen
  • Beispiele zur Übertragbarkeit von Erfahrungen anderer Fach-Communities auf das Feld der Biomedizinischen Informatik.
• Erarbeitung eines gemeinsamen Verständnis von FAIR Data, Open Data und Open Access (6 Prinzipien), u.a. in Bezug auf unterschiedliche Datenarten sowie Datenschutzanforderungen und rechtliche Rahmenbedingungen
• Zielgruppenanalyse vornehmen und Anforderungen sammeln und kanalisieren
• Perspektivisch: Bündeln der Anforderungen aus dem FAIR data management an die Industrie (TMF, BVMI, NSG MI-I, VUD)

Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities.  The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).

In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.