Helmholtz Metadata Collaboration – Promoting FAIR data for Helmholtz

In 2019, the Helmholtz Association of German Research Centres launched the Helmholtz Metadata Collaboration (HMC) platform to translate global metadata concepts into standardized formats and streamline scientific practices across the Association. HMC's mission is to enhance the visibility and reusability of data within Helmholtz and beyond, while turning FAIR data into reality. Our long-term vision is to build a sustainable, distributed, and semantically enriched FAIR data space that covers all 18 autonomous Helmholtz centres and our six Helmholtz research fields Aeronautics, Space & Transport, Earth & Environment, Energy, Health, Information, and Matter. At HMC, our work is focused on three areas of activity: (1) Assessing and monitoring the state of FAIR data across Helmholtz, (2) Facilitating the connectivity of Helmholtz research data, and (3) Implementing (meta)data recommendations into implementations. Through our activities, we engage with a wide range of stakeholders (e.g., data professionals, research data infrastructures, technicians, or scientists,) to improve the consistency and interconnectivity of metadata within Helmholtz. In recent years, we have examined the state of research data management and FAIRness of data within Helmholtz through surveys and FAIR metrics. We have raised awareness about the importance of metadata through outreach events, training, and consultations. We developed the technical infrastructure needed to connect FAIR data within Helmholtz and to establish the Helmholtz FAIR data space. In collaboration with our communities, we have worked to implement aligned FAIR metadata practices and recommendations. Additionally, we have funded 28 community projects within Helmholtz to address practical metadata challenges. Our involvement in various panels and committees has connected us closely with national (e.g., NFDI) and international (e.g., EOSC, RDA) research data management initiatives. In this poster, we will share insights into our approach, present some results and activities, and discuss key lessons learned on our journey towards a Helmholtz FAIR data space. By sharing our experiences, we aim to foster active discussions about (meta)data, the relevant stakeholders, and how to jointly shape the development of a FAIR data ecosystem.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-07
DOI
https://doi.org/10.5281/zenodo.14832659
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Helmholtz Metadata Collaboration – Promoting FAIR data for Helmholtz

Zenodo (CERN European Organization for Nuclear Research)
Research Data Management Practices
article

Helmholtz Metadata Collaboration – Promoting FAIR data for Helmholtz

article en

Abstract

In 2019, the Helmholtz Association of German Research Centres launched the Helmholtz Metadata Collaboration (HMC) platform to translate global metadata concepts into standardized formats and streamline scientific practices across the Association. HMC's mission is to enhance the visibility and reusability of data within Helmholtz and beyond, while turning FAIR data into reality. Our long-term vision is to build a sustainable, distributed, and semantically enriched FAIR data space that covers all 18 autonomous Helmholtz centres and our six Helmholtz research fields Aeronautics, Space & Transport, Earth & Environment, Energy, Health, Information, and Matter. At HMC, our work is focused on three areas of activity: (1) Assessing and monitoring the state of FAIR data across Helmholtz, (2) Facilitating the connectivity of Helmholtz research data, and (3) Implementing (meta)data recommendations into implementations. Through our activities, we engage with a wide range of stakeholders (e.g., data professionals, research data infrastructures, technicians, or scientists,) to improve the consistency and interconnectivity of metadata within Helmholtz. In recent years, we have examined the state of research data management and FAIRness of data within Helmholtz through surveys and FAIR metrics. We have raised awareness about the importance of metadata through outreach events, training, and consultations. We developed the technical infrastructure needed to connect FAIR data within Helmholtz and to establish the Helmholtz FAIR data space. In collaboration with our communities, we have worked to implement aligned FAIR metadata practices and recommendations. Additionally, we have funded 28 community projects within Helmholtz to address practical metadata challenges. Our involvement in various panels and committees has connected us closely with national (e.g., NFDI) and international (e.g., EOSC, RDA) research data management initiatives. In this poster, we will share insights into our approach, present some results and activities, and discuss key lessons learned on our journey towards a Helmholtz FAIR data space. By sharing our experiences, we aim to foster active discussions about (meta)data, the relevant stakeholders, and how to jointly shape the development of a FAIR data ecosystem.

Zenodo (CERN European Organization for Nuclear Research)
Karlsruhe Institute of Technology (DE), Forschungszentrum Jülich (DE), German Cancer Research Center (DE), Helmholtz-Zentrum Berlin für Materialien und Energie (DE), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), GEOMAR Helmholtz Centre for Ocean Research Kiel (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 40%
Research Data Management Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.