🎉 HMC Dashboard on Open and FAIR Data in Helmholtz 2.0 is finally released!
Dashboard
Overview

Documentation: HMC FAIR Data Dashboard

Overview

The HMC FAIR Data Dashboard was developed by a team of the Helmholtz Metadata Collaboration (opens in a new tab). The dashboard presents interactive statistics about open and FAIR data publications in the Helmholtz Association. All statistics are based on the data collected by the HMC Toolbox for Data Mining.

The dashboard addresses a variety of target groups ranging from the management level, research data professionals, research data management staff, to providers of research data infrastructure and researchers who are interested in improving their FAIR data practices. The dashboard therefore includes separate subpages, providing each target group an individual view on the data.

Subpages

  • The subpage Welcome provides a high level overview about publication numbers.
  • The subpage Data in Helmholtz allows Helmholtz staff to explore where research data produced by specific Helmholtz centers is published, to estimate how much data is published every year and what the progression over time is. The filter menu allows to filter all data on this subpage for a specific research center.
  • The subpage Repositories provides useful information to repository managers who are interested in analyzing the target groups of their repository or in identifying gaps in the FAIRness of the data.
  • The subpage My data allows researchers interested in FAIR data practices to evaluate data publications and learn about how the FAIRness of research data can be improved.
  • The subpage About allows users to find useful background information about this software project.

The HMC-instance of the dashboard is accessible at https://fairdashboard.helmholtz-metadaten.de (opens in a new tab).

The source code of all project parts is available in a joint repository on GitLab (opens in a new tab).

Structure of the project

The project is divided into two parts, represented by two project repositories and two sections in this documentation, respectively:

Disclaimer

Please note that the list of data publications obtained from data harvesting using the HMC Toolbox for Data Mining, as presented in the HMC FAIR Data Dashboard is affected by method-specific biases and is neither complete nor entirely free of falsely identified data. If you wish to reuse the data shown in this dashboard for sensitive topics such as funding mechanisms, we highly recommend a manual review of the data.

We also recommend careful interpretation of evaluation-results derived from automatized FAIR assessment. The FAIR principles are a set of high-level principles and applying them depends on the specific context such as discipline-specific aspects. There are various quantitative and qualitative methods to assess the FAIRness of data (see also FAIRassist.org (opens in a new tab) but no definitive methodology (see Wilkinson et al. (opens in a new tab)). For this reason, different FAIR assessment tools can provide different scores for the same dataset. We may include alternate, complementary methodologies in future versions of this project. To illustrate the potentials of identifying systematic gaps with automated evaluation approaches, in this dashboard you can observe evaluation results obtained from F-UJI (opens in a new tab) as one selected approach. Both, the F-UJI framework and the underlying metrics (opens in a new tab) are subject of continuous development. The evaluation results can be useful in providing guidance for improving the FAIRness of data and repository infrastructure, respectively, but focus on machine-actionable aspects and are limited with respect to human-understandable and discipline-specific aspects of metadata. Evaluations results obtained from F-UJI can be useful in providing guidance for improving the FAIRness of data and repository infrastructure, respectively, but cannot truly assess how FAIR research data really is.

How to cite this work

Scientific papers

  • Kubin, M., Sedeqi, M.R., Schmidt, A., Gilein, A., Glodowski, T., Serve, V., Günther, G., Weisweiler, N.L., Preuß, G. and Mannix, O. (2024) "A Data-Driven Approach to Monitor and Improve Open and FAIR Research Data in a Federated Research Ecosystem", Data Science Journal, 23(1), p. 41. Available at: https://doi.org/10.5334/dsj-2024-041 (opens in a new tab).

Software publications

Data availability

  • A selective export of the dataset included in the dashboard is published on Zenodo (opens in a new tab) and will be updated in regular intervals.
Installation and startup