SIG quality assurance & metrics for FAIR data
The SIG quality assurance and metrics for FAIR data provides and discusses standards, metrics and guidelines for organizing data curation based on existing best practices and the FAIR data principles. The SIG focuses on challenges and applications of quality assurance and metrics in the context of NFDI4Ing, but many issues are transferable and apply to many types of data and research data management processes. Furthermore, the methods and tools developed by NFDI4Ing for the self-organization and self-monitoring of quality and maturity of research data and data management processes require an ongoing exchange of experience between different national and international stakeholders. For this reason, close collaboration with researchers in other NFDI consortia and the larger scientific community is a major goal of the SIG.
For example, international preliminary work in the subfield of Data Management Plans (DPM) shows that researchers desire a subject-specific adaptation of DMP templates and existing tools (e.g. the Research Data Management Organiser – RDMO). Therefore, the SIG is active in the working group discipline-specific guidance for DMP of the Research Data Alliance.
key challenges & objectives
The main objectives of the group’s work are research-supported development, and the exchange of various products, models and services for quality assurance of engineering research data and processes, e.g.:
- requirements & needs for products and services to be developed;
- discussion and validation of RDM QA-tools, methods and implementation results;
- leverage synergies through cooperation of different archetypes, community clusters, other consortia, and (inter-)national partners and initiatives;
- ensuring connectivity to the overall NFDI.
Further issues for discussion and validation, e.g. in regard to the implementation and evaluation of tools include:
- identification of standard and archetype-specific data and data management processes;
- presentation of methods for self-monitoring of data and process quality;
- recommendations and practices for implementing the maturity model;
- requirements of content for engineering-specific data management planning (DMP templates and guidance for engineering-specific editing of DMPs);
- collection and prioritization of (software) development goals for RDMO, especially for interfacing with other systems in NFDI4Ing;
- clarification of institutional needs for access to RDMO;
- presentation of data and process quality metrics.
how to get into contact
We invite researchers, doctoral students, RDM-teams from universities and research institutions, quality assurance working groups and initiatives of other NFDI-consortia as well as all other parties of individuals interested in issues of quality assurance in RDM to join us.
The next meeting of the group will take place on 13th of January 2023 at 9:00 a.m.
Feel free to join our mailinglist: email@example.com!
For topic specific questions or suggestions you can also contact us directly:
Research data management maturity models:
Max Leo Wawer
Data management planning with RDMO:
DMP-templates in engineering:
FAIR data metrics: