FAIR & XAIR Data Literacy
FAIR & XAIR Data Literacy
Description
The micromodule addresses the societal challenge of insufficient FAIR data literacy and research data management (RDM) across disciplines. The FAIR principles - findability, accessibility, interoperability, and reusability - emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. In addition XIAR principles ensure data and models are inherently explainable, aligning with the urgent need for transparency and interpretability in AI applications. While illustrated through a biocatalysis use case, the primary focus lies on transferable competencies in structuring, documenting, contextualizing, and reusing research data in heterogeneous, interdisciplinary, and international environments. Students learn how emerging standards, semantic models, and open-source tools enable interoperable, machine-readable, and AI-ready (XAIR) research workflows
The Team
Teamchers
These are the teamchers you'll work with on the challenge.
Learners
- Study period
- 1 June – 28 June 2026
- Study format
- Hybrid
- Application period
- 9 March – 2 May 2026
- Credits
- 1 ECTS
- Hosting university
- Hamburg University of Technology
Information
Value and progress
Aplly FAIR/XAIR principles
At the end of the micromodule students are able to produce, describe, store, preserve and (re) use scientific data based on FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making data as open as possible, and as closed as necessary.
ESCO SKILLS
Assess and improve data quality ad reproducibility
At the end of the micromodule students are able to analyse scientific data originating from qualitative and quantitative research methods as well as support the re-use of scientific data and be familiar with open data management principles.
ESCO SKILLS
Soft skills relating to interdisciplinary and international collaboration
By the end of the Challenge learners will cultivate soft skills relating to interdisciplinary and international collaboration, critical thinking, communication, ideation, and collective creativity.
ESCO SKILLS
Digital and Research Data Management Skills
By the end of the Challenge learners will develop practical competencies in managing research data across the scientific data lifecycle. They will learn how to document, structure, store, and curate experimental datasets according to FAIR principles and prepare data for reuse in computational and collaborative research environments. Students will also gain experience using digital tools that support reproducible workflows, including version control systems and structured data formats.
ESCO SKILLS
Scientific Data Literacy in Chemical and Bioprocess Engineering
By the end of the Challenge learners will develop competencies in scientific data literacy and the critical interpretation of experimental datasets in engineering contexts. Students will explore how experimental measurements, analytical data, and model-based representations interact within chemical and bioprocess engineering workflows. They will evaluate the completeness, quality, and reproducibility of research data and understand how structured and well-documented datasets support modeling, simulation, and data-driven research.
ESCO SKILLS
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Hosting university
Hamburg University of Technology
