Research Data Management¶
This site is an onboarding guide to Research Data Management (RDM) for anyone interested in the topic and looking for a starting point. It is built around eight modules that move from foundational concepts to more advanced topics, and it combines general RDM knowledge with concrete tools and standards used in catalysis research, such as Voc4Cat, DCAT-AP+, Repo4Cat, PID4Cat, and Reac4Cat. No prior background in data management is assumed.
Why this matters¶
Research data is at the core of every result a research project produces, from raw measurement series to simulation output and the code used to analyze them. Funders such as the DFG and the European Commission increasingly require a Data Management Plan as a condition of funding, and good practice in data management protects a project against data loss, duplicated effort, and results that cannot be reproduced or reused once a contract or thesis ends. Making data findable, accessible, interoperable, and reusable from the outset turns these good intentions into daily practice rather than a last-minute step before publication.
Getting Started¶
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New to Research Data Management?
Work through all eight modules in order to build a complete foundation, from what research data is through to ontologies and long-term archiving.
Best for: first-time visitors with no prior RDM experience.
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Looking for something specific?
Each module is self-contained and links to related modules, so it is possible to jump straight to the topic that is currently relevant.
Best for: readers who need a refresher on one topic, such as Git or metadata standards.
Modules¶
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1. Foundations of RDM
What research data management is, why it matters, and the FAIR principles.
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2. Data Organization & Documentation
Folder structures, naming conventions, metadata standards, and electronic lab notebooks.
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3. Version Control with Git
Why version control matters, core Git workflows, and .gitignore for research data.
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4. Collaboration with GitHub & GitLab
Issues, pull requests, GitHub Pages, Zenodo DOIs, CI/CD, and licensing.
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5. Data Standards & Interoperability
Open versus proprietary formats, controlled vocabularies, and Voc4Cat.
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6. Ontologies & Semantic Technologies
RDF, OWL, SKOS, linked data, persistent identifiers, and Reac4Cat.
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7. Data Archiving & Publication
Backup strategy, choosing a repository, Repo4Cat, and data citation.
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8. Further Topics & Integration
Reproducible workflows, containerization, case studies, and further reading.