Introduction to Research Data Management

Introduction to Research Data Management Seasonal School on Data Management in Biodiversity and Environmental Science 2025 NFDI4Biodiversity & HeFDI & iDiv Dr. Sabrina Jordan https://orcid.org/0000-0002-4248-6147 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 2 Unless otherwise noted, the slides of this presentation are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Third-party content remains under its original licenses or copyright and is marked on the respective slides, e. g. Slide No. 4 PHD Comics (Jorge Cham, 2007): © Jorge Cham (educational use only) or Slide No. 5 Adobe Stock (ID 229078273): Licensed to University of Kassel. Previously published Slides Some slides in this presentation are based on earlier training materials and presentations created by the author and / or co-authors. They have been partially adapted, translated, or modified for this publication and are not individually marked as reused content. Licensing information Use of AI Tools Parts of this presentation (e.g. outline development, draft formulations of learning objectives, translations, and citation formatting) were supported using OpenAI’s ChatGPT (GPT-5, 2025). All contents were reviewed, edited, and finalized by the author. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 3 Agenda Illustration: unDraw (Katerina Limpitsouni), MIT license. 1. Never have I ever 2. Data lifecycle with core elements 1. Planning 2. Collecting, processing, documenting 3. Storing and sharing 4. Archiving and publishing 5. Finding and using 3. Wrap up Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 4 Never have I ever… •Accidentally deleted data / documents •Accidentally edited multiple versions of the same file in parallel •Saved several versions with "final" in the name •Assigned file names without a system •Found files/data and couldn't assign them directly •Searched for data / files Herrema, A. (2014): FOSTER Cartoon: Data for Future Generations. FOSTER Project (EU-Funding 612425). Available at: https://web.archive.org/web/20240308101405/https://www.fosteropenscience.eu/conten t/cartoondata-future-generations, CC-BY. © Jorge Cham, PHD Comics (2007). The Data Collection Continuum. Used with permission. https://phdcomics.com/comics/archive.php?comicid=382 Munroe, R. (2015): Documents. XKCD. Available at: https://xkcd.com/1459/, CC-BY-ND Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 5 Typical timewasters •"I can't find my files." •"I don't know if I have the current version." •"I forget what was meant in my notes." •"I don't know if I'm even allowed to save / use it like that." Adobe Stock (ID: 229078273), licensed to University of Kassel. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 6 Created by S. Jordan, 2025, with Imgflip Meme Generator. Template: What do we want. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 7 Why spend (more) time on RDM? Adobe Stock (ID: 212877427), licensed to University of Kassel. •We want to… find our data again, share it, back up our results. •We need… order, documentation, legal clarity, trusted repositories. •This helps us to… save time, ensure credibility, foster collaboration. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 8 Research data management and the data life cycle For each step there are mandatory 📋 and optional 🌟elements! Research data management (RDM) includes all aspects of data administration and data processing, especially the planning of data collection procedures, the generation and processing of data, ensuring data integrity, documentation, sustainable storage, and making data available for reuse. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 9 Principles for Ensuring Good Scientific and Artistic / Design Practice at the University of Kassel •Implement the DFG Code of Conduct: •to comprehensively document collected data and results, presenting them transparently and traceably, including individual findings that do not support a hypothesis, and •to clearly identify the sources of data, organisms, materials, and software used, providing evidence for their reuse. Guideline for Handling Research Data at the University of Kassel •Supports Open Access publication of data and/or storage for usually 10 years. •A data management plan must be maintained to document data management, •and, if necessary, a record of processing activities (RPA) must be created (Art. 30 GDPR). Statute for the Central Ethics Committee of the University of Kassel •Consultation upon request after prior „Self-Assessment“. •Statements on research ethics or safety-related concerns. Doctoral regulations Where do obligations come from? Example: Framework for RDM in Kassel I →Similar in most institutions Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 16 Nagoya Protocol (Access & Benefit Sharing) 📋 •Legally binding treaty under the Convention on Biological Diversity (CBD) •Fair & equitable sharing of benefits from genetic resources & traditional knowledge •EU implementation: Regulation (EU) No. 511/2014 •Germany: Federal Agency for Nature Conservation (BfN) as authority •DFG Guidelines: due diligence, Prior Informed Consent (PIC), Mutually Agreed Terms (MAT) Practical relevance for researchers: •Check if resources fall under Nagoya •Obtain PIC & MAT if needed • Keep documentation → publications & funding proposals Convention on Biological Diversity (2011). Nagoya Protocol –Cover page. Secretariat of the CBD. Available at: https://www.cbd.int/abs/doc/protocol/nagoyaprotocol-en.pdf Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 17 Back to: Research data lifecycle Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 18 „Data management is documented by a data management plan, which is a part of every research project. It is updated regularly during the research process and sets out responsibilities, obligations, ownership/rights of use, and conventions for the practical management of data.“ -Guideline for Handling Research Data at the University of Kassel Planning RDM: Sometimes Mandatory 📋 What‘s the point? •Optimization of research data management in advance •Recognizing challenges at an early stage •Determine requirements (e.g. storage space) and react •Work efficiently and keep track of things •Fulfilment of requirements (funding organisations, publishers) What is it? •Guide for the structured handling of research the during the project and beyond written by you! Illustration: Jørgen Stamp, CC BY 2.5 DK, via Wikimedia Commons Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 19 Planning RDM Think about this at the beginning of a project: •What data is collected and how •Who is responsible for the data •Who owns the rights to the data •What are the discipline-specific standards ✓Document your considerations in a data management plan and update it as the project progresses Datenmanagementplanung für Forschungsprojekte. Praktische Tipps und Best Practices [only in German] https://www.youtube.com/watch?v=EtJKQkrXQiY Illustration: Jørgen Stamp, CC BY 2.5 DK, via Wikimedia Commons Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 20 Planning RDM https://www.uni-kassel.de/forschung/forschungsdatenmanagement/daten-managen/daten-planen https://www.forschungsdaten.info/praxis-kompakt/tools/ There are various tools: - Checklists - Sample plans - Special web applications - Guide through the process in the form of a questionnaire - Often adapted questionnaires for funders - In most cases, more or less finished DMPs can be exported 🌟 Illustration: Jørgen Stamp, CC BY 2.5 DK, via Wikimedia Commons Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 21 Example: GFBio Model DMP Screenshot from https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/W3YEEQ, accessed 03.10.2025. Citation Model DMP: Mau, Franziska; Timmermann, Britta; Astor, Tina, 2020, "GFBio Model Data Management Plan (DMP)", https://doi.org/10.25625/W3YEEQ, GRO.data, V1 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 22 Creating a DMP with GFBio DMPT With free feedback! Screenshot from: https://www.gfbio.org/plan/, accessed 03.10.2025. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 23 „Researchers document all information relevant to the production of a research result […] this also includes […] individual results that do not support the research hypothesis.” Guideline 12 Collecting, processing, documenting data 📋 “The origin of the data, organisms, materials and software used in the research process is disclosed and the reuse of data is clearly indicated; original sources are cited.” Guideline 7 Explanation “Where subject-specific recommendations exist for review and assessment, researchers create documentation in accordance with these guidelines.” Guideline 12 Screenshot of https://wissenschaftliche-integritaet.de/en/, accessed 03.10.2025 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 24 🌟Concrete structure with very(!) much room for personal judgement and highly individual. Collecting, processing, documenting data Example of documentation content: •Description of the research project; project objectives •Hypotheses •Detailed information on data collection (methods, units, time periods, locations, technology used) •Measures for data cleansing (code used for software, if applicable) •Structure of the data and their relationship to each other •Explanation of variables (names), labels and codes •Differences between different versions •Information on access and terms of use Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 25 Why documentation matters https://youtu.be/FN2RM-CHkuI?si=Cjq_FNty_Q-ihGNB Keywords: Replicability and reproducability! Replicability: same methods, new data → similar results. →Focus on repeating the experiment. Reproducibility: same data and code → identical results. →Focus on verifying the analysis. Both are key for trust and transparency in research - and good RDM supports both. Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 32 Good practices Simple versioning in the file name: Based on the "v1-0-0" version, the following will be changed: 1. the first digit if multiple cases, variables, waves, or samples have been added or deleted 2. the second digit when data is corrected so that the analysis is influenced 3. the third digit if simple revisions are made without relevance to meaning © Jorge Cham, PHD Comics (2012). notFinal.doc. Used with permission. https://phdcomics.com/comics/archive.php?comicid=1531 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 33 Good practices Folder structures for file organization Bearbeitender Choose a logical folder structure that reflects the workflow well. It should be hierarchical, but as flat as possible, no more than three sub-levels. Folder naming should be systematic, content-related and also be understandable for third parties. Screenshot: Birte Cordes Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 34 BACKUP Protection against data loss •Automatic backup of all data •and all versions •Data beeing processed •for a limited period of time •Access only for data owners Selecting data for archiving [german] Differentiation between backup, storage, publication ARCHIVING Backup for long-term storage (GAP: 10 years+) •Selected data •physical preservation •Final versions only •As FAIR as possible •Data access if necessary •Important: Archiving in repositories does not necessarily mean publication! PUBLICATION Access for external parties FAIR •Unambiguous •Invariable •quotable Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 35 Mandatory or optional? –Storage / archiving Retention obligations Mandatory •According to GWP Data, on which publications are based, are generally archived in an accessible and identifiable manner for a period of 10 years (1) ➢Selection is allowed ➢There may be reasons against retention •+Obligations by third-party funders, if applicable Optional: •Who should be able to continue working with the data? ➢Knowledge transfer in the working group ➢External partners if applicable •Can / should (parts of) the data be published? ➢Good for the publication list! ➢Data can also be reused by others 📋🌟 (1) https://wissenschaftliche-integritaet.de/en/code-of-conduct/archiving/ Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 36 Should any accusations arise, you need to be able to retrieve and comprehend the data to show that you acted correctly. Retention: fulfilling duties 📋 Exception: Data protection •If you have determined –for data protection reasons –that the data must be deleted after the end of the project, you are obligated to do so. •Be sure to provide a written justification and document it in the data management plan! •This includes data protection– compliant deletion. Where? •Requirements from third-party funders? •Is there a shared folder or storage system your group uses? ➢If not, it might be a good idea to set one up to ensure consistent access and organization. •If not, consider storing the data in institutional repositories (access restrictions can be applied) How? •With all the information needed to open and understand the data Graphic: https://undraw.co/search/attention Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 37 Slide derived from: Team Research Data Management of LUH/TIB. (2024, August 2). Managing digital research data. Zenodo. https://doi.org/10.5281/zenodo.13305129 Mandatory or optional? –Publication 🌟 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 38 Derived from: Jessica Rex. (29.03.2021). Einführung ins Forschungsdatenmanagement. Zenodo. https://doi.org/10.5281/zenodo.4644229 🌟 Access and publication Supplement to article / book Data Journal Repository ???? Overview Data Journals in Physics List of Data Journals 1. Discipline specific (e.g. https://www.hepdata.net/ ) 2. Institutional (e.g. DaKS) 3. Interdisciplinary (e.g. Zenodo) Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 39 •Preferably subject-specific! •Specifications or recommendations of the third-party funder? •DFG: https://risources.dfg.de/ •Horizon Europe: https://open-research-europe.ec.europa.eu/for-authors/data-guidelines#approvedrepositories •Recommendations / advice from NFDI consortium? (https://www.nfdi.de/konsortien/) •Directories / Search Engines: •https://www.re3data.org/ •https://v2.sherpa.ac.uk/opendoar/ •https://repositoryfinder.datacite.org/ •Ask the Research Data Service! Further information: •https://www.forschungsdaten.info/themen/veroeffentlichen-undarchivieren/repositorien/ •https://www.eresearch.uni-goettingen.de/de/knowledge-base/howto/dataand-publication-repositories/ •https://youtu.be/N8EN6HHS-PU 🌟 Finding a suitable repository 📋 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 40 Repository Focus URL GBIF (Global Biodiversity Information Facility) International infrastructure for biodiversity & species occurrence data https://www.gbif.org ENA (European Nucleotide Archive) Nucleotide sequence data (DNA/RNA) https://www.ebi.ac.uk/ena GenBank International nucleotide sequence database (part of INSDC) https://www.ncbi.nlm.nih.gov/genbank BOLD (Barcode of Life Data Systems) DNA barcoding data https://www.boldsystems.org TRY International plant trait database https://www.try -db.org MorphoBank Morphological data, esp. for phylogenetic research https://morphobank.org Edaphobase Soil organism data (operated by Senckenberg) https://portal.edaphobase.org Biodiversity-specific repositories Repository Focus URL PANGAEA Data publisher for Earth & environmental sciences https://www.pangaea.de Dryad Open access repository for research data, biology & medicine https://datadryad.org Zenodo General-purpose repository (all disciplines, incl. software) https://zenodo.org Generic repositories List after: https://www.senckenberg.de/de/ueber-uns/strategische-leitlinien/leitfaden-forschungsdaten/, accessed 26.09.2025 Introduction to RDM | Seasonal School 2025 | 06.10.2025 | Seite 41 Find and use Various approaches: −Directly in repositories −Data are cited or presented as supplementary material in publications −By means of so-called metasearch engines or harvester −Base −B2find (EUDAT) −gesisDataSearch −Datacite metadata-search −Google Dataset Search −NFDI4Ing Data Collections Explorer 🌟 📋