• Online


  • Justine Vandendorpe (ZB MED - Information Centre for Life Sciences)
  • Julia Fürst (ZB MED - Information Centre for Life Sciences)
  • Till Sauerwein (ZB MED - Information Centre for Life Sciences)
  • Rabea Müller (ZB MED - Information Centre for Life Sciences)

March 5, 2024 - March 6, 2024

Online (Zoom Webinar) --> Registration: https://www.cecam.org/workshop-details/1349 

BioNT - BIO Network for Training - is an international consortium of academic entities and small and medium-sized enterprises (SMEs). BioNT is dedicated to providing a comprehensive training program and fostering a community for digital skills relevant to the biotechnology industry and biomedical sector. With a curriculum tailored for both beginners and advanced professionals, BioNT aims to equip individuals with the necessary expertise in handling, processing, and visualising biological data, as well as utilising computational biology tools. Leveraging the consortium's strong background in digital literacy training and extensive network of collaborations, BioNT is poised to professionalise life sciences data management, processing, and analysis skills. [Information about deNBI]

This hands-on workshop will introduce you to data management processes and activities in academia and industry. You will learn how to make your data reusable, your analyses reproducible and your processes transparent. Good data management prevents data loss and saves time, money and resources. For researchers, it also increases visibility and reputation (by ensuring the quality of research), ensures data ownership (i.e. possession and responsibility for data), and makes them eligible for funding. Good data management also helps to meet formal and legal requirements, improves teamwork and collaboration, and ensures transparency, verifiability and reproducibility.

Here we offer a two-day workshop with the primary aim of introducing participants to good enough practices for managing their data. On the first day, participants will learn about the basics of data management, good research practices, Common European Data Spaces, data management and governance in industry and data management plans. On the second day, participants will learn how to organise their data, how to make it FAIR, about electronic lab notebooks and how to make their computational results reproducible (e.g. using tools and techniques suggested by Piccolo and Frampton 2016 [1]).

This workshop is based on the FAIRsFAIR Adoption Handbook [2] and online training materials from ZB MED [3], The Carpentries [4] and Code Refinery [5]. FAIRsFAIR - Fostering Fair Data Practices in Europe aims to provide practical solutions for using the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles [6]. ZB MED - Information Centre for Life Sciences is an infrastructure and research centre for information and data in the life sciences. ZB MED aims to ensure the national provision of information and literature in the life sciences for practical applications, teaching and research. The Carpentries is a non-profit organisation that teaches software engineering and data science skills to researchers to enable them to conduct efficient, open and reproducible research. All their teaching materials are freely reusable under the Creative Commons - Attribution licence [7]. CodeRefinery provides training and infrastructure for researchers to make their research more reproducible and transparent, furthering the goals of open science and FAIR data management.

[1] Piccolo, S. R., & Frampton, M. B. (2016). Tools and techniques for computational reproducibility. In GigaScience (Vol. 5, Issue 1). Oxford University Press (OUP)https://doi.org/10.1186/s13742-016-0135-4

[2] Engelhardt, C., Biernacka, K., Coffey, A., Cornet, R., Danciu, A., Demchenko, Y., Downes, S., Erdmann, C., Garbuglia, F., Germer, K., Helbig, K., Hellström, M., Hettne, K., Hibbert, D., Jetten, M., Karimova, Y., Kryger Hansen, K., Kuusniemi, M. E., Letizia, V., … Zhou, B. (2022). D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions (V1.2.1). Zenodo, https://doi.org/10.5281/ZENODO.6674301

[3] Vandendorpe J, Lindstädt B, Shutsko A, Markus K. Online Training Workshop on Research Data Management in (Bio-)Medicine. ZB MED – Information Centre for Life Sciences; 2023,  https://repository.publisso.de/resource/frl:6452660

[4] Library Carpentry “FAIR Data and Software”, retrieved 2023-12-22, https://librarycarpentry.org/lc-fair-research/index.html

[5] CodeRefinery “Reproducible research - Preparing code to be usable by you and others in the future”, retrieved 2023-12-22, https://coderefinery.github.io/reproducible-research/

[6] Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship [Internet]. Vol. 3, Scientific Data. Springer Science and Business Media LLC; 2016, http://dx.doi.org/10.1038/sdata.2016.18

[7] Software Carpentry “About us”, retrieved 25.08.2023, https://software-carpentry.org/about/

Learning goals:

By the end of this workshop, you will be able to:

  • Know the importance of research data management in both academia and industry.
  • Know good research practices.
  • Know Common European Data Spaces concept and initiative.
  • Be aware of European policies and regulations.
  • Be aware of the enterprise data management processes and activities.
  • Be aware of the enterprise data governance policies and procedures.
  • Know the key organisational roles in data management and governance.
  • Define Data Management Plans (DMPs).
  • Articulate the purpose and benefits of DMPs for a project or organisation.
  • Be able to create a DMP.
  • Define, articulate the uses and benefits of electronic lab notebooks (ELNs).
  • Articulate the role of ELNs in data security and privacy. 
  • Be able to organise your files and folders appropriately.
  • Know the FAIR data principles.
  • Know tools and techniques to make data analysis reproducible.


Data management, reproducible science, FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles 


  • To follow the workshop more efficiently, we recommend having a two-screen setup
  • To actively communicate during the workshop, please familiarise yourself with Markdown formatting by reviewing the HedgeDoc features document

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