training

Enabling reproducible research throughout its lifecycle

At 4TU.ResearchData, we recognize that reproducibility is not just about sharing results at the end of a project. Rather, it starts from the very first step of research. To ensure that data, code, and research outputs remain transparent, reusable, and well-documented, we offer training that supports best practices throughout the entire research lifecycle.

Our courses focus on foundational skills for reproducible research, helping researchers and data professionals apply best practices from data creation to analysis and publication. By integrating version control, structured programming, and command-line tools into research workflows, we enable efficient collaboration, long-term usability, and seamless sharing of scientific outputs.

gthOur training aims to strenghten four main directions:

  1. Foundational skills for reproducible research in collaboration with ongoing training at the technical universities
  2. Develop domain specific training to produce high quality research outputs in domains from Natural and Engineering sciences
  3. Knowledge about the services that the 4TU.ResearchData repository provides e.g. the API, OpenDAP server, IIIF server
  4. Data support skills as part of the RDNL coalition

Our training offerings:

1. Foundational skills for reproducible research

✅ Introduction to version control with Git – Learn to track and manage changes in research code and collaborate effectively using Git and GitHub.

✅ Introduction to good practices in programming for research (Python) – Develop clean, reusable, and well-documented code following best practices in Python.

✅ Introduction to the Unix Shell – Gain command-line skills to automate tasks and work more efficiently .

If you are interested in organizing one of those trainings at your institution (current 4TU.ResearchData member’s institutions), please approach our trainer Leila Iñigo!

2. Domain specific training to produce high quality research outputs

The value of scientific data depends not only on its scientific content but on how easily it can be found, accessed, integrated, and reused by others, whether they are human researchers or automated computational workflows. We are currently developing and piloting a course that focuses on how to create first-class research outputs using the NetCDF format and publish them through the 4TU.ResearchData repository. We aim to teach how to produce NetCDF datasets that meet these standards, datasets that are not only scientifically valuable today, but that remain accessible, interoperable, and reusable for years to come.

We are using the infrastructure of the Carpentries to develop this training and we aim that other trainers and researchers can reuse the material worldwide.

We expect to have the materials ready by the end of 2026.

3. Knowledge about the services that the 4TU.ResearchData repository provides

Since 2025, we’ve started developing workshops about how to use the API (Application Programming interface) of 4TU.ResearchData.

If you are a support staff or researcher interested on using the API of 4TU.ResearchData for a specific use case and you need help on the process, you can fill out this form, and get in contact with our trainer Leila Iñigo to discuss what are the possible options for your request.

4. Data support skills

✅ Essentials for Data Support – A worldwide course aimed at data professionals and research support staff, equipping them with the skills to assist researchers in managing, and preserving data effectively.

Through our training, we empower researchers to embed reproducibility into their daily practices, ensuring that data and software are well-managed, open, and reusable—advancing both individual research and the broader scientific community.

Training instances

Foundational skills for reproducible research

Knowledge about the services that the 4TU.ResearchData repository provides

Domain specific training

  • Interoperability for Climate and Atmospheric sciences
    Pilot stage
    Pilot workshop t.b.a

Data support skills

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