Research Data Management: Welcome!

Welcome!

In this training you will learn the basics of Research Data Management (RDM). A data avalanche has now hit experimental sciences and requires new technical skills especially for scientists for which RDM is not currently part of the initial education. Furthermore, funding requirements and institutions alike are moving towards a more Open Science in which Open Data plays a key role.

This lesson should help you to understand the main concepts and good practices related to Research Data Management. Eventually, you will be able to implement these good RDM practices into your daily research work which will pave the way to a more Open Science in general.

This lesson is primarily developed for students and employees at the University of Amsterdam (UvA, The Netherlands) from the Life Sciences. While some sections might be specific to the UvA, be domain-specific (e.g. biology) or not directly applicable to your research, most of these lesson materials should be useful to a broader audience.

Finally, these RDM knowledge and practice can lead you to a new emerging profession called “Data Steward”. Combining domain-specific knowledge with skills in RDM and programming can nurture your career development.

Episode list

• 1. Introduction: the Research Life Cycle.
• 2. Plan: the Data Management Plan.
• 3. Plan: the project and file management.
• 4. Analyse: analysing data.
• 5. Analyse: version control your data and workflow.
• 6. Preserve: archiving data.
• 7. Preserve: generic data repository.
• 8. Preserve: domain-specific data repository.
• 9. Share: the metadata: data about the data.
• 10. Share: add a license to your data.
• 11. The FAIR principles.
• 12. Open Science: the big picture.

What you will learn in this lesson

1. How do you define the Research Data Life Cycle?
   • What is considered research data?
   • What are the main steps?
   • What can I do to preserve and reuse my research data?.
   • What are biological replicates and why are they important?
2. What concepts and practices are comprised in Research Data Management?
   • Definition.
   • What can I do to preserve and reuse my research data?.
   • What are biological replicates and why are they important?
3. Why should you care about Research Data Management in general?
   • Protect your valuable datasets for you, your team, other scientits and society in general.
   • Speed up your research findings by avoiding time loss when performing data-related tasks (saving, retrieving or analysing data).
   • Funders are increasingly requiring good data management practices to grant proposals.
   • Enhance your scientific reputation by showing that you embrace best practices on data management.
   • Some if not all of these good practices might become de facto standards in the future.
4. Where can you get help at your local institution?
   • At the University of Amsterdam.
   • In the Netherlands.
   • In Europe.
   • In the rest of the world.

Before you start

Before the training, please make sure you have done the following:

1. Consult what you need to do in the lesson Setup.
2. Read the workshop Code of Conduct to make sure this workshop stays welcoming for everybody.
3. Get comfortable: if you’re not in a physical workshop, be set up with two screens if possible. You will be following along on your own computer while also following this tutorial on your own.
4. Although not strictly required, a basic command of the Shell and R/RStudio will help you to follow some episodes on data archiving for instance. You can self-study the Software Carpentry Unix Shell and the R and RStudio lesson to fill these two knowledge gaps. More instructions are available on the workshop website in the Setup section.

Citation

If you make use of this material in some way (teaching, vocational training, research), please cite us: “Frederick White and Marc Galland” (eds): “Research Data Management.” Version 2020.12.

Credits

This lesson builds on many different resources from the University of Amsterdam or around the Internet. We are grateful to people active on Research Data Management and hope they can in turn build upon our own efforts.

• University of Amsterdam Research Data Management website.
• Arctic University of Norway (Tromso) Research Data Management website.
• The European Open Science Cloud catalogue of RDM training and support resources.
• The Open Science Training Handbook.
• University of Utrecht, Bioinformatics Center RDM section.