Introduction to Research Data Management (RDM)

Contributors

Alexander Botxki	Bruna Piereck
Flora D'Anna	Laura Standaert
Rafael Buono	René Custers
Veerle Van den Eynden	Dilza Campos

About this course

This course is composed by 6 sessions that complement each other aiming to give an overview of the steps of Research Data Management (RDM) based on practical and fun activities, as much as discussions. All along we focus in practical and analytical view on the impact of this practices on writing and publishing the results of researchers in scientific journals. This course contains generalized examples and life sciences dedicated examples, but the content is easily applicable to any areas of science and research.

Material license

CC-BY

Soon suggestion on how to make the citation.

Program

Morning 1

Time	Session
9h00	No data, no paper: better to start with the end in mind
10h30	Coffee break
10h45	A closer look at the repositories world
12h30	End of the day

Morning 2

Time	Session
9h00	Planning for efficiency
10h30	Coffee break
10h45	Organising and standardising research data that underpin your Publication
12h30	End of the day

Morning 3

Time	Session
9h00	Make writing easier: Document & describe your data
10h30	Coffee break
10h45	Ethical and legal constraints on the sharing of personal data
12h30	End of the day

Complement (Links for materials and Reading)

• Extra material for trainers: List of material

• Interactive actives links for students: List of links

• Extra reading material for students: List of links

• Course presentation:

  • Latest

  • Previous courses

Learning Outcomes

Find out what you should be able to achieve after each session.

Session: No data, no paper: better to start with the end in mind

• Define what is research data management.

• Explain the meaning of FAIR.

• Differentiate FAIR and open data.

• Find information and resources about research data management.

• List the benefits of good data management for the research/er.

• List the aspects to take into account when implementing data management practices to reach FAIR data as end goal.

• Find and explain data policy and recommendations of few journals and funders.

Session: Organising and standardising research data that underpin your publication

• To implement a system to organise and structure all data and documentation files linked to a publication during and after research.

• To apply logical, structured and descriptive file names in their project.

• To implement file versioning in their project (manually).

• To use suitable data standards to make data interoperable, commonly understandable and reusable.

Session: Make writing easier: Document & describe your data

• Implement SOP (standardoperating procedure) type of approach for your daily documentation of experiments.

• Discuss the benefitis of make versioning more persistent by using github or related.

• Apply minimal metadata standards for domain-specific data.

• Describe the impact of documentation on the publication preparation

Session 5: Ethical and legal constraints on the sharing of personal data

• Recognize and discuss the main GDPR principles.

• Explain when is a dataset subject to the GDPR.

• Recognize practical consequences of the GDPR.

• Differentiate anonymous, pseudonymous and, when are data highly unique.

• Know how to protect personal data.

• Apply anonymization to publish/upload onto a repository and share human datasets.

Session : A closer look at the repositories world

• Recognize generic and discipline specific repositories.

• Explain the different access levels and access types.

• List considerations to be taken into account when sharing human data.

• Mention few domain specific and restricted access repositories.

• Verify if the data is suitable for reuse.

Session : Planning for efficiency

• Describe what a data management plan (DMP) is.

• List which areas should be covered in a DMP.

• Create a plan and select the appropriate template in DMPonline(.be)

• Describe what types of data exist.

• Recognize characteristics of data that need specific RDM measures (e.g. confidential data, large data).

LO's From previous course format : Sessions are either merged or not presented in the new format

Session : Data publication 101

• Explain what a trusted data repository is and how to find it.

• Finding trusted repositories and identifying those that are certified. Submit metadata for publication.

• Deposit metadata in a repository.

• Use a trusted generic repository to share research output.

• Apply PIDs to their own research outputs.

Session : Reusing data

• Find databases of existing data.

• Verify if the data is suitable for reuse.

• List what aspects to check to evaluate data quality.

• Explain possible ways to deal with versioning of existing data.

• Be able to cite data correctly.