Qualitative FAIR Data ♻️

Welcome to the CRD Coursebook - Qualitative FAIR Data edition!! 🤓
The Coursebook is a branch of the original Circular Research Data (CDR)

Keywords: Research Data Management, Qualitative Research, Qualitative Data, FAIR, FAIR Digital Objects.

The Course 🎓

Dates: November 10 and 11 , 2022

The theme, “Circular Research Data” (CDR), is inspired by the socio-economic transition we live through, moving from a linear to a circular model acknowledging sustainability. One of the barriers to research reproducibility is precisely this “linear” mindset. A significant share of the research output produced in the last decades has followed the linear model: 1) Take the data, 2) Analyze and publish it, 3) Dispose of the data. We have the responsibility to change into a circular model.

Sometimes research involves collecting or handling qualitative data, like interviews or policy analyses. We will discuss the 6 steps of Research Data Management following the FAIR principles within the context of Open Science. You will look at real-world examples and learn the steps you need to take in your research to close the circle and achieve data sustainability following the FAIR principles of research data management.

Link to the original event page: Qualitative FAIR Data

Learning Objectives 📗

Usually, training materials on FAIR principles are tedious and contain extensive theory. We want to teach the implementation of FAIR principles differently. In this Bootcamp coursebook, you have low entry-level materials and examples that the average researcher can understand and immediately apply. Following a bold apporach, less focus on the theory and more focus on examples

• Get to know the importance of a “Research Digital Object”.
• Be able to explore the available Open-Source tools for interoperability to make Research Digital Objects sustainable.
• You will learn what “Data Terms of Use” are and “Data Access Protocols” and how to create them.
• You will review the differences and similarities of “Data Descriptions” in different science fields to discuss what we can do (as a scientific community) to standardise these practices.
• You will learn what “Rich Metadata” technically means on the Semantic Web and its relation to sustainable research output for future generations of researchers.

💡 Following a bit more bold apporach we want to teach the implementation of FAIR principles differently. Less focus on the theory and more focus on examples. Guided by the 6 steps to Circular Researh Data

Standing in the shoulder of giants ♻️

These efforts are made possible thanks to the Netherlands eScience Center Fellowship for promoting best practices and the support of Maastricht University Library.

The content has broader inspiration from the FAIR Teaching Handbok, various elements and illustrations of The Turing way. In creating this course, we take a low entry-level approach for the average researcher to learn the FAIR principles (more examples and less theory). Still, the reader can continue on this path by looking into the FAIR Cookbook or similar resources.

In-site Sessions 💻

• Day 1 - Thursday, 10 Nov 2022 - 10.00-16.00 CET

• Day 2 - Friday, 11 Nov 2022 - 10.00-16.00 CET

Cite this Coursebook

Hernández Serrano, Pedro; Vivas Romero, Maria; Library Carpentries: “Qualitative FAIR Data” Maastricht University Library, 10, 11 Nov. 2022, maastrichtu-library.github.io/qualitative-FAIR-data/