FAIR Data Management

The FAIR data management training learning pathway teaches you how to organise, describe, and store research data according to the FAIR principles (Findable, Accessible, Interoperable, Reusable).

Licence: Creative Commons Attribution 4.0 International

Keywords: FAIR, DMP, Data management, data stewardship

Authors: Simone Leo, Luca Pireddu, Stian Soiland-Reyes, Paul De Geest, Katarzyna Kamieniecka, Krzysztof Poterlowicz

Scientific topics: Data management

Status: Active

Learning objectives:

Module 1: FAIR Data Management

  • Learn the FAIR principles
  • Recognise the relationship between FAIR and Open data
  • Learn best practices in data management
  • Learn how to introduce computational reproducibility in your research
  • Learn how to make clinical datasets FAIR
  • Recognise why FAIR datasets are important

Module 2: FAIR Pointers

  • Identify the FAIR principles and their origin.
  • Explain the difference between FAIR and open data.
  • Contextualise the main principles of FAIR around the common characteristics of identifiers, access, metadata and registration.
  • Define the term ‘metadata’.
  • Recall examples of community/domain standards that apply to data and metadata.
  • Describe why indexed data repositories are important.
  • Summarise resources enabling you to choose a searchable repository.
  • To illustrate data access in terms of the FAIR Principles using companion terms including communications protocol and - authentication.
  • To interpret the data usage licence associated with different data sets.
  • Explain the definition and importance of using identifiers.
  • Illustrate what are the persistent identifiers.
  • Give examples of the structure of persistent identifiers.

1

Module 1: FAIR Data Management

3 materials

This FAIR data management learning pathway empowers clinicians to effectively organise, document, and share patient data for research and improved care.”

Time estimation: 1 hour 20 minutes

2

Module 2: FAIR Pointers

5 materials

This learning path aims to teach you the basics FAIR data and signpost to other useful learning materials and resources. You will learn FAIR from the perspective of the 15 FAIR Principles published in 2016. You will learn about FAIR, its origins and the FAIR Principles using real examples of FAIR data in the public domain. The 15 FAIR Principles will be summarised using four encompassing characteristics: metadata, data registration, access and persistent identifiers.

Time estimation: 3 hours 20 minutes

Activity log