FAIR is a collection of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. This module provides a number of lessons to ensure that a researcher’s data is properly managed and published to ensure it enables reproducible research.
It is based on the lesson template used in Neurohackweek, Data Carpentry and Software Carpentry workshops.
| 09:00 | Module Overview: Data and the FAIR Principles |
Why is FAIR important?
Who is this module for? How can I get some help if I get stuck solving an exercise or answering a question? When and where are the future ReproNim training workshops? |
| 09:00 | Lesson 1: Introduction to the Web of Data |
What is a research object and how do I properly identify it?
What is linked open data? What are the FAIR Data principles? |
| 09:02 | Lesson 2: Ethics | What ethics policies and issues surround privacy, data sharing, and the use of data? |
| 09:02 | Lesson 3: Data Publishing |
Am I ready to publish my data?
What resources are available for your research data needs? |
| 09:03 | Lesson 4: Your Laboratory Datastore | What resources are available for me to be a good steward of my laboratory’s data |
| 09:03 | Lesson 5: Semantic Data Representations | How do I represent my data as linked data? |
| 09:03 | Finish |