Overview

Teaching: 70 min
Exercises: 35 min

Questions

• What is FAIR?

• How does FAIR apply to me?

• Towards FAIR neuroimaging data.

• Overview of BIDS and data standardization

Objectives

• This module should provide you with the ability to work with your data in a FAIR manner

• Provide researchers with the proper information on FAIR data so that they can be submitted to the specified workflows and executions environments in a reproducible fashion

• Provide an overview of current data standards, such as BIDS, with hands on excercises

• Slides with solutions can be found in the sfn2018-training repository: https://github.com/ReproNim/sfn2018-training/tree/master/FAIR

Key Points

• There are a number of practical guidelines and best practices for ensuring data supports reproducible research

• This module is in line with our overall goal of making science (including scientific training) more open by ensuring that data is made FAIR (Findabile, Accessible, Interoperable, and Reusable).

• There are a number of tools and standards to assist in making data FAIR.

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