Exercises

Overview

Teaching: 0 min
Exercises: 10 min

Questions

• What makes this dataset FAIR?

Objectives

• Analyze a dataset to see if it is FAIR.

(10 min exercise)

Exercise 11: What aspect of this dataset are FAIR? (10 minutes)

At bare minimum, any dataset can probably benefit from having the below information available:

• a name/title
• its purpose or experimental hypothesis

Analyze the below dataset from HydroShare. Hydroshare: Annual soil moisture predictions across conterminous United States using remote sensing and terrain analysis across 1 km grids (1991-2016)

Use the ARDC FAIR self assessment tool

Solution

Solutions will probably contain the following:

• Findable: mostly FAIR
• Accessible: mostly FAIR
• Interoperable: mostly FAIR
• Reusable: mostly FAIR

Exercise 12: What aspect of this dataset are FAIR? (10 minutes)

Analyze the below dataset from HydroShare. Long-term, gridded standardized precipitation index for Hawai‘i

Use the ARDC FAIR self assessment tool

Solution

Solutions will probably contain the following:

• Findable: mostly FAIR
• Accessible: mostly FAIR
• Interoperable: mostly FAIR
• Reusable: mostly FAIR

Exercise 13: What aspect of this dataset are FAIR? (10 minutes)

Analyze the below dataset from HydroShare. ‘Ike Wai: Groundwater Chemistry - Nutrient Data

Use the ARDC FAIR self assessment tool

Solution

Solutions will probably contain the following:

• Findable: mostly FAIR
• Accessible: mostly FAIR
• Interoperable: mostly FAIR
• Reusable: mostly FAIR

Attribution

Content of this episode was adapted from:

• Metadata - FAIR data for climate sciences.
• Metadata - How to FAIR
• MIBBI

Key Points

• A spectrum exists for FAIR data sharing.