What We’re Reading — September 25, 2019

The FireOak team keeps an eye out for and shares the most interesting articles, reports, and case studies related to managing, sharing, and securing information, data, and knowledge. Here are some snippets from what we’re reading right now. This week, our read examines how to measure the FAIR principles for re-use of data.

The article we’re reading this week, from the International Journal of Digital Curation, focuses on how we can measure the findability, accessibility, interoperability, and resusability, of data.  When assessing the FAIRness of data, specifically reusability, the fitness for reuse will require the analysis of key questions and metrics. This study also identifies that there are measures that react differently when fitness is evaluated from a machine-centered or human-centered perspective.

Key Takeaways

• Metadata is essential. Creating “fitness for use frameworks” help to identify what metadata elements are being used to discover and evaluate data. 
• Increased awareness of FAIR principles is needed. It became clear that data re-users and consumers do not always know the key role metadata plays to the discovery and evaluation of data.

FAIR Principles
The following questions were presented for each of the FAIR principles in this study:

Findability:

1. How did you find the data? 
2. Did the data have a persistent identifier? 
3. Did the data have metadata? 
4. Did the metadata help you locate the data?

Accessibility:

1. How did you access the data? 
2. Was the data in an open format?
3. Was the data free?
4. Did the data have use constraints (e.g., limitations of use)?
5. Was the metadata accessible?

Interoperability:

1. Was the data in a useable format? 
2. How was the data encoded? 
3. Was the data encoded using encoding common to other data used in your research (i.e., syntactically heterogeneous; same format)? 
4. Was the data using shared controlled vocabularies, data dictionaries, and/or other common ontologies (i.e., semantically heterogeneous)? 
5. Was the data machine-actionable (e.g., to be processed without humans)?

Re-usability:

1. Were there any issues with data quality that impacted re-use of the data?
2. Did the data geographic scale used impact reuse of the data?
3. Did the coordinate systems used impact reuse of the data?
4. Did the metadata provide sufficient information for data re-use? 

Citation:
Bishop, B. W., Hank, C. (2018). Measuring FAIR principles to inform fitness for use. International Journal of Digital Curation, 13(1), 35-46. https://doi.org/10.2218/ijdc.v13i1.630

Index of What We’re Reading Posts