Date of Award

2-2-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Elizabeth Pierce

Abstract

Thousands of scientists, researchers, students, and organizations consume government-produced data daily that are often difficult to locate because they are collected and stored in multiples at varying levels of quality, inhibiting their usefulness for data science investigations and analysis. To address these challenges, United States Government agencies and bureaus have been implementing Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles into their data-sharing strategies since 2016. However, differing interpretations of the FAIR Data Principles lead to data that must be documented uniformly and adequately integrated for reuse. Select datasets have been analyzed with peer-reviewed FAIR assessment tools to establish a FAIR baseline. A case study was performed applying the FAIR Data Principles with the United States Department of the Interior (DOI). Delphi panels with DOI Chief Data Officers and DOI Federal Data Consumers were used to gain insights on delivering these data according to the FAIR Data Principles. User expectations from the Data Consumers were provided to Chief Data Officers in the Delphi panels, and the Chief Data Officers were also queried for their FAIR awareness and expectations. The study results will help guide Chief Data Officers in the Department of the Interior based on their expectations and understanding paired with the needs of the data consumers to better share data according to FAIR Data Principles. Analyzing datasets shared by DOI bureaus in data.gov will also provide a baseline to establish opportunities for growth and improvement.

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