Date of Award

8-23-2024

Degree Type

Dissertation

Degree Name

Doctor of Professional Studies

Department

Information Management

Advisor(s)

Stephen Wallace

Keywords

Data Curation;FAIR Compliance;FAIR Principals;Metadata

Abstract

This thesis investigates the relationship between data curation and FAIR (Findable, Accessible, Interoperable, and Reusable) compliance in research data management, with a focus on the role of metadata. Through case investigations on Data.gov and Google Dataset Search platforms, the research assesses their efficacy in dataset discovery and the impact of metadata. The thesis examines how data curation influences FAIR compliance throughout the research data lifecycle and explores metadata’s role in FAIR Principles compliance for both curated traditional research datasets and open datasets. Findings reveal a disparity in FAIR compliance between different dataset types and platforms, with open datasets, particularly those on Data.gov, demonstrating higher compliance due to standardized metadata and formats. In contrast, datasets found through Google Dataset Search exhibit lower compliance levels. While metadata quality generally improves FAIR compliance across repositories, it does not resolve all related issues. The thesis highlights the limitations of heuristic-based approaches in data curation, identifying vulnerabilities such as human error and lack of robust control mechanisms. Results underscore the need for strong data policies to ensure consistent, high-quality research data management practices throughout the data lifecycle. Keywords: metadata, data curation, FAIR principles, research data management.

Access

Open Access

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