Datagraphy: toward a systematic approach to dataset discovery.

Area of Science:

• Data Science
• Research Methodology
• Information Science

Background:

• Scientific discovery increasingly relies on data, yet reusing existing datasets is hindered by fragmentation and heterogeneity.
• Current dataset discovery methods lack standardization, leading to inefficiencies and potential bias.
• There's a need for a formal methodology for dataset selection, akin to bibliographic research.

Purpose of the Study:

• Introduce datagraphy, a structured approach for systematic dataset identification and evaluation.
• Formalize dataset search as a research practice to enhance transparency, reproducibility, and collaboration.
• Address challenges in dataset discovery and reuse within the big data ecosystem.

Main Methods:

• Developed a nine-step framework to operationalize datagraphy.
• Applied the framework through a datagraphic search focused on the exposome.
• Analyzed challenges including metadata availability, repository heterogeneity, and dataset quality.

Main Results:

• Datagraphy provides a systematic foundation for identifying and synthesizing reusable datasets.
• The framework can enhance transparency, reproducibility, and efficiency at the researcher level.
• Identified key challenges in metadata, repositories, accessibility, and quality impacting datagraphy.

Conclusions:

• Datagraphy complements repository improvements by standardizing researcher-level practices.
• Integrating datagraphy with FAIR principles and technological advancements can enable automated discovery and sustainable data reuse.
• This structured methodology offers a scalable approach for FAIR-aligned, data-driven research across disciplines.