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A FAIR Data Ecosystem for Science of Science
Jian Qin1, Sarah Bratt2, Jeff Hemsley1
1Syracuse University, USA.
Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology
|April 8, 2024
Summary
This study introduces Automated Research Workflows (ARWs) within a FAIR data ecosystem for science of science research. It guides researchers in automating data science projects for broader applicability.
Area of Science:
- Information Science
- Computational Social Science
- Science of Science Research
Background:
- Developing robust data ecosystems is crucial for advancing science of science research.
- Current data challenges hinder efficient research processes in this domain.
- Integrating automated workflows is key to overcoming these data hurdles.
Purpose of the Study:
- To conceptually discuss Automated Research Workflows (ARWs) within a FAIR data ecosystem.
- To illustrate characteristics and expectations for FAIR data ecosystem designers and developers.
- To provide a guide for automating research workflows in science of science and beyond.
Main Methods:
- Conceptual discussion informed by information science and technology perspectives.
- Case studies of data problems in science of science research.
- Drawing from a decade-long data science project on GenBank metadata workflows.
Main Results:
- Identified key characteristics and expectations for designing FAIR data ecosystems.
- Demonstrated the integration of ARWs into the FAIR data ecosystem framework.
- Established a foundation for generalizing ARW applications across research domains.
Conclusions:
- Automated Research Workflows are essential for building effective FAIR data ecosystems.
- The proposed framework enhances the generalizability of data automation in scientific research.
- Researchers can leverage this guide to implement automated workflows in their data science projects.
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