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A FAIR Data Ecosystem for Science of Science.

Jian Qin1, Sarah Bratt2, Jeff Hemsley1

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Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology
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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.

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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.