A FAIR Data Ecosystem for Science of Science.

Jian Qin, Sarah Bratt, Jeff Hemsley, Alexander Smith, Qiaoyi Liu
Author Information
  1. Jian Qin: Syracuse University, USA.
  2. Sarah Bratt: University of Arizona, USA.
  3. Jeff Hemsley: Syracuse University, USA.
  4. Alexander Smith: Syracuse University, USA.
  5. Qiaoyi Liu: Syracuse University, USA.

Abstract

This poster discusses Automated Research Workflows (ARWs) in the context of a FAIR data ecosystem for the science of science research. We offer a conceptual discussion from the point of view of information science and technology using several cases of "data problems" in the science of science research to illustrate the characteristics and expectations for designers and developers of a FAIR data ecosystem. Drawing from a 10-year data science project developing GenBank metadata workflows, we incorporate the ideas of ARWs into the FAIR data ecosystem discussion to set a broader context and increase generalizability. Researchers can use these as a guide for their data science projects to automate research workflows in the science of science domain and beyond.

Keywords

References

  1. Sci Data. 2016 Mar 15;3:160018 [PMID: 26978244]
  2. Science. 2018 Mar 2;359(6379): [PMID: 29496846]
  3. Quant Sci Stud. 2022 Winter;3(1):174-193 [PMID: 35434639]

Grants

  1. R01 GM137409/NIGMS NIH HHS

Word Cloud

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