Data Intelligence (Jan 2023)

Building Community Consensus for Scientific Metadata with YAMZ

  • Jane Greenberg,
  • Scott McClellan,
  • Christopher Rauch,
  • Xintong Zhao,
  • Mat Kelly,
  • Yuan An,
  • John Kunze,
  • Rachel Orenstein,
  • Claire Porter,
  • Vanessa Meschke,
  • Eric Toberer

DOI
https://doi.org/10.1162/dint_a_00211
Journal volume & issue
Vol. 5, no. 1
pp. 242 – 260

Abstract

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ABSTRACTThis paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps.