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Credit for Data Reuse Is Driven by Making Data FAIR: The PARSEC Project Approach
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  • Shelley Stall,
  • Alison Specht,
  • Margaret O’Brien,
  • Anne Cambon-Thomsen,
  • Laurence Mabile,
  • Yasuhiro Murayama,
  • Pedro Luiz Pizzigatti Corrêa,
  • Helen Glaves,
  • Helena Cousijn,
  • Eric Olson
Shelley Stall
American Geophysical Union

Corresponding Author:[email protected]

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Alison Specht
University of Queensland
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Margaret O’Brien
University of California Santa Barbara
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Anne Cambon-Thomsen
University Paul Sabatier Toulouse III
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Laurence Mabile
University Paul Sabatier Toulouse III
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Yasuhiro Murayama
National Institute of Information and Communications Technology
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Pedro Luiz Pizzigatti Corrêa
University of São Paulo
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Helen Glaves
British Geological Survey
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Helena Cousijn
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Eric Olson
Center for Open Science
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Research data are a vital component of the scientific record. Discovering and assessing data for possible reuse in future research is challenging. The Belmont Forum has recently awarded funds to three international teams as part of a four-year Collaborative Research Action (CRA) on Science-driven e-Infrastructure Innovation (SEI) for the Enhancement of Transnational, Interdisciplinary and Transdisciplinary Data Use to improve data management practices that will increase data reuse. One of these awardees, PARSEC, comprises two interwoven strands, one focused on improving data practices for reuse and credit, and one for synthesis science. The data specialists work alongside synthesis science researchers as they determine the influence of natural protected areas on socioeconomic outcomes for local communities. They collaborate with the researchers to better understand their motivations and work practices, and to aid them in the data-related steps that need to be taken during the research lifecycle. This will ensure their data and code are FAIR-compliant and thus enhance the likelihood of their data being reused and their analyses reproducible. The PARSEC team is working with Research Data Alliance (RDA), Earth Science Information Partners (ESIP), DataCite and ORCID to build awareness of the elements required for data creators to receive credit and automated attribution for their data contributions, and the tools that will make it easier to observe usage. Credit for data is an important incentive for researchers to make their data reusable. When data are FAIR and cited, their related publications have higher visibility. We shall discuss various ways in which we are working across the science-data interface in our multi-country and multi-disciplinary working environment to improve data (and code) reuse through better management and crediting. Make your Data FAIR, Cite your Data, Get Credit, Increase Reuse and reap the rewards!