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\begin{document}

\title{Credit for Data Reuse Is Driven by Making Data FAIR: The PARSEC Project
Approach}



\author[1]{Shelley Stall}%
\author[2]{Alison Specht}%
\author[3]{Margaret O’Brien}%
\author[4]{Anne Cambon-Thomsen}%
\author[4]{Laurence Mabile}%
\author[5]{Yasuhiro Murayama}%
\author[6]{Pedro Luiz Pizzigatti Corrêa}%
\author[7]{Helen Glaves}%
\author[8]{Helena Cousijn}%
\author[9]{Eric Olson}%
\affil[1]{American Geophysical Union}%
\affil[2]{University of Queensland}%
\affil[3]{University of California Santa Barbara}%
\affil[4]{University Paul Sabatier Toulouse III}%
\affil[5]{National Institute of Information and Communications Technology}%
\affil[6]{University of São Paulo}%
\affil[7]{British Geological Survey}%
\affil[8]{DataCite}%
\affil[9]{Center for Open Science}%


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  \date{May 24, 2024}


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\begin{abstract}
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!%
\end{abstract}%



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