TY - JOUR
T1 - Data Management Plan Implementation, Assessments, and Evaluations
T2 - Implications and Recommendations
AU - Wade Bishop, Bradley
AU - Neish, Peter
AU - Kim, Ji Hyun
AU - Bats, Raphaëlle
AU - Million, A. J.
AU - Carlson, Jake
AU - Moulaison-Sandy, Heather
AU - Pham, Minh T.
N1 - Publisher Copyright:
© 2023 The Author(s).
PY - 2023
Y1 - 2023
N2 - Data management plans (DMPs) have become nearly a worldwide requirement for research funding. To meet these new funding agency expectations, information professionals across domains and the world have worked to create resources and services to successfully implement and sometimes assess DMPs. This essay presents a series of case studies from different institutions across the globe to highlight current practices and share recommendations for future work. A summary of various projects related to DMP implementation, assessment, and evaluation in different contexts provides a useful overview of current practices. The essay concludes with recommendations for practical oversight and scoring to improve DMPs’ utility in enabling the sharing of data.
AB - Data management plans (DMPs) have become nearly a worldwide requirement for research funding. To meet these new funding agency expectations, information professionals across domains and the world have worked to create resources and services to successfully implement and sometimes assess DMPs. This essay presents a series of case studies from different institutions across the globe to highlight current practices and share recommendations for future work. A summary of various projects related to DMP implementation, assessment, and evaluation in different contexts provides a useful overview of current practices. The essay concludes with recommendations for practical oversight and scoring to improve DMPs’ utility in enabling the sharing of data.
UR - http://www.scopus.com/inward/record.url?scp=85172098983&partnerID=8YFLogxK
U2 - 10.5334/dsj-2023-027
DO - 10.5334/dsj-2023-027
M3 - Article
AN - SCOPUS:85172098983
SN - 1683-1470
VL - 22
JO - Data Science Journal
JF - Data Science Journal
M1 - 27
ER -