TY - JOUR
T1 - Call to action for global access to and harmonization of quality information of individual earth science datasets
AU - Peng, Ge
AU - Downs, Robert R.
AU - Lacagnina, Carlo
AU - Ramapriyan, Hampapuram
AU - Ivánová, Ivana
AU - Moroni, David
AU - Wei, Yaxing
AU - Larnicol, Gilles
AU - Wyborn, Lesley
AU - Goldberg, Mitch
AU - Schulz, Jörg
AU - Bastrakova, Irina
AU - Ganske, Anette
AU - Bastin, Lucy
AU - Khalsa, Siri Jodha S.
AU - Wu, Mingfang
AU - Shie, Chung Lin
AU - Ritchey, Nancy
AU - Jones, Dave
AU - Habermann, Ted
AU - Lief, Christina
AU - Maggio, Iolanda
AU - Albani, Mirko
AU - Stall, Shelley
AU - Zhou, Lihang
AU - Drévillon, Marie
AU - Champion, Sarah
AU - Hou, C. Sophie
AU - Doblas-Reyes, Francisco
AU - Lehnert, Kerstin
AU - Robinson, Erin
AU - Bugbee, Kaylin
N1 - Publisher Copyright:
© 2021 The Author(s).
PY - 2021
Y1 - 2021
N2 - Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision-and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.
AB - Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision-and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.
KW - Data Quality
KW - Earth Science Information
KW - FAIR
KW - Interoperability
KW - Quality Dimension
KW - Stewardship
UR - http://www.scopus.com/inward/record.url?scp=85106189806&partnerID=8YFLogxK
U2 - 10.5334/dsj-2021-019
DO - 10.5334/dsj-2021-019
M3 - Article
SN - 1683-1470
VL - 20
JO - Data Science Journal
JF - Data Science Journal
IS - 1
M1 - 19
ER -