G. Peng, R. Downs, C. Lacagnina, H. Ramapriyan, I. Ivánová, D. Moroni, Yaxing Wei, Larnicol Gilles, L. Wyborn, Mitchell Goldberg, J. Schulz, I. Bastrakova, A. Ganske, L. Bastin, S. Khalsa, Mingfang Wu, C. Shie, N. Ritchey, Dave Jones, T. Habermann, C. Lief, Iolanda Maggio, M. Albani, S. Stall, Lihang Zhou, M. Drévillon, Sarah M. Champion, C. Hou, F. Doblas-Reyes, K. Lehnert, E. Robinson, K. Bugbee
{"title":"Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets","authors":"G. Peng, R. Downs, C. Lacagnina, H. Ramapriyan, I. Ivánová, D. Moroni, Yaxing Wei, Larnicol Gilles, L. Wyborn, Mitchell Goldberg, J. Schulz, I. Bastrakova, A. Ganske, L. Bastin, S. Khalsa, Mingfang Wu, C. Shie, N. Ritchey, Dave Jones, T. Habermann, C. Lief, Iolanda Maggio, M. Albani, S. Stall, Lihang Zhou, M. Drévillon, Sarah M. Champion, C. Hou, F. Doblas-Reyes, K. Lehnert, E. Robinson, K. Bugbee","doi":"10.31219/osf.io/nwe5p","DOIUrl":null,"url":null,"abstract":"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 and 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, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31219/osf.io/nwe5p","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 6
Abstract
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 and 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, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.
期刊介绍:
The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.