Call to action for global access to and harmonization of quality information of individual Earth Science datasets
| Autor: | Gilles Larnicol, David Moroni, Carlo Lacagnina, Lucy Bastin, Dave Jones, Iolanda Maggio, Mingfang Wu, Lihang Zhou, Yaxing Wei, Marie Drévillon, Shelley Stall, Siri Jodha Singh Khalsa, Ge Peng, Nancy A. Ritchey, Jörg Schulz, Sarah M Champion, Lesley Wyborn, Ivana Ivánová, Francisco J. Doblas-Reyes, Mitch Goldberg, Irina Bastrakova, Chung-Lin Shie, Christina Lief, Mirko Albani, Erin Robinson, Kerstin Lehnert, Kaylin Bugbee, Ted Habermann, C. Sophie Hou, Robert R. Downs, Anette Ganske, Hampapuram Ramapriyan |
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| Přispěvatelé: | Barcelona Supercomputing Center |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Data Quality
Quality Dimension Earth Science Information Interoperability FAIR Stewardship Open science Science (General) 010504 meteorology & atmospheric sciences Computer science media_common.quotation_subject Earth science 01 natural sciences Internetworking (Telecommunication) Q1-390 Data Management and Stewardship Conjunts de dades Interoperabilitat en xarxes d'ordinadors Computer Science (miscellaneous) Quality (business) 0105 earth and related environmental sciences media_common End user business.industry 05 social sciences Usability Computer Science Applications Call to action Metadata Earth sciences Data quality Enginyeria agroalimentària::Ciències de la terra i de la vida [Àrees temàtiques de la UPC] Data sets 0509 other social sciences 050904 information & library sciences business |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Data Science Journal; Vol 20 (2021); 19 Data Science Journal, Vol 20, Iss 1 (2021) |
| ISSN: | 1683-1470 |
| Popis: | 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. The virtual pre-ESIP workshop held on July 13, 2020 was sponsored by ESIP and co-organized by the ESIP IQC and the BSC EQC team, in collaboration with the ARDC AU/NZ DQIG. An additional community engagement event was carried out by the AU/NZ DQIG prior to the pre-ESIP workshop. ESIP is primarily supported by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA) and the United States Geological Survey (USGS). The technological and infrastructural support during the preparation and conduct of the workshop was invaluable. In particular, we thank Megan Carter, ESIP Community Director, for supporting us throughout the workshop and providing helpful advice during the planning stage of the virtual workshop, and ESIP Community Fellow, Alexis Garretson, for supporting the ESIP SM20 report-out session. We thank all participants for attending the pre-ESIP workshop and the ESIP SM20 session and contributing to productive discussions during the live sessions and the two weeks of the ESIP SM20 period. Portions of this work have been extracted from Peng et al. (2020a), which reported on the workshop and the ESIP SM20 report-out session. The Australian participants acknowledge the support of the ARDC. The constructive suggestions from two anonymous reviewers of Data Science Journal have helped improve the quality of the paper. Peer Reviewed "Article signat per 33 autors/es: Ge Peng , Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee" |
| Databáze: | OpenAIRE |
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