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Data management matters

delete2022-01-01
delete10
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OA
AI
C
Cerys Willoughby *
J
Jeremy G. Frey
DOI:10.1039/d1dd00046bdelete
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Abstract

Abstract

En 中文
There are a number of issues that inhibit the replication and reproduction of research, and make it hard to utilise existing scientific data to make new discoveries. These include poor data management, competing standards, a lack of consideration of the usability of data, and a disconnect between the publication of science and the data and methods behind it. In this paper, we examine the benefits of good data management for not only ensuring that data are well organised, easy to find, and preserved for the future, but also for facilitating reproducibility and new discoveries in science. We consider the importance of documenting data and making them usable by both humans and machines, and consider the development of tools to support these processes in the future. The implementation of good data management practices are essential to ensure that scientific data is findable, accessible and usable for verification and reuse.

Journal

Digital Discovery cover
Digital Discovery
IF:
5.6
Papers:
981
Citations:
1.7K

Organization

U
university of southampton
Scholars:
3.3W
Papers: 3.2W
Citations: 52
Cited Papers

Cited Papers

Native Language Experience Influences the Topography of the Mismatch Negativity to Speech
err2010-01-01
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errJason D. Zevin; Hia Datta; Urs Maurer; Kara A. Rosania; Bruce D. McCandliss
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Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases
err2021-02-16
err14
errOAAI
errMayer, Gerhard; Mueller, Wolfgang; Schork, Karin; Uszkoreit, Julian; Weidemann, Andreas; Wittig, Ulrike; Rey, Maja; Quast, Christian; Felden, Janine; Gloeckner, Frank Oliver; Lange, Matthias; Arend, Daniel; Beier, Sebastian; Junker, Astrid; Scholz, Uwe; Schueler, Danuta; Kestler, Hans A.; Wibberg, Daniel; Puehler, Alfred; Twardziok, Sven; Eils, Juergen; Eils, Roland; Hoffmann, Steve; Eisenacher, Martin; Turewicz, Michael
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Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants
err2014-09-16
err112
errOAAI
errZhu, Hao; Zhang, Jun; Kim, Marlene T.; Boison, Abena; Sedykh, Alexander; Moran, Kimberlee
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FAIRsharing as a community approach to standards, repositories and policies
err2019-04-02
err182
errOAAI
errSansone, Susanna-Assunta; McQuilton, Peter; Rocca-Serra, Philippe; Gonzalez-Beltran, Alejandra; Izzo, Massimiliano; Lister, Allyson L.; Thurston, Milo
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Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
err2011-06-10
err509
errOAAI
errSushko, Iurii; Novotarskyi, Sergii; Koerner, Robert; Pandey, Anil Kumar; Rupp, Matthias; Teetz, Wolfram; Brandmaier, Stefan; Abdelaziz, Ahmed; Prokopenko, Volodymyr V.; Tanchuk, Vsevolod Y.; Todeschini, Roberto; Varnek, Alexandre; Marcou, Gilles; Ertl, Peter; Potemkin, Vladimir; Grishina, Maria; Gasteiger, Johann; Schwab, Christof; Baskin, Igor I.; Palyulin, Vladimir A.; Radchenko, Eugene V.; Welsh, William J.; Kholodovych, Vladyslav; Chekmarev, Dmitriy; Cherkasov, Artem; Aires-de-Sousa, Joao; Zhang, Qing-You; Bender, Andreas; Nigsch, Florian; Patiny, Luc; Williams, Antony; Tkachenko, Valery; Tetko, Igor V.
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