Towards Effective and Rewarding Data Sharing

Gardner, Daniel; Toga, Arthur W.; Ascoli, Giorgio A.; Beatty, Jackson; Brinkley, James F.; Dale, Anders M.; Fox, Peter T.; Gardner, Esther P.; George, John; Goddard, Nigel; Harris, Kristen M.; Herskovits, Edward H.; Hines, Michael L.; Jacobs, Gwen A.; Jacobs, Russell E.; Jones, Edward G.; Kennedy, David N.; Kimberg, Daniel Y.; Mazziotta, John C.; Miller, Perry L.; Mori, Susumu; Mountain, David C.; Reiss, Allan L.; Rosen, Glenn D.; Rottenberg, David A.; Shepherd, Gordon M.; Smalheiser, Neil R.; Smith, Kenny; Strachan, Tom; Essen, David C. Van; Williams, Robert W.; Wong, Stephen T.C.

doi:10.1385/ni:1:3:289

Cited by 84 publications

(82 citation statements)

References 3 publications

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“…would prevent him or her from sharing data. Besides the actual sharing effort [21,23,29,33,[36][37][38][39][40][41], scholars utter concerns regarding the effort required to help others to make sense of the data [42]. The knowledge factor becomes apparent in Sieber's study [43] in which most researchers stated that data sharing is advantageous for science, but that they had not thought about it until they were asked for their opinion.…”

Section: Resources Neededmentioning

confidence: 99%

What Drives Academic Data Sharing?

2014

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Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher's point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

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Section: Resources Neededmentioning

confidence: 99%

What Drives Academic Data Sharing?

2014

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“…Tal vez las reticencias más severas provengan del propio investigador, al temer que se ponga en cuestión su trabajo y el tratamiento que han recibido los datos recogidos así como los resultados extraídos de los mismos (Vickers, 2006). Otro de los impedimentos es la falta de reconocimiento y protección a los investigadores que comparten datos (Borgman, 2008;Gardner, 2003).…”

Section: Consideraciones Finales: Papel De Los Profesionales De La Inunclassified

Compartir los datos de investigación en ciencia: introducción al <i>data sharing</i>

Torres‐Salinas¹,

Robinson-García²,

Cabezas-Clavijo³

2012

El Profesional de la Informacion

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“…What is important is that the database structure be accurately described, and that this description be publicly available. Once the analysis is completed and the data submitted to a central repository (e.g., fMRIDC), standard metadata conventions could be applied to the data (see, e.g., Gardner, 2003, for standards in central and peer-to-peer repositories).…”

Section: Standards Conventionsmentioning

confidence: 99%

Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing

et al. 2008

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The increasingly complex research questions addressed by neuroimaging research impose substantial demands on computational infrastructures. These infrastructures need to support management of massive amounts of data in a way that affords rapid and precise data analysis, to allow collaborative research, and to achieve these aims securely and with minimum management overhead. Here we present an approach that overcomes many current limitations in data analysis and data sharing. This approach is based on open source database management systems that support complex data queries as an integral part of data analysis, flexible data sharing, and parallel and distributed data processing using cluster computing and Grid computing resources. We assess the strengths of these approaches as compared to current frameworks based on storage of binary or text files. We then describe in detail the implementation of such a system and provide a concrete description of how it was used to enable a complex analysis of fMRI time series data.

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Towards Effective and Rewarding Data Sharing

Cited by 84 publications

References 3 publications

What Drives Academic Data Sharing?

What Drives Academic Data Sharing?

Compartir los datos de investigación en ciencia: introducción al <i>data sharing</i>

Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing

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