“Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with “old” data. It is therefore not surprising that data sharing is advocated by funding agencies, journals, and researchers alike. We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole-still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation-expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others-namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.”
Archives des mots-clés : data sharing
What Drives Academic Data Sharing?
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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.”
URL : What Drives Academic Data Sharing?
DOI :10.1371/journal.pone.0118053
Incentives and motivations for sharing research data: researcher’s perspectives
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“This study, commissioned by Knowledge Exchange, has gathered evidence, examples and opinions on current and future incentives for research data sharing from the researcher’s point of view, in order to provide recommendations for policy and practice development on how best to incentivise data access and reuse. Whilst most researchers appreciate the benefits of sharing research data, on an individual basis they may be reluctant to share their own data. This study is based on qualitative interviews with 22 selected researchers of five research teams that have established data sharing cultures, in the partner countries of Knowledge Exchange: Denmark, Finland, Germany, the Netherlands and the United Kingdom. The five case studies span various academic disciplines: arts and humanities, social sciences, biomedicine, chemistry and biology.”
URL : Incentives and motivations for sharing research data: researcher’s perspectives
Alternative URL : http://knowledge-exchange.info/Default.aspx?ID=733
The Research Data Alliance: globally co-ordinated action against barriers to data publishing and sharing
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“This article discusses the drivers behind the formation of the Research Data Alliance (RDA), its current state, the lessons learned from its first full year of operation, and its anticipated impact on data publishing and sharing. One of the pressing challenges in data infrastructure (taken here to include issues relating to hardware, software and content format, as well as human actors) is how best to enable data interoperability across boundaries. This is particularly critical as the world deals with bigger and more complex problems that require data and insights from a range of disciplines. The RDA has been set up to enable more data to be shared across barriers to address these challenges. It does this through focused Working Groups and Interest Groups, formed of experts from around the world, and drawing from the academic, industry, and government sectors.”
Data Producers Courting Data Reusers: Two Cases from Modeling Communities
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“Data sharing is a difficult process for both the data producer and the data reuser. Both parties are faced with more disincentives than incentives. Data producers need to sink time and resources into adding metadata for data to be findable and usable, and there is no promise of receiving credit for this effort. Making data available also leaves data producers vulnerable to being scooped or data misuse. Data reusers also need to sink time and resources into evaluating data and trying to understand them, making collecting their own data a more attractive option. In spite of these difficulties, some data producers are looking for new ways to make data sharing and reuse a more viable option. This paper presents two cases from the surface and climate modeling communities, where researchers who produce data are reaching out to other researchers who would be interested in reusing the data. These cases are evaluated as a strategy to identify ways to overcome the challenges typically experienced by both data producers and data reusers. By working together with reusers, data producers are able to mitigate the disincentives and create incentives for sharing data. By working with data producers, data reusers are able to circumvent the hurdles that make data reuse so challenging.”
URL : Data Producers Courting Data Reusers
Alternative URL : http://www.ijdc.net/index.php/ijdc/article/view/9.1.98
Datasharing guía práctica para compartir datos de investigación…
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Datasharing: guía práctica para compartir datos de investigación :
“Asociar los datos de investigación a la publicación favorece que la comunidad científica los reutilice, pero no tiene suficientes garantías de preservación. Almacenarlos en bases de datos solventa esta contingencia y aporta visibilidad, pero en España no existen demasiados servicios de estas características. Por esta razón, se describen, evalúan y exponen los pros y contras de depósitos de datos multidisciplinares extranjeros que pueden ser de utilidad para investigadores y gestores de información: Dryad, Figshare, Zenodo y Dataverse. Todavía es pronto para escoger de forma óptima y definitiva entre una u otra aplicación, por lo que se concluye con unas recomendaciones que orienten a la comunidad de usuarios e intermediarios.”
“To associate research data to the published results favors their reuse by the scientific community, but this does not afford sufficient guarantees of preservation. To store them in databases solves this contingency and provides visibility, but in Spain there are not many services of this kind. For this reason, we describe, evaluate and discuss the pros and cons of foreign multidisciplinary data repositories that can be useful for researchers and information managers: Dryad, Figshare, Zenodo and Dataverse. It is still early to choose optimally and definitively one or the other application, so we conclude with recommendations to guide the user community and intermediaries.”
URL : http://eprints.rclis.org/20907/
The Open Access Divide This paper is…
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The Open Access Divide :
“This paper is an attempt to review various aspects of the open access divide regarding the difference between those academics who support free sharing of data and scholarly output and those academics who do not. It provides a structured description by adopting the Ws doctrines emphasizing such questions as who, what, when, where and why for information-gathering. Using measurable variables to define a common expression of the open access divide, this study collects aggregated data from existing open access as well as non-open access publications including journal articles and extensive reports. The definition of the open access divide is integrated into the discussion of scholarship on a larger scale.”
URL : http://www.mdpi.com/2304-6775/1/3/113