Solving bottlenecks in data sharing in the life sciences

Dalgleish, Raymond; Molero, Eva; Kidd, Richard; Jansen, Maarten; Past, Daniela; Robl, Anika; Mons, Barend; Díaz, Carlos; Mons, Albert; Brookes, Anthony J.

doi:10.1002/humu.22123

Cited by 10 publications

(8 citation statements)

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“…The basic attitude of the references describes a lack of recognition for data donors [29,31,[48][49][50][51]. Both sources -review and surveyargue that donors do not receive enough formal recognition to justify the individual efforts and that a safeguard against uncredited use is necessary [36,[52][53][54].…”

Section: Returnsmentioning

confidence: 99%

“…Costello [34] encapsulates this issue: "If I release data, then I may be scooped by somebody else producing papers from them." Many other authors in our sample examine the issue of competitive misuse [5,22,23,46,48,[102][103][104]. Another issue regarding the recipient's use of data concerns a possible flawed interpretation (e.g., [21,59,89,105,106]).…”

Section: Adverse Usementioning

confidence: 99%

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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: Returnsmentioning

confidence: 99%

Section: Adverse Usementioning

confidence: 99%

What Drives Academic Data Sharing?

2014

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“…Data sharing is not a tradition in ecology and faces multiple sociological and technological obstacles. Overall, new practices are needed to make data sharing fully part of the culture in Life Sciences (Pauly, 1988;Reichman et al, 2011;Thessen and Patterson, 2011;Dalgleish et al, 2012;Hampton et al, 2013). The two critical stages at which practices have to be improved to allow for data sharing are the very first, i.e., the collection of the data, and the very last, i.e., their publication.…”

Section: Introductionmentioning

confidence: 98%

Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository

Colléter¹,

Valls²,

Guitton³

et al. 2015

Ecological Modelling

236

103

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“…Further, the fear of competitive misuse is an argument that can be found throughout literature on the topic (Acord and Harley, 2012;Dalgleish et al, 2012;Masum et al, 2013;Noor et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing

et al. 2015

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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.JEL Code: C81, D02, D23, L89

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Solving bottlenecks in data sharing in the life sciences

Cited by 10 publications

References 0 publications

What Drives Academic Data Sharing?

What Drives Academic Data Sharing?

Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository

A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing

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