What Should Be the Data Sharing Policy of Cognitive Science?

Pitt, Mark A.; Tang, Yun

doi:10.1111/tops.12006

Cited by 23 publications

(34 citation statements)

References 30 publications

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“…Eschenfelder and Johnson [24] suggest more control for researchers over deposited data (see also [25][26][27][28][29][30][31][32]). According to some scholars, a priority right for publications, for example an embargo on data (e.g., [33]), would enable academic data sharing.…”

Section: Degree Of Controlmentioning

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: Degree Of Controlmentioning

confidence: 99%

What Drives Academic Data Sharing?

2014

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“…how about using variable X as a covariate? ), which may provide readers with more insight into the impact of published results (Pitt & Tang 2013).…”

Section: Why Open Data?mentioning

confidence: 99%

“…For instance, data can be used in courses on statistics and research methods (Whitlock 2011), as well as in the development and validation of new statistical methods (Pitt & Tang 2013).…”

Section: Why Open Data?mentioning

confidence: 99%

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Issues in Open Research Data

Moore

2014

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“…Sharing 37 data opens access to and reinforces open scientific inquiry; encourages diversity of analysis 38 and opinion; promotes new research; facilitates the education of new researchers; enables 39 the exploration of topics not envisioned by the initial investigators; permits the creation of 40 new data sets when data from multiple sources are combined; and it sets the stage for new While sharing data has obvious group benefits for the scientific community and 48 society, decisions to archive data are made by individual researchers, and it is less obvious 49 that the benefits of sharing data outweigh the costs for all individuals [Tenopir et al, 2011]. 50 Many researchers are reluctant to share their data publicly because of real or perceived 51 individual costs [Roche et al, 2014;Pitt and Tang, 2013] which probably explains why 52 sharing data is far from universal. Improving participation in sharing data will require 53 lowering costs and/or increasing benefits for primary data collectors [Smith, 2009] resulting in faulty conclusions [Atici et al, 2013], misuse [Bezuidenhout, 2013], and possible 58 infringement of the privacy of test subjects [Antman, 2014].…”

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confidence: 99%

A research data sharing game

Pronk

Wiersma

Weerden

2014

Preprint

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7While reusing research data has evident benefits for the scientific community as a whole, 8 decisions to archive and share these data are primarily made by individual researchers. For 9 individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, 10 for example time and money. In this sense the problem of data sharing resembles a typical 11 game in interactive decision theory, more commonly known as game theory. Within this 12 framework we analyse how measures to promote sharing and reuse of research data affect 13 individuals who do and do not share data. We find that the scientific community can benefit 14 from top-down policies to enhance sharing data even when the act of sharing itself implies a 15 cost. Namely, if (almost) everyone shares, many individuals receive benefits, as datasets in 16 our model can be reused to achieve a higher efficiency (i.e. more publications, higher quality 17 papers). Surprisingly, as sharing implies a cost, even sharing individuals themselves in a 18 community in which sharing is common can gain a higher efficiency than individuals who do 19 not share in a community in which sharing is not common. In addition to these findings, we 20 find that measures to ensure better data retrieval and quality can compensate for sharing 21 costs by further enabling reuse. Nevertheless, an individual researcher who decides not to 22 share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy 23 that will lead to maximisation of individual efficiency, we see the average scientific 24 community efficiency in our model steadily drop as more individuals decide not to share. 25 With this in mind, we conclude that the key to motivate the researcher to share data lies in 26 reducing the costs associated with sharing, or even better, turning it into a benefit. 27 28

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What Should Be the Data Sharing Policy of Cognitive Science?

Cited by 23 publications

References 30 publications

What Drives Academic Data Sharing?

What Drives Academic Data Sharing?

Issues in Open Research Data

A research data sharing game

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