Publish (your data) or (let the data) perish! Why not publish your data too?

Wicherts, Jelte M.; Bakker, Marjan

doi:10.1016/j.intell.2012.01.004

Cited by 70 publications

(59 citation statements)

References 18 publications

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“…It is also argued that shared data increases quality assurance and makes the review process better and that it increases the networking and the exchange with other researchers [21]. Wicherts and Bakker [77] argue that researchers who share data commit less errors and that data sharing encourages more research (see also [78]). …”

Section: Scientific Valuementioning

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: Scientific Valuementioning

confidence: 99%

What Drives Academic Data Sharing?

2014

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“…The data consist of the responses of 2,301 first-year psychology students to the Raven's Progressive Matrices (Raven, 2000) collected between 2001 and 2009 at the University of Amsterdam (Bakker & Wicherts, 2013; see also Wicherts & Bakker, 2012). From the 36 items, 10 48.…”

Section: Illustration 2: Scores On the Raven Testmentioning

confidence: 99%

Heteroscedastic Latent Trait Models for Dichotomous Data

Molenaar

2014

Psychometrika

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Effort has been devoted to account for heteroscedasticity with respect to observed or latent moderator variables in item or test scores. For instance, in the multi-group generalized linear latent trait model, it could be tested whether the observed (polychoric) covariance matrix differs across the levels of an observed moderator variable. In the case that heteroscedasticity arises across the latent trait itself, existing models commonly distinguish between heteroscedastic residuals and a skewed trait distribution. These models have valuable applications in intelligence, personality and psychopathology research. However, existing approaches are only limited to continuous and polytomous data, while dichotomous data are common in intelligence and psychopathology research. Therefore, in present paper, a heteroscedastic latent trait model is presented for dichotomous data. The model is studied in a simulation study, and applied to data pertaining alcohol use and cognitive ability.

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“…By placing data in one of these curated sites rather than on personal websites the community may be assured of its long-term accessibility. In contrast, data placed on personal websites may not last long or be properly documented (Klein et al, 2014;Krawczyk and Reuben, 2012;Vines, 2014;Wicherts & Bakker, 2012).…”

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confidence: 99%

The what, why, and how of born-open data

Rouder

2015

Behav Res

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Publish (your data) or (let the data) perish! Why not publish your data too?

Cited by 70 publications

References 18 publications

What Drives Academic Data Sharing?

What Drives Academic Data Sharing?

Heteroscedastic Latent Trait Models for Dichotomous Data

The what, why, and how of born-open data

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