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Open Data: Nice People Can'T Share!

Authors: Green, Aurelia; McClung, Jennifer S; Nichola, Raihani; Bshary, Redouan; Roche, Dominique;

Open Data: Nice People Can'T Share!

Abstract

Poster for FORCE 2018 meeting in Montreal. One of the most meaningful ways by which scientists can engage with other scientists and with the broader community is by sharing one of their most treasured possessions: data. Indeed, publishers and funders increasingly acknowledge the importance of data as a scientific output. As such, many journals and funders now require that scientists make their data openly accessible, both to promote transparency and to accelerate the pace of scientific discovery. To date, journal policies that mandate data sharing have successfully increased the accessibility to datasets underlying scientific publications. However, is the quality of these data sufficient to allow reuse and reanalysis? Even when journals mandate data sharing, our survey of the ecological and evolutionary literature found that 56% of open datasets were incomplete, and 64% were shared in a way that partially or entirely prevented reuse. Given the highly competitive nature of academia, authors might be wary of openly sharing their data for fear of criticism or of others benefiting from their work at their expense. As such, ‘scholarly altruism’ is often cited as a key reason for why some authors are willing to share high quality data that are complete and readily reusable. Our latest (unpublished) work tests this hypothesis by assessing how researchers’ psychological motivations and level of cooperation in real-world situations relate to the quality of their shared datasets.

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Keywords

data sharing, open science, evolution, open data, ecology

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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