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Article Dans Une Revue CODATA Data Science Journal Année : 2022

Barriers to Full Participation in the Open Science Life Cycle among Early Career Researchers

Résumé

Open science (OS) is currently dominated by a small subset of practices that occur late in the scientific process. Early career researchers (ECRs) will play a key role in transitioning the scientific community to more widespread use of OS from pre-registration to publication, but they also face unique challenges in adopting these practices. Here, we discuss these challenges across the OS life cycle. Our essay relies on the published literature, an informal survey of 32 ECRs from 14 countries, and discussions among members of the Global Working Group on Open Science (Global Young Academy and National Young Academies). We break the OS life cycle into four stages—study design and tracking (pre-registration, open processes), data collection (citizen science, open hardware, open software, open data), publication (open access publishing, open peer review, open data), and outreach (open educational resources, citizen science)—and map potential barriers at each stage. The most frequently discussed barriers across the OS life cycle were a lack of awareness and training, prohibitively high time commitments, and restrictions and/or a lack of incentives by supervisors. We found that OS practices are highly fragmented and that awareness is particularly low for OS practices that occur during the study design and tracking stage, possibly creating ‘path-dependencies’ that reduce the likelihood of OS practices at later stages. We note that, because ECRs face unique barriers to adopting OS, there is a need for specifically targeted policies such as mandatory training at the graduate level and promotion incentives.
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Dates et versions

halshs-03927528 , version 1 (06-01-2023)

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Natasha Gownaris, Koen Vermeir, Martin-Immanuel Bittner, Lasith Gunawardena, Sandeep Kaur-Ghumaan, et al.. Barriers to Full Participation in the Open Science Life Cycle among Early Career Researchers. CODATA Data Science Journal, 2022, 21 (1), pp.2. ⟨10.5334/dsj-2022-002⟩. ⟨halshs-03927528⟩
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