A funder-imposed data publication requirement seldom inspired data sharing

Couture, Jessica; Blake, Rachael E.; McDonald, Gavin; Ward, Colette L.

doi:10.1371/journal.pone.0199789

Cited by 52 publications

(49 citation statements)

References 27 publications

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“…Funding agencies, for example, might seek to streamline their efforts by requiring data to be shared and preventing costly re-collection. Funders have both incentives and enforcement mechanisms readily available (i.e., "carrots and sticks") (Couture et al, 2018;Diekema et al, 2014). Other stakeholders, including scientific journals, manage a delicate balance between incentives and enforcement.…”

Section: Introductionmentioning

confidence: 99%

Enforcing public data archiving policies in academic publishing: A study of ecology journals

2019

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To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.

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Section: Introductionmentioning

confidence: 99%

Enforcing public data archiving policies in academic publishing: A study of ecology journals

2019

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“…These researchers were affected by their funder's data sharing policy. This finding could be in contrast to [40], who found it very difficult to recover data that are required by the funder to be shared. However, this (possible) change in the sharing behaviour of researchers is too new to be assessed finally.…”

Section: Discussion Rq4: Bilateral Data Sharing Dominates Currently-imentioning

confidence: 68%

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

2020

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Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency's policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.

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“…Journals have adopted a spectrum of public data archiving (PDA) policies, ranging from the policies that data should be made "available upon request" all the way to mandated data deposition in peer-reviewed journals dedicated to open data (Sholler et al, 2019). While an "available upon request" PDA policy is better than no policy at all (Stodden et al, 2018), such datasets are often difficult to retrieve in practice as corresponding authors become unreachable or original datasets are lost (Couture et al, 2018;Stodden et al, 2018;Wicherts et al, 2006). Sharing data with published papers would remove these impediments for accessing data, with synthetic data offering a solution for when it is not possible to share the original dataset due to disclosure concerns.…”

Section: Discussionmentioning

confidence: 99%

A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis-generation

Quintana¹

2019

Preprint

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Open research data provides considerable scientific, societal, and economic benefits. However, disclosure risks can sometimes limit the sharing of open data, especially in datasets that include sensitive details or information from individuals with rare disorders. This article introduces the concept of synthetic datasets, which is an emerging method originally developed to permit the sharing of confidential census data. Synthetic datasets mimic real datasets by preserving their statistical properties and the relationships between variables. Importantly, this method also reduces disclosure risk to essentially nil as no record in the synthetic dataset represents a real individual. This practical guide with accompanying R script enables behavioural researchers to create synthetic datasets and assess their utility via the synthpop R package. By sharing synthetic datasets that mimic original datasets that could not otherwise be made open, researchers can ensure the reproducibility of their results and facilitate data exploration while maintaining participant privacy.

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A funder-imposed data publication requirement seldom inspired data sharing

Cited by 52 publications

References 27 publications

Enforcing public data archiving policies in academic publishing: A study of ecology journals

Enforcing public data archiving policies in academic publishing: A study of ecology journals

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis-generation

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