The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

Perrier, Laure; Blondal, Erik; MacDonald, Heather

doi:10.1371/journal.pone.0229182

Cited by 57 publications

(67 citation statements)

References 62 publications

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“…Aspects of researchers' experiences and attitudes about sharing research data are, relative to other aspects of open science, well-studied. A meta-synthesis of 45 qualitative studies of researchers' practices and perceptions about data sharing found that researchers lack time, resources, and skills to effectively share their data in public repositories (Perrier et al 2020). In previous research, a lack of suitable infrastructure for data sharing is commonly cited as a barrier to the availability of research data along with a lack of incentives (Science et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A Survey of Researchers’ Needs and Priorities for Data Sharing

Hrynaszkiewicz¹,

Harney²,

Cadwallader³

2021

Data Science Journal

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One of the ways in which the publisher PLOS supports open science is via a stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data.In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers' satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data.In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 617 completed responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data.

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Section: Introductionmentioning

confidence: 99%

A Survey of Researchers’ Needs and Priorities for Data Sharing

Hrynaszkiewicz¹,

Harney²,

Cadwallader³

2021

Data Science Journal

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“…Regardless, some scientific disciplines are significantly ahead in terms of data sharing, for example in genomics [ 66 ], as confirmed in the present study for molecular epidemiology in animal species. In other disciplines, barriers to making research datasets available have been explored [ 57 , 61 , 67 , 68 ] and some of the factors which can positively impact data sharing have been identified [ 69 – 71 ]. Top-down pressure, in the form of strong encouragement or policies of mandatory sharing, from funding bodies and journal publishers appears to be highly effective.…”

Section: Discussionmentioning

confidence: 99%

Systematic review of the status of veterinary epidemiological research in two species regarding the FAIR guiding principles

Meyer

Faverjon²,

Hostens

et al. 2021

BMC Vet Res

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Background The FAIR (Findable, Accessible, Interoperable, Reusable) principles were proposed in 2016 to set a path towards reusability of research datasets. In this systematic review, we assessed the FAIRness of datasets associated with peer-reviewed articles in veterinary epidemiology research published since 2017, specifically looking at salmonids and dairy cattle. We considered the differences in practices between molecular epidemiology, the branch of epidemiology using genetic sequences of pathogens and hosts to describe disease patterns, and non-molecular epidemiology. Results A total of 152 articles were included in the assessment. Consistent with previous assessments conducted in other disciplines, our results showed that most datasets used in non-molecular epidemiological studies were not available (i.e., neither findable nor accessible). Data availability was much higher for molecular epidemiology papers, in line with a strong repository base available to scientists in this discipline. The available data objects generally scored favourably for Findable, Accessible and Reusable indicators, but Interoperability was more problematic. Conclusions None of the datasets assessed in this study met all the requirements set by the FAIR principles. Interoperability, in particular, requires specific skills in data management which may not yet be broadly available in the epidemiology community. In the discussion, we present recommendations on how veterinary research could move towards greater reusability according to FAIR principles. Overall, although many initiatives to improve data access have been started in the research community, their impact on the availability of datasets underlying published articles remains unclear to date.

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“…(i) Systematic reviewers should share review data, analytic code and other materials routinely; (ii) Potential facilitators or barriers apply to the sharing of systematic review material, which we will draw from previous studies examining facilitators and barriers to adopting open science practices [41][42][43][44][45][46][47].…”

Section: Survey Contentmentioning

confidence: 99%

The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence

et al. 2021

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Background Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions. Methods The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4). Discussion The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.

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The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

Cited by 57 publications

References 62 publications

A Survey of Researchers’ Needs and Priorities for Data Sharing

A Survey of Researchers’ Needs and Priorities for Data Sharing

Systematic review of the status of veterinary epidemiological research in two species regarding the FAIR guiding principles

The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence

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