Archives des mots-clés : Lauren Cadwallader

An analysis of the effects of sharing research data, code, and preprints on citations

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Authors : Giovanni Colavizza, Lauren Cadwallader, Marcel LaFlamme, Grégory Dozot, Stéphane Lecorney, Daniel Rappo, Iain Hrynaszkiewicz

Calls to make scientific research more open have gained traction with a range of societal stakeholders. Open Science practices include but are not limited to the early sharing of results via preprints and openly sharing outputs such as data and code to make research more reproducible and extensible. Existing evidence shows that adopting Open Science practices has effects in several domains.

In this study, we investigate whether adopting one or more Open Science practices leads to significantly higher citations for an associated publication, which is one form of academic impact. We use a novel dataset known as Open Science Indicators, produced by PLOS and DataSeer, which includes all PLOS publications from 2018 to 2023 as well as a comparison group sampled from the PMC Open Access Subset. In total, we analyze circa 122’000 publications. We calculate publication and author-level citation indicators and use a broad set of control variables to isolate the effect of Open Science Indicators on received citations.

We show that Open Science practices are adopted to different degrees across scientific disciplines. We find that the early release of a publication as a preprint correlates with a significant positive citation advantage of about 20.2% on average. We also find that sharing data in an online repository correlates with a smaller yet still positive citation advantage of 4.3% on average.

However, we do not find a significant citation advantage for sharing code. Further research is needed on additional or alternative measures of impact beyond citations. Our results are likely to be of interest to researchers, as well as publishers, research funders, and policymakers.

Arxiv : https://arxiv.org/abs/2404.16171

A survey of researchers’ needs and priorities for data sharing

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Authors : Iain Hrynaszkiewicz, James Harney, Lauren Cadwallader

PLOS has long supported Open Science. One of the ways in which we do so is via our 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 728 completed and 667 partial 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.

There may however be opportunities – unmet researcher needs – in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.

DOI : https://doi.org/10.31219/osf.io/njr5u

Establishing, Developing, and Sustaining a Community of Data Champions

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Authors : James L. Savage, Lauren Cadwallader

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines.

While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit.

One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally.

This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK.

We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term.

URL : Establishing, Developing, and Sustaining a Community of Data Champions

DOI : http://doi.org/10.5334/dsj-2019-023