Biomedical supervisors’ role modeling of open science practices

AuthorsTamarinde L Haven, Susan Abunijela, Nicole Hildebrand

Supervision is one important way to socialize Ph.D. candidates into open and responsible research. We hypothesized that one should be more likely to identify open science practices (here publishing open access and sharing data) in empirical publications that were part of a Ph.D. thesis when the Ph.D. candidates’ supervisors engaged in these practices compared to those whose supervisors did not or less often did.

Departing from thesis repositories at four Dutch University Medical centers, we included 211 pairs of supervisors and Ph.D. candidates, resulting in a sample of 2062 publications. We determined open access status using UnpaywallR and Open Data using Oddpub, where we also manually screened publications with potential open data statements. Eighty-three percent of our sample was published openly, and 9% had open data statements.

Having a supervisor who published open access more often than the national average was associated with an odds of 1.99 to publish open access. However, this effect became nonsignificant when correcting for institutions. Having a supervisor who shared data was associated with 2.22 (CI:1.19–4.12) times the odds to share data compared to having a supervisor that did not.

This odds ratio increased to 4.6 (CI:1.86–11.35) after removing false positives. The prevalence of open data in our sample was comparable to international studies; open access rates were higher. Whilst Ph.D. candidates spearhead initiatives to promote open science, this study adds value by investigating the role of supervisors in promoting open science.

URL : Biomedical supervisors’ role modeling of open science practices

DOI : https://doi.org/10.7554/eLife.83484

“We Share All Data with Each Other”: Data-Sharing in Peer-to-Peer Relationships

Author : Eva Barlösius

Although the topic of data-sharing has boomed in the past few years, practices of datasharing have attracted only scant attention within working groups and scientific cooperation (peer-to-peer data-sharing).

To understand these practices, the author draws on Max Weber’s concept of social relationship, conceptualizing data-sharing as social action that takes place within a social relationship. The empirical material consists of interviews with 34 researchers representing five disciplines—linguistics, biology, psychology, computer sciences, and neurosciences.

The analysis identifies three social forms of data-sharing in peer-to-peer relationships: (a) closed communal sharing, which is based on a feeling of belonging together; (b) closed associative sharing, in which the participants act on the basis of an agreement; and (c) open associative sharing, which is oriented to “institutional imperatives” (Merton) and to formal regulations.

The study shows that far more data-sharing is occurring in scientific practice than seems to be apparent from a concept of open data alone. If the main goal of open-data policy programs is to encourage researchers to increase access to their data, it could be instructive to study the three forms of data-sharing to improve the understanding of why and how scientists make their data accessible to other researchers.

URL : “We Share All Data with Each Other”: Data-Sharing in Peer- to-Peer Relationships

DOI : https://doi.org/10.1007/s11024-023-09487-y

Initial insight into three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles

Authors : Yukiko SakaiYosuke MiyataKeiko YokoiYuqing WangKeiko Kurata

While data sharing has received research interest in recent times, its real status remains unclear, owing to its ambiguous concept. To understand the current status of data sharing, this study examined primary reuse, data integration, and dataset release as the actual practices of data sharing.

A total of 963 articles, chosen from those published in 2018 and registered in the Web of Science global citation database, were manually checked. Existing data were reused in the mode of data integration (13.3%) as frequently as they were for the mode of primary reuse (12.1%). Dataset release was the least common mode (9.0%).

The results show the variation in data sharing and indicate the need for standardization of data description in articles based on thorough registration and expansion in public data archives to close the loop that results in the virtuous cycle of research data.

URL : Initial insight into three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles

DOI : https://doi.org/10.1002/leap.1546

Do Open Access Mandates Work? A Systematized Review of the Literature on Open Access Publishing Rates

Authors : Elena Azadbakht, Tara Radniecki, Teresa Schultz, Amy W. Shannon

To encourage the sharing of research, various entities—including public and private funders, universities, and academic journals—have enacted open access (OA) mandates or data sharing policies.

It is unclear, however, whether these OA mandates and policies increase the rate of OA publishing and data sharing within the research communities impacted by them. A team of librarians conducted a systematized review of the literature to answer this question. A comprehensive search of several scholarly databases and grey literature sources resulted in 4,689 unique citations.

However, only five articles met the inclusion criteria and were deemed as having an acceptable risk of bias. This sample showed that although the majority of the mandates described in the literature were correlated with a subsequent increase in OA publishing or data sharing, the presence of various confounders and the differing methods of collecting and analyzing the data used by the studies’ authors made it impossible to establish a causative relationship.

URL : Do Open Access Mandates Work? A Systematized Review of the Literature on Open Access Publishing Rates

DOI : https://doi.org/10.31274/jlsc.15444

What constitutes equitable data sharing in global health research? A scoping review of the literature on low-income and middle-income country stakeholders’ perspectives

Authors : Natalia Evertsz, Susan Bull, Bridget Pratt

Introduction

Despite growing consensus on the need for equitable data sharing, there has been very limited discussion about what this should entail in practice. As a matter of procedural fairness and epistemic justice, the perspectives of low-income and middle-income country (LMIC) stakeholders must inform concepts of equitable health research data sharing.

This paper investigates published perspectives in relation to how equitable data sharing in global health research should be understood.

Methods

We undertook a scoping review (2015 onwards) of the literature on LMIC stakeholders’ experiences and perspectives of data sharing in global health research and thematically analysed the 26 articles included in the review.

Results

We report LMIC stakeholders’ published views on how current data sharing mandates may exacerbate inequities, what structural changes are required in order to create an environment conducive to equitable data sharing and what should comprise equitable data sharing in global health research.

Conclusions

In light of our findings, we conclude that data sharing under existing mandates to share data (with minimal restrictions) risks perpetuating a neocolonial dynamic. To achieve equitable data sharing, adopting best practices in data sharing is necessary but insufficient. Structural inequalities in global health research must also be addressed.

It is thus imperative that the structural changes needed to ensure equitable data sharing are incorporated into the broader dialogue on global health research.

URL : What constitutes equitable data sharing in global health research? A scoping review of the literature on low-income and middle-income country stakeholders’ perspectives

DOI : http://dx.doi.org/10.1136/bmjgh-2022-010157

Analysis of U.S. Federal Funding Agency Data Sharing Policies 2020 Highlights and Key Observations

Authors : Reid I. Boehm, Hannah Calkins, Patricia B. Condon, Jonathan Petters, Rachel Woodbrook

Federal funding agencies in the United States (U.S.) continue to work towards implementing their plans to increase public access to funded research and comply with the 2013 Office of Science and Technology memo Increasing Access to the Results of Federally Funded Scientific Research.

In this article we report on an analysis of research data sharing policy documents from 17 U.S. federal funding agencies as of February 2021. Our analysis is guided by two questions: 1.) What do the findings suggest about the current state of and trends in U.S. federal funding agency data sharing requirements? 2.) In what ways are universities, institutions, associations, and researchers affected by and responding to these policies?

Over the past five years, policy updates were common among these agencies and several themes have been thoroughly developed in that time; however, uncertainty remains around how funded researchers are expected to satisfy these policy requirements.

URL : Analysis of U.S. Federal Funding Agency Data Sharing Policies 2020 Highlights and Key Observations

DOI : https://doi.org/10.2218/ijdc.v17i1.791

Data Management Plans: Implications for Automated Analyses

Authors : Ngoc-Minh Pham, Heather Moulaison-Sandy, Bradley Wade Bishop, Hannah Gunderman

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements.

This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements.

The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2).

This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

URL : Data Management Plans: Implications for Automated Analyses

DOI : http://doi.org/10.5334/dsj-2023-002