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

A focus groups study on data sharing and research data management

Authors : Devan Ray Donaldson, Joshua Wolfgang Koepke

Data sharing can accelerate scientific discovery while increasing return on investment beyond the researcher or group that produced them. Data repositories enable data sharing and preservation over the long term, but little is known about scientists’ perceptions of them and their perspectives on data management and sharing practices.

Using focus groups with scientists from five disciplines (atmospheric and earth science, computer science, chemistry, ecology, and neuroscience), we asked questions about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans.

Participants identified metadata quality control and training as problem areas in data management. Additionally, participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. We present their desired repository features as a rubric for the research community to encourage repository utilization. Future directions for research are discussed.

URL : A focus groups study on data sharing and research data management

DOI : https://doi.org/10.1038/s41597-022-01428-w

Practices Before Policy: Research Data Management Behaviours in Canada

Authors : Melissa Cheung, Alexandra Cooper, Dylanne Dearborn, Elizabeth Hill, Erin Johnson, Marjorie Mitchell, Kristi Thompson

In anticipation of the then forthcoming Tri-Agency Research Data Management Policy, a consortium of professionals from Canadian university libraries surveyed researchers on their research data management (RDM) practices, attitudes, and interest in data management services.

Data collected from three surveys targeting researchers in science and engineering, humanities and social sciences, and health sciences and medicine were compiled to create a national dataset.

The present study is the first large-scale survey investigating researcher RDM practices in Canada, and one of the few recent multi-institutional and multidisciplinary surveys on this topic.

This article presents the results of the survey to assess researcher readiness to meet RDM policy requirements, namely the preparation of data management plans (DMPs) and data deposit in a digital repository.

The survey results also highlight common trends across the country while revealing differences in practices and attitudes between disciplines. Based on our survey results, most researchers would have to change their RDM behaviors to meet Tri-Agency RDM policy requirements.

The data we gathered provides insights that can help institutions prioritize service development and infrastructure that will meet researcher needs.

URL : Practices Before Policy: Research Data Management Behaviours in Canada

DOI : https://doi.org/10.21083/partnership.v17i1.6779