Archives des mots-clés : research data

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

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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

Publishers, funders and institutions: who is supporting UKRI-funded researchers to share data?

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Authors : Beth Montague-Hellen, Kate Montague-Hellen

Researchers are increasingly being asked by funders, publishers and their institutions to share research data alongside written publications, and to include data availability statements to support their readers in finding this data.

In the UK, UKRI (UK Research and Innovation) is one of the largest funding bodies and has had data-sharing policies for several years. This article investigates the reasons why a researcher may or may not share their data and assesses whether funders, publishers and institutions are supporting data-sharing behaviour through their policies and actions.

A survey with 166 responses gave an indicative assessment of researcher opinions around data sharing, and a corpus of 3,277 journal articles retrieved from four UK institutions was analysed using multivariate logistic regression models to provide empirical evidence as to researcher behaviour around data sharing.

The regression models provide insight into how this is affected by the funder, institution and publisher of the research. This study identifies that those publishers and funders who give clear guidance in their policies as to which data should be shared, and where this data should be shared, are most likely to encourage good practice in researchers.

URL : Publishers, funders and institutions: who is supporting UKRI-funded researchers to share data?

DOI : https://doi.org/10.1629/uksg.602

Researchers and their data: A study based on the use of the word data in scholarly articles

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Authors : Frédérique Bordignon, Marion Maisonobe

Data is one of the most used terms in scientific vocabulary. This article focuses on the relationship between data and research by analyzing the contexts of occurrence of the word data in a corpus of 72,471 research articles (1980–2012) from two distinct fields (Social sciences, Physical sciences).

The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process, and how they gain value for researchers who hold them.

Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word data mostly occurs at the beginning and end of research articles. Adjectives and verbs accompanying the noun data turn out to be even more important than data itself in specifying data.

The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data-handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.

URL : Researchers and their data: A study based on the use of the word data in scholarly articles

DOI : https://doi.org/10.1162/qss_a_00220

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

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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

What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

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Authors : Brigitte Mathiak, Nick Juty, Alessia Bardi, Julien Colomb, Peter Kraker

Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users’ wishes.

This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 ‘topics’ and eight types of users.

While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conducted and analysed a survey among data infrastructure specialists in a first attempt at ranking the requirements.

Between these data professionals, these rankings were very different, excepting the availability of metadata and data quality assessment.

URL : What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases

DOI : http://doi.org/10.5334/dsj-2023-003

Cluster Analysis of Open Research Data: A Case for Replication Metadata

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Author : Ana Trisovic

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR.

Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis.

I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results.

About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.

URL : Cluster Analysis of Open Research Data: A Case for Replication Metadata

DOI : https://doi.org/10.2218/ijdc.v17i1.833

Data Management Plans: Implications for Automated Analyses

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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