Open Science – Page 2

"The OpenCitations Index"

This article presents the OpenCitations Index, a collection of open citation data maintained by OpenCitations, an independent, not-for-profit infrastructure organisation for open scholarship dedicated to publishing open bibliographic and citation data using Semantic Web and Linked Open Data technologies. The collection involves citation data harvested from multiple sources. To address the possibility of different sources providing citation data for bibliographic entities represented with different identifiers, therefore potentially representing same citation, a deduplication mechanism has been implemented. This ensures that citations integrated into OpenCitations Index are accurately identified uniquely, even when different identifiers are used. This mechanism follows a specific workflow, which encompasses a preprocessing of the original source data, a management of the provided bibliographic metadata, and the generation of new citation data to be integrated into the OpenCitations Index. The process relies on another data collection: OpenCitations Meta, and on the use of a new globally persistent identifier, namely OMID (OpenCitations Meta Identifier). As of July 2024, OpenCitations Index stores over 2 billion unique citation links, harvest from Crossref, the National Institute of Heath Open Citation Collection (NIH-OCC), DataCite, OpenAIRE, and the Japan Link Center (JaLC). OpenCitations Index can be systematically accessed and queried through several services, including SPARQL endpoint, REST APIs, and web interfaces. Additionally, dataset dumps are available for free download and reuse (under CC0 waiver) in various formats (CSV, N-Triples, and Scholix), including provenance and change tracking information.

https://arxiv.org/abs/2408.02321

"Reproducible and Attributable Materials Science Curation Practices: A Case Study"

While small labs produce much of the fundamental experimental research in Material Science and Engineering (MSE), little is known about their data management and sharing practices and the extent to which they promote trust in and transparency of the published research. In this research, a case study is conducted on a leading MSE research lab [at MIT] to characterize the limits of current data management and sharing practices concerning reproducibility and attribution. The workflows are systematically reconstructed, underpinning four research projects by combining interviews, document review, and digital forensics. Then, information graph analysis and computer-assisted retrospective auditing are applied to identify where critical research information is unavailable orat risk.

Data management and sharing practices in this leading lab protect against computer and disk failure; however, they are insufficient to ensure reproducibility or correct attribution of work,especiallywhen a group member withdraws before the project completion.Therefore, recommendations for adjustments in MSE data management and sharing practices are proposed to promote trustworthiness and transparency by adding lightweight automated file-level auditing and automated data transfer processes.

https://doi.org/10.2218/ijdc.v18i1.940

Paywall: "What Is Research Data ‘Misuse’? And How Can It Be Prevented or Mitigated?"

In the article, we emphasize the challenge of defining misuse broadly and identify various forms that misuse can take, including methodological mistakes, unauthorized reuse, and intentional misrepresentation. We pay particular attention to underscoring the complexity of defining misuse, considering different epistemological perspectives and the evolving nature of scientific methodologies. We propose a theoretical framework grounded in the critical analysis of interdisciplinary literature on the topic of misusing research data, identifying similarities and differences in how data misuse is defined across a variety of fields, and propose a working definition of what it means to "misuse" research data. Finally, we speculate about possible curatorial interventions that data intermediaries can adopt to prevent or respond to instances of misuse.

https://doi.org/10.1002/asi.24944

"Training to Act FAIR: A Pre-Post Study on Teaching FAIR Guiding Principles to (Future) Researchers in Higher Education"

With a pre-post test design, the study evaluates the short-term effectiveness of FAIR training on students’ scientific suggestions and justifications in line with FAIR’s guiding principles. The study also assesses the influence of university legal frameworks on students’ inclination towards FAIR training. Before FAIR training, 81.1% of students suggested that scientific actions were not in line with the FAIR guiding principles. However, there is a 3.75-fold increase in suggestions that adhere to these principles after the training. Interestingly, the training does not significantly impact how students justify FAIR actions. The study observes a positive correlation between the presence of university legal frameworks on FAIR guiding principles and students’ inclination towards FAIR training.

https://doi.org/10.1007/s10805-024-09547-2

"The Promotion and Implementation of Open Science Measures among High-Performing Journals from Brazil, Mexico, Portugal, and Spain"

This study empirically examined the promotion and implementation of open science measures among high-performing journals of Brazil, Mexico, Portugal, and Spain. Journal policy related to data sharing, materials sharing, preregistration, open peer review, and consideration of preprints and replication studies was gathered from the websites of the journals. . . . Analyses found a higher promotion of open science measures among Brazilian journals than their Portuguese counterparts, and higher promotion of open science measures among international journals than their domestic counterparts. Analyses found higher implementation of open science measures among Brazilian journals than their Portuguese and Mexican counterparts. One journal out of 40 encouraged preregistration of studies; none encouraged replication studies and none had implemented open peer review.

https://doi.org/10.1002/leap.1616

"Privacy Protection Framework for Open Data: Constructing and Assessing an Effective Approach"

This framework [Privacy Protection Framework for Open Data] aims to establish clear privacy protection measures and safeguard individuals’ privacy rights. Existing privacy protection practices were examined using content analysis, and 36 indicators across five dimensions were developed and validated through an empirical study with 437 participants. The PPFOD offers comprehensive guidelines for data openness, empowering individuals to identify privacy risks, guiding businesses to ensure legal compliance and prevent data leaks, and assisting libraries and data institutions in implementing effective privacy education and training programs, fostering a more privacy-conscious and secure data era.

https://doi.org/10.1016/j.lisr.2024.101312

"The Societal Impact of Open Science: A Scoping Review"

Open Science (OS) aims, in part, to drive greater societal impact of academic research. Government, funder and institutional policies state that it should further democratize research and increase learning and awareness, evidence-based policy-making, the relevance of research to society’s problems, and public trust in research. Yet, measuring the societal impact of OS has proven challenging and synthesized evidence of it is lacking. This study fills this gap by systematically scoping the existing evidence of societal impact driven by OS and its various aspects, including Citizen Science (CS), Open Access (OA), Open/FAIR Data (OFD), Open Code/Software and others. Using the PRISMA Extension for Scoping Reviews and searches conducted in Web of Science, Scopus and relevant grey literature, we identified 196 studies that contain evidence of societal impact. The majority concern CS, with some focused on OA, and only a few addressing other aspects. Key areas of impact found are education and awareness, climate and environment, and social engagement. We found no literature documenting evidence of the societal impact of OFD and limited evidence of societal impact in terms of policy, health, and trust in academic research. Our findings demonstrate a critical need for additional evidence and suggest practical and policy implications.

https://doi.org/10.1098/rsos.240286

Research Data Alliance: Recommendations on Open Science Rewards and Incentives

Open Science contributes to the collective building of scientific knowledge and societal progress. However, academic research currently fails to recognise and reward efforts to share research outputs. Yet it is crucial that such activities be valued, as they require considerable time, energy, and expertise to make scientific outputs usable by others, as stated by the FAIR principles. To address this challenge, several bottom-up and top-down initiatives have emerged to explore ways to assess and credit Open Science activities (e.g., Research Data Alliance, RDA) and to promote the assessment of a broad spectrum of research outputs, including datasets and software (e.g., Coalition for Advancement of Research Assessment, CoARA). As part of the RDA-SHARC (SHAring Rewards and Credit) interest group, we have developed a set of recommendations to help implement various rewarding schemes at different levels. The recommendations target a broad range of stakeholders. For instance, institutions are encouraged to provide digital services and infrastructure, organise training and cover expenses associated with making data available for the community. The funders should establish policies requiring open access to data produced by funded research and provide corresponding support. The publishers should favour open peer-review models and open access to articles, data and software. Government policymakers should set up a comprehensive Open Science strategy, as recommended by UNESCO and followed by a growing number of countries. The present work details different measures that are proposed to the stakeholders. The need to include sharing activities in research evaluation schemes as an overarching mechanism to promote Open Science practices is specifically emphasised.

https://tinyurl.com/4rhk44mn

"An Empirical Examination of Data Reuser Trust in a Digital Repository"

Most studies of trusted digital repositories have focused on the internal factors delineated in the Open Archival Information System (OAIS) Reference Model—organizational structure, technical infrastructure, and policies, procedures, and processes. Typically, these factors are used during an audit and certification process to demonstrate a repository can be trusted. The factors influencing a repository’s designated community of users to trust it remains largely unexplored. This article proposes and tests a model of trust in a data repository and the influence trust has on users’ intention to continue using it. Based on analysis of 245 surveys from quantitative social scientists who published research based on the holdings of one data repository, findings show three factors are positively related to data reuser trust—integrity, identification, and structural assurance. In turn, trust and performance expectancy are positively related to data reusers’ intentions to return to the repository for more data. As one of the first studies of its kind, it shows the conceptualization of trusted digital repositories needs to go beyond high-level definitions and simple application of the OAIS standard. Trust needs to encompass the complex trust relationship between designated communities of users that the repositories are being built to serve.

https://doi.org/10.1002/asi.24933

"Copyright, the Right to Research and Open Science: About Time to Connect the Dots"

In this contribution, we highlight the necessity to design a research-enabling copyright framework that provides researchers with access to the necessary knowledge, information and data, and to tackle the challenges of the future.

For that purpose, we examine copyright through the prism of the Open Science movement and in the light of a "right to research " and connect both to a larger, constitutional argument which suggests that enabling research through copyright law is a pressing constitutional imperative. Based on this theoretical framework, we suggest substantive and institutional modifications to copyright law, through legislative interventions and judicial interpretations that would remove significant barriers towards open science as envisaged by European and international institutions. The conflict between the proprietary interests of rightholders and the societal interests in unhindered, purpose-bound research should, in case of doubt, be decided in favour of research and open science as crucial enablers for innovation and progress. For authors, remuneration is most of the time not the primary motivation or incentive to produce research; they can often rely on other revenues (e.g. through institutional employment) and other interest prevail, such as the broadest possible dissemination of their works that will secure them reputation and career advancement. The incentive mechanisms therefore are entirely different in the research field compared to other creative sectors, an aspect that must be taken into account when designing a research-friendly copyright system.

https://ssrn.com/abstract=4857765

"DMPs as Management Tool for Intellectual Assets by SMART-metrics"

Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives.

To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability.

Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results.

The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

https://tinyurl.com/25ymtyyk

"Understanding the Value of Curation: A Survey of Us Data Repository Curation Practices and Perceptions"

Data curators play an important role in assessing data quality and take actions that may ultimately lead to better, more valuable data products. This study explores the curation practices of data curators working within US-based data repositories. We performed a survey in January 2021 to benchmark the levels of curation performed by repositories and assess the perceived value and impact of curation on the data sharing process. Our analysis included 95 responses from 59 unique data repositories. Respondents primarily were professionals working within repositories and examined curation performed within a repository setting. A majority 72.6% of respondents reported that "data-level" curation was performed by their repository and around half reported their repository took steps to ensure interoperability and reproducibility of their repository’s datasets. Curation actions most frequently reported include checking for duplicate files, reviewing documentation, reviewing metadata, minting persistent identifiers, and checking for corrupt/broken files. The most "value-add" curation action across generalist, institutional, and disciplinary repository respondents was related to reviewing and enhancing documentation. Respondents reported high perceived impact of curation by their repositories on specific data sharing outcomes including usability, findability, understandability, and accessibility of deposited datasets; respondents associated with disciplinary repositories tended to perceive higher impact on most outcomes. Most survey participants strongly agreed that data curation by the repository adds value to the data sharing process and that it outweighs the effort and cost. We found some differences between institutional and disciplinary repositories, both in the reported frequency of specific curation actions as well as the perceived impact of data curation. Interestingly, we also found variation in the perceptions of those working within the same repository regarding the level and frequency of curation actions performed, which exemplifies the complexity of a repository curation work. Our results suggest data curation may be better understood in terms of specific curation actions and outcomes than broadly defined curation levels and that more research is needed to understand the resource implications of performing these activities. We share these results to provide a more nuanced view of curation, and how curation impacts the broader data lifecycle and data sharing behaviors.

https://doi.org/10.1371/journal.pone.0301171

Paywall: "Journal Requirement for Data Sharing Statements in Clinical Trials: A Cross-Sectional Study"

Despite ICMJE [International Committee of Medical Journal Editors] recommendations, more than 27% of biomedical journals do not require clinical trials to include data sharing statements, highlighting room for improved transparency.

https://doi.org/10.1016/j.jclinepi.2024.111405

"The NIH Data Management and Sharing Policy for Non-data librarians" (Video)

The NIH Data Management and Sharing (DMS) Policy went into effect early last year. That means that the policy that so many medical data librarians have been talking about is finally in place and affecting researchers. Libraries do not need a data expert or an institutional repository to get started with supporting NIH grants with this new policy. Reference interviewing skills and a basic knowledge of the NIH DMS Plan format can be combined to walk researchers through the basics. In this session, librarians who are new to the NIH DMS Policy will learn the essentials: what is the NIH DMS policy, who is affected, and how do researchers incorporate it into an NIH grant application.

https://www.youtube.com/watch?v=6JAj5rHpFd0

"Maggot: An Ecosystem for Sharing Metadata within the Web of Fair Data"

We developed Maggot which stands for Metadata Aggregation on Data Storage, specifically designed to annotate datasets by generating metadata files to be linked into storage spaces. Maggot enables users to seamlessly generate and attach comprehensible metadata to datasets within a collaborative environment. This approach seamlessly integrates into a data management plan, effectively tackling challenges related to data organisation, documentation, storage, and frictionless FAIR metadata sharing within the collaborative group and beyond. Furthermore, for enabling metadata crosswalk, metadata generated with Maggot can be converted for a specific data repository or configured to be exported into a suitable format for data harvesting by third-party applications.

https://doi.org/10.1101/2024.05.24.595703

Paywall: "K-Means Clustering of Dermatology Journals: Comparing the Distribution of ‘Free-to-Publish’ and ‘Pay-to-Publish’ Models"

The study reveals a higher proportion of F2P journals, especially in higher-tier journals, indicating a preference for quality-driven research acceptance. Conversely, a rising proportion of P2P journals in lower tiers suggests potential bias towards the ability to pay. This disparity poses challenges for researchers from less-funded institutions or those early in their careers. The study also finds significant differences in APCs between F2P and P2P journals, with hybrid OA being more common in F2P.

https://doi.org/10.1007/s00403-024-03105-x

Classifying Open Access Business Models

The proliferation of Open Access (OA) business models has been rapid, presenting challenges for stakeholders in academic publishing in communicating and working effectively with one another. This article offers a comprehensive classification system for OA models, categorizing them into five core types (transactional, bundled, cooperative, sponsored, and alternative), each with distinct characteristics and implications for funding, equity, and implementation. This classification aims to clarify the myriad labels and terminologies used, addressing the inconsistencies and gaps in previous attempts to categorize OA models. By providing descriptions and analyses of different business models, the article seeks to enhance transparency around and understanding of OA options, ultimately supporting informed decision-making in the evolving landscape of academic publishing.

https://doi.org/10.5281/zenodo.11242106

"Opening Science to Society: How to Progress Societal Engagement into (Open) Science Policies"

A broad understanding of the aims and objectives of the international open science movement was recently adopted with the 2021 UNESCO Recommendation on Open Science, expanding the focus of open science to include scientific knowledge, infrastructures, knowledge systems and the open engagement of societal actors. In response, recent discussions on science policy practice are shifting to the implementation of open science via national policy. While policy instruments to support some aspects of open science are well-studied, guidance on the emerging ‘social’ aspects of open science has lagged, prompting UNESCO to generate guidance. In this paper, authors of a UNESCO Open Science Toolkit guidance document on ‘Engaging societal actors in Open Science’ synthesize the scholarly underpinnings behind the guidance document’s recommendations. This work draws upon a targeted search from academic, policy, and grey literature in the fields of open science and community engagement, with a special focus on citizen science, to derive guidance on how to overcome barriers to the uptake of societal engagement approaches. The results present building blocks of what an enabling environment for the open engagement of societal actors could look like, identifying key considerations and reflecting on opportunities and challenges for progressing and evaluating sound open engagement of societal actors into regional & national (open) science policies.

https://doi.org/10.1098/rsos.231309

"The O3 Guidelines: Open Data, Open Code, and Open Infrastructure for Sustainable Curated Scientific Resources"

Here, we introduce the Open Data, Open Code, and Open Infrastructure (O3) Guidelines for the creation and maintenance of curated resources which promote sustainability through a combination of technical workflows, social workflows, and progressive governance models. Together, these support and encourage community-facing curation (Fig. 1). In summary, (1) the technical aspect of O3 necessitates using open data, open code, and open infrastructure. Both data and code are permissively licensed and kept together under public version control. This enables anyone to directly suggest improvements and updates. Further, it recommends using hardware and software infrastructure that supports automation in response to various actions performed by contributors and maintainers. For example, this includes running quality assurance workflows in response to new contributions and the generation of exports in multiple formats in response to running a release workflow. (2) The social aspect of O3 prescribes the composition of training material, curation guidelines, contribution guidelines, and a community code of conduct that encourage and support potential community curators. It requires the use of public tools for suggestions, questions, discussion as well as social workflows for the submission and review of changes. (3) The governance aspect of O3 necessitates the division of responsibilities and authority across multiple institutions, making the resource more robust to fluctuation in funding and personnel, such as when reviewing and applying changes to the data or code. O3 prescribes liberal attribution and acknowledgment of the individuals and institutions, both internal and external to the project, who contribute on a variety of levels such as to data, code, discussion, and funding. More generally, the O3 Guidelines suggest that a minimal governance model be codified and instituted as early as possible in a project’s lifetime.

https://doi.org/10.1038/s41597-024-03406-w

"The FAIR Assessment Conundrum: Reflections on Tools and Metrics"

Several tools for assessing FAIRness have been developed. Although their purpose is common, they use different assessment techniques, they are designed to work with diverse research products, and they are applied in specific scientific disciplines. It is thus inevitable that they perform the assessment using different metrics. This paper provides an overview of the actual FAIR assessment tools and metrics landscape to highlight the challenges characterising this task. In particular, 20 relevant FAIR assessment tools and 1180 relevant metrics were identified and analysed concerning (i) the tool’s distinguishing aspects and their trends, (ii) the gaps between the metric intents and the FAIR principles, (iii) the discrepancies between the declared intent of the metrics and the actual aspects assessed, including the most recurring issues, (iv) the technologies used or mentioned the most in the assessment metrics. The findings highlight (a) the distinguishing characteristics of the tools and the emergence of trends over time concerning those characteristics, (b) the identification of gaps at both metric and tool levels, (c) discrepancies observed in 345 metrics between their declared intent and the actual aspects assessed, pointing at several recurring issues, and (d) the variety in the technology used for the assessments, the majority of which can be ascribed to linked data solutions. This work also highlights some open issues that FAIR assessment still needs to address.

https://doi.org/10.5334/dsj-2024-033

2024 State of Open Infrastructure Report

In this inaugural report, we dive deep into the characteristics of open infrastructure powering research and scholarship and what (we believe) sets them apart from their competitors. We take a closer look at the governing bodies and decision-makers behind the technologies your community relies on. We share the latest data and analysis of over US$415M in grant funding powering open infrastructures and research surrounding them, and bring you the latest infrastructure and policy developments in regions such as Latin America, Africa, and the European Union. We highlight success stories and the key trends in the adoption of open infrastructure. We share the latest on trends we’re tracking, such as the global movement towards Diamond Open Access and the underlying infrastructure needs, Artificial Intelligence (AI) and the intersection with open research, and "digital sovereignty" and its impact on research across borders.

https://tinyurl.com/ycxs547k

"The Global Lens: Highlighting National Nuances in Researchers’ Attitudes to Open Data"

This report investigates the variations seen in researcher’s attitudes towards open data across Ethiopia, Japan and the United States, using responses from the State of Open Data surveys. Highlighting: what open data is and its importance towards global scientific advancement, outlining methods that were used, outward context and overall suggestions towards policy makers.

Ethiopia and Japan were found to display contrasting responses, with the United States often representing the middle ground. Researchers in Ethiopia show the highest familiarity with FAIR principles (36.50%), support for a national open data mandate (76.96%), and agreement with penalising non-compliance (56.74%). In contrast, Japan has the lowest familiarity with FAIR principles (10.20%), support for a national mandate (41.86%), and agreement with penalties (35.78%). The United States falls in between, with 37.60% familiarity with FAIR principles, 61.22% supporting a national mandate, and 54.09% supporting penalties.

The factors shaping these attitudes, including funding policies, research culture, and individualistic behaviours have also been examined. Recommendations suggest Ethiopia could leverage its strong support by establishing clear national policies, the United States could build on existing federal policies, and Japan may need a more gradual approach, engaging researchers in policy development.

https://tinyurl.com/45xsvc8h

"Recognising Open Research Data in Research Assessment: Overview of Practices and Challenges"

The literature review aims at identifying content and key issues regarding the assessment of ORD practices nationally and internationally. It starts from the observation that research assessment needs to be reformed as they are currently biased towards scientific publications. Internationally, discussions and projects thereon have emerged. To contextualise recORD and this literature review, we first describe international and Swiss initiatives for reforming research assessment and how they include ORD recognition. The remainder of the review follows an innovative methodology as it identifies first core values in responsible research assessment, and second existing frameworks, to thirdly derive propositions to keep in mind when developing concrete ORD-specific research assessment recommendations. In a final section, the review presents further readings and useful weblinks on the recognition of ORD in research assessment.

https://zenodo.org/doi/10.5281/zenodo.11060206

"An Analysis of the Effects of Sharing Research Data, Code, and Preprints on Citations"

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.

https://arxiv.org/abs/2404.16171

"Health Data Sharing Attitudes Towards Primary and Secondary Use of Data: A Systematic Review"

Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. . ..

Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people’s concerns, and focus on spreading awareness about the data sharing process and benefits.

https://doi.org/10.1016/j.eclinm.2024.102551