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      Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research

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          Abstract

          The data ecosystem is complex and involves multiple stakeholders. Researchers and scientists engaging in data-intensive research collect, analyse, store, manage and share large volumes of data. Consequently, capturing researchers’ and scientists’ views from multidisciplinary fields on data use, sharing and governance adds an important African perspective to emerging debates. We conducted a descriptive cross-sectional survey and received 160 responses from researchers and scientists representing 43 sub-Saharan African countries. Whilst most respondents were satisfied with institutional data storage processes, 40% indicated that their organisations or institutions did not have a formally established process for storing data beyond the life cycle of the project. Willingness to share data was generally high, but increased when data privacy was ensured. Robust governance frameworks increased the willingness to share, as did the regulation of access to data on shared platforms. Incentivising data sharing remains controversial. Respondents were satisfied with exchanging their data for co-authorship on publications (89.4%) and collaboration on projects (77.6%). However, respondents were split almost equally in terms of sharing their data for commercial gain. Regarding the process of managing data, 40.6% indicated that their organisations do not provide training on best practices for data management. This could be related to a lack of resources, chronic institutional under-investment, and suboptimal research training and mentorship in sub-Saharan Africa. The sustainability of data sharing may require ethical incentive structures to further encourage researchers and scientists. Tangible infrastructure to facilitate such sharing is a prerequisite. Capacity development in data governance for researchers and scientists is sorely needed.

          Significance:

          Data sharing is necessary to advance science, yet there are many constraints. In this study, we explored factors that promote a willingness to share, as well as constraining factors. Seeking potential solutions to improve data sharing is a scientific and ethical imperative. The standardisation of basic data sharing and data transfer agreements, and the development of a Data Access Committee will strengthen data governance and facilitate responsible data sharing in sub-Saharan Africa. Funders, institutions, researchers and scientists ought to jointly contribute to fair and equitable data use and sharing during and beyond the life cycle of research projects.

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          Most cited references81

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            A manifesto for reproducible science

            Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.
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              Data Sharing by Scientists: Practices and Perceptions

              Background Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.
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                Author and article information

                Journal
                0066654
                8688
                S Afr J Sci
                S Afr J Sci
                South African journal of science
                0038-2353
                1996-7489
                26 May 2024
                May-Jun 2023
                30 May 2023
                31 October 2024
                : 119
                : 5-6
                : 15129
                Affiliations
                [1 ]Centre for Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
                [2 ]School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
                [3 ]Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
                Author notes

                Authors’ contributions

                S.M.K.: Made substantial contributions to the analysis and interpretation of data for the work; drafted/revised the work critically for important intellectual content. N.C.: Made substantial contributions to the design of the work, acquisition and interpretation of data for the work; drafted/revised the work critically for important intellectual content. K.R.: Drafted/revised the work critically for important intellectual content. B.W.W.: Drafted/revised the work critically for important intellectual content. Q.B.: Made substantial contributions to the analysis and interpretation of data for the work; drafted/revised the work critically for important intellectual content. T.M.E.: Made substantial contributions to the analysis of data for the work; drafted/revised the work critically for important intellectual content. K.M.: Made substantial contributions to the conception of the work; drafted/revised the work critically for important intellectual content. All authors approved the final version and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

                CORRESPONDENCE TO: Nezerith Cengiz, ncengiz@ 123456sun.ac.za
                Author information
                http://orcid.org/0000-0002-1316-734X
                http://orcid.org/0000-0002-7650-2897
                http://orcid.org/0000-0001-5916-2723
                http://orcid.org/0000-0003-0511-1837
                http://orcid.org/0000-0002-6158-7971
                http://orcid.org/0000-0002-8703-1664
                http://orcid.org/0000-0003-3404-4901
                Article
                NIHMS1997167
                10.17159/sajs.2023/15129
                11526389
                39483790
                6d73fe89-73db-4dd2-8f20-8b1bc05a6ef8

                Published under a Creative Commons Attribution Licence.

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                big data,data governance,sub-saharan africa,researchers,scientists,data transfer agreements,data sharing

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