Experiences of Health Research Data Sharing Among Researchers in Sub-Saharan Africa: Cross-Sectional Study
Bibliographic record
Abstract
Background: Digital platforms play a vital role in improving the availability and access to health research outputs, enhancing the engagement of policy makers and practitioners in the research processes. Despite their potential, it needs to be explored how digital platforms are used to manage and share health research datasets and publications, and to translate research findings among health networks or institutions in sub-Saharan Africa (SSA). Objective: This study aimed to assess the practices of health research data management, including sharing among researchers and their support staff within 3 large research networks for health innovations in SSA. Methods: A cross-sectional mixed methods survey was conducted across 3 research networks in SSA, showing experiences of sharing research data using digital platforms among researchers of 3 large research and innovation networks in SSA and affiliated institutions in the Global North. A total of 160 respondents completed a self-administered web-based questionnaire, and following data cleansing, the survey data were analyzed using both descriptive and inferential statistics. Results: Most respondents (91/160, 56.9%) used electronic data collection tools to collect research data. Almost half (79/160, 49.4%) of the respondents have a digital research data management platform. More than half of the respondents shared their research datasets (102/160, 63.8%), and 61.3% (98/160) shared research findings with the research community through different channels. Furthermore, most respondents shared their research datasets and research outputs through institutional data repositories (42/160, 26.1%), scientific conferences (123/160, 76.9%), and journal articles (110/160, 68.8%). This study found that parameters such as sex, professional category (health professional, information and communication technology professional, and data managers), and the role (researcher or student) influence health research data sharing within the community. The results show that the roles of the individual have the strongest association with the sharing of research datasets, followed by years of experience in research, then sex, and profession. Females were less likely to share their research datasets than males. Data managers and information and communication technology professionals exchanged datasets less frequently in the professional group, and the researcher's role was statistically significant in sharing research datasets. Conclusions: This study demonstrates that most researchers share research datasets and outputs through various channels. It was further found that digital platforms were essential in managing and sharing research datasets and publications since more than half (85/160, 53.1%) of the respondents have and use digital platforms. In addition, the study identified factors that influenced researchers' practices of sharing research datasets and publications. Furthermore, key gaps limit the sharing of these research datasets, including inadequate infrastructure, insufficient African dataset sharing platforms, a lack of institutional policy, and limited skills to use available platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".