Data sharing and reuse in clinical research: Are we there yet? A cross-sectional study on progress, challenges and opportunities in LMICs.

Data sharing holds promise to accelerate innovative discoveries through artificial intelligence (AI) and traditional analytics. However, it remains unclear whether these prospects translate into tangible benefits in improving health care and scientific progress. In this cross-sectional study, we investigate current data reuse practices and explore ways to enhance the use of existing data in clinical research, focusing on low- and middle-income countries. 643 clinical researchers and data professionals participated in the study. 55.5% analysed clinical trial data. 75.3% of data users analysed data from observational studies obtained mainly through personal requests or downloads from publicly available sources. Data was mainly used to influence the design of new studies or in pooled and individual patient-level data meta-analyses. Key benefits realised were career progression and academic qualification, with more gains reported by users affiliated with high-income and upper-middle-income countries (p = 0.046, chi = 8.0). Scientific progress through publications and collaborations was associated with gender (p = 0.012, chi = 10.9), with males more likely to contribute. Benefits to the public although minimal, were associated with career seniority (p = 0.001, chi = 18.8), with works by senior researchers being more likely to influence health policy or treatment guidelines. Although 54% of the respondents accessed at least 3 datasets in the past 5 years, 79.4% of data users encountered difficulty finding relevant data for planned analyses. Researchers affiliated with low and middle income institutions reported more difficulty interpreting data (p = 0.012, chi = 25.7), while challenges with language were regionally influenced (p = 0.000, chi = 51.3) and more commonly reported by researchers in Latin America and South and East Asia institutions. While the utilisation of shared data is lower than expected, focused efforts to enrich existing data with extensive metadata using standard terminologies can enhance data findability. Investment in training programmes, building professional networks, and mentorship in data science may improve the quality of data generated and increase researchers' ability to use existing datasets.