Introduction
Research Data Management (RDM) has emerged as a critical component of modern scientific research, particularly in the field of clinical and medical sciences where large volumes of heterogeneous, sensitive, and complex data are generated, maintained, and used for further research and for clinical purposes. Effective management of clinical research data contributes to data integrity, security, accessibility, reproducibility, and long-term preservation of scientific outputs. The rapid expansion of digital technologies, electronic health records, clinical trials, and data-intensive research has increased the need for systematic approaches to managing research data throughout its lifecycle.
Clinical research data encompass diverse forms of information, including patient records, laboratory findings, diagnostic images, and observational data. Given the significance of these data in evidence-based medicine and healthcare decision-making, understanding the evolution of scholarly research on clinical research data management is essential. Scientometric studies provide valuable insights into the intellectual structure, development patterns, influential contributors, and collaboration networks within a scientific field. Therefore, this study aimed to analyze and visualize the global scientific publications related to clinical research data management from 1990 to 2023.
Literature Review
Previous studies have highlighted the growing importance of research data management across various disciplines. Whyte and Tedds (2011) emphasized the necessity of systematic data management practices to ensure data preservation and reuse. Cox and Pinfield (2014) examined the evolving role of libraries in supporting research data management services, while Marlina et al. (2022) identified organizational and technological factors affecting RDM readiness. Several scientometric investigations have explored the broader field of research data management. Gupta et al. (2021) conducted a bibliometric analysis of RDM literature and found that the United States and the United Kingdom were leading contributors to the field. Lee et al. (2024) similarly reported substantial international growth in RDM-related publications and identified Germany, the United States, and the United Kingdom as major scientific producers. Within the medical and clinical domains, studies have focused on data sharing, ethical considerations, and challenges associated with managing health research data. Bull et al. (2015) discussed ethical frameworks for sharing individual-level health research data, while Rosenbaum (2018) examined the growing movement toward data sharing in medical research. Despite these contributions, few studies have specifically investigated the scientific landscape of clinical research data management using an integrated scientometric approach. This research addresses that gap by combining data from both Scopus and Web of Science databases to provide a comprehensive overview of the field.
Methodology
This study employed an applied scientometric research design. The statistical population consisted of all publications related to clinical research data management indexed in Scopus and Clarivate Analytics (Web of Science) between 1990 and 2023. Data retrieval was conducted through structured search strategies applied to titles, abstracts, and keywords in both databases. After data extraction and removal of duplicate records, a total of 13,978 publications remained for analysis. Multiple bibliometric and network analysis tools were utilized, including ScientoPy, Ravar Matrix, VOSviewer, UCINET, Microsoft Excel, and Python programming. These tools facilitated the examination of publication growth trends, geographical distribution of research output, collaboration networks among countries, and co-authorship patterns among researchers. Network analysis indicators such as degree centrality, closeness centrality, and betweenness centrality were calculated to identify influential countries and researchers within the scientific collaboration networks. Visualization techniques were employed to illustrate the structure and dynamics of scientific production in the field.
Results
The findings revealed a substantial increase in scientific publications related to clinical research data management over the study period. While only a limited number of publications appeared during the early years of the 1990s, scientific output grew steadily after 2005 and accelerated significantly from 2018 onward. The highest number of publications was recorded in 2021 and 2022, reflecting the increasing importance of data-driven healthcare research and digital health technologies. Geographical analysis showed that the United States ranked first in scientific production, followed by the United Kingdom and Germany. These countries consistently maintained their leading positions throughout the study period. Other significant contributors included Italy, France, Canada, and the Netherlands. The international collaboration network demonstrated that the United Kingdom, the United States, Switzerland, Germany, and Australia occupied central positions within global research partnerships. According to degree centrality measures, the United Kingdom exhibited the strongest collaborative activity, while the United States showed the highest closeness and betweenness centrality values, indicating its strategic role in connecting different research communities. Analysis of co-authorship networks identified several influential scholars in the field. Researchers such as Axel Junger, Hans-Ulrich Prokosch, Rainer Röhrig, Martin Sedlmayr, Dagmar Waltemath, and Carole Goble were among the most productive and influential contributors. These researchers played key roles in fostering collaboration and advancing the development of clinical research data management.
Discussion
The results indicated that clinical research data management has evolved into a rapidly expanding area of scientific inquiry. The notable growth in publications after 2018 may be attributed to advances in information technology, the proliferation of electronic health systems, increasing emphasis on data sharing, and the growing adoption of data-driven approaches in healthcare and biomedical research. The dominance of the United States, the United Kingdom, and Germany reflected their strong research infrastructures, substantial investment in health research, supportive policy environments, and advanced technological capabilities. Their leadership in international collaboration networks further demonstrated the importance of global partnerships in addressing complex challenges associated with clinical data management.
The study also highlighted the significance of influential researchers who act as bridges between different scientific communities. Such collaborative networks contributed to knowledge dissemination, methodological innovation, and the establishment of best practices in data management. Nevertheless, challenges remain, including issues related to data standardization, interoperability, privacy protection, ethical governance, and management of large-scale datasets. The findings were consistent with previous bibliometric studies on research data management and reinforced the growing recognition of data as a strategic scientific asset. As healthcare systems continue to generate increasingly complex datasets, effective data management will become even more critical for ensuring research quality and facilitating evidence-based decision-making.
Conclusion
This scientometric analysis provided a comprehensive overview of global research trends in clinical research data management between 1990 and 2023. The results demonstrated significant growth in scientific output, expanding international collaboration, and increasing scholarly attention to the management of clinical research data. The United States, the United Kingdom, and Germany emerged as the leading contributors, while several prominent researchers played central roles in shaping the intellectual structure of the field. The continued expansion of clinical research data management reflected its strategic importance in supporting high-quality healthcare research and innovation. Future developments are expected to involve greater integration of artificial intelligence, machine learning, automation technologies, and advanced data analytics. Moreover, increasing attention to data standardization, interoperability, and privacy protection will be essential for maximizing the value of clinical research data. The findings of this study can assist policymakers, research institutions, and scholars in understanding the evolution of the field, identifying influential contributors, and developing strategies to strengthen research capacity and international collaboration in clinical research data management.