CHAPTER ONE
INTRODUCTION AND BACKGROUND TO THE STUDY
Introduction
This study aims to examine research data management practices and the level of readiness in selected health institutions in Uganda. The findings will provide a foundation for recommending interventions that align with best practices and global trends in health research.
Research Data Management (RDM) broadly refers to the processes, policies, and services that govern how research data are created, organized, described, stored, preserved, and shared. Its purpose is to ensure continuous access, promote reuse, and maintain the security and long-term value of data. RDM has increasingly been recognized as a practical approach to improving data quality, integrity, and accessibility for both current and future research needs. It is also considered a global best practice that supports the principles of making data Findable, Accessible, Interoperable, and Reusable (FAIR).
Additionally, RDM encompasses all activities involved in handling data throughout the research lifecycle, including its creation, storage, sharing, preservation, and reuse. While developed countries have established policies to promote RDM, developing countries are still in the process of adopting these practices, often with limited infrastructure and support systems. Despite these challenges, compliance with RDM standards is becoming increasingly necessary, as it is often a requirement for research funding and publication.
Research data are valuable assets that benefit not only current studies but also future research and societal development. The growing recognition of data as a critical resource has led funders and publishers to require data management plans in research projects. These plans outline how data will be handled, shared, and made accessible for broader use beyond the original research scope. Proper implementation of RDM helps prevent data loss, improves organization, and enhances the usability of large and complex datasets generated across institutions.
In the field of health and biomedical sciences, research involves the systematic collection, analysis, and interpretation of data to improve health outcomes. Within this context, RDM plays a crucial role in ensuring that data are properly managed throughout the research lifecycle. Effective RDM enhances collaboration, protects research integrity, reduces errors, and improves the quality of data used for analysis. It also facilitates access to original datasets, enabling validation and replication of research findings.
Given that health research is often resource-intensive, effective data management helps optimize investments by promoting data sharing and reuse. This accelerates knowledge generation and increases research productivity. It also enables new discoveries by allowing other researchers to explore existing datasets from different perspectives. Furthermore, RDM ensures compliance with ethical standards, legal requirements, and funding guidelines, thereby enhancing the competitiveness of research institutions.
Globally, RDM has contributed to advancements in research quality across disciplines. However, many countries still lack comprehensive policies and standardized frameworks to guide its implementation. International organizations and initiatives continue to promote RDM by developing guidelines, infrastructures, and collaborative platforms that support data sharing and accessibility. These efforts have encouraged wider adoption of FAIR data principles as a means of improving research transparency and usability.
Despite these global developments, many developing countries face challenges in implementing RDM due to limited resources, lack of policies, and insufficient technical support. As a result, research data in these regions are often poorly managed, leading to issues such as data loss, misuse, and limited accessibility. Studies conducted in several African countries, including South Africa, Kenya, Tanzania, Malawi, and Zimbabwe, highlight common barriers such as inadequate legal frameworks, lack of infrastructure, and limited awareness of RDM practices.
