Research consultancy
Research Data Management (RDM) refers to the processes, services, and policies involved in creating, organizing, describing, storing, and preserving data used and generated for research purposes. These practices ensure continuous access, sharing, reuse, security, and long-term value to its holders (Science Europe, 2018; Schöpfel, et al., 2018). RDM is widely regarded as a practical approach to ensuring the quality, integrity, and accessibility of research data, both now and in the future (Heuer, 2020; Wilms, et al., 2020). Globally, RDM is recognized as a best practice and provides research institutions with a competitive advantage by supporting the FAIR principles—making research data Findable, Accessible, Interoperable, and Reusable (Fuhr, 2019; Perrier et al., 2017).
RDM encompasses both activities and practices undertaken during the research life cycle, including data creation, organization, storage, sharing, preservation, and reuse. It has gained traction in developed nations through emerging policies and is increasingly endorsed by governments, funders, publishers, and research councils worldwide (Lämmerhirt, 2016; Perrier et al., 2017b; Tam, et al., 2014). However, in low-income countries, research institutions are often pressured to adopt RDM practices without adequate infrastructure or support systems, though these practices are increasingly required for FAIR data, a recognized best practice and essential for securing funding and publishing opportunities (Wilms, et al., 2020b). Compliance with these standards appears to be the main driver for RDM adoption in these regions. While funders require data management plans for funding approval, publishers focus on ensuring data accessibility to validate research findings. Although the FAIR concept is more common in developed countries, RDM is gradually being adopted in developing nations, facilitating global research collaborations (Patterton, 2016).
Research data are valuable assets that must be preserved for both current and future use by researchers, institutions, and society (Matlatse, 2016). The growing recognition of research data as a valuable resource has prompted funders and publishers to demand the inclusion of data management plans in funded projects. These plans outline how data will be managed, made accessible, and shared, allowing for broader use beyond the original researchers’ intentions (Park, 2018; Renaut, et al. 2018; Sa and Dora, 2019). RDM is praised for managing the vast and diverse volumes of research data generated across institutions, improving data quality and mitigating potential disorder (Berman and Cerf, 2013; Borgman, 2012; Choi and Lee, 2020).
In health and biomedical research, data management is integral to the research process, extending across the entire research life cycle. RDM is especially important for safeguarding valuable research data and ensuring it remains accessible, usable, and reusable for societal benefit. It protects research integrity, increases data quality, and facilitates data sharing, which is vital for validating findings and enhancing research productivity. By optimizing the resources required for data collection and sharing, RDM contributes to knowledge generation and allows other researchers to ask new questions of existing data (Park, 2018). Additionally, it enforces compliance with ethical codes, data protection laws, and funding requirements, which increases institutions’ competitiveness (Chawinga and Zinn, 2020a). The role of RDM in health research is exemplified by its early adoption in biomedical research, contributing to the rapid development of COVID-19 treatments and vaccines (Bjormmaln, 2020).
Despite RDM’s global recognition, policies to guide its adoption remain underdeveloped (Liu, et al., 2020). However, initiatives like CODATA (2019) and the Research Data Alliance (RDA) (2013) are promoting RDM adoption across disciplines and regions. These organizations have advocated for new policies, principles, and infrastructures to enhance research data management. CODATA, for example, has called for global policy changes to support the quality and reuse of research data, while the RDA focuses on overcoming barriers to open data sharing. The FAIR principles, introduced in 2014, are also increasingly adopted to ensure balanced access to research data (Wilkinson, 2016).
Nevertheless, research data management in low-income countries is hindered by a lack of standards, guidelines, and support services (Fuhr, 2019). In countries like South Africa, Kenya, Tanzania, Malawi, and Zimbabwe, challenges such as insufficient legal frameworks, inadequate infrastructure, and limited funding impede RDM practices (Chawinga, 2019; Chiparausha and Chigwada, 2019; Mushi, et al., 2020). Moreover, the scarcity of RDM literature in low-income nations reflects the fragmentation and infancy of these practices, which are only emerging through international research collaborations (Mohammed and Ibrahim, 2019; Mushi, et al., 2020; Tripathi, et al., 2017).
