Research Data Management practices is a social real-life issue that can be explained using different theoretical frames. However, for purpose of answering the research question, a pragmatic philosophical stance is adopted. This is concerned with what works and provides solutions to an identified problem (Creswell, 2013; Patton, 2002. Pragmatism allows the researcher to emphasize the research problem and use all approaches available to address the problem. It is an approach that uses mixed methods. How “research data is managed” and why there has been a slow readiness to adopt and uptake of RDM practices” as constructed by respondents and its implications are explored. Thus pragmatism will give the researcher the freedom of choice of methods, techniques, and procedures of research that best meets the needs and purpose of the study (Creswell, 2013b.).
The research design associated with the pragmatic paradigm involves mixed methods (Creswell, 2012). The current study adopted a concurrent parallel design combining the survey design applied within a case study. The mixing of the two designs provided a better understanding of the research problem since it utilizes and is built upon the strengths of both quantitative and qualitative data (Creswell, 2008; Saunders, et al., 2012).
It was imperative o analyze the current data management practices
Current Data Management practices
Different disciplines have specific data management practices although may not necessarily adhere to the best practices (Borgman, 2012; Der, 2015). Best practices ensure; good use of public funds, following standards to make experiments and studies replicable, and research data and results as open as possible and as closed as necessary (Bishop, & Borden, 2020). However, failure to adhere to best practices may lead to complete loss of data and a waste of research effort (Briney, 2015). Schouppe & Burgelman, (2018) noted the varying data management practices by different research communities, noting that research data infrastructures usually generate complex ecosystems of poorly interoperable data. The resulting data in silos slows down the flow of knowledge and prevents the exchange of data in interdisciplinary research across different regions of the world. Researchers and institutions continue to manage research data centered on individual research project protocols which define the different approaches through which data is: created, organized, documented, accessed, preserved, stored, and reused (Borghi et al., 2018). In health institutions, research data is highly regulated and controlled by legal, regulatory, and confidentiality requirement which shape its management (Knight, 2015; Marutha, 2020). The different legal frameworks under which research data is managed create discrepancies which in most cases prevent better data management practices (Manurung, 2019; Wiley, 2020).
Existing practices around data management are varied across the discipline and institutions. Differing data formats, access methods, security systems, and intellectual property restrictions are in place. The unnecessary differences in practices and data characteristics within different research groups and institutions leave researchers to develop ad hoc measures for managing research data and there is no substantive evidence of how it’s accomplished (Wallis, Rolando & Borgman, 2013). Previous work on RDM practices was built around a variety of approaches (Tuyl and Michalek 2015) most of which relied on self-reports (Perrier et al. 2017) which were sensitive and confidential and many have remained unpublished (Patterton, Bothma, and Deventer, 2018), making data management is multi-faceted, diverse and complex (Cox, et al., 2014).
At the global level, most studies about RDM practices originated from the USA, UK, Australia, and Europe, USA and UK with increasing research output from Australia, Canada, and China. These together have generated over 60% of the total RDM related literature globally (Patterton, 2016). Much of the literature is open and easily accessible due to increasing pressure from international research funding agencies (European Commission, 2018; Tenopir, et al., 2015; Welcome Trust, 2015). The exponential growth of literature related to RDM practices is seen from under a decade ago when studies conducted focused mainly on different aspects of RDM. However, in Africa RDM studies have merged from the Republic of South Africa (Patterton, 2016; Patterton, et al., 2018; van Wyk, 2018), Kenya (Bull et al., 2015; Ng ’ Eno, 2018; Ng’eno and Mutula, 2018), Tanzania (Mushi et al., 2020), Malawi (Chawinga, 2019a; Chiware, 2020) and Zimbabwe (Chigwada, et al., 2017b), but no substantive study has been conducted in Uganda to-date (March 2021).
Respondents in previous studies have been drawn from a range and variety of positions, responsibilities, and levels of experience. Although there may be differences in behaviors and practice between the different groups of the respondent, no study has shown differences among the different categories of respondents and no explanations have been given though in most of the cases respondents were heterogeneous and linked by a community of practice.
In most of the RDM studies, sample sizes have been pruned to variances depending on the population and scope of the study. The sample size varied depending on the types of the population of the study. Online questionnaires/web surveys, interviews, document review, cross-sectional or case studies, and focus groups were methods applied in many RDM studies (Perrier, et al., 2017). In some studies more than one method was used, however, online questionnaires and personal interviews were the most commonly used methods (Patterton, 2016). Given the experience of the past studies, the current study shall adopt the questionnaire, interviews, and document reviews as data collection methods.
According to Vision 2040 and the National Development Plan III, Uganda is desirous of improving health services delivery (National Planning Authority, 2020). Yet, the existing health information systems and infrastructure are not well organized and aligned to the country’s health needs. Even the frameworks to speed up knowledge generation to improve health and biomedical care are inadequate (Ministry of Health e-health Strategy, 2018). Although the country is participating in health research of global importance, it remains highly dependent on donor funding and collaborative research dictated by donors and not necessarily a priority to the country’s research agenda. Health and biomedical disciplines are at the forefront of producing massive journal articles and are well recognized globally. However, research data management (RDM) practices though becoming increasingly mandatory research standards espoused in policy statements by a growing number of international funders and publishers (National Science Foundation (NSF), 2011; Medical Research Council, UK, 2013; The Gates Foundation, 2014; Welcome Trust and seven UK research councils, Hahnel, 2015), the country has not put in place a framework to guide researchers and institutions on the best way forward to remain competitive (Ministry of Health, 2017; UNCST, 2014). This may have a long-term impact on the local health and biomedical research affecting Uganda’s participation in this important global enterprise.
The existing research data presents significant assets with opportunities to benefit researchers, institutions, and society today and in the future. Unfortunately, enormous research data remains inaccessible as volumes are stored under different conditions, protocols, and technologies. Research data in electronic formats suffer bit rot while data in physical formats could be lost, misplaced, locked, and abandoned in storage facilities where they are deteriorating to oblivion (Joint Clinic Research Centre, 2017; Stover, 2019; Uganda National Council of Science and Technology, 2014). The general research terrain seems to be characterized by limited awareness of existing research data, data locations, storage, preservation measures, and lack of public knowledge of how such data could be accessed, shared, and reused. This is further complicated by limited competencies required for effective RDM, absence of supportive technical infrastructure, and absence of comprehensive national legal frameworks supporting research data management. This may be contributing to difficulties in finding, accessing, using, and or reusing data which presents a growing volume of dark data across health institutes (Stover, et al., 2019). Consequently, research data is lost or rendered inaccessible during or after research projects (Ministry of Health National eHealth Policy, 2016). Across health institutes, much of the existing research data is remotely owned by donors who dictate any possible access and reuse. The current practices where research data is remitted directly to the funders’ repositories leave local researchers with limited opportunities to access and use the data in which they have participated to generate. Even the publications generated from such data recognize only a few individuals in senior positions leaving out the majority of participants in the research processes. Due to limited analytical skills in research data and the required rigorous processes for ethical clearance, the majority of local research project participants hardly benefit from the enormous research data generated. This continues to pose challenges to scholarly productivity and limits overall benefits from the accumulated research data currently existing in health institutes across the country.
