Research Data Management in Kenya’s Agricultural Research Institutes

Abstract

Research Data Management (RDM) refers to the collection, organization, validation, and preservation of data for analysis, discovery, sharing, reuse and transformation. RDM consists of a number of diff erent activities and processes that include creation of data, storage, security, preservation, retrieval, sharing, and reuse while taking into account technical capabilities, human resource capability, ethical considerations, legal issues and government . T he strategic importance of RDM within agricultural research institutes is to: enable scrutiny of research finding s , prevent duplication of effort by enabling others to use the same data; promote innovation through retrieval, co - analysis of data, ensuring r esearch data gathered is not lost or destroyed, and that the research meet funders’ requirements. The purpose of this study was to examine Research Data Management (RDM) in Kenya’s agricultural research institutes with the view to proposing interventions to imp rove management, sharing and re use of agricultural research output. The objectives of the study were to : 1) assess the status of research data management in Kenya’s agricultural research institutes; and 2 ) to determine the legal and policy framework, ICT infrastructure and human capital that is available to facilitate RDM in Kenya’s agricultural research institutes. The study was underpinned by the Community Capability Model ( CCM) framework (Lyon , Ball, Duk e and Day, 2012) and Data Curation Centre (DCC) L ifecycle M odel (Higgins, 2008). The study adopted pragmatism ontology with mixed methods epistemology that enabled the researcher to collect quantitative data from a large sample of researchers in six purpos ively selected research institutes. Census was used to select the respondents who consisted of directors of institute s , heads of research, heads of IT and librarians. Both quantitative and qualitative data were collected. Quantitative data was analyzed usi ng SPSS to generate descriptive and inferential statistics while the qualitative data was analyzed thematically. The finding s of the study revealed that RDM legal framework did not exist in the institutes surveyed ; the RDM policies and regulations were outdated ; the institutes lacked unit/department to coordinate functions of RDM; there was limited RDM awareness and advocacy; the institutes lacked RDM security systems; the institutes suffered from lack of or inadequate RDM guidelines on standardization; technical infrastructure; skills and collaborative partnerships . Overall, the findings revealed that RDM was poorly managed . The study recommend ed among others, the e stablish ment of a formal data governance structure to address RDM issues , a legislati ve and policy framework for RDM; capacity building programs and plans, incentivisation of researchers; and a sound technical infrastructure .