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dc.contributor.advisorMutula, Stephen O.
dc.creatorNg'eno, Emily Geruto.
dc.date.accessioned2018-11-29T10:40:51Z
dc.date.available2018-11-29T10:40:51Z
dc.date.created2018
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10413/15908
dc.descriptionDoctor of Philosophy in Information Studies. University of KwaZulu-Natal, Pietermaritzburg, 2018.en_US
dc.description.abstractResearch 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 different 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. The strategic importance of RDM within agricultural research institutes is to: enable scrutiny of research findings, prevent duplication of effort by enabling others to use the same data; promote innovation through retrieval, co-analysis of data, ensuring research 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 improve management, sharing and reuse 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, Duke and Day, 2012) and Data Curation Centre (DCC) Lifecycle Model (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 purposively selected research institutes. Census was used to select the respondents who consisted of directors of institutes, heads of research, heads of IT and librarians. Both quantitative and qualitative data were collected. Quantitative data was analyzed using SPSS to generate descriptive and inferential statistics while the qualitative data was analyzed thematically. The findings 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 recommended among others, the establishment of a formal data governance structure to address RDM issues, a legislative and policy framework for RDM; capacity building programs and plans, incentivisation of researchers; and a sound technical infrastructure.en_US
dc.language.isoen_ZAen_US
dc.subject.otherResearch data management.en_US
dc.subject.otherKenya agricultural research institutes.en_US
dc.subject.otherCommunity Capability Model (CCM) framework (Lyon, Ball, Duke and Day, 2012).en_US
dc.subject.otherData Curation Centre (DCC) Lifecycle Model (Higgins, 2008).en_US
dc.subject.otherPragmatism.en_US
dc.subject.otherMixed methods.en_US
dc.subject.otherAgricultural research output.en_US
dc.titleResearch data management in Kenya's agricultural research institutes.en_US
dc.typeThesisen_US


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