Research data management is considered a critical step in the research process among researchers. Researchers are required to submit RDM plans with details about data storage, data sharing, and reuse procedures when submitting research proposals for grants. This chapter presents findings of an investigation into the perceptions and practices of ZARI researchers towards research data management. Mixed methods research using a self-administered questionnaire was adopted for data collection. Fifty-one researchers were sampled and recruited for participation into the study. The study established that the majority of the researchers were not depositing their research data in central repositories; data was kept on individual's devices and was therefore not readily available for sharing. The major challenges being faced by researchers included lack of a policy, lack of a repository, and inadequate knowledge in RDM. The study concludes that research data at ZARI was not being professionally managed. The study recommends for formulation of policies, establishment of repository and staff training.
TopBackground
Research data has been defined by Corti et al (2014: Viii) as “any research material resulting from primary data collection or generation, qualitative or quantitative or derived from existing sources intended to be analysed in the course of a research project”. Research data can be “numerical, textual, digitized materials, images, recordings or model of scripts”. Research data is actually collected, observed, or created, for purposes of analysis to produce original research results. Research data varies: it can be laboratory notebooks, field notebooks and documents which contain text. The data can also be numerical, descriptive, visual or tactile. It can be raw, cleaned or processed. Usually, all the data is included as research data even though much of it is currently being created in digital format.
The management of research data is commonly referred to as research data management (RDM). RDM is defined as “the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results” (Whyte & Tedds, 2011:1). The activities involved in research data management include: “design and creation of data, storage, security, preservation, retrieval, sharing, and reuse” of data (Cox & Pinfield, 2013). RDM is complex, often requiring collaborative input from different stakeholders in institutions. In essence RDM “requires technical capabilities, ethical considerations, legal and governance frameworks” (Cox & Pinfield, 2013:1-2).
The management of research data is categorized into two basic types namely “data management” and “digital curation”. Data management is carried out “during the active phase of the data life cycle”; that is when researchers are generating and making use of the data themselves. Digital curation is the management of research data during and after the research life cycle phase and involves digital storing and preservation as well as providing access to the data (Witt, 2010). As already mentioned RDM is important and has manifold benefits to the research community and the public at large. Fry et al (2009: iii) summarises the importance of RDM as follows: