Speakers
Description
Data management is organizing, describing, sharing, and preserving the research data in research repositories. Data management plays an important role in data dissemination, and reuse of data for future purposes. The data is shared to broaden the impact and visibility of the research, encourage collaboration between data users and data creators, and allow others to replicate or validate your results thereby improving scientific integrity, to provide credits as a research output in its own right. Data management practices are integral to the entire research lifecycle, from planning for what kind of data you will collect to depositing your data set in a repository. Due to poor data management, challenges such as the finding, accessibility, availability, sharing, and re-use of data remains crucial in data repositories. Therefore, there is a need for an effective data management practices in the research repositories that will enhance data sharing. This paper, therefore, focuses on methods that intend to make research repositories available and accessible in order to improve data sharing quality. Machine learning (ML), as a promising emerging technology, will therefore be utilized in this study to improve the availability and the accessibility of research repositories sharing. These ML resources sharing algorithms will increase the visibility and accessibility of the research to a broader audience instead of sharing data with constrained audience. The ML algorithms will further be evaluated to increase the audience of researchers accessing and sharing opinions on research repositories. Repositories also manage and maintain data ensuring long-term access and additional demands on the audience.
Keywords: Data management, research repositories, resource sharing, accessibility and availability.
