Promoting Open Access to Scholarly Data: A Study of the Electronic Thesis and Dissertation (ETD) Initiative at the Public and Private Universities of Bangladesh
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Date
2023-11-17
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INFLIBNET Centre, Gandhinagar
Abstract
By providing ideas on how to develop a good research topic, problem statement formulation,
how to write a literature review without plagiarism, research methodology, data analysis,
and recommendations and conclusions, scholarly data such as ETDs play an increasingly
important role in improving the understanding of research and development. Academic
articles, papers, citations, figures, tables, research reports, theses, and dissertations, as well
as scholarly networks and digital libraries, are all included in the rapidly expanding category
of scholarly data. Open access (OA) refers to the unrestricted use of electronic resources and
open access to information. Any type of digital content, including text, data, software, audio,
video, and multi-media, is eligible for open access. The majority of them merely deal with text,
but an increasing number combine text with graphics, data, and executable code, nonscholarly
content, like as books, movies, and music, can also be covered by OA. This article
examines several facets of the "electronic dissertation and thesis (ETD) projects in private
and public universities, including software selection, metadata collection, long-term
preservation of digitized data, and other issues. The holistic approach to such projects that
this paper proposes is sound project planning, which considers not only technical issues but
also issues related to project policies, procedures, and copyright acknowledgments that
arise in the process of providing open access, and it concluded that it played an important
role in its success.
Description
26th International Symposium, ETD 2023, Gandhinagar, Gujarat, 26-28 October, 2023
Keywords
Bangladesh, Electronic Theses and Dissertations (ETD), Institutional Repositories, Open Access, Open Science, Private Universities, Research Data, Scholarly Data