From Research Data and Datasets to Artificial Intelligence and Discovery: Online Data Research Repositories and Digital Scholarly Ecosystems

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dc.contributor.authorUzwyshyn, Raymond
dc.date.accessioned2022-08-03T13:28:21Z
dc.date.available2022-08-03T13:28:21Z
dc.date.issued2022-07
dc.description.abstractOnline networked data research repositories allow sharing and archiving of research data for open science and global research. This sharing opens data to modern interoperability and metadata for search, retrieval, and larger possibilities of open scholarly research ecosystems. Data research repositories are currently being leveraged to accelerate global research, promote international collaboration, and innovate on levels previously thought impossible. Research data repositories may also link data to further content from online publications and other digital communication and aggregation tools. This article pragmatically overviews such a data and content-centered ecosystem at Texas State University Libraries in the United States. The research then discusses the ecosystem's next level of planning and construction involving both bigger data possibilities for AI infrastructures\enabling researchers and their data towards Deep Learning (Neural Net) possibilities. The research uses examples of recent digitized medical image datasets for Cancer/melanoma detection through Deep Learning/Neural Net for global open science possibilities. These methodologies show large promise in making good use of online open data repositories, digital library ecosystems and online datasets. Recent AI research highlights the utility of several easily available online open-source digital library data repository and ecosystem components. An online data-centered research ecosystem accelerates open science, research and discovery on global levels. This open-source ecosystem and software infrastructure may be easily replicated by research institutions. Creating open online data infrastructures for research communities enables future global data and research, collaboration and the advancement of science, the academic research cycle on networked global levels.
dc.description.departmentUniversity Libraries
dc.description.sponsorshipSpecial Interest Group in Big Data, International Federation of Library Associations Information Technology Standing Committee, International Federation of Library Associations
dc.formatText
dc.format.extent16 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationUzwyshyn, R. (2022). From research data and datasets to Artificial Intelligence and discovery: Online data research repositories and digital scholarly ecosystems. Proceedings for International Federation of Library Associations World Library Information Congress (WLIC 2022).
dc.identifier.urihttps://hdl.handle.net/10877/16022
dc.language.isoen
dc.publisherInternational Federation of Library Associations
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceNew Horizons in Artificial Intelligence in Libraries, International Federation of Library Associations and Institutions, July 2022, National University of Ireland, Galway, Ireland.
dc.subjectartificial intelligence
dc.subjectneural nets
dc.subjectdeep learning
dc.subjectbig data
dc.subjectresearch data repositories
dc.subjectonline data research ecosystems
dc.subjectonline data research repositories
dc.subjectDataverse
dc.subjectTexas Data Repository
dc.titleFrom Research Data and Datasets to Artificial Intelligence and Discovery: Online Data Research Repositories and Digital Scholarly Ecosystems
dc.title.alternativeOnline Data Research Repositories and Digital Scholarly Ecosystems: From Research Data and Datasets to Artificial Intelligence and Discovery
dc.typeArticle

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