Uzwyshyn, Raymond2022-08-082022-08-082022-07-26Uzwyshyn, R. (2022). From research data and datasets to Artificial Intelligence and discovery: Online data research repositories and digitals scholarly ecosystems. Presented at the New Horizons in Artificial Intelligence in Libraries IFLA WLIC Satellite Conference, Dublin, Ireland.https://hdl.handle.net/10877/16040Online 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.Image39 pages1 file (.pdf)1 file (.pptx)enonline data librariesonline data research repositoriesbig dataartificial Intelligenceonline research ecosystemsmachine learningdeep learningneural networksPresentation