OPEN ACCESS DATA RESEARCH REPOSITORIES FROM DATA AND RESEARCH ECOSYSTEMS TO ARTIFICIAL INTELLIGENCE AND DISCOVERY
dc.contributor.author | Uzwyshyn, Raymond | |
dc.date.accessioned | 2022-11-04T05:32:44Z | |
dc.date.available | 2022-11-04T05:32:44Z | |
dc.date.issued | 2022-10-27 | |
dc.description.abstract | Data research repositories allow sharing and archiving of research data for global research. Libraries open this sharing of data to modern metadata and interoperability for search, retrieval, and larger possibilities of global scholarly research ecosystems. Data research repositories are being leveraged to accelerate global research, promote international collaboration, and innovate on levels previously thought impossible. They link data to further content from online publications to multimedia digital communication and aggregation tools. This article pragmatically overviews a data and content-centered ecosystem and then discusses the ecosystem's next level of possibilities. This involves questions of big data and AI infrastructures for \enabling researchers towards Deep Learning (Neural Net) possibilities. These new areas show large promise in making good use of online open data repositories, digital library ecosystems and online datasets Recent AI research also highlights the utility of several 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 and universities globally. | en_US |
dc.identifier.citation | Uzwyshyn, R. (2022). Open Access Data Research Repositories From Data and Research Ecosystems to Artificial Intelligence and Discovery. [Presentation]. Nazarbayev University, Astana, Kazakhstan | en_US |
dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/6740 | |
dc.language.iso | en | en_US |
dc.publisher | Nazarbayev University library | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | Type of access: Open Access | en_US |
dc.subject | EALC 2022 | en_US |
dc.subject | Data Research Repositories | en_US |
dc.subject | open access | en_US |
dc.subject | Research Libraries | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Neural Nets | en_US |
dc.subject | Academic Libraries | en_US |
dc.subject | Big Data | en_US |
dc.subject | Open Source Research Ecosystems | en_US |
dc.title | OPEN ACCESS DATA RESEARCH REPOSITORIES FROM DATA AND RESEARCH ECOSYSTEMS TO ARTIFICIAL INTELLIGENCE AND DISCOVERY | en_US |
dc.type | Presentation | en_US |
workflow.import.source | science |
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