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Metcalfe's Law (1993). The Value of Compatibly Communicating Components.

Metcalfe's Law (1993). The Value of Compatibly Communicating Components.

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Conference Paper
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This research presents elements necessary to develop a Digital Scholarship Research Ecosystem for a university , college or research institution. Software systems, hardware, human resources and timelines are outlined with brief theoretical overviews and a pragmatic focus on 'open-source' (freely available) software, best-in-class applications and g...

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Online 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 and foundational AI infrastructure. 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.
Preprint
Full-text available
This research overviews methodologies for building new AI services within the 'third interdisciplinary space' of the academic library, utilizing pragmatic steps taken by Texas State University Libraries, USA. Data-centered steps for setting up digital scholarly research ecosystem infrastructure are reviewed. Setting needed data-centered groundwork for library AI services enables research, data and media towards wider global online AI possibilities. Library AI external scholarly communications infrastructures and services are discussed as well as educational methodologies involving incremental steps for foundational AI scaffolding. Pathways from data collection to data cleaning, analytics and data visualization to AI applications are clarified and preliminary focused steps needed are forwarded to move library staff, research faculty and graduate students towards these new AI possibilities. Data-centered ecosystems, retooling and building on present library staff expertise as well as Data research repositories, algorithmic and programmatic literacy are recommended for later AI possibilities. Preliminary AI library working groups and R&D prototype methodologies for scaling up future library services and human resource infrastructures are considered. Recommended emergent pathways are prescribed to create library AI infrastructures to better prepare for a currently occurring global AI paradigm shift.