Leadership to advance data and information science at Virginia Tech library

Griffin, J.

doi:10.3233/isu-190042

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Through this collaboration, they realized core skills for librarians that they already have and provided advice on how to further enhance these skills on the go. Virginia Tech Newman Library also took the collaborative approach and formed a team of disciplinary data and informatics consultants, enabling collaborations across disciplines and various units on campus (Griffin & Lawlor 2019). A similar approach was used by three institutions -University of Wisconsin Madison, University of Massachusetts Amherst Libraries and Tufts Universitythat developed their research data management services after the National Science Foundation began requiring Data Management Plans in grant proposals (Raboin et al 2013).…”

Section: Data Management Services (N=21)mentioning

confidence: 99%

Research Support Services in STEM Libraries: A Scoping Review

Tchangalova

Coalter

Trost

et al. 2021

ISTL

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As science and technology libraries continue to evolve, specialized research support services are developed and offered at academic institutions or research organizations. Making sense of this changing landscape and determining the best programs for an institution can be a daunting task, especially for early-career librarians. This article aims to provide an overview of various small to medium size non-traditional or specialized research support services in academic and special libraries serving Science, Technology, Engineering and Mathematics (STEM) disciplines. A systematic search of five databases was conducted for articles that described the development and implementation of research support services. Non-traditional or specialized research support services identified in this scoping review fall in the following areas: bibliometrics/altmetrics, data management services, geographic information systems, patents, and systematic reviews. The paper provides a detailed foundation for novice and experienced STEM librarians to offer innovative library services or enhance existing research support services.

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Section: Data Management Services (N=21)mentioning

confidence: 99%

Research Support Services in STEM Libraries: A Scoping Review

Tchangalova

Coalter

Trost

et al. 2021

ISTL

View full text Add to dashboard Cite

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“…Let D be the nodes with minsize obtained by formula (23); let UZ (D) and RZ (D) be the out-degree and in-degree of D, respectively. If node D is a book or document, UZ (D) can be calculated by…”

Section: Calculation Of Meta-path Importancementioning

confidence: 99%

“…e information isolated island and overloading of book data hinder users from acquiring and sharing of L&I [20,21]. L&I information is commonly organized in two modes: information fusion and information aggregation [22,23]. Paramonova [24] enhanced the semantics of L&I information through information classification, ontology analysis, and data association method and relied on the semantics for knowledge organization and visual metering.…”

Section: Introductionmentioning

confidence: 99%

Information Visualization from the Perspective of Big Data Analysis and Fusion

Lin

2021

Scientific Programming

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In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. The previous research into the information association of L&I visualization network rarely tries to construct such a network or explore the information association of the network. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in detail. Experimental results verify the effectiveness of the proposed algorithm.

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Information Visualization from the Perspective of Big Data Analysis and Fusion

Lin

2021

Wseas Transactions on Computers

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In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. However, there are several defects with the research on information association of L&I visualization network: the lack of optimization of network layout algorithms, and the absence of L&I information fusion and comparison in multiple disciplines, in the big data environment. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network, and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in details. Experimental results verify the effectiveness of the proposed algorithm

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Leadership to advance data and information science at Virginia Tech library

Cited by 3 publications

References 6 publications

Research Support Services in STEM Libraries: A Scoping Review

Research Support Services in STEM Libraries: A Scoping Review

Information Visualization from the Perspective of Big Data Analysis and Fusion

Information Visualization from the Perspective of Big Data Analysis and Fusion

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