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Methods and Practices for Institutional Benchmarking based on Research Impact and Competitiveness: A Case Study of ShanghaiTech University

CNCI in the discipline. In the WoS data, Keywords Plus extracted from titles of cited publications and footnotes provide supplementary terms to the Author’s Keywords ( Chen, 2017 ; Garfield, 1990 ). Based on keywords and using the full counting method maps were generated by VOSviewer (version 1.6.10). Univ E was selected as the leading university in the area of molecular biology & genetics. Its keyword co-occurrence map led to six main clusters (indicated with colored nodes and links, Figure 2a) . The themes of these clusters are stem cell and genes (blue cluster

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Disclosing and Evaluating Artistic Research

discrediting the potential value and contributions of artistic research, we illustrate its artificial nature—stemming from the sudden elevation of higher art education to university status and its associated expectations. Arguing that this precipitous introduction of higher art education into the traditional university sector has deprived the newly established notion of artistic research from the time needed to organize disciplinary demarcations and goals, we point to the difficulties of assuming a broader, more abstract perspective on the issue of evaluating and assessing

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Usage Count: A New Indicator to Detect Research Fronts

2011.59 detected by usage count and 2009.07 by times cited. Table 2 Recentness of the research fronts detected by times cited. Clusters No. of references Mean cited year No. of citing articles Recentness Emerging peptide nanomedicine 29 2008 54 2010.44 Pluripotent stem cell 25 2007 56 2008.89 Adipose-derived stem cell 25 2003 83 2010.14 Somatic cell 22 2008 52 2009.33 Mesenchymal stem cell 22 2003 55 2010.07 Induced pluripotent stem cell 22 2008 41 2009

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Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

maps of science based on the new Web-of-Science categories Scientometrics 94 2 589 593 Leydesdorff, L., & Wagner, C. (2009). Is the United States losing ground in science? A global perspective on the world science system. Scientometrics, 78(1), 23–36. 10.1007/s11192-008-1830-4 Leydesdorff L. Wagner C. 2009 Is the United States losing ground in science? A global perspective on the world science system Scientometrics 78 1 23 36 Lin, C.S.L., & Ho, Y.S. (2015). A bibliometric analysis of publications on pluripotent stem cell research. Cell Journal

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A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

is the fierce competition that stems from extreme homogenization, making it difficult to generate a cluster effect and achieve collaboration effectively ( Huang, 2014 ). Therefore, methods that are solely based on technical similarities are not necessarily effective in identifying IURC partners. The method proposed in this paper is meant to improve the matching process for collaborative partners, and thereby, overcome barriers in the innovation process that prevent collaborations from forming and to encourage innovations to move along the chain. In this study, we

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Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

through interest groups within a larger association, several targeted conferences, and focused publications, such as the International Journal of Metadata , Semantics , and Ontologies . Despite strong, historical grounding, metadata research in data science, and the larger digital ecosystem, is restrained by not being considered a true scientific endeavor. More specifically, challenges to metadata research stem, to a large degree, from two intersecting factors: 1) the utilitarian nature of metadata, and 2) historical and traditional perceptions of metadata. 3

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Measuring and Visualizing Research Collaboration and Productivity

Porter, A.L., Schoeneck, D.J., Roessner, D., & Garner, J. (2010). Practical research proposal and publication profiling. Research Evaluation, 19(1), 29–44. 10.3152/095820210X492512 Porter A.L. Schoeneck D.J. Roessner D. Garner J. 2010 Practical research proposal and publication profiling Research Evaluation 19 1 29 44 Porter, A.L., Schoeneck, D.J., Solomon, G., Lakhani, H., & Dietz, J. (2013). Measuring and mapping interdisciplinarity: Research & evaluation on education in science & engineering (“REESE”) and STEM. In American Education Research Association Annual

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CitationAS: A Tool of Automatic Survey Generation Based on Citation Content

results in higher ranking. Table 2 Top 20 Phrases According to High Frequency. Phrase (Frequency) Phrase (Frequency) cell line (37507) reactive oxygen species (5160) gene expression (37001) central nervous system (4418) amino acid (35165) smooth muscle cell (3439) transcription factor (25626) protein protein interaction (3286) cancer cell (25605) single nucleotide polymorphism (2535) stem cell (22567) tumor necrosis factor (2482) growth factor (17531) genome wide association (2386

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A Local Adaptation in an Output-Based Research Support Scheme (OBRSS) at University College Dublin

first year the OBRSS was implemented, 85% of academic staff updated their profiles as opposed to 75% over the previous three years. Using the publication records in CRIS, we can compare the coverage of research outputs per School and College in Scopus and the OBRSS ranked publication list. Although many international university ranking organisations use either Scopus or Web of Science as a data source to evaluate the research performance of an institution, the data source only works well for STEM (Science, Technology, Engineering & Mathematics) disciplines where

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Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

similarities between a search term and each source in the semantic network. Usually the angle (cosine similarity) between two search query vectors is calculated as the index of the similarity between these two search queries. Lucchese et al. (2011) first processed the search log, including the removal of empty log records and stop words, as well as stemming and deleting sessions that last too long or include too many queries, which indicates it is likely produced by machines. Then they calculated the word and semantic similarities between queries using two methods to

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