181,871 research outputs found
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Ascopolyporus tibetensis F. M. Yu, Q. Zhao & T. Luangharn
No full text<i>Ascopolyporus tibetensis</i> F.M. Yu, Q. Zhao & T. Luangharn <p> <b>Host:</b> On the living stem of bamboo</p> <p> <b>Distribution:</b> China (Tibet)</p>Published as part of <i>Yu, Feng-Ming, Wei, De-Ping, Zhao, Qi, Tang, Song-Ming & Luangharn, Thatsanee, 2023, Ascopolyporus tibetensis (Cordycipitaceae, Hypocreales): a new species from Tibet, China, pp. 88-98 in Phytotaxa 592 (2)</i> on page 95, DOI: 10.11646/phytotaxa.592.2.2, <a href="http://zenodo.org/record/7840405">http://zenodo.org/record/7840405</a>
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Online Supplement 2 Appendix for Bowers, Zhao, and Ho (2023) Towards Hierarchical Cluster Analysis Heatmaps: R Tutorial and code
No full textThis document is the Hierarchical Cluster Analysis Heatmaps in R Tutorial and code for the Online Supplement 2 Appendix for the paper:
Bowers, A.J., Zhao, Y., Ho, E. (2023) Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College. The High School Journal
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Online Supplement 1 Appendix for Bowers, Zhao, and Ho (2023) Towards Hierarchical Cluster Analysis Heatmaps: R Shiny application code
No full textThis document is the R Shiny application code for the Online Supplement 1 Appendix for the paper:
Bowers, A.J., Zhao, Y., Ho, E. (2023) Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College. The High School Journal
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation
No full textCitation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods
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