1,721,007 research outputs found
Fig. 3 in Predicting the global mammalian viral sharing network using phylogeography
No full textFig. 3 Taxonomic and geographic patterns of mean predicted viral sharing link numbers (degree centrality). Top row: all viral sharing links; middle row: viral sharing links with species in the same order; bottom row: viral sharing links with species in another order. a, c, e Average species-level viral sharing link numbers for mammalian orders in our dataset. Bars represent means; error bars represent standard errors. b, d, f Geographic distributions of mean viral sharing link numbers. Distributions were derived by summing the viral sharing link numbers of all species inhabiting a 25 km2 grid square and dividing them by the number of species inhabiting the grid square, giving mean degree number at the grid level.Published as part of Albery, Gregory F., Eskew, Evan A., Ross, Noam & Olival, Kevin J., 2020, Predicting the global mammalian viral sharing network using phylogeography, pp. 364001 in Nature Communications 11 (1) on page 5, DOI: 10.1038/s41467-020-16153-4, http://zenodo.org/record/381812
Fig. 1 Viral sharing GAMM outputs and data distribution. a in Predicting the global mammalian viral sharing network using phylogeography
No full textFig. 1 Viral sharing GAMM outputs and data distribution. a Predicted viral sharing probability increases with increasing phylogenetic relatedness; the different coloured lines represent different geographic overlap values. b Predicted viral sharing probability increases with increasing geographic overlap; the different coloured lines represent different phylogenetic relatedness values. c The geographic overlap:phylogenetic similarity interaction surface, where the darker colours represent increased probability of viral sharing. White contour lines denote 10% increments of sharing probability. Labels have been removed from some contours to avoid overplotting. d Hexagonal bin chart displaying the data distribution, which was highly aggregated at low values of phylogenetic similarity and especially of geographic overlap.Published as part of Albery, Gregory F., Eskew, Evan A., Ross, Noam & Olival, Kevin J., 2020, Predicting the global mammalian viral sharing network using phylogeography, pp. 364001 in Nature Communications 11 (1) on page 3, DOI: 10.1038/s41467-020-16153-4, http://zenodo.org/record/381812
Comparative Network Studies Database
No full textDataset containing information on all comparative social network studies used in the paper Comparative Approaches in Social Network Ecology. Dataset has 49 rows. Columns as follows: Title: The title of the study Author: Information on the authorship team ASNR: Whether or not the study used the animal social network repository (ASNR). Two-level variable (Y/N) Disease simulation?: Whether or not the study included epidemiological simulations. Two-level variable (Y/N) Number of networks: Number of distinct networks in study (where information is clear) Number of species: Number of species in study (where information is clear) Humans: Whether or not the study included humans. Two-level variable (Y/N) Research Topic: Research area of study. Free text. Research Field: Broad research field of study. (Beh=Behaviour; Dis=Disease; Wel=Welfare; Meth=Methodological; Dat=Dataset) Primates?: Whether or not the study included primates. Two-level variable (Y/N) Comments: Any additional information. Free text. Link: Link to the studySilk, Matthew; Albery, Gregory F. (2023). Comparative Network Studies Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24552514.v
Fig. 2 in Predicting the global mammalian viral sharing network using phylogeography
No full textFig. 2 The predicted viral sharing network predicts observed trends in an independent dataset. In all figures, points are jittered along the x-axis according to a density function; the black points and associated error bars are means ± standard errors. a Species pairs with higher predicted viral sharing probability from our model were more likely to be observed sharing a virus in the independent EID2 dataset. This comparison excludes species pairs that were also present in our training data. b Species that hosted a zoonotic virus in our dataset had more viral sharing links in the predicted all-mammal network than those without zoonotic viruses. c Species that had never been observed with a virus have fewer links in the predicted network than species that were known to host viruses in the EID2 dataset only, in our training data only, or in both. The y-axis represents viral sharing link number, scaled to have a mean of 0 and a standard deviation of 1 within each order for clarity. Black points represent means; error bars represent standard errors. Supplementary Figure 5 displays these same data without the within-order scaling.Published as part of Albery, Gregory F., Eskew, Evan A., Ross, Noam & Olival, Kevin J., 2020, Predicting the global mammalian viral sharing network using phylogeography, pp. 364001 in Nature Communications 11 (1) on page 4, DOI: 10.1038/s41467-020-16153-4, http://zenodo.org/record/381812
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
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