1,721,027 research outputs found
Supplemental Material, DS1_VET_10.1177_0300985818806049 - Lesions and Cellular Tropism of Natural Rift Valley Fever Virus Infection in Adult Sheep
No full textSupplemental Material, DS1_VET_10.1177_0300985818806049 for Lesions and Cellular Tropism of Natural Rift Valley Fever Virus Infection in Adult Sheep by Lieza Odendaal, Sarah J. Clift, Geoffrey T. Fosgate, and A. Sally Davis in Veterinary Pathology</p
Combined_supplemental_materials-Odendaal_et_al - Lesions and Cellular Tropism of Natural Rift Valley Fever Virus Infection in Young Lambs
No full textCombined_supplemental_materials-Odendaal_et_al for Lesions and Cellular Tropism of Natural Rift Valley Fever Virus Infection in Young Lambs by Lieza Odendaal, A. Sally Davis, Geoffrey T. Fosgate and Sarah J. Clift in Veterinary Pathology</p
sj-pdf-1-vdi-10.1177_10406387231154537 – Supplemental material for Diagnostic sensitivity and specificity of immunohistochemistry for the detection of rabies virus in domestic and wild animals in South Africa
No full textSupplemental material, sj-pdf-1-vdi-10.1177_10406387231154537 for Diagnostic sensitivity and specificity of immunohistochemistry for the detection of rabies virus in domestic and wild animals in South Africa by Drienie D. Claassen, Lieza Odendaal, Claude T. Sabeta, Geoffrey T. Fosgate, Debrah K. Mohale, June H. Williams and Sarah J. Clift in Journal of Veterinary Diagnostic Investigation</p
Practical Sample Size Calculations for Surveillance and Diagnostic Investigations
No full text The likelihood that a study will yield statistically significant results depends on the chosen sample size. Surveillance and diagnostic situations that require sample size calculations include certification of disease freedom, estimation of diagnostic accuracy, comparison of diagnostic accuracy, and determining equivalency of test accuracy. Reasons for inadequately sized studies that do not achieve statistical significance include failure to perform sample size calculations, selecting sample size based on convenience, insufficient funding for the study, and inefficient utilization of available funding. Sample sizes are directly dependent on the assumptions used for their calculation. Investigators must first specify the likely values of the parameters that they wish to estimate as their best guess prior to study initiation. They further need to define the desired precision of the estimate and allowable error levels. Type I (alpha) and type II (beta) errors are the errors associated with rejection of the null hypothesis when it is true and the nonrejection of the null hypothesis when it is false (a specific alternative hypothesis is true), respectively. Calculated sample sizes should be increased by the number of animals that are expected to be lost over the course of the study. Free software routines are available to calculate the necessary sample sizes for many surveillance and diagnostic situations. The objectives of the present article are to briefly discuss the statistical theory behind sample size calculations and provide practical tools and instruction for their calculation. </jats:p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
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
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