1,720,965 research outputs found
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
Random Forest for Hypothesis Testing: Development and Application to Cancer Detection
Full text linkHypothesis testing is the foundation of scientific inquiry. Contemporary data for hypothesis testing includes thousands of variables collected on a cohort composed of a small number of samples. From these data, machine learning technologies are employed to evaluate various hypotheses and statistics about an outcome, such as the presence or absence of a disease. We here ask a question that has been challenging to answer: does a set of variables provide enough relevant information about an outcome? We answer this question in this thesis by: (1) reducing this question (also known as the k-sample testing problem) to the well-known independence testing problem, (2) using a kernel decision forests, which are popular tools for classification and regression, to develop a new hypothesis test, and (3) estimate information-theoretic quantities directly from random forest which allows us to quantify uncertainty within the data set. We show the value of these approaches through extensive mathematical theory, simulated experiments, and applications to cancer detection. Specifically, when developing cancer detection models, we find that combinations of variable sets often decrease rather than increase sensitivity over the optimal single variable set. Based on these results, we suggest that our algorithms can more efficiently and reliably answer this question than existing approaches
Random Forest for Hypothesis Testing: Development and Application to Cancer Detection
Full text linkHypothesis testing is the foundation of scientific inquiry. Contemporary data for hypothesis testing includes thousands of variables collected on a cohort composed of a small number of samples. From these data, machine learning technologies are employed to evaluate various hypotheses and statistics about an outcome, such as the presence or absence of a disease. We here ask a question that has been challenging to answer: does a set of variables provide enough relevant information about an outcome? We answer this question in this thesis by: (1) reducing this question (also known as the k-sample testing problem) to the well-known independence testing problem, (2) using a kernel decision forests, which are popular tools for classification and regression, to develop a new hypothesis test, and (3) estimate information-theoretic quantities directly from random forest which allows us to quantify uncertainty within the data set. We show the value of these approaches through extensive mathematical theory, simulated experiments, and applications to cancer detection. Specifically, when developing cancer detection models, we find that combinations of variable sets often decrease rather than increase sensitivity over the optimal single variable set. Based on these results, we suggest that our algorithms can more efficiently and reliably answer this question than existing approaches
Multivariate Independence and k-sample Testing
Full text linkWith the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are multivariate, univariate tests are not applicable. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. We introduce hyppo, which includes many state of the art multivariate testing procedures. This thesis provides details for the implementations of each of the tests within a test hyppo as well as extensive power and run-time benchmarks on a suite of high-dimensional simulations previously used in different publications. The documentation and all releases for hyppo are available at https://hyppo.neurodata.io
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
