1,720,971 research outputs found
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
Modélisation multi-échelles des défauts d'irradiation dans les métaux cubiques centrés
No full textThe modelling of metals under extreme conditions requires a multi-scale approach. Indeed, for reasons of numerical complexity, it is not possible to use a unique formalism, accurate and transferable for all scales of simulation. In general, one or more methods have to be used for a given spatial and temporal scale. These methods are based on approximations - physical and/or numerical - which are more and more numerous when the simulated space and time scales increase. Multi-scale modelling can therefore be summarised as a - utopian - balance between simulated "space-time" scales and representativeness of phenomena involved in the transformations of the system studied.Multi-scale methods applied to materials science must also take into account the effects of finite temperatures in order to simulate structures in their nominal conditions of industrial use. In recent years, temperature effects have been treated in the context of local approximations: harmonic and/or quasi-harmonic. Taking into account anharmonic effects remains difficult - but necessary to be representative of physical phenomena - and is a main research topic for the improvement of multi-scale models.The objectives of this thesis are (i) to develop new simulation tools to extend the applicability of multi-scale methods (ii) and to estimate finite temperature quantities. We rely on a set of methods, in full expansion in the field of materials science: Machine Learning. These methods allow to develop systematic statistical tools and to study correlations more easily.In a first step, we develop methods for fast estimation of quantities derived from the harmonic vibrational properties: the defect formation entropy and the attack frequencies. The formalism developed is accurate, transferable and allows to greatly reduce the numerical cost (traditionally evolving as O(N^3) where N is the number of particles in the system) whose numerical complexity evolves as O(N).In a second step, we are interested in quantifying the influence of anharmonic effects for metallic systems. We develop a direct computational approach (coupling Machine Learning and free energy methods) to calculate the self-diffusion coefficient, with an accuracy of ab initio, in body-centered cubic metals. We directly confront our results with the experiment and we give a - general - explanation, for the cubic centered metals, of the anomalous behavior of the self-diffusion coefficient at high temperatures.La modélisation des métaux sous conditions extrêmes nécessite une approche de type multi-échelles. En effet, pour des raisons de complexité numérique, il n'est pas possible d'utiliser un formalisme unique, précis et transférable pour toutes les échelles de simulation. Il correspond, en général, une ou plusieurs méthodes utilisables pour une échelle spatiale et temporelle donnée. Ces méthodes se basent sur des approximations - physiques et/ou numériques - dont le nombre croît lorsque les échelles d'espace et de temps simulés augmentent. La modélisation multi-échelle peut donc se résumer comme un - utopique - équilibre entre échelles "spatio-temporelles" simulées et représentativité des phénomènes mis en jeu lors des transformations du système étudié.Les méthodes multi-échelles appliquées à la science des matériaux doivent aussi prendre en compte les effets de températures finies afin de simuler des structures dans leurs conditions nominales d'utilisation industrielles. Ces dernières années, les effets de température ont été traités dans le cadre d'approximations locales : harmonique et/ou quasiharmonique. La prise en compte des effets anharmoniques reste difficile - mais nécessaire pour rendre compte de certains phénomènes physiques - et est un sujet de recherche à part entière pour l'amélioration des modèles multi-échelles.Les objectifs de cette thèse sont de (i) développer de nouveaux outils de simulations afin d’étendre les domaines d’applicabilité des méthodes multi-échelles (ii) et d’estimer des grandeurs de températures finies. Nous nous basons sur un ensemble de méthodes en plein essor dans le domaine de la science des matériaux : le Machine Learning. Ces méthodes permettent de développer des outils statistiques systématiques et d'étudier plus facilement des corrélations.Dans un premier temps, nous développons des méthodes d’estimations rapides de quantités dérivées de propriétés vibrationnelles harmoniques : l’entropie de formation de défauts et les fréquences d’attaque. Le formalisme développé est précis, transférable et permet de réduire grandement le coût numérique (évoluant traditionnellement comme O(N^3) où N est le nombre de particules dans le système) dont la complexité numérique évolue comme O(N).Dans un deuxième temps, nous nous intéressons à quantifier l’influence des effets anharmoniques pour des systèmes métalliques. Nous développons une approche de calcul direct (couplant Machine Learning et méthodes d’énergie libre) permettant de calculer le coefficient d’auto-diffusion, avec une précision "ab initio", des métaux cubiques centrés. Nous confrontons directement nos résultats avec l’expérience et nous donnons une explication, générale pour les métaux cubiques centrés, du comportement anormal du coefficient d’auto-diffusion à hautes températures
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
Multi-scale modelling of point defects in body-centered cubic metals
No full textLa modélisation des métaux sous conditions extrêmes nécessite une approche de type multi-échelles. En effet, pour des raisons de complexité numérique, il n'est pas possible d'utiliser un formalisme unique, précis et transférable pour toutes les échelles de simulation. Il correspond, en général, une ou plusieurs méthodes utilisables pour une échelle spatiale et temporelle donnée. Ces méthodes se basent sur des approximations - physiques et/ou numériques - dont le nombre croît lorsque les échelles d'espace et de temps simulés augmentent. La modélisation multi-échelle peut donc se résumer comme un - utopique - équilibre entre échelles "spatio-temporelles" simulées et représentativité des phénomènes mis en jeu lors des transformations du système étudié.Les méthodes multi-échelles appliquées à la science des matériaux doivent aussi prendre en compte les effets de températures finies afin de simuler des structures dans leurs conditions nominales d'utilisation industrielles. Ces dernières années, les effets de température ont été traités dans le cadre d'approximations locales : harmonique et/ou quasiharmonique. La prise en compte des effets anharmoniques reste difficile - mais nécessaire pour rendre compte de certains phénomènes physiques - et est un sujet de recherche à part entière pour l'amélioration des modèles multi-échelles.Les objectifs de cette thèse sont de (i) développer de nouveaux outils de simulations afin d’étendre les domaines d’applicabilité des méthodes multi-échelles (ii) et d’estimer des grandeurs de températures finies. Nous nous basons sur un ensemble de méthodes en plein essor dans le domaine de la science des matériaux : le Machine Learning. Ces méthodes permettent de développer des outils statistiques systématiques et d'étudier plus facilement des corrélations.Dans un premier temps, nous développons des méthodes d’estimations rapides de quantités dérivées de propriétés vibrationnelles harmoniques : l’entropie de formation de défauts et les fréquences d’attaque. Le formalisme développé est précis, transférable et permet de réduire grandement le coût numérique (évoluant traditionnellement comme O(N^3) où N est le nombre de particules dans le système) dont la complexité numérique évolue comme O(N).Dans un deuxième temps, nous nous intéressons à quantifier l’influence des effets anharmoniques pour des systèmes métalliques. Nous développons une approche de calcul direct (couplant Machine Learning et méthodes d’énergie libre) permettant de calculer le coefficient d’auto-diffusion, avec une précision "ab initio", des métaux cubiques centrés. Nous confrontons directement nos résultats avec l’expérience et nous donnons une explication, générale pour les métaux cubiques centrés, du comportement anormal du coefficient d’auto-diffusion à hautes températures.The modelling of metals under extreme conditions requires a multi-scale approach. Indeed, for reasons of numerical complexity, it is not possible to use a unique formalism, accurate and transferable for all scales of simulation. In general, one or more methods have to be used for a given spatial and temporal scale. These methods are based on approximations - physical and/or numerical - which are more and more numerous when the simulated space and time scales increase. Multi-scale modelling can therefore be summarised as a - utopian - balance between simulated "space-time" scales and representativeness of phenomena involved in the transformations of the system studied.Multi-scale methods applied to materials science must also take into account the effects of finite temperatures in order to simulate structures in their nominal conditions of industrial use. In recent years, temperature effects have been treated in the context of local approximations: harmonic and/or quasi-harmonic. Taking into account anharmonic effects remains difficult - but necessary to be representative of physical phenomena - and is a main research topic for the improvement of multi-scale models.The objectives of this thesis are (i) to develop new simulation tools to extend the applicability of multi-scale methods (ii) and to estimate finite temperature quantities. We rely on a set of methods, in full expansion in the field of materials science: Machine Learning. These methods allow to develop systematic statistical tools and to study correlations more easily.In a first step, we develop methods for fast estimation of quantities derived from the harmonic vibrational properties: the defect formation entropy and the attack frequencies. The formalism developed is accurate, transferable and allows to greatly reduce the numerical cost (traditionally evolving as O(N^3) where N is the number of particles in the system) whose numerical complexity evolves as O(N).In a second step, we are interested in quantifying the influence of anharmonic effects for metallic systems. We develop a direct computational approach (coupling Machine Learning and free energy methods) to calculate the self-diffusion coefficient, with an accuracy of ab initio, in body-centered cubic metals. We directly confront our results with the experiment and we give a - general - explanation, for the cubic centered metals, of the anomalous behavior of the self-diffusion coefficient at high temperatures
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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