1,720,962 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
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
Développement de méthodes computationnelles pour la transcriptomique spatiale basée sur l'image
No full textSpatial transcriptomics methods provide crucial insights into the cellular heterogeneity and spatial organization of complex multicellular systems. Combining molecular and spatial information helps elucidate tissue architecture during development and disease. Although several commercial approaches are available, they are expensive and offer limited flexibility. In contrast, homebuilt methods provide cost-effective and customizable alternatives. Still, Both commercial and homebuilt pose analytical challenges. One the crucial analysis step is to identify RNA expression profiles at the single-cell level. Therefore, precise cell segmentation is critical, as accurate RNA profiles depend on correctly segmented cells.In the first part of this thesis, I present as a step-by-step guide to implement and perform homebuilt spatial transcriptomics. This guide spans the entire life cycle of a project,from its initial definition to experimental choices, wet lab approaches, instrumentation,and analysis. Beyond a litteratrue ressource, I present two of my contribution. First a data analysis pipeline for our recently submitted manuscript, where we present autoFISH, a complete toolbox to perform automated single molecule FISH (smFISH). Second I present a simulation environment to guide the choice of marker genes by anticipating analysis challenges.In a second part I describe my work on cell segmentation in image-based spatialtranscriptomics (IST). I present two novel segmentation methods, each tailored fora different experimental set-up. First, I developed Comseg, apoint-cloud segmentation method based on a graph of RNA nodes weighted by co-expression. ComSeg can be used on IST data missing cell staining. Secondly, we developed RNAseg, a cell staining-based method taking advantage of RNA position to identify cell on various cell staining quality.Les méthodes de transcriptomique spatiale offrent des informations essentielles sur l'hétérogénéité cellulaire et l'organisation spatiale des systèmes multicellulaires complexes. La combinaison d'informations moléculaires et spatiales aide à étudier l'architecture des tissus pendant le développement et les maladies. Bien que plusieurs approches commerciales soient disponibles, elles sont coûteuses et offrent une flexibilité limitée. En revanche, les méthodes développées en laboratoire "open source" offrent des alternatives économiques et personnalisables. Cependant, tant les approches commerciales que celles "open source" développées en laboratoire posent des défis analytiques. L'une des étapes d'analyse cruciales consiste à identifier les profils d'expression de l'ARN au niveau de la cellule unique. Par conséquent, une segmentation cellulaire précise est essentielle, car des profils d'ARN fiables dépendent de cellules correctement segmentées.Dans la première partie de cette thèse, je propose un guide étape par étape pour mettre en œuvre et réaliser une transcriptomique spatiale développée en laboratoire. Ce guide couvre l'ensemble du cycle de vie d'un projet, depuis sa définition initiale jusqu'aux choix expérimentaux,, à l'instrumentation et à l'analyse. Au-delà d'une ressource bibliographique, je présente deux de mes contributions. Tout d'abord, un pipeline d'analyse de données pour notre manuscrit récemment soumis, où nous présentons autoFISH, une boîte à outils complète pour effectuer des expériences automatisées de FISH à molécule unique (smFISH). Ensuite, je présente un environnement de simulation conçu pour guider le choix des gènes marqueurs en anticipant les défis analytiques.Dans une deuxième partie, je décris mon travail sur la segmentation cellulaire pour transcriptomique spatiale basée sur l'image (IST). Je présente deux nouvelles méthodes de segmentation, chacune adaptée à un type IST. Premièrement, j'ai développé ComSeg, une méthode de segmentation basée sur un nuage de points et utilisant un graphe de nœuds d'ARN pondérés par co-expression. ComSeg peut être utilisé sur des données IST ne comportant pas de coloration cellulaire. Deuxièmement, nous avons développé RNAseg, une méthode basée sur la coloration cellulaire qui exploite la position de l'ARN pour identifier les cellules dans des données où la coloration cellulaire est de qualité variable
Développement de méthodes computationnelles pour la transcriptomique spatiale basée sur l'image
No full textSpatial transcriptomics methods provide crucial insights into the cellular heterogeneity and spatial organization of complex multicellular systems. Combining molecular and spatial information helps elucidate tissue architecture during development and disease. Although several commercial approaches are available, they are expensive and offer limited flexibility. In contrast, homebuilt methods provide cost-effective and customizable alternatives. Still, Both commercial and homebuilt pose analytical challenges. One the crucial analysis step is to identify RNA expression profiles at the single-cell level. Therefore, precise cell segmentation is critical, as accurate RNA profiles depend on correctly segmented cells.In the first part of this thesis, I present as a step-by-step guide to implement and perform homebuilt spatial transcriptomics. This guide spans the entire life cycle of a project,from its initial definition to experimental choices, wet lab approaches, instrumentation,and analysis. Beyond a litteratrue ressource, I present two of my contribution. First a data analysis pipeline for our recently submitted manuscript, where we present autoFISH, a complete toolbox to perform automated single molecule FISH (smFISH). Second I present a simulation environment to guide the choice of marker genes by anticipating analysis challenges.In a second part I describe my work on cell segmentation in image-based spatialtranscriptomics (IST). I present two novel segmentation methods, each tailored fora different experimental set-up. First, I developed Comseg, apoint-cloud segmentation method based on a graph of RNA nodes weighted by co-expression. ComSeg can be used on IST data missing cell staining. Secondly, we developed RNAseg, a cell staining-based method taking advantage of RNA position to identify cell on various cell staining quality.Les méthodes de transcriptomique spatiale offrent des informations essentielles sur l'hétérogénéité cellulaire et l'organisation spatiale des systèmes multicellulaires complexes. La combinaison d'informations moléculaires et spatiales aide à étudier l'architecture des tissus pendant le développement et les maladies. Bien que plusieurs approches commerciales soient disponibles, elles sont coûteuses et offrent une flexibilité limitée. En revanche, les méthodes développées en laboratoire "open source" offrent des alternatives économiques et personnalisables. Cependant, tant les approches commerciales que celles "open source" développées en laboratoire posent des défis analytiques. L'une des étapes d'analyse cruciales consiste à identifier les profils d'expression de l'ARN au niveau de la cellule unique. Par conséquent, une segmentation cellulaire précise est essentielle, car des profils d'ARN fiables dépendent de cellules correctement segmentées.Dans la première partie de cette thèse, je propose un guide étape par étape pour mettre en œuvre et réaliser une transcriptomique spatiale développée en laboratoire. Ce guide couvre l'ensemble du cycle de vie d'un projet, depuis sa définition initiale jusqu'aux choix expérimentaux,, à l'instrumentation et à l'analyse. Au-delà d'une ressource bibliographique, je présente deux de mes contributions. Tout d'abord, un pipeline d'analyse de données pour notre manuscrit récemment soumis, où nous présentons autoFISH, une boîte à outils complète pour effectuer des expériences automatisées de FISH à molécule unique (smFISH). Ensuite, je présente un environnement de simulation conçu pour guider le choix des gènes marqueurs en anticipant les défis analytiques.Dans une deuxième partie, je décris mon travail sur la segmentation cellulaire pour transcriptomique spatiale basée sur l'image (IST). Je présente deux nouvelles méthodes de segmentation, chacune adaptée à un type IST. Premièrement, j'ai développé ComSeg, une méthode de segmentation basée sur un nuage de points et utilisant un graphe de nœuds d'ARN pondérés par co-expression. ComSeg peut être utilisé sur des données IST ne comportant pas de coloration cellulaire. Deuxièmement, nous avons développé RNAseg, une méthode basée sur la coloration cellulaire qui exploite la position de l'ARN pour identifier les cellules dans des données où la coloration cellulaire est de qualité variable
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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