1,721,000 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
Mesures passives sans fil : outil, redondance, mesures et analyses
No full textUnderstanding wireless traffic is fundamental for improving networks and designing advanced algorithms and protocols. In this context, passive measurements have the edge over active measurements, as there is no requirement for any modification in existing network devices. Passive measurements are often less expensive and easier to deploy than other methods. This approach involves monitoring the wireless medium and collecting data on various network parameters, such as signal strength, channel occupancy, and packet loss. It consists of deploying multiple sniffers throughout the target area (sniffers are devices operating in monitor mode that collect the wireless packets regardless of their nature). However, one of the main challenges with passive measurements is ensuring trace completeness, or the ability to collect a complete and accurate dataset. We know that a single sniffer cannot capture all the traffic due to the inherent characteristics of the wireless medium where the environment can be highly dynamic and unpredictable. Several factors can impact trace completeness in wireless passive measurements. These include environmental factors, such as interference from other wireless devices, changes in the physical environment (such as moving objects), and variations in wireless signal propagation due to changes in atmospheric conditions. Additionally, issues with the measurement equipment itself, such as calibration errors or data processing issues, can also impact trace completeness. The importance of trace completeness in wireless passive measurements cannot be overstated. Inaccurate or incomplete data can lead to incorrect conclusions about network performance, which can have significant implications for network planning, optimization, and troubleshooting. For example, incomplete data can result in missed opportunities to identify and address network issues, and incorrect or incomplete trajectory reconstruction. In this thesis, we study the quality of traces captured by a sniffer and investigate the resulting improvements by introducing redundancy in the number of sniffers. We explore the impact of the following two aspects on the quality of wireless traces: the number of sniffing devices and the type of hardware used. We study the variation in the Received Signal Strength Indicator (RSSI) and its impact on distance estimation. The analysis is helped by the development of a readily-usable and easily-available tool, called PyPal, for the synchronization and merging of Wi-Fi traces collected simultaneously.La compréhension du trafic sans fil est fondamentale pour améliorer les réseaux et concevoir des algorithmes et des protocoles avancés. Dans ce contexte, les mesures passives ont l'avantage sur les mesures actives, car elles ne dépendent d'aucune modification des équipements réseau existants. Elles sont souvent moins coûteuses et plus faciles à déployer que d'autres méthodes. Cette approche consiste à surveiller le support sans fil et à collecter des données sur divers paramètres de réseau, tels que la force du signal, l'occupation des canaux et la perte de paquets. Elle consiste à déployer plusieurs sniffeurs dans la zone cible (les sniffeurs sont des dispositifs fonctionnant en « monitor mode » qui collectent les paquets sans fil indépendamment de leur nature). Cependant, l'un des principaux défis des mesures passives est d'assurer la complétude de la trace, c'est-à-dire la capacité à collecter un ensemble de données complet et précis. Nous montrons qu'un seul sniffeur ne peut pas capturer tout le trafic en raison des caractéristiques inhérentes du support sans fil, où l'environnement peut être hautement dynamique et imprévisible. Il existe plusieurs facteurs qui peuvent affecter la complétude de la trace dans les mesures passives sans fil. Celles-ci incluent des facteurs environnementaux, tels que les interférences provenant d'autres dispositifs sans fil, les changements dans l'environnement physique (comme les objets en mouvement) et les variations de propagation du signal sans fil dues aux changements des conditions atmosphériques. De plus, des problèmes avec l'équipement de mesure lui-même, tels que des erreurs de calibration ou des problèmes de traitement des données, peuvent également affecter la complétude de la trace. L'importance de la complétude de la trace dans les mesures passives sans fil ne peut être surestimée. Des données inexactes ou incomplètes peuvent conduire à des conclusions incorrectes sur les performances du réseau, ce qui peut avoir des implications significatives pour la planification, l'optimisation et le dépannage du réseau. Par exemple, des données incomplètes peuvent entraîner des opportunités manquées pour identifier et résoudre des problèmes de réseau, ainsi qu'une reconstruction de trajectoire incorrecte ou incomplète. Dans cette thèse, nous étudions la qualité des traces capturées par des sniffeurs et examinons les améliorations résultantes en introduisant de la redondance dans le nombre de sniffeurs. Nous étudions l'impact des deux aspects suivants sur la qualité des traces sans fil : le nombre de sniffeurs et le type de matériel utilisé. Nous étudions la variation de l'indicateur de force du signal reçu (RSSI) et son impact sur l'estimation de la distance. L'analyse est facilitée par le développement d'un outil facilement utilisable et disponible appelé PyPal pour la synchronisation et la fusion de traces Wi-Fi collectées simultanément
Mesures passives sans fil : outil, redondance, mesures et analyses
No full textLa compréhension du trafic sans fil est fondamentale pour améliorer les réseaux et concevoir des algorithmes et des protocoles avancés. Dans ce contexte, les mesures passives ont l'avantage sur les mesures actives, car elles ne dépendent d'aucune modification des équipements réseau existants. Elles sont souvent moins coûteuses et plus faciles à déployer que d'autres méthodes. Cette approche consiste à surveiller le support sans fil et à collecter des données sur divers paramètres de réseau, tels que la force du signal, l'occupation des canaux et la perte de paquets. Elle consiste à déployer plusieurs sniffeurs dans la zone cible (les sniffeurs sont des dispositifs fonctionnant en « monitor mode » qui collectent les paquets sans fil indépendamment de leur nature). Cependant, l'un des principaux défis des mesures passives est d'assurer la complétude de la trace, c'est-à-dire la capacité à collecter un ensemble de données complet et précis. Nous montrons qu'un seul sniffeur ne peut pas capturer tout le trafic en raison des caractéristiques inhérentes du support sans fil, où l'environnement peut être hautement dynamique et imprévisible. Il existe plusieurs facteurs qui peuvent affecter la complétude de la trace dans les mesures passives sans fil. Celles-ci incluent des facteurs environnementaux, tels que les interférences provenant d'autres dispositifs sans fil, les changements dans l'environnement physique (comme les objets en mouvement) et les variations de propagation du signal sans fil dues aux changements des conditions atmosphériques. De plus, des problèmes avec l'équipement de mesure lui-même, tels que des erreurs de calibration ou des problèmes de traitement des données, peuvent également affecter la complétude de la trace. L'importance de la complétude de la trace dans les mesures passives sans fil ne peut être surestimée. Des données inexactes ou incomplètes peuvent conduire à des conclusions incorrectes sur les performances du réseau, ce qui peut avoir des implications significatives pour la planification, l'optimisation et le dépannage du réseau. Par exemple, des données incomplètes peuvent entraîner des opportunités manquées pour identifier et résoudre des problèmes de réseau, ainsi qu'une reconstruction de trajectoire incorrecte ou incomplète. Dans cette thèse, nous étudions la qualité des traces capturées par des sniffeurs et examinons les améliorations résultantes en introduisant de la redondance dans le nombre de sniffeurs. Nous étudions l'impact des deux aspects suivants sur la qualité des traces sans fil : le nombre de sniffeurs et le type de matériel utilisé. Nous étudions la variation de l'indicateur de force du signal reçu (RSSI) et son impact sur l'estimation de la distance. L'analyse est facilitée par le développement d'un outil facilement utilisable et disponible appelé PyPal pour la synchronisation et la fusion de traces Wi-Fi collectées simultanément.Understanding wireless traffic is fundamental for improving networks and designing advanced algorithms and protocols. In this context, passive measurements have the edge over active measurements, as there is no requirement for any modification in existing network devices. Passive measurements are often less expensive and easier to deploy than other methods. This approach involves monitoring the wireless medium and collecting data on various network parameters, such as signal strength, channel occupancy, and packet loss. It consists of deploying multiple sniffers throughout the target area (sniffers are devices operating in monitor mode that collect the wireless packets regardless of their nature). However, one of the main challenges with passive measurements is ensuring trace completeness, or the ability to collect a complete and accurate dataset. We know that a single sniffer cannot capture all the traffic due to the inherent characteristics of the wireless medium where the environment can be highly dynamic and unpredictable. Several factors can impact trace completeness in wireless passive measurements. These include environmental factors, such as interference from other wireless devices, changes in the physical environment (such as moving objects), and variations in wireless signal propagation due to changes in atmospheric conditions. Additionally, issues with the measurement equipment itself, such as calibration errors or data processing issues, can also impact trace completeness. The importance of trace completeness in wireless passive measurements cannot be overstated. Inaccurate or incomplete data can lead to incorrect conclusions about network performance, which can have significant implications for network planning, optimization, and troubleshooting. For example, incomplete data can result in missed opportunities to identify and address network issues, and incorrect or incomplete trajectory reconstruction. In this thesis, we study the quality of traces captured by a sniffer and investigate the resulting improvements by introducing redundancy in the number of sniffers. We explore the impact of the following two aspects on the quality of wireless traces: the number of sniffing devices and the type of hardware used. We study the variation in the Received Signal Strength Indicator (RSSI) and its impact on distance estimation. The analysis is helped by the development of a readily-usable and easily-available tool, called PyPal, for the synchronization and merging of Wi-Fi traces collected simultaneously
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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