1,721,053 research outputs found
CabriTrack: Accelerometer data for automated behavioural monitoring of grazing goats
No full textThis dataset contains annotated acceleration data of goats grazing outdoors. We used 59 different Creole goats that were equipped with a tri-axial accelerometer of the brand Axivity, model AX3. Two to five animals were set up at the same time inside an experimental field, each animal having an accelerometer located on the horn. The accelerometer was located on the inside of the left horn (for an observer located at the back of the animal). The X axis is along the horn, the Y axis along the back of the animal, and the Z axis perpendicular and on the same plane as the Y axis. Accelerometers recorded the acceleration with a frequency of 25Hz. Animals were monitored with a CCTV camera for subsequent data annotation. Videos were later manually analyzed with the software Boris to label the activity of each animal. Five different behaviors were considered: grazing, ruminating/chewing, displacement, resting, and other. Grazing is when the animal is either with the head down collecting forage or head up chewing. Chewing sequences that lasted less than 5 seconds were labeled as grazing. When chewing sequences lasted more than 5 seconds, they were labeled as ruminating/chewing. Ruminating/chewing thus included chewing sequences of more than 5 seconds, as well as any regular rumination sequences, with the jaw moving. During rumination sequences, the jaw can stop moving for some time. When it stopped for less than 3 seconds, the sequence was still labeled as ruminating. When the sequence stopped for more than 3 seconds, it was labeled as resting. Resting is used for all the sequences where the animal has no activity, either lying on the ground or standing, with the head up or down. Note that alert behavior, with the head of the animal moving rapidly in different directions, was not labeled as resting. Displacement is when the animal explores the field, walking or running. Transitions between postures were also accounted for in displacement, such as when the animal moves from standing to lying or from lying to standing. The dataset is composed of more than 144 hours of annotated behavior from 59 different animals. It contains 12,756,669 lines and 8 columns, each line being an acceleration data point with the metadata. The first column, Time, is the timestamp of the data point. The Animal_id column is the ID of the animal from which the data were acquired. We did not use the national identification number, but instead integers from 1 to 59. The Behaviour column gives the labeled behavior for the sequence, with values in {Displacement, Grazing, Ruminating_Chewing, Other, Resting}. The Behaviour_num column gives the behavior of the sequence as an integer, with the same order used in the previous list (e.g., 1 for Displacement). The columns X, Y, and Z give the acceleration value for the corresponding axis. The Sequence_num is used to identify the behavior sequences. Lines of the dataset with the same Sequence_num are from the same behavior sequence, i.e., they are from consecutive timestamps, from the same animal, and with the same behavior.Ce jeu de données contient des données d'accélération annotées de chèvres Créoles au pâturage. Nous avons utilisé 59 chèvres différentes équipées d'un accéléromètre tri-axial de la marque Axivity, modèle AX3. Deux à cinq animaux étaient équipés en même temps dans une parcelle expérimentale, chaque animal ayant un accéléromètre fixé sur la corne. L'accéléromètre était situé à l'intérieur de la corne gauche (pour un observateur placé à l'arrière de l'animal). L'axe X est le long de la corne, l'axe Y le long du dos de l'animal, et l'axe Z est perpendiculaire et sur le même plan que l'axe Y. Les accéléromètres enregistraient l'accélération à une fréquence de 25 Hz. Les animaux étaient surveillés avec une caméra de vidéosurveillance pour une annotation ultérieure des comportements. Les vidéos ont ensuite été analysées manuellement avec le logiciel Boris afin de marquer le comportement de chaque animal. Cinq comportements différents ont été pris en compte : pâturage, rumination/mastication, déplacement, repos et autre. Le pâturage correspond à l'animal soit avec la tête baissée en train de ramasser de la nourriture, soit avec la tête relevée en train de mâcher. Les séquences de mastication durant moins de 5 secondes étaient annotées comme du pâturage. Lorsque les séquences de mastication duraient plus de 5 secondes, elles étaient annotées comme rumination/mastication. La rumination/mastication incluait donc les séquences de mastication de plus de 5 secondes, ainsi que les séquences de rumination régulière, avec des mouvements de la mâchoire. Pendant les séquences de rumination, la mâchoire peut s'arrêter de bouger pendant un certain temps. Si elle s'arrêtait pendant moins de 3 secondes, la séquence était toujours annotée comme rumination. Si l'arrêt durait plus de 3 secondes, la séquence était annotée comme repos. Le repos est utilisé pour toutes les séquences où l'animal n'a aucune activité, qu'il soit couché ou debout, la tête baissée ou relevée. Il est à noter que le comportement d'alerte, avec la tête de l'animal bougeant rapidement dans différentes directions, n'était pas annoté comme repos. Le déplacement correspond à l'exploration de la parcelle par l'animal, en marchant ou en courant. Les transitions entre les postures étaient également prises en compte dans le déplacement, comme lorsque l'animal passe de la position debout à la position couchée, ou inversement. Le jeu de données est composé de plus de 144 heures de comportements annotés provenant de 59 animaux différents. Il contient 12 756 669 lignes et 8 colonnes, chaque ligne correspondant à un point de données d'accélération avec les métadonnées. La première colonne, Time, est l'horodatage du point de données. La colonne Animal_id est l'identifiant de l'animal à partir duquel les données ont été acquises. Nous n'avons pas utilisé le numéro d'identification national, mais plutôt des entiers allant de 1 à 59. La colonne Behaviour indique le comportement annoté pour la séquence, avec des valeurs parmi {Déplacement, Pâturage, Rumination_Mastication, Autre, Repos}. La colonne Behaviour_num donne le comportement de la séquence sous forme d'entier, dans le même ordre que la liste précédente (par exemple, 1 pour Déplacement). Les colonnes X, Y et Z donnent la valeur d'accélération pour l'axe correspondant. La colonne Sequence_num est utilisée pour identifier les séquences comportementales. Les lignes du jeu de données ayant le même Sequence_num proviennent de la même séquence comportementale, c'est-à-dire qu'elles proviennent de points de données consécutifs, du même animal, et avec le même comportement
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
Solving adaptive sampling problems in graphical models using Markov decision process
No full textIn environmental management problems, decision should ideally rely on knowledge of the whole system. However, due to limited budget, in practice only a small part of the system is sampled and the complete system state is reconstructed from the sampled observations. In this article we consider the situation where the biological system under study is structured and can be modeled as a graphical model. Optimal sampling in such models still raises some methodological questions, like adaptive sampling, or the measure of the quality of a sample in terms of quality of reconstruction. Here, we present a way to formalise these two questions. The sample is chosen as the one which maximises the expected utility of information brought by the observations minus the sample cost.
The utily is derived from the notion of Maximum a Posteriori. This problem is known to be NP-hard. We present how to modelit as a Markov decision process in order to build approximate solution methods based on Reinforcement Learning
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