1,720,970 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
DELiVR FIJI plugin
No full text<p>Initial Release of the FIJI plugin for the DELiVR cleared-brain analysis pipeline, as described in our 2024 publication: </p>
<p>Virtual reality-empowered deep-learning analysis of brain cells</p>
<p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p>
<p>https://doi.org/10.1038/s41592-024-02245-2</p>
<p>This repository contains the FIJI plugin that creates the config file and starts either the inference or the training docker containers. It is built this code https://github.com/erturklab/delivr_fiji using Maven. </p>
Docker container for DELiVR pipeline (training, version 6.04)
No full text<p>Initial Release of the docker containers for the DELiVR cleared-brain analysis pipeline, as described in our 2024 publication: </p>
<p>Virtual reality-empowered deep-learning analysis of brain cells</p>
<p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p>
<p>https://doi.org/10.1038/s41592-024-02245-2</p>
<p>This repository contains the training container: </p>
<ul>
<li>This container is based on the github repository https://github.com/erturklab/delivr_train</li>
<li>The pipeline in this container requires annotated 3D stacks (preferably annotated in VR as it's faster) as training data. </li>
<li>The training pipeline can be started via the FIJI plugin or by the "docker run" command</li>
<li>Please refer to the handbook accompanying the publication for details. </li>
</ul>
<p>As a proof-of-concept, we include the training data used to train the c-fos model.</p>
DELiVR supporting files (training weights, additional components for inference docker container)
No full text<p>Initial Release of the supporting data for the DELiVR cleared-brain analysis pipeline, as described in our 2024 publication: </p>
<p>Virtual reality-empowered deep-learning analysis of brain cells</p>
<p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p>
<p>https://doi.org/10.1038/s41592-024-02245-2</p>
<p>This repository contains the following datasets:</p>
<ul>
<li>Training weights for microglia inference</li>
<li>Training weights for c-fos inference (as used in the paper)</li>
<li>The copy of Terastitcher that we used to build the inference container </li>
<li>The copy of mBrainAligner that we used to build the inference container</li>
<li>The Dockerfile that we used to build the inference container </li>
<li>The Ilastik version that we used to build the inference container </li>
</ul>
<p>Source code for TeraStitcher: https://github.com/abria/TeraStitcher</p>
<p>Source code for mBrainaligner: https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/mBrainAligner </p>
<p>Download for this specific version of the Ilastik binaries: https://files.ilastik.org/ilastik-1.4.0b8-Linux.tar.bz2</p>
Docker containers for DELiVR, initial release (inference, version 12.19)
No full text<p>Initial Release of the docker containers for the DELiVR cleared-brain analysis pipeline, as described in our 2024 publication: </p>
<p>Virtual reality-empowered deep-learning analysis of brain cells</p>
<p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p>
<p>https://doi.org/10.1038/s41592-024-02245-2</p>
<p>This repository contains the inference container: </p>
<ul>
<li>Inference container (version 12.29, using code from github: https://github.com/erturklab/delivr_cfos).</li>
<li>This container will run the entire preprocessing, cell detection, postprocessing, and visualization pipeline after being started (either via the command line with the "docker run" command, or via the FIJI plugin. Please refer to the handbook accompanying the publication for details. </li>
<li>In addition, it contains the additional files required to build the container, as well as the Dockerfile. </li>
</ul>
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
DELiVR example dataset
No full text<p>Example dataset for our DELiVR pipeline, as published in: </p>
<p>Virtual reality-empowered deep-learning analysis of brain cells</p>
<p>Kaltenecker, Al-Maskari, Negwer, et al., Nature Methods 2024</p>
<p>https://doi.org/10.1038/s41592-024-02245-2</p>
<p> </p>
<p>This archive contains one stack of 16-bit tiff images, taken from a cleared mouse brain stained for c-Fos and imaged using a light-sheet microscope (voxel resolution 1.625 x 1.625 x 6 µm in X/Y/Z) </p>
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