1,721,044 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
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
No full text<h2>General</h2>
<p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p>
<h2>Summary</h2>
<ul>
<li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
<ul>
<li>30 completely labeled (segmented) images</li>
<li>71 partly labeled images</li>
<li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li>
</ul>
</li>
<li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li>
<li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li>
<li>An evaluation of three baseline methods in terms of the above metrics and score</li>
</ul>
<h2>Abstract</h2>
<p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p>
<h2>Dataset documentation:</h2>
<p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p>
<p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p>
<p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p>
<h2>Files</h2>
<ul>
<li>fisbe_v1.0_{completely,partly}.zip
<ul>
<li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li>
</ul>
</li>
<li>fisbe_v1.0_mips.zip
<ul>
<li>maximum intensity projections of all samples, for convenience.</li>
</ul>
</li>
<li>sample_list_per_split.txt
<ul>
<li>a simple list of all samples and the subset they are in, for convenience.</li>
</ul>
</li>
<li>view_data.py
<ul>
<li>a simple python script to visualize samples, see below for more information on how to use it.</li>
</ul>
</li>
<li>dim_neurons_val_and_test_sets.json
<ul>
<li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li>
</ul>
</li>
<li>Readme.md
<ul>
<li>general information</li>
</ul>
</li>
</ul>
<h2>How to work with the image files</h2>
<p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p>
<p>We recommend to work in a virtual environment, e.g., by using conda:</p>
<p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p>
<h3>How to open <em>zarr</em> files</h3>
<ol>
<li>Install the python zarr package:
<pre><code>pip install zarr</code></pre>
</li>
<li>Opened a zarr file with:<br>
<p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p>
<p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p>
</li>
</ol>
<p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p>
<h3>How to view <em>zarr</em> image files</h3>
<p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p>
<ol>
<li>Install napari:
<pre><code>pip install "napari[all]"</code></pre>
</li>
<li>Save the following Python script: <br>
<p><code>import zarr, sys, napari</code></p>
<p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p>
<p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p>
</li>
<li>Execute:
<pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre>
</li>
</ol>
<h2>Metrics</h2>
<ul>
<li>S: Average of avF1 and C</li>
<li>avF1: Average F1 Score</li>
<li>C: Average ground truth coverage</li>
<li>clDice_TP: Average true positives clDice</li>
<li>FS: Number of false splits</li>
<li>FM: Number of false merges</li>
<li>tp: Relative number of true positives</li>
</ul>
<p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p>
<h2>Baseline</h2>
<p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p>
<h2>License</h2>
<p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p>
<h2>Citation</h2>
<p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p>
<pre><code>@misc{mais2024fisbe,
title = {FISBe: A real-world benchmark dataset for instance
segmentation of long-range thin filamentous structures},
author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
year = 2024,
eprint = {2404.00130},
archivePrefix ={arXiv},
primaryClass = {cs.CV}
}</code></pre>
<h2>Acknowledgments</h2>
<p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p>
<h2>Changelog</h2>
<p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p>
<h2>Contributing</h2>
<p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p>
<p>All contributions are welcome!</p>
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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