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9,302 results for “DETECT”
Supporting data for 2019SW002408: Detection of VLF attenuation in the Earth-ionosphere waveguide caused by X-class solar flares using a global lightning location network
<p>This folder contains data from the World Wide Lightning Location Network (WWLLN) used in the paper "<strong>Detection of VLF attenuation in the Earth-ionosphere waveguide caused by X-class solar flares using a global lightning location network</strong>". </p> <p>Contents:</p> <p>1. readme_2019SW002408.pdf: A detailed explanation of the data files in this folder</p> <p>2. station_list_20170906-10.csv: A table of WWLLN stations used in this analysis</p> <p>3. stroke-station_20170906.csv: A list of lightning strokes detected by WWLLN, and the stations detecting each stroke, for the day September 6, 2017.</p> <p>4. stroke-station_20170910.csv: A list of lightning strokes detected by WWLLN, and the stations detecting each stroke, for the day September 10, 2017.</p>
Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition"
<p>Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition" . 720p Version. </p>
A Real-time System for Vision-based Road Cracks Detection via Shallow and Deep Learnings
<p>Code and data of "A Real-time System for Vision-based Road Cracks Detection via Shallow and Deep Learnings"</p>
PAN12 Deception Detection: Sexual Predator Identification
<p>This archive contains the training corpus for the "Sexual Predator Identification" task of the PAN 2012 Lab, held in conjunction with the CLEF 2012 conference.</p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2.</p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 1
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 1. Part 2 accessible here: <a href="https://doi.org/10.5281/zenodo.3719225">https://doi.org/10.5281/zenodo.3719225</a></p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Tagged formatted datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged formatted datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems' T.Murovič, A.Trost.</p>
Assessment of real-time PCR for Helicobacter pylori DNA detection in stool with co-infection of intestinal parasites: a comparative study of DNA extraction methods
<p>Background</p> <p>Many studies reported high prevalence of <em>H. pylori</em> infection among patients co-infected with intestinal parasites. Molecular approach for the DNA detection of those microbes in stool have been proposed. However there are a few reports that evaluated the effect of bead-beating in relation to the <em>H. pylori</em> outcome. Therefore, we developed and evaluated two TaqMan-based real-time PCR (rt-PCR) qualitative assays for the detection of <em>ureC</em> (<em>glmM</em>) and <em>cagA</em> of <em>Helicobacter pylori</em> on DNA extracted by three procedures.</p> <p>Results</p> <p>The two PCRs were analysed on 100 stool samples from patients who were screened for intestinal parasites. Three DNA extraction procedures were used: 1) automation with bead beating, 2) automation without bead beating and 3) hand column. The specificity of the new assays was confirmed by sequencing the PCR products and by the lack of cross-reactivity with other bacteria or pathogens DNA. Rt-PCR assays showed a detection limit of 10^4 bacteria/200 mg stool. The <em>ureC</em>_PCR with bead beating process was compared to conventional stool antigen test (SAT), with 94.12 and 93.75% of respectively sensitivity and specificity. However, the discordant samples were confirmed by DNA sequencing suggesting a potential higher sensitivity and specificity of PCR.</p> <p>Conclusions</p> <p>Our findings showed that the automation with bead-beating –suggested procedure for intestinal parasitic infections- can reach highly sensitive results in <em>H. pylori</em> detection on stool compared also with SAT. Thus, this work can provide new insights into the practice of a clinical microbiology laboratory in order to optimize detection of gastro-intestinal infections. Further studies are needed to better define the clinical value of this technique.</p>
Face detection ensemble with methods using depth information to filter false positives
<p>%files (File.rar):<br> %ColorImageX.jpg is the color image<br> %SegImageX.jpg is the segmentation map <br> %SegImage2_X.jpg is the segmentation map obtained considering only Depth<br> %map</p> <p>%the .mat file<br> %DatasetFaceD{x}{1} depth map<br> %DatasetFaceD{x}{2} eyes coordinates<br> %DatasetFaceD{x}{3} calibration matrix, e.g used in face_dimension.m</p> <p> </p> <p>PS few images (7) used in the paper are no more available</p> <p> </p>
A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing
<p>We provide a large data set consisting of <strong>2,057 sentences</strong> from 90 news articles and annotations of crowdworkers with respect to <strong>bias itself</strong> and the following <strong>bias dimensions</strong>:</p> <ol> <li><strong>hidden assumptions</strong></li> <li><strong>subjectivity</strong></li> <li><strong>representation tendencies</strong></li> </ol> <p>Our data set contains <strong>44,547 labels in total</strong> (43,197 sentence labels and 1,350 article labels).</p> <p>The news articles deal with the <strong>Ukraine crisis</strong>. They were published in 33 countries in total and were selected based on the data set of Cremisini et al. (Cremisini, A., Aguilar, D., & Finlayson, M. A. <em>A Challenging Dataset for Bias Detection: The Case of the Crisis in the Ukraine</em>, Proc. of SBP-BRiMS'19, pp. 173-183, 2019).</p> <p>Each sentence was annotated by 5 crowdworkers. In total, we spent $ 3,335 for the crowdworkers annotations.</p> <p>More information can be found in our <a href="https://github.com/michaelfaerber/ukraine-news-bias">GitHub repository</a>. A description of the used file format is given in the codebook attached to the dataset.</p> <p>Please cite our data set as follows:</p> <pre><code>@unpublished{Faerber2020Bias, author = {Michael F{\"{a}}rber and Victoria Burkard and Adam Jatowt and Sora Lim}, title = {{A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing}}, year = {2020} }</code></pre>
A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader
<p>The data supports the manuscript entitled “A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader” that is submitted to GRL. These files can be opened by MATLAB. The data can be used freely for scientific purposes with appropriate citation.</p>
An annotated visual dataset for Automatic weed detection and identification
<p>This dataset is provided and linked to the Paper :<br> <strong>Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots, </strong><em>in APPS, Special Issue: Machine Learning in Plant Biology: From Genomics to Field Studies</em></p> <p> </p> <p>The training and test images are provided there.</p> <p><br> This research received financial support from the Agence Nationale de la Recherche (grant No. ANR-17-ROSE-0003).</p>
German Traffic Sign Detection Benchmark (resized to 128x128)
<p>German Traffic Sign Detection Benchmark Dataset</p> <p>All images resized to 128x128</p> <p>Split in Training and Test datasets according to source</p> <p> </p> <p>Original data available under: http://benchmark.ini.rub.de/?section=gtsrb</p>
Cluster-based Table Detection Dataset
<p>This data set contains PDF segments and document features, combined with a label whether a segment is part of a table or not.</p> <p>The contained features are:</p> <ul> <li>file: Corresponding PDF file name</li> <li>page: Page where the cluster is located, starting with 0</li> <li>bbox: Bounding box of the cluster, stored as (x_0,x_1,y_0,y_1)</li> <li>text: This information had to be removed because it can be confidential.</li> <li>n_nodes: Number of layout elements in the cluster</li> <li>approx_size: Approximate number of cells when assuming a tabular cluster structure</li> <li>tabular_fill_score: Percentage of filled cells. This is calculated by building an artificial grid over the cluster and set the cells which would be filled relative to the maximal possible number, namely the approx_size.</li> <li>loop_score: Percentage of loops present in a cluster, relative to the maximal possible number.</li> <li>rectangle_score: Percentage of unique rectangles in a cluster, relative to the maximal possible number (which would be one rectangle per element)</li> <li>x_sparsity_abs: Average length of horizontal edges in a cluster</li> <li>x_sparsity_rel: Average length of horizontal edges in a cluster (i.e. x_sparsity_abs), relative to the horizontal sparsity the same page</li> <li>font_size_entropy: Shannon entropy of the font sizes in a cluster</li> <li>font_name_entropy: Shannon entropy of the font names in a cluster</li> <li>bold_pct: Percentage of bold texts in a cluster</li> <li>italic_pct: Percentage of italic texts in a cluster</li> <li>font_size_entropy_doc: Shannon entropy of the font sizes in a document</li> <li>font_name_entropy_doc: Shannon entropy of the font names in a document</li> <li>bold_pct_doc: Percentage of bold texts in a document</li> <li>italic_pct_doc: Percentage of italic texts in a document</li> <li>font_size_entropy_diff: Deviation of the font size entropy of a cluster (i.e. font_size_entropy) compared to the corresponding measurement on document-level (i.e. font_size_entropy_doc)</li> <li>font_name_entropy_diff: Deviation of the font name entropy of a cluster (i.e. font_name_entropy) compared to the corresponding measurement on document-level (i.e. font_name_entropy_doc)</li> <li>bold_pct_diff: Deviation of the percentage of bold texts in a cluster (i.e. bold_pct) compared to the corresponding measurement on document-level (i.e. bold_pct_doc)</li> <li>italic_pct_doc_diff: Deviation of the percentage of italic texts in a cluster (i.e. italic_pct) compared to the corresponding measurement on document-level (i.e. italic_pct_doc)</li> <li>is_table: Label indicating with 1 that a cluster contains pure table content and 0 otherwise</li> </ul>
Figure 3 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 3 Eidophasia assmanni sp. nov., holotype, male genitalia. Scale bar: 0.2 mm.
Figure 5 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 5 Eidophasia assmanni sp. nov., type locality (Russia, Altai Mountains).
Figure 2 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 2 Eidophasia assmanni sp. nov., holotype, male pre-genitalia segments.
Figure 1 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 1 Eidophasia assmanni sp. nov., holotype, scale bar: 5 mm.
Supplementary Material for A Systematic Mapping Study on Architectural Smells Detection
<p>This is the supplementary material for a systematic mapping study on architectural smells detection.The material is in the form of a spreadsheet. First sheet is for detection techniques and second for detection tools.</p>
Machine Learning for Anomaly Detection in Cyanobacterial Fluorescence Signals
<p>Excel files containing chlorophyll a and phycocyanin fluorescence data imported from <a href="https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/habTracker.html">https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/habTracker.html</a> for buoys WE2, WE4, WE8, and WE13. The Python code used to manipulate the data is also included.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.