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9,302 results for “DETECT”
Figure 5 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 5. Examples of images used: high, low, and zero density, respectioely.
Figure 3 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 3. The experimental setup for the proposed application one.
Sainfoin Fruit Processing - Object Detection Dataset
<p>This dataset consists of 500 images of sainfoin (Onobrychis viciifolia) seed pods, seed, and split seeds. The images were taken as a part of an experiment to determine minimum sample size of seed pods needed to accurately estimate pod threshing trait heritability within sainfoin breeding lines.</p><p>The experiment was a complete factorial design with the following factors:</p><ul><li>Sainfoin named varieties: AAC Mountainview, Delaney, Eski , Rocky Mountain Remont, and Shoshone</li><li>Sample Size: 1, 2, 3, 4, and 5 grams of dried seed pods</li><li>Two different threshing types: Belt thresher processed 3X, Haldrup Impact Thresher (35sec @ Speed 9)</li></ul><p>This makes for a total factorial combination set of 5 varieties X 5 sample sizes X 2 threshing types = 50.</p><p>Each combination was comprised of 10 individual replicates where each replicate in a combination was a unique, random sample of seeds of the same mass (So, 10 random, 2g samples of Eski seed, processed by belt thresher; 10 random, 5g samples of Delaney seed processed by the Haldrup thresher, etc.). This makes for a total of 500 experimental units that comprise the sample set.</p><p>Once the seeds were sampled, weighed, and processed through the threshing equipment, they were weighed again and imaged.</p><p>The threshed seeds were scattered onto an imaging platform with a blue background, lit by 2 LED panels, and photographed with a Sony ILCE-7RM2 at the following settings:</p><ul><li>ISO: 100</li><li>Exposure: 1/40s</li><li>Focal Length: 55mm</li><li>Format: TIFF</li><li>Size: 7968x5320</li></ul><p>The raw images were converted from TIFF files to JPEG format and annotated in image labeling software. The seed objects were annotated with bounding boxes classified as the following classes</p><ol><li>pod: an enclosed seed pod</li><li>seed: a seed which was successfully threshed from the legume pod carpel</li><li>split: a seed threshed from the pod, but which split in two halves during the threshing process</li></ol><p>All image annotations were exported into the convenient <a href="https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-coco-overview.html">COCO format</a>.</p><p>No further image processing was performed.</p><p>The image set was split into a 80/20 training and validation step using `scikit-learn` in Python 3.11 stratifying the datasets equally over the various experimental factor levels.</p><p>The zip file 'train_val_images.zip' contains a 'train' folder with 400 training images, 'val' containing 100 validation images, an image taken with a color correction card named 'color_test.jpg', and a json file with all the annotations.</p><p>Another file called 'seed_weights.csv' contains the image_name to global-key mapping in tabular format as well as the before and after threshing seed weights for each experimental sample.</p><p><strong>Labeling Metrics:</strong></p><ul><li>Pod (48.58%)<ul><li>36,599 objects</li></ul></li><li>Seed (33.83%)<ul><li>25,488 object</li></ul></li><li>Split (17.59%)<ul><li>13,255 objects</li></ul></li><li><strong>TOTAL (100%)</strong><ul><li><strong>75,342 objects</strong></li></ul></li></ul>
Data sets for "On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector" by Wright et al.
<p>Copy of data described in 'On the detection of a solar radio burst event that occurred on 28 August 2022 and its effect on GNSS signals as observed by ionospheric scintillation monitors distributed over the American sector' by Wright et. al and to appear in the Journal of Space Weather and Space Climate (JSWSC), 2023.</p><p> </p>
Artifact: SoK: Evaluations in Industrial Intrusion Detection Research
<p>This collection resembles the artifact of our publication "SoK: Evaluations in Industrial Intrusion Detection Research," published in the Journal of Systems Research 2023, in which we performed a systematic mapping study on the literature of Industrial Intrusion Detection Systems (IIDSs). Within this paper, we systematically analyzed the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. This artifact contains our data extracted from the 609 publications under consideration in the survey. For further information, please refer to the respective publication.</p>
Detectability of very low frequency earthquakes in Nankai subduction zone
<p><strong>How to cite this data</strong></p> <ul> <li>data DOI of this repository</li> <li>method and conception<br>Shunsuke Takemura, Satoru Baba, Suguru Yabe, Yusuke Yamashita, Katsuhiko Shiomi, Takanori Matsuzawa, Detectability analysis of very low frequency earthquakes: methods and application in Nankai using F-net and DONET broad-band seismometers, <em>Geophysical Journal International</em>, Volume 237, Issue 1, April 2024, Pages 49–63, <a href="https://doi.org/10.1093/gji/ggae033">https://doi.org/10.1093/gji/ggae033</a></li> </ul> <p><strong>source_meca.dat</strong></p> <p>Assumed focal mechanisms. Longitude, Latitude, depth, strike, dip, and rake are listed. Focal mechanisms at each source grid were assumed to be low-angle thrust faulting mechanisms from the plate geometry of Koketsu et al. (2012) and the convergence direction of NUVEL-1A (DeMets et al. 2010).</p> <p><strong>VLFEdetectability_Fnet.dat</strong></p> <p>The detectable limits of very low frequency earthquakes (VLFEs) in Nankai using F-net only. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components. </p> <p><strong>VLFEdetectability_FnetDONET.dat</strong></p> <p>The detectable limits of VLFEs in Nankai using F-net and DONET. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components. </p>
Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
<p>#############</p><p>Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity, ICCV 2023</p><p>#############</p><p> </p><p>Authors: Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander</p><p>Affiliation: EPFL</p><p>Date: October, 2023</p><p> </p><p>Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth</p><p>These pth files can be loaded with PyTorch. The code to load and use the models is available at The code to load and use the models is available at: <a href="https://github.com/amathislab/BUCTD">https://github.com/amathislab/BUCTD</a></p><p><strong>Note: The weights for OCHuman</strong>, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X</p><p>We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.</p><p> </p><p>Link to the ICCV article: </p><p><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf">https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf</a></p><p> </p><p>The weights and predictions are released with Creative Commons Attribution 4.0 license. The code is released under the Apache 2.0 license, see https://github.com/amathislab/BUCTD </p><p><i>If you find our weights, code or ideas useful, please cite:</i></p><p> </p><p>@InProceedings{Zhou_2023_ICCV,</p><p> author = {Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander},</p><p> title = {Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity},</p><p> booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},</p><p> month = {October},</p><p> year = {2023},</p><p> pages = {14689-14699}</p><p>}</p><p> </p>
Detective's Desk
A small project that helped me learn more about texturing and how Maya works. Thank you! Source: Objaverse 1.0 / Sketchfab
Data for: Detection of oomycete pathogens in UK peat-free growing media and implications for plant health
<p>This dataset on Zenodo accompanies the manuscript Frederickson-Matika <em>et al.</em> (2024), Detection of oomycete pathogens in UK peat-free growing media and implications for plant health.</p> <p>There are two files:</p> <ul> <li>metadata.tsv - plain text table as tab-separated variables</li> <li>raw_data.tar.gz - compressed archive of 43 paired raw FASTQ files</li> </ul> <p>This represents a subset of two complete Illumina MiSeq plates (in two dated folderes) run at the James Hutton Institute containing other environmental samples using the same protocol. Only the synthetic controls and peat-free samples are provided here.<br><br>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.14 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz<br> $ ls -1 plate_20220505/ plate_20230608/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd plate_20220505/ $ md5sum -c MD5SUM.txt<br> $ cd ../plate_20230608/ $ md5sum -c MD5SUM.txt<br> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code><code> $ mkdir -p intermediate/ summary/</code></code></pre> <pre>Run the THAPBI PICT pipeline:</pre> <pre><code> $ thapbi_pict pipeline -m 1s3g \<br> -i plate_*/ -o summary/peat-free \<br> -y plate_*/GBL*.fastq.gz \<br> -n plate_*/GBL*.fastq.gz \<br> -s intermediate/ \<br> -t metadata.tsv -u \<br> -x 9 -c 1,2,3,4,5,6,7,8</code><br><br></pre> <p>The options here are as follows:</p> <ul> <li>-i - two input directories of paired raw FASTQ files.</li> <li>-n - negative controls used to increase the absolute abundance threshold</li> <li>-y - synthetic controls used to increase the fractional abundance threshold</li> <li>-s - optional location to store intermediate files</li> <li>-o - output stem for reports</li> <li>-t - filename for tab-separated-variable metadata</li> <li>-u - show unsequenced samples defined in the metadata</li> <li>-x - which metadata column contains Illumina FASTQ filename stems</li> <li>-c - which metadata columns to include in the report.</li> </ul> <p>This assumes the following key default settings:</p> <ul> <li>-a 100 (default absolite abundance threshold)</li> <li>-f 0.001 (default fractional abundance threshold)</li> <li>-d -(default provided ITS1 database).</li> </ul> <p>With these settings, only synthetic sequences were found in the controls, and therefore the thresholds were not automatically increased any further.</p> <p>Opening the output file summary/peat-free.ITS1.samples.1s3g.xlsx in Excel or similar should show you a table resembling Table 1 in the paper, but one row per sequencing sample, and additional columns with per-sample per-species read counts etc.</p>
Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data"
<p>"<span>Benchmarking automated detection and classification approaches for long-term acoustic monitoring of endangered species: a case study on gibbons from Cambodia</span>"</p> <div> <p><span>Recent advances in deep learning and transfer learning have revolutionized our ability for the automated detection of acoustic signals from long-term soundscape recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (<em>Nomascus gabriellae</em>) calls recorded in Jahoo, Cambodia. For the benchmarking, we compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with ResNet50 models trained on the ‘ImageNet’ dataset (ResNet), and transfer learning with embeddings from a global birdsong model (BirdNET). We also investigated the impact of varying the number of training samples on the performance of these models. Transfer learning models based on <span>BirdNET embeddings had superior performance with a smaller number of training samples, whereas Koogu and ResNet models only had acceptable performance with a larger number of training samples (>200 gibbon samples). We deployed the BirdNET-based model over </span>> 130,000 hours<span> of continuous soundscape data, which, after manual review, resulted in >12,000 verified true positive detections. We found that gibbon calling events occurred mostly in the early morning hours between 05:00 to 0:600 local time. We had fewer gibbon detections during the monsoon period and found substantial variation in spatial patterns of calling events across months and years. </span>We show that automated detection can be used to investigate long-term spatial and temporal patterns of gibbon calling events. Reliable automated detection approaches are a critical first step for using passive acoustic monitoring to assess endangered gibbon populations at ecologically relevant temporal- and spatial-scales. </span></p> <p> Detailed instructions regarding use are provided on GitHub.</p> </div> <p>Link to GitHub: https://github.com/DenaJGibbon/benchmark-gibbon-calls.</p> <p>Please cite both if you use these data: </p> <p>Clink, D., Cross-Jaya, H., Kim, J., Ahmad, A. H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T. T., Thi, H. N., Chi, T. N., & Klinck, H. (2024). Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12706803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12706803</a></p> <p>Clink DJ, Cross-Jaya H, Kim J, Ahmad AH, Hong M, Sala R, Birot H, Agger C, Vu TT, Thi HN, Chi TN. Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data. bioRxiv. 2024:2024-08.</p>
A Multi-Center Study of Near-Infrared Spectroscopy (NIRS) for Hematoma Detection
ClinicalTrials.gov study NCT00576147. IPD Sharing: Not stated. Countries: 1. Publications: 3.
NIRS Monitoring to Detect AKI in Preterm Infants
ClinicalTrials.gov study NCT03384173. IPD Sharing: NO. Countries: 1. Publications: 5.
Narrow-Band Imaging Versus Standard White Light for the Detection of Serrated Lesions in the Proximal Colon
ClinicalTrials.gov study NCT01572428. IPD Sharing: Not stated. Countries: 1. Publications: 1.
External Validation of the CLOVER Score for Detecting Occult Cancer in Venous Thromboembolism Patients
ClinicalTrials.gov study NCT07310693. IPD Sharing: YES. Countries: 1. Publications: 1.
RoBotic TCD Ultrasound BubbLe Study Compared to Transthoracic Echocardiography for Detection of Right to Left Shunt
ClinicalTrials.gov study NCT04604015. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Near Infrared Autofluorescence (NIRAF) Detection for Identifying Parathyroid Glands During Parathyroidectomy
ClinicalTrials.gov study NCT05152927. IPD Sharing: NO. Countries: 1. Publications: 3.
Doppler Ultrasound Probe for Blood Flow Detection in Severe Upper Gastrointestinal Hemorrhage
ClinicalTrials.gov study NCT00732212. IPD Sharing: NO. Countries: 1. Publications: 1.
The Detection Of Circulating Tumor Cells (CTC) In Patients With NSCLC Undergoing Definitive Radiotherapy Or Chemoradiotherapy
ClinicalTrials.gov study NCT02135679. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Ultra-high-resolution CT vs. Conventional Angiography for Detecting Coronary Heart Disease
ClinicalTrials.gov study NCT04272060. IPD Sharing: NO. Countries: 1. Publications: 7.
Transcutaneous Electrical Stimulation for Apnea Detected by Capnography
ClinicalTrials.gov study NCT06823661. IPD Sharing: NO. Countries: 1. Publications: 4.
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.