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66 results for “detection technique”
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
Detection of very low frequency earthquakes based on matched-filter technique
<p>List of origin times of detected Very low frequency earthquakes based on Baba et al. (2020, Journal of Geophysical Research).</p> <p>Analysis period: January 2003-June 2019</p> <p>The content of this analysis is published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2020GL088089">https://doi.org/10.1029/2020GL088089</a></p> <p>First column: year, second column: month, third column: day, forth column: hour, fifth column: minute, sixth column: second, seventh column: longitude, eighth column: latitude, ninth column: depth, and tenth column: region name. Times are described in JST (UT+9).</p> <p>Data Set S1 of supporting information of GRL corresponds to version 2. We updated the catalog because the information on Line 19035 in version 2 was incomplete.</p>
deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques
<p>In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades.</p> <p>Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies.</p> <p>We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively.</p> <p>We also release <em>11</em> fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.</p>
DeLTA (Deep Learning Techniques for noise Annoyance detection) Dataset
<p>The Deep Learning Techniques for noise Annoyance detection (DeLTA) dataset comprises 2,980 15-second binaural audio recordings collected in urban public spaces across London, Venice, Granada, and Groningen (sourced from <a href="https://doi.org/10.5281/zenodo.5578572">International Soundscape Database</a>). A remote listening experiment was designed and hosted on Gorilla Experiment Builder, a professional online platform used for studying complex behaviours. The survey was then distributed via Prolific to a pool of pre-registered participants (N=1,221), and data collected between July 5th and July 23rd, 2021.</p> <p>During the listening experiment, participants listened to ten 15-second-long binaural recordings of urban environments and were instructed to select all the sound sources they could identify within the recording and then to provide an annoyance rating (from 1 to 10). For the sound source recognition task, participants were provided with a list of 24 labels they could select from. To collapse these into a single set of sound sources per recording, a “consensus” approach was considered, i.e., if two or more participants identified a source as being present in a recording, this source was considered to be effectively present. This resulted in a 2890 by 24 data frame (2890 recordings, each with up to 23 possible labels present and an average annoyance rating). On average, each recording has 3.2 identified sound sources present.</p> <p>Due to the constraints of the online survey software, Mp3 files were used for the listening experiment. Higher quality 24- or 32-bit 48kHz WAV files can be made available from the authors upon request. Each binaural audio recording consists of a 2 channel Mp3 file.</p>
High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory
<p>The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.</p>
Supplementary data for a systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
<p>The Microsoft Excel files containing the supplementary data and diagrams for the paper:</p> <p><strong>A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges</strong></p> <p>The article is under review in the Journal of Systems and Software.</p> <p>In this version, the literature search has been performed between April and May 2023, and studies published until that date has been mentioned in the attached Excel files.</p> <p>This version (v3.4.0) corresponds to the third revision (R3) of the manuscript.</p> <p> </p>
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
<p>This dataset contains the files and annotations for <a href="https://propaganda.qcri.org/semeval2020-task11/index.html">SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles</a>. The task was composed by two subtasks: span identification (SI) and technique classification (TC). This dataset includes the following:</p> <ul> <li>The text files for training, development, and testing sets for both the SI and the TC tasks.</li> <li>The gold-standard files for the training sets, for both the SI and the TC task</li> </ul> <p>Our propaganda identification initiative remains active. We keep a <a href="https://propaganda.qcri.org/ptc/leaderboard.php">live leader-board</a> reporting the performance of models submited up to date.</p> <p><strong>Reference</strong></p> <p>Giovanni Da San Martino, Alberto Barrón-Cedeño, Henning Wachsmuth, Rostislav Petrov, and Preslav Nakov. 2020. <a href="https://propaganda.qcri.org/">Task 11: Detection of Propaganda Techniques in News Articles</a>. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020). Barcelona, Spain (2020)</p> <p> </p> <pre><code>@InProceedings{SemEval20-11-DaSanMartino, author = "Da San Martino, Giovanni and Barr\'{o}n-Cede\~no, Alberto and Wachsmuth, Henning and Petrov, Rostislav and Nakov, Preslav", title = "{SemEval}-2020 Task 11: {D}etection of Propaganda Techniques in News Articles", pages = "", abstract = "We describe the outcome of the SemEval 2020 Task 11 on the detection of propaganda in news articles. We present two tasks. In the first task, systems are asked to identify specific text spans in a free text where propaganda is being applied. In the second task, systems are asked to identify the propaganda technique being applied in a text span. We describe the construction of the evaluation framework (dataset and evaluation metrics) as well as the approaches explored by the different participants. ", crossref = "SemEval20" }</code></pre> <p> </p>
Dataset for Black Tea Fermentation Detection based on Image Processing and Machine Learning Techniques
<p>This is a dataset on black tea fermentation. The dataset contains black tea fermentation conditions and images. The fermentation conditions captured are: temperature, humidty and time. The images belong to black tea as they underwent the fermentation process. The dataset was collected in Sisibo tea factory, Kenya in July and August 2020.</p>
Database for comparison between Hybrid Capture and PCR techniques for HPV-HR detection
<p>This database displays the results from the HPV-HR detection with the gold standard technique Hybrid Capture 2 (HC2) and different PCR-based techniques. These results are compared in order to determine the degree of agreement between theses techniques. </p>
Evaluation of Low-cost Techniques for Detecting Sickle Cell Disease and β-thalassemia in Nepal and Canada
ClinicalTrials.gov study NCT05506358. IPD Sharing: YES. Countries: 2. Publications: 1.
A Comparison of the Detection of Hypoventilation During Deep Sedation Utilizing Nasal End Title CO@ Versus Transcutaneous CO2 Measurement Techniques
ClinicalTrials.gov study NCT00954733. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A Phase 4 Trial Assessing the ImPact of Residual Inflammation Detected Via Imaging TEchniques, Drug Levels and Patient Characteristics on the Outcome of Dose TaperIng of Adalimumab in Clinical Remissi
ClinicalTrials.gov study NCT02198651. IPD Sharing: YES. Countries: 14. Publications: 2.
Advances in Leukemia Detection and Classification: A Systematic Review of AI and Image Processing Techniques
Open the record for dataset details and reuse information.
From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions
<p>Data for all figures included in "From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions". Amino acid concentrations, enantiomeric excesses, and relative distributions for E. coli cells and B. subtilis spores after subcritical water extraction for 30 min at 200 °C using water and dilute acid.</p>
A synchronous spectrofluorometric technique for simultaneous detection of alfuzosin and tadalafil: Applied to tablets and spiked biological samples
<p>A facile, accurate, eco-friendly, and sensitive spectrofluorometric method was evolved to assay alfuzosin hydrochloride (AFH) and tadalafil (TDF) in different matrices. Such a co-administered combination is clinically used for the treatment of lower urinary tract symptoms. Both compounds are characterized by their native fluorescence spectra upon excitation at specific wavelengths. Their characteristic fluorescence spectra were utilized for sensitive assay of the studied analytes in tablets and human biological samples. The assay principle is based on first-order synchronous spectrofluorometric scan using Dλ = 60 nm in which AFH peaks were recorded at 366 nm. Meanwhile, TDF measurements were recorded at 293 nm in the same scans without overlap with AFH spectra. Recent analytical chemistry trends were implemented to lessen occupational and environmental perils, utilizing ethanol as a diluting solvent for method optimization and application. Linearity ranges were 5.0–90.0 and 10.0–100.0 ng. <span>mL⁻1</span> for AFH and TDF, respectively in their raw materials with average % recoveries of 100.44 % and 99.73 % in raw materials, 100.15 % and 100.20 % in spiked plasma, and 97.14 % and 99.99 % in spiked urine. The proposed method was successfully applied to Prostetrol® and Starkoprex® commercial tablets with no interference with common tablet additives.</p>
Data for: Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques
<p>This dataset on Zenodo accompanies the manuscript Salvatore <em>et al.</em> (2024), Detection and diversity of <em>Phytophthora</em> species from declining <em>Quercus suber</em> stands using both DNA metabarcoding and soil baiting techniques.</p> <p>There are two files here on Zenodo:</p> <ul> <li><code>metadata.tsv</code> - plain text table as tab-separated variables</li> <li><code>raw_data.tar.gz</code> - compressed archive of 56 paired raw FASTQ files</li> </ul> <p>This represents a subset of one complete Illumina Nano MiSeq plate run at the James Hutton Institute also containing a small number of unrelated samples using the same protocol.</p> <p>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, v1.0.16 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</code><br><code> $ ls -1 raw_data/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd raw_data/ $ md5sum -c MD5SUM.txt</code><br><code> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code> $ mkdir -p intermediate/ summary/</code></pre> <p>Run the THAPBI PICT pipeline:</p> <pre><code> $ thapbi_pict pipeline -m 1s3g -f 0 -a 15 -i raw_data/ \</code><br><code> -s intermediate/ -o summary/sardinia \</code><br><code> -t metadata.tsv -u -x 8 -c 4,5,3,2,7,6</code></pre> <p>The options here are as follows:</p> <ul> <li><code>-m</code> - use the 1s3g classifier (see methods)</li> <li><code>-f</code> - set to zero to disable the fractional abundance threshold</li> <li><code>-a</code> - set a lower absolute abundance threshold</li> <li><code>-i</code> - location of the input raw data</li> <li><code>-s</code> - optional location to store intermediate files</li> <li><code>-o</code> - output stem for reports</li> <li><code>-t</code> - filename for tab-separated-variable metadata</li> <li><code>-u</code> - show unsequenced samples defined in the metadata</li> <li><code>-x</code> - which metadata column contains Illumina FASTQ filename stems</li> <li><code>-c</code> - which metadata columns to include in the report.</li> </ul> <p>This leaves the <code>-d</code> option with the default provided ITS1 database. We are NOT taking advantage of the negative controls to automatically set a blanket minimum abundance as Control-Plate-3-Mix-3-Dry-P3-c_S56_L001 sadly has over 3000 <em>Phytophthora</em> reads.</p> <p>That takes under a minute to run, and classifies most of the samples.</p> <p>Opening the output file <code>summary/sardinia_20240912_v1.0.16.ITS1.samples.1s3g.xlsx</code> in Excel or similar should show you a table resembling Table 3 in the paper, without the baiting results, but with one row per sequencing sample, and additional columns with per-sample per-species read counts etc. The similarly named reads file as one row per unique amplicon sequence variant (ASV), and columns for each sequencing sample.</p> <p>All the <em>Phytophthora</em> classifications were to species level except a single ASV from E5BTB-DILUTE-Wet-P6_S17_L001 (S3 Bultei) with 15 reads, a perfect match to partial sequences MH588088.1 and MH593844.1, isolates Y1 and Y2, which is in the THAPBI PICT database but only at genus level:</p> <pre><code>>bad82a53fff502146e7ea00cbf2d9d3e Phytophthora<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAACTTTCCACGTGAACCGTTTCAAACCAAATAGTTGGGGGTCTTGTCTGGTGGCGGCTGCTGGCTTTATTGTTGGCGGCTGCTGCTGGGTGAGCCCTATCATGGCGAGCGTTTGGGCTTCGGCCTGAGCTAGTAGCATTTCTTTTAAACCCATTCCTTAATACTGATTATACT</code></pre> <p>There are 9 samples left simply as "Unknown" (all six from S2 Buddusò, one each from S3 Bultei, S4 Nuoro, and S6 Tempio). A further two samples contained low levels of an unknown sequence in addition to knowns.</p> <p>The unknown ASV from S1BTC-DILUTE-Wet-P6_S9_L001 (S3 Bultei) looks to be a novel <em>Phytophthora</em> if real, similar to <em>P. quercina</em>:</p> <pre><code>>3cd110db0a0b8fa8b971ea6b61c53970 Phytophthora?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAAAACTTTCCACGTGAACCGTTTCAACCAATATTTTGGGGGTCTCGTCTGGCGTGCGGCTGTTGCTGTAAAAGGCGGCGGCTGTTGCTGGGTGAGCCCTATCATGGCAAACGTTTGGGCTTCGGTCTGAACAAGTAGCTCTTTTTTAAACCATTACTTATTACTGATTATACT</code></pre> <p>The unknown ASV from S4OC-DILUTE-Wet-P6_S2_L001 (S4 Nuoro) looks to be a <em>Pythium</em> (a perfect match to partial sequences MN269744 and KY822489 from uncultured clones):</p> <pre><code>>eac8c1931b4f8c57803e6ad9ffb4eb56 Pythium?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCAAAAAACTATCCACGTGAACCGTTAAGCAAAAGTCTAGTTGGCTTGTGTTGTTCGGGAGTGTGTTGGGAAGAGCTTGGAGATGTCTTCGGATATTTCGATGCCTAGTACTGGACATCCTGGCGAGCGAGTCGGCTAGCAACGAAGGTCGGGAGTTCGCTTGCGGACTGATGTGCGCTTGTCGCATGTCGGTCGAAAGGCTTGAGCAAACGGCTGATCTATTACTTTTAAACCATACCATAACTACTGATGATACT</code></pre> <p>The unknown ASV from S9OC-Wet-P5_S12_L001 (S4 Nuoro) at only 38 reads seems to be an artefact of some kind, possibly chimeric:</p> <pre><code>>74933d826f6077cd5f3dd036f894429d Artefact?<br>TAGCCGTAGGGGAACCTGCGGCTGGATCACCTCCTTTCTGGATTCGGAAGGCAGGGATCAGTGATCAGTTATCAGAACCGATTGCTGCTCCTCTTCCGAGCATCCACAACGCCAGCTCTGGCAGGAATTCTGATATCTGATATCTGGTTTCTGCGATCTGGCAACGGCGCCGCCGTCTGCGCATCCCTTCTGCCGCGTTATTCCAGAGGGCAGTGATCAGTCATCAGAACCGATGGCGGATCCGCCCCCGGCTTCACGGCGAGCGCCCGAGCCTG</code></pre> <p>The remaining unknown ASVs were likely <em>Plasmopara</em>.</p> <p> </p>
Exploration of Biomarkers for the Susceptibility, Early Detection and Treatment Response of Brain Tumours using Epidemiological Techniques
<p>The following resource is from Lily Andrews thesis entitled: Exploration of Biomarkers for the Susceptibility, Early Detection and Treatment Response of Brain Tumours using Epidemiological Techniques. This presents tables that were too large to place in the appendix of the thesis.</p>
CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques
<p>This dataset provides a collection of color images taken from the orange leaves of <em>Citrus sinensis</em> (L.) Osbeck species with diseases, nutritional deficiencies, and pest symptoms, proper to develop abnormality detection algorithms based on digital image analysis techniques. The dataset comprises 953 color images divided into 12 classes of orange leaves: Healthy, Huanglongbing (HLB), Greasy spot, Iron deficiency, Magnesium deficiency, Manganese deficiency, Nitrogen deficiency, Zinc deficiency, Texas citrus mite, Red scale, Red scale sequelae, and Citrus leafminer. Each color image was segmented by a thresholding method to obtain a binary mask of the leaf region. Samples were analyzed by the quantitative real-time polymerase chain reaction (qPCR) diagnostic test to detect the <em>Ca.</em> L. asiaticus bacterium that causes HLB disease.</p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Liquid Biopsy With Immunomagnetic Beads Capture Technique for Malignant Cell Detection in Body Fluid
ClinicalTrials.gov study NCT02891642. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.