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9,302 results for “DETECT”
The photovoltaic inventory dataset in Japan detected in Sentinel-2 imagery
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Colorimetric Detection of Aqueous N-Nitrosodimethylamine via Pho-tonitrosation of a Naphtholsulfonate Indicator
<p>Underlying dataset for Colorimetric Detection of Aqueous N-Nitrosodimethylamine via Pho-tonitrosation of a Naphtholsulfonate Indicator</p>
Supplementary data for the article: "Towards an widely applicable earthquake detection algorithm for fibreoptic and hybrid fibreoptic-seismometer networks"
<p>Repository of supporting data associated with the article submitted to GJI, titled: "Towards an widely applicable earthquake detection algorithm for fibreoptic and hybrid fibreoptic-seismometer networks".</p> <p> </p> <p>Contents include:</p> <ol> <li>A full working example of modified QuakeMigrate version and seismic time-series data used to detect an earthquake.</li> <li>Earthquake catalogues for the three datasets described in the publication.</li> </ol>
Benchmarking Illumina RNA-seq fusion transcript detection methods - simulated 101 base PE reads
<p>Simulated 101 base paired-end reads used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </p>
Optical Detection of Interleukin-6 using Liquid Janus Emulsions using Hyperthermophilic Affinity Proteins
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Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Anonymised Phone Call Dataset for Anomaly Detection
<p>The dataset provides anonymized information related to phone calls, including the following details:</p> <p>1. Origin Numbers (A-Numbers)<br>2. Destination Numbers (B-Numbers)<br>3. Timestamp of the call<br>4. Call Result, indicating whether the call was blacklisted (coded as 001) or not (coded as 000)</p> <p>The dataset is divided into two subsets with the following characteristics:</p> <p>Dataset 1<br>- Collection Period: 24th July 2018 to 21st October 2018<br>- Duration: 89 days<br>- Total Records: 83,366,367 examples<br>- Unique A-Numbers: 9,006,011<br>- Unique B-Numbers: 2,387,932</p> <p>Dataset 2<br>- Collection Period: 1st June 2019 to 30th June 2019<br>- Duration: 29 days<br>- Total Records: 32,879,670 examples<br>- Unique A-Numbers: 3,217,069<br>- Unique B-Numbers: 1,380,235</p>
Supplementary data: ONT for Listeria monocytogenes outbreak detection
<p>Supplementary data for the publication "Oxford Nanopore’s 2024 sequencing technology for Listeria monocytogenes outbreak detection and source attribution: progress and clone-specific challenges"</p> <ol> <li><em>L. monocytogenes </em>assemblies generated from <ul> <li>Illumina-only data (shovill)</li> <li>ONT-only data (100x coverage, sup@v5.0 + flye + flye polisher + medaka [model r103_min_high_g360])</li> <li>ONT + Illumina data (100x ONT coverage, sup@v5.0 + flye + flye polisher + polypolish)</li> </ul> </li> <li>Raw ONT pod5 data from three<em> L. monocytogenes </em>isolates (N19-1094, N21-2956, N23-1883) with GAAG6mAC / GT4mCTTC methylations, among other methylated motifs</li> </ol>
EEG Data - Depression Detection
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FIGURE 2 in Detecting the phylogenetic position of Bovista acuminata (Agaricales, Basidiomycota) by an ITS-LSU combined analysis: the new genus Bryoperdon and revisitation of Lycoperdon subgen. Apioperdon
FIGURE 2. Bryoperdon acuminatum. Fresh basidiomes (TO HG191016). a in field; b in studio; c particular of the exoperidium; d particular of the exoperidium and ellipsoid-lacerate stoma. Scale bars: a, b = 10 mm; c = 5 mm; d = 2 mm. Pictures by A. Vizzini.
FIGURE 1 in Detecting the phylogenetic position of Bovista acuminata (Agaricales, Basidiomycota) by an ITS-LSU combined analysis: the new genus Bryoperdon and revisitation of Lycoperdon subgen. Apioperdon
FIGURE 1. Bayesian phylogenetic analysis based on the combined nrITS/nrLSU dataset of selected Lycoperdaceae. BPP values ≥ 0.7 are shown on the branches. For each sequenced taxon the Genbank number is given. The newly sequenced collections are in bold.
Figure 2 in Density, occupancy and detectability of tortoise species (Chelonoidis spp.) in the Atlantic Forest: implications for conservation and management
Figure 2. Relationship between the probability of detection of Chelonoidis denticulatus (A) and Chelonoidis carbonarius (B) with the accumulated rainfall of the day before sampling (previous rainfall) in the Vale Natural Reserve, Brazil.
Figure 1 in Density, occupancy and detectability of tortoise species (Chelonoidis spp.) in the Atlantic Forest: implications for conservation and management
Figure 1. Location of the Vale Natural Reserve, municipality of Linhares, Espírito Santo, south-eastern Brazil, showing the location of the linear transects and the types of vegetation present in the Reserve.
Figure 3 in Density, occupancy and detectability of tortoise species (Chelonoidis spp.) in the Atlantic Forest: implications for conservation and management
Figure 3. Relationship between the probability of occupancy of Chelonoidis denticulatus (A) and Chelonoidis carbonarius (B) with the distance from the water resource, and relationship between the probability of occupancy of Chelonoidis denticulatus and the percentage of forest cover (C) in the Vale Natural Reserve, Brazil.
Dataset: Gold standard dataset for explainability need detection in app reviews.
<p>We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.</p> <p>The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.</p> <p>We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.</p> <p>For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.</p>
Corpus Nummorum - Object Detection Coin Dataset
<p>This Object Detection dataset is a collection of ancient coin images from three different sources: the <a href="https://www.corpus-nummorum.eu/">Corpus Nummorum (CN)</a> project, the <a href="https://ikmk.smb.museum/home?lang=en">Münzkabinett Berlin</a> and the <a href="https://www.bnf.fr/fr/departement-monnaies-medailles-antiques">Bibliothèque nationale de France, Département des Monnaies, médailles et antiques</a>. It covers Greek and Roman coins from ancient Thrace, Moesia Inferior, Troad and Mysia. This is a selection of the coins published on the <a href="https://www.corpus-nummorum.eu/search/coins">CN</a> portal (due to copyrights). </p> <p>This dataset contains 506 different classes with about 179.000 coin images (approx. 29.000 unique coins). The classes come from four different categories: persons, objects, animals and plants. The coin images were assigned to the classes using our <a href="https://github.com/Frankfurt-BigDataLab/NLP-on-multilingual-coin-datasets">NLP pipeline</a>. For this purpose, our Named Entity Recognition and Relation Extraction were performed on every coin's description (separated into obverse and reverse). Each coin image assigned to this description was then <span>copied to the folder of the predicted classes</span>. A coin image can therefore also be assigned to different classes. The file name contains both the coin id and the coin type of the <a href="https://www.corpus-nummorum.eu/">CN database</a>. Whether the image belongs to a coin obverse or reverse can be recognized by the suffix obv or rev. An "sources" csv file holds the sources for every image. Due to copyrights the image size is limited to 299*299 pixels. However, this should be sufficient for most ML approaches.</p> <p>Due to the numerically different occurrences of the individual entities, the data set is not balanced. In addition, a class can contain very different representations of the same entity. Therefore, some classes can be difficult to train. Unfortunately, we cannot provide any annotations for the data set.</p> <p>During the summer semester 2024, we held the "Data Challenge" event at our Department of Computer Science at the Goethe-University. Our students could choose between the Object Detection dataset and a Natural Language dataset as their challenge. One team opted for the Object Detection challenge. We gave them this dataset with the task to use to try out their own ideas. Here are their results:</p> <p><a href="https://github.com/PatrickMelnic/DataChallenges_ObjectDetection/">Multilabel Classification as Backbone for Object Detection</a></p> <p> </p> <p>Now we would like to invite you to try out your own ideas and models on our coin data.</p> <p>If you have any questions or suggestions, please, feel free to contact us. </p>
Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes
<p>The dataset comprises three types of data. First, multi-electrode array recordings of mouse retinal ganglion cells performed by Awen Louboutin and Tom Quetu. Second, point spread functions from mouse and human optical eye models, and associated convolved natural images. Third, results from a convolutional neural network model trainings.</p>
Quantitative real-time PCR assays Q2 for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates
<p>These are the data behind figures 2 to 7 in the paper: Brink AM, Kremp A and Gorokhova E (2024) Quantitative real-time PCR assays for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates. Front. Microbiol. 15:1421101. doi: 10.3389/fmicb.2024.1421101</p>
Dangerous Items Dataset for 5-Class Object Detection (Pascal VOC annotation)
<p>This repository contains the data from the manuscript "Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN". It contains 4,000 images representing 5 classes of objects: baseball bat, gun, knife, machete and rifle (<a href="https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger one is no longer than 1,000 pixels. The full set was randomly divided into training and testing parts (75% and 25% of the full set respectively). As a result, the training part contains 3,000 images, and the testing part – 1,000 images. Both parts are balanced, that is, they contain similar number of objects to be detected. Images were annotated using bounding boxes in the Pascal VOC format, according to which the description about each image is included in the corresponding XML file.</p> <p>Using this dataset please cite:<br>Omiotek, Z. (2025). Dangerous items’ detection in surveillance camera images using Faster R‑CNN. Przegląd Elektrotechniczny, 101(5), 156-168. https://www.red.pe.org.pl/articles/2025/5/36.pdf</p>
Popular Animals Dataset for 6-Class Object Detection (Pascal VOC annotation)
<p>This dataset contains images used in the monograph titled <em>Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej</em> (Application of Selected Deep Learning Methods in Computer Vision) to build the Faster R-CNN model. The full collection consists of 600 image files showing 6 classes of objects: <em>kot</em> (cat), <em>krowa</em> (cow), <em>pies</em> (dog), <em>koń</em> (horse), <em>człowiek</em> (human), <em>owca</em> (sheep) (<a title="Popular Animals Dataset" href="https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger side is no longer than 1000 pixels. The set was randomly divided into a training part (75% of the full set) and a test part (25% of the full set). As a result, the training part contains 450 files, and the test part - 150. Both parts are balanced - they contain a similar number of detected objects. The images are labeled with bounding boxes in the Pacal VOC format, according to which the description of each file is contained in an XML file with the same name. The dataset can be used to build models for object detection.</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.