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9,302 results for “DETECT”
Supplementary material 1 from: Roques A, Ren L, Rassati D, Shi J, Akulov E, Audsley N, Auger-Rozenberg M-A, Avtzis D, Battisti A, Bellanger R, Bernard A, Bernadinelli I, Branco M, Cavaletto G, Cocquempot C, Contarini M, Courtial B, Courtin C, Denux O, Dvořák M, Fan J-t, Feddern N, Francese J, Franzen EKL, Garcia A, Georgiev G, Georgieva M, Giarruzzo F, Gossner M, Gross L, Guarneri D, Hoch G, Hölling D, Jonsell M, Kirichenko N, Loomans A, Luo Y-q, McCullough D, Maddox C, Magnoux E, Marchioro M, Martinek P, Mas H, Mériguet B, Pan Y-z, Phélut R, Pineau P, Ray AM, Roques O, Ruiz M-C, Sarto i Monteys V, Speranza S, Sun J-h, Sweeney JD, Touroult J, Valladares L, Veillat L, Yuan Y, Zalucki MP, Zou Y, Žunič-Kosi A, Hanks LM, Millar JG (2023) Worldwide tests of generic attractants, a promising tool for early detection of non-native cerambycid species. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 169-209. https://doi.org/10.3897/neobiota.84.91096
Total trapping network
Supplementary material 2 from: Green S, Dehnen-Schmutz K, Drakulic J, Eschen R, Orazio C, Douma JC, Lundén K, Colombari F, Jactel H (2023) Awareness, detection and management of new and emerging tree pests and pathogens in Europe: stakeholders' perspectives. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 9-40. https://doi.org/10.3897/neobiota.84.95761
Supplementary tables and figures
Supplementary material 3 from: Colombari F, Battisti A (2023) Citizen science at school increases awareness of biological invasions and contributes to the detection of exotic ambrosia beetles. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 211-229. https://doi.org/10.3897/neobiota.84.95177
Number of individuals of each species captured at each school
Supplementary material 1 from: Garcia A, Samalens J-C, Grillet A, Soares P, Branco M, van Halder I, Jactel H, Battisti A (2023) Testing early detection of pine processionary moth Thaumetopoea pityocampa nests using UAV-based methods. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 267-279. https://doi.org/10.3897/neobiota.84.95692
Supplementary images
Supplementary material 1 from: Green S, Dehnen-Schmutz K, Drakulic J, Eschen R, Orazio C, Douma JC, Lundén K, Colombari F, Jactel H (2023) Awareness, detection and management of new and emerging tree pests and pathogens in Europe: stakeholders' perspectives. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 9-40. https://doi.org/10.3897/neobiota.84.95761
HOMED stakeholder survey
Supplementary material 1 from: Colombari F, Battisti A (2023) Citizen science at school increases awareness of biological invasions and contributes to the detection of exotic ambrosia beetles. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 211-229. https://doi.org/10.3897/neobiota.84.95177
Lecture questionnaires
Dataset of the paper "High Resolution Kernel Exploit Detection"
<p>N/A</p>
WASMDYPA: Effectively Detecting WebAssembly Bugs via Dynamic Program Analysis
<p>This contains all the experimental code, data sets, and result files for our experiments.</p>
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
<p>The dataset is composed of 10,348 tweets: 5,284 for English and 5,064 for Turkish. Tweets in the dataset are human-annotated in terms of "false", "true", or "other". The dataset covers multiple topics: the Russia-Ukraine war, COVID-19 pandemic, Refugees, and additional miscellaneous events. The details can be found at https://github.com/avaapm/mide22</p>
Clinical applicability of COVID-19 detection in chest radiography with deep learning - radiologist annotations
<p>Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. In order to evaluate the performance of radiologists in the detection of COVID-19 radiological features in CXR, manual annotation of a subset of random CXR images was performed by two radiologists using an in-house software. The software presented CXRs from a randomly selected subset and allowed for window center/width adjustment, zooming and panning. Radiologists were asked to label CXRs into one of 4 classes: Normal, Not indicative of COVID-19 (pathological), Indicative of COVID-19 and Undetermined. The Indicative of COVID-19 class was defined as CXRs where the patient presented findings indicative of COVID-19, namely bilateral pulmonary opacities of low/medium density. The Undetermined class was defined as CXRs where the patient presented findings that could be indicative of COVID-19 but which could also be indicative of another condition, namely unilateral lung opacities, diffuse bilateral opacities of ARDS pattern or diffuse reticular opacities. The Not indicative of COVID-19 (pathological) class was defined as CXRs where the patient presented findings indicative of any other pathology except for COVID-19. CXRs where the patient presented medical devices were classified as Normal if the underlying pathology was not visible. Additionally, CXRs without sufficient quality for visual assessment by the radiologists due to bad image quality, patient positioning or any other factors could be labelled as Compromised for exclusion. A total of 1,845 CXRs were selected for annotation by two radiologists. Of these, 1,256 belong to the Mixed dataset(a combination of multiple public sources), 289 belong to BIMCV and 300 belong to COVIDGR. Selection of CXRs for annotation was performed randomly: for the Mixed dataset, a balanced selection strategy was used during image selection, whereas for BIMCV and COVIDGR, the dataset class distribution was maintained in the subset selected for annotation. Manual labelling of CXRs was performed in two stages. First, both radiologists independently classified each CXR. CXRs where the two radiologists disagreed were then selected for the second stage where the two radiologists assessed the CXRs together to achieve consensus. At no point were radiologists given access to the ground truth label, RT-PCR results or any other information besides the CXR image. To ensure that written information present in the CXR image (such as hospital system, health service, laterality markers, patient positioning, etc.) did not bias the annotation, all written labels were blacked out during before annotation. This dataset contains the information regarding each annotated CXR and the resulting radiologist labels for each image before and after consensus.</p> <p>The public datasets used in this study are available in the following repositories:</p> <p>CheXpert: <a href="https://stanfordmlgroup.github.io/competitions/chexpert/">https://stanfordmlgroup.github.io/competitions/chexpert/</a></p> <p>ChestXRay-8: <a href="https://nihcc.app.box.com/v/ChestXray-NIHCC">https://nihcc.app.box.com/v/ChestXray-NIHCC</a></p> <p>COVID-19 IDC: <a href="https://github.com/ieee8023/covid-chestxray-dataset">https://github.com/ieee8023/covid-chestxray-dataset</a></p> <p>COVIDx: <a href="https://github.com/lindawangg/COVID-Net">https://github.com/lindawangg/COVID-Net</a></p> <p>RSNA-PDC: <a href="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge">https://www.kaggle.com/c/rsna-pneumonia-detection-challenge</a></p> <p>SAVE LIVES: <a href="https://www.hmhospitales.com/coronavirus/covid-data-save-lives">https://www.hmhospitales.com/coronavirus/covid-data-save-lives</a></p> <p>SERAM: <a href="https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf">https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf</a></p> <p>Twitter: <a href="https://twitter.com/ChestImaging">https://twitter.com/ChestImaging</a></p> <p>BIMCV PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/padchest/">https://bimcv.cipf.es/bimcv-projects/padchest/</a></p> <p>BIMCV COVID-19-PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/">https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/</a></p> <p>COVIDGR: <a href="https://dasci.es/transferencia/open-data/covidgr-2/">https://dasci.es/transferencia/open-data/covidgr-2/</a></p>
Fig. 3 in A new R package and web application for detecting bilateral asymmetry in parasitic infections
Fig. 3. Volcano plot: statistical significance (-log (p-value)) of 10 G-tests for asymmetry of parasite distributions in individual hosts plotted against fold difference (log2 ratio) in parasite abundance between right and left eyes. Each point in the scatterplot represents the parasite distribution between eyes (excluding lenses) in an individual host. The G-test p-values were adjusted using the Benjamini and Hochberg (1995) procedure before -log10 transformation. The dashed horizontal line represents the p-value threshold for a 5% false discovery rate. Parasite distributions deviating significantly from symmetry are shown as red squares, whereas those not differing significantly from a 1: 1 ratio are represented by blue circles.
Structure, Luminescence and Temperature Detection Capability of [C(NH2)3]M(HCOO)3 (M = Mg2+, Mn2+, Zn2+) Hybrid Organic–Inorganic Formate Perovskites Containing Cr3+ Ions
<p>Project NCN SONATA 16 2020/39/D/ST5/01289<br><br>Experimental data for the publication<strong> </strong><em>Structure, Luminescence and Temperature Detection Capability of [C(NH<sub>2</sub>)<sub>3</sub>]M(HCOO)<sub>3</sub> (M = Mg<sup>2+</sup>, Mn<sup>2+</sup>, Zn<sup>2+</sup>) Hybrid Organic–Inorganic Formate Perovskites Containing Cr<sup>3+</sup> Ions, </em><a href="https://doi.org/10.3390/s23146259">https://doi.org/10.3390/s23146259</a></p>
Figure 1 in First Field Collection of the Rough Sweetpotato Weevil, Blosyrus asellus (Olivier) (Coleoptera: Curculionidae), on Hawaii Island, with Notes on Detection Methods
Figure 1. Adult rough sweetpotato weevil, Blosyrus asellus (Coleoptera: Curculionidae). Photo by GTM.
Dataset and figures for "Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS"
<p>This dataset contains the LiCSBAS results and figures (LOS velocities, decomposed vertical and EW velocities, optical images, SB network, mask) mentioned in the paper “Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS”.</p>
Impact of an inexperienced operator on photoplethysmogram for bovine heat detection
<p>Data fot the article with DOI: <a href="https://doi.org/10.1117/12.2650499">https://doi.org/10.1117/12.2650499</a></p>
Figure 3 in Recovery of Sweetpotato Vine Borer, Omphisa anastomosalis (Lepidoptera: Crambidae), in Sweetpotato Fields in Hawaii Through Field Collections and Detection Trapping
Figure 3. Sweetpotato vine borer caterpillars: A. Caterpillar leaving sweetpotato root. The root shows internal damage from the sweetpotato vine borer caterpillar and internal frass accumulation. B. Caterpillar within sweetpotato vine.
Focal mechanisms and hypocenter locations detected by temporal deployed seismic networks in and around hypocentral area of the 2000 Western Tottori Earthquake
<p>Hypocenter and focal mechanism data in the hypocentral area of the 2000 Western Tottori Earthquake. Detailed description of the datafiles is in README.docx.</p>
Fig. 4 in Detection of candidate proteins in the indican biosynthetic pathway of Persicaria tinctoria (Polygonum tinctorium) using protein-protein interactions and transcriptome analyses
Fig. 4. Scheme of the hypothesized indican biosynthetic pathway.
Synthetic Airborne Intruder Dataset: A dataset based on High-Resolution Inpainting for Safety Critical Detect and Avoid
<p>Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, e.g. recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset and present it to an independent object detector that was fully trained on real data.</p> <p>This dataset is represented in the following repository. The dataset is structured as follows:</p> <p># Synthetic Airborne Intruder Dataset</p> <p>This dataset was syntheticaly generated using an adapted [Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) with different background images and object segementations. Each image contains one object instance.</p> <p>The annotations are in the COCO annotation format.</p> <p>## Data Structure<br> Synthetic Dataset Root:<br> --train<br> |--images<br> |--instances.json<br> --val<br> |--images<br> |--instances.json<br> --test<br> |--images<br> |--instances.json<br> --Background_Sources<br> |--sources_train.csv<br> |--sources_val.csv<br> |--sources_test.cs<br> --README.md</p> <p>## Categories</p> <p>| Id | Name | Instances over all splits |<br> | ---| --- | --- |<br> | 0 | large airplane | 1695 | <br> | 1 | small airplane | 1255 | <br> | 2 | very small airplane | 46 | <br> | 3 | helicopter | 2201 | <br> | 4 | drone | 961 |<br> | 5 | hot air balloon | 315 |<br> | 6 | paraglider | 565 |<br> | 7 | airship | 42 |<br> | 8 | UFO | 0 |</p> <p>### Note:<br> UFO is a placeholder for future expansion of the dataset.</p> <p>## Splits<br> The dataset consists of 3 splits: train 5900 images, val 590 images, test 590 images.<br> The Number of instances per class and per split can be seen in the table below:</p> <p>Class | train | val | test<br> -------|-------|-----|--------<br> large airplane | 1416 | 142 | 137<br> small airplane | 1046 | 96 | 113<br> very small airplane | 38 | 2 | 6<br> helicopter | 1812 | 206 | 183<br> drone | 800 | 86 | 75<br> hot air balloon | 268 | 21 | 26<br> paragliders | 492 | 32 | 41<br> airship | 28 | 5 | 9<br> UFO | 0 | 0 | 0</p> <p>## Sources<br> The sources of the background images can be found in the files [here](./Background_Sources/).</p>
TRIBE AGROset - An agricola visual dataset for object detection
<p>This a computer vision dataset for object detection of fruits and trunks in agriculture. In its current stage, the dataset has 22 classes, such as:</p> <ul> <li>background</li> <li>arugula_minus9 (arugula in an early stage of development with less than 9 leaves)</li> <li>arugula_plus9 (arugula in an advanced stage of development with more than 9 leaves)</li> <li>carrot</li> <li>carrot_small_leaves</li> <li>coriander</li> <li>coriander_small_leaves</li> <li>coty (lettuce in the starting stage of development, with about two very small leaves)</li> <li>grape_bunch (bunches of full-size grapes)</li> <li>grape_bunch_small (bunches of pee-size grapes)</li> <li>lettuce_minus9 (lettuce in an early stage of development with less than 9 leaves)</li> <li>lettuce_plus9 (lettuce in an advanced stage of development with more than 9 leaves)</li> <li>lettuce_ready (lettuce ready for being harvested)</li> <li>radish_root</li> <li>radish_small_leaves</li> <li>radish_big_leaves</li> <li>spinach</li> <li>spinach_big</li> <li>swiss_chard</li> <li>tomato</li> <li>trunk (vine and forestry trunk)</li> <li>turnip</li> <li>turnip_small_leaves</li> </ul> <p>All the images were self-acquired in other datasets, under real-world conditions and using typical visual sensors for agricultural robots and IoT. This dataset is the result of the merging of some other datasets after fixing some annotations.</p> <p>Magalhães, Sandro A. ‘Dataset of Tomato inside Greenhouses for Object Detection in Pascal VOC’. INESC TEC, 2021. https://doi.org/10.25747/pc1e-nk92.</p> <p>Aguiar, André Silva, and Sandro Magalhães. ‘Grape Bunch and Vine Trunk Dataset for Deep Learning Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5139598.</p> <p>Rodrigues, Leandro, Francisco Terra, Sandro Magalhães, Filipe Santos, Pedro Moura, and Mário Cunha. ‘PixelCropRobot Dataset: Images of Vegetables Crops in Different Phenological Stages Taken in Greenhouses’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.7433286.</p> <p>Moreira, Germano, Sandro Augusto Magalhães, Tiago Padilha, Filipe Neves dos Santos, and Mário Cunha. ‘RpiTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596363.</p> <p>Sarmento, José, Filipe Neves Dos Santos, and André Silva Aguiar. ‘VinePoint’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5038646.</p> <p>Magalhães, Sandro Augusto, Germano Moreira, Filipe Neves dos Santos, and Mário Cunha. ‘AgRobTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596799.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDet -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5213825.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDetV2 -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection (Augmented Version)’. Zenodo, 2022. https://doi.org/10.5281/ZENODO.7186052.</p> <p> </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.