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9,302 results for “DETECT”
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
Dataset: Swarms in Central Utah – event detection lists, arrival picks, relocations & moment tensor solutions
<p>Dataset containing results of moment tensor inversions, relocations and event detections for our study "Petersen & Pankow (2023): Small-magnitude seismic swarms in Central Utah: Interactions of regional tectonics, local structures and hydrothermal systems" (<a href="https://doi.org/10.1029/2023GC010867">https://doi.org/10.1029/2023GC010867</a>).<br>Please read the pdf-README file for more information on the dataset.</p>
Selection of Low Frequency Extensions of Saturn Kilometric Radiation detected by Cassini/RPWS.
<p>Selection of Low Frequency Extensions (LFEs) of Saturn Kilometric Radiation (SKR) observed by Cassini RPWS. The catalogue of LFES are presented in TFCAT format (Time-Frequency Catalogue, https://gitlab.obspm.fr/maser/catalogues/tfcat/-/tree/master/). This work was funded by Science Foundation Ireland grant 18/FRL/6199. </p>
Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]
<p>Dataset of all TROPOMI detected methane plumes in 2021, including estimates for the source location, emission quantification and source type. Corresponds to Figure 6 of Schuit et al. 2023 [Automated detection and monitoring of methane super-emitters using satellite data, https://doi.org/10.5194/acp-23-9071-2023]. Additional details and context are provided in Section 3 of the paper.</p> <p> </p> <p><em>Contents and data formats</em></p> <p><strong>date</strong>, date of the TROPOMI observation. format: YYYYMMDD</p> <p><strong>time_UTC</strong>, time of the TROPOMI observation in UTC. format: HH:MM:SS</p> <p><strong>lat</strong>, latitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>lon</strong>, longitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>source_rate_t/h</strong>, estimated emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>uncertainty_t/h</strong>, the uncertainty of the emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>estimated_source_type</strong>, the locally dominant anthropogenic source sector based on bottom-up inventories, the methodology is described in Section 2.5.3 of the paper. format: str</p> <p> </p> <p>Full citation of the paper:</p> <p>Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Automated detection and monitoring of methane super-emitters using satellite data, Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, 2023.</p>
Probability of Detection applied to X-ray inspection using numerical simulations
<p>In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).</p>
Polar atmospheric and aerosol river detection catalogs
<p>These detection catalogs of atmospheric and aerosol rivers were created for the publication <em>Lapere et al., "Polar aerosol atmospheric rivers: detection, characteristics and potential applications", Journal of Geophysical Research, Submitted</em>. The associated methodology is described in this publication.</p> <p>They provide binary detection of Atmospheric river (AR), Black carbon aerosol atmospheric river (BC_AER), Dust aerosol atmospheric river (DU_AER), Sea salt aerosol atmospheric river (SS_AER) and Organic carbon aerosol atmospheric river (OC_AER), in NetCDF format, for the period 1980-2022, with a 3-hour time resolution and 1x1° spatial resolution, for the regions 30°-90°N (indicated by the suffix "NH") and 30°-90°S (indicated by the suffix "SH").</p> <p>The code for pre-processing raw MERRA2 data, along with the detection algorithm are also provided here as Python Jupyter notebooks.</p>
Wintertime Arctic warm air intrusion detection algorithm for satellite sea ice concentration analysis
<p>This Dataset is related to the Article <em>Relevance of warm air intrusions for Arctic satellite sea ice concentration time </em>series in <em>The Cryosphere</em> (https://doi.org/10.5194/tc-2023-69).</p> <p>Provided are the core detection algorithm and a minimal working example as well as a list of all detected warm air intrusions between November 1979 and April 2020 (monthly data).</p>
Convolutional neural network for automated surface crack detection using inductive thermography
<p>Two phase images of the samples AIT_01 and AIT_08, analysed in the publication "Convolutional neural network for automated surface crack detection using inductive thermography", submitted to the Journal of Electronic Imaging.</p>
Photoconductive receivers at 1030 nm for high average power pulsed THz detection
<p>This dataset is accompanying the paper "Photoconductive receivers at 1030 nm for high average power pulsed THz detection"</p> <p><strong>General data acquisition:</strong></p> <p>THz is generated with the tilted pulse front approach in lithium niobate at room temperature. The pump power is controlled by a motorized lambda/2-waveplate (PI DT-80) in connection with a thin-film polarizer and calibrated to a THz power meter (Ophir 3A-P-THz). The computer-controlled rotation stage allows to sweep the THz power in a reliable and reproduceable way. The probe beam is guided over an oscillating delay line, having a delay range of approximately 15 ps and a shaking frequency of 20 Hz, leading to 4800 THz traces in over 2 min measurement time. In the path of the probe beam is also a motorized rotation stage and a polarizer positioned, to control the laser probe power in the same way as the THz pump power.</p> <p>The THz is received by a ErAs:InAlGaAs photoconductive antenna (PCA) developed at TU Darmstadt, which is optimized for 1030 nm. The THz receiving side has a silicon lens, concentrating the THz radiation on an H-dipole antenna with a center dipole length of 25 µm and a photoconductive gap of 5 µm. On the backside, the probe beam is coupled with a microscope objective for the near infrared range (Mitutoyo M Plan Apo NIR 20X). The small current of the PCA receiver is converted to a useable voltage range with a transimpedance amplifier (Femto DLPCA-200) with a gain of 10^7 V/A.</p> <p>For each combination of THz pump power and laser probe power (sampling beam), a measurement file with 2 min recording length and a sampling rate of 200 kSa/s is recorded with the DAQ (Dewesoft Sirius Mini). The exported file-format HDF-5.</p> <p>HDF-5 is an efficient (binary), cross-platform data format and can be read easily by i.e. Python or Matlab. The graphical user interface “HDFView” can be downloaded for free (after registration) from <a href="https://www.hdfgroup.org/downloads/hdfview/">https://www.hdfgroup.org/downloads/hdfview/</a>. It allows to explore the folder structure of an HDF-5 file but is not necessary when using Python or Matlab.</p> <p>The structure of a single HDF-5 from the uploaded raw data is</p> <ul> <li>A folder called "AI", standing for analog input <ul> <li>The dataset "AI 1" stands for the first analog channel, containing the position of the scanning delay line. The voltage can be converted to “THz time” with a conversion factor, where 20 V correspond to 15 ps delay.</li> <li>The dataset “AI 2”, which is voltage signal from the transimpedance amplifier and proportional to the THz electric field.</li> </ul> </li> </ul> <p>There are additional attributes in the root-folder of the HDF-5 file.</p> <p>The processed data (used in the figures) can be found in the files labeled Fig.X.h5, except for Fig.7, which only contains a single curve and is a .txt file.</p>
SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm
<p>This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean"</p> <p><strong>SST_FRONT_data.zip</strong> contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD). </p> <p>SST_FRONT_data.zip also contains <strong>Fdens_Ffreq_Fstre_example.mlx</strong>, which<strong> </strong>is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre). </p> <p><strong>Fdens_Ffreq_Fstre_example.pdf</strong> is intended for quick viewing of the script above.</p> <p> </p>
Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema
<p><strong>Objectives:</strong> This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images.</p> <p><strong>Description:</strong> This dataset consists of a collection of chest X-rays extracted from the <a href="https://physionet.org/content/mimic-cxr-jpg/2.0.0/" target="_blank" rel="noopener">MIMIC database</a>, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, pleural effusions, bat wings, and infiltrates. The dataset includes a wide variety of radiological features, with a total of 4263 annotations (<em>Table 1</em>). Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema".</p> <p><strong>Annotation Method:</strong> The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments. </p> <p>Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called "mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications.</p> <p><strong>Datasets:</strong></p> <ol> <li><strong>SLY dataset:</strong> The dataset contains chest X-ray images labeled by clinicians, including both stacked frontal and lateral images. We obtained this dataset by annotating it on the <a href="https://supervisely.com/" target="_blank" rel="noopener">Supervisely platform</a>, and it is stored in JSON and PNG formats.</li> <li><strong>Source dataset:</strong> The dataset is a transformed version of the SLY dataset. In this dataset, all annotations are consolidated into a single spreadsheet, and only frontal view images are represented.</li> <li><strong>Processed dataset: </strong>The dataset focuses exclusively on the lung area for analysis, as other areas surrounding the lung typically contain extraneous information that clinicians do not use in their decision-making process.</li> <li><strong>COCO dataset:</strong> A collection of subsets prepared in the <a href="https://towardsdatascience.com/how-to-work-with-object-detection-datasets-in-coco-format-9bf4fb5848a4" target="_blank" rel="noopener">COCO format</a> and suitable for training and testing. It includes subsets for each feature and for all features evaluated in this study.</li> </ol> <div> <p><strong>Access to the Study:</strong> Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories:</p> <ul> <li><strong>Source code:</strong> <a href="https://github.com/ViacheslavDanilov/edema_quantification" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/edema_quantification</a></li> <li><strong>Dataset:</strong> <a href="https://doi.org/10.5281/zenodo.8383776" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8383776</a></li> <li><strong>Lung segmentation models:</strong> <a href="https://doi.org/10.5281/zenodo.8393555" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393555</a></li> <li><strong>Radiographic feature detection models:</strong> <a href="https://doi.org/10.5281/zenodo.8393565" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393565</a></li> </ul> <p> </p> <p><em><strong>Table 1.</strong> Summary of annotated radiological features and severity labels</em></p> <table> <tbody> <tr> <td> <p><strong>Radiological feature</strong></p> </td> <td> <p><strong>Number </strong><strong>of objects</strong></p> </td> <td> <p><strong>Severity </strong><strong>l</strong><strong>abel</strong></p> </td> <td> <p><strong>Number of cases</strong></p> </td> </tr> <tr> <td> <p>Cephalization</p> </td> <td> <p>1656</p> </td> <td> <p>No edema</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Kerley line</p> </td> <td> <p>609</p> </td> <td> <p>Vascular congestion</p> </td> <td> <p>74</p> </td> </tr> <tr> <td> <p>Pleural effusion</p> </td> <td> <p>317</p> </td> <td> <p>Interstitial edema</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Bat wing</p> </td> <td> <p>1604</p> </td> <td> <p>Alveolar edema</p> </td> <td> <p>595</p> </td> </tr> <tr> <td> <p>Infiltrate</p> </td> <td> <p>77</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>TOTAL</p> </td> <td> <p>4263</p> </td> <td> <p>TOTAL</p> </td> <td> <p>741</p> </td> </tr> </tbody> </table> <p> </p> </div>
The Object Detection for Olfactory References (ODOR) Dataset.
<p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level<br> annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image<br> descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).</p> <p> </p>
Software Plagiarism Detection on Intermediate Representation Data Set
<p>This data set contains the necessary files used for the bachelor's thesis Software Plagiarism Detection on Intermediate Representation.</p> <p>This includes the data sets for the tests, the implemented code and scripts for the evaluation as well as referenced work.</p> <p> </p>
Reproduction package for the publication 'Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight'
<p>The uploaded files can be used to reproduce the dataset and figures in the paper<strong> Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight</strong> by Lýdia Štofanová, Aurora Simionescu, Nastasha A. Wijers, Joop Schaye, Jelle Kaastra, Yannick M. Bahé, and Andrés Arámburo-García.</p><p>NOTE: Files will be published with a new version. </p>
Detecting small environmental differences: Risk-response curves for predator-induced behavior and morphology. 2008.
Most organisms possess traits that are sensitive to changes in the environment (i.e. plastic traits) which results in the expression of environmentally-induced polymorphisms. While most phenotypically plastic traits have traditionally been treated as threshold switches between induced and uninduced states, there is growing evidence that many traits can respond in a continuous fashion. In this experiment we exposed larval anurans (wood frog tadpoles, Rana sylvatica) to an increasing gradient of predation risk to determine how organisms respond to small environmental changes. We manipulated predation risk in two ways: by altering the amount of prey consumed by a constant number of predators (Dytiscus sp.) and by altering the number of predators that consume a constant amount of prey. We then quantified the expression of predator-induced behavior, morphology, and mass to determine the level of risk that induced each trait, the level of risk that induced the maximal phenotypic response for each trait, whether the different traits exhibited a plateauing response, and whether increasing risk via increasing predator number or via increasing prey consumption induced similar phenotypic changes. We found that all of the traits exhibited fine-tuned, graded responses and most of them exhibited a plateauing response with increased predation risk, suggesting either a limit to plasticity or the reflection of high costs of the defensive phenotype. For many traits, a large proportion of the maximum induction occurred at low levels of risk, suggesting that the chemical cues of predation are effective at extremely low concentrations. In contrast to earlier work, we found that behavioral and morphological responses to increased predator number were simply a response to increased total prey consumption. These results have important implications for models of plasticity evolution, models of optimal phenotypic design, expectations for how organisms respond to fine-grained changes (i.e. wi
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
Daily presence of individual tagged striped bass as measured by stationary receiver detections in Plum Island Estuary in 2009
Stationary receiver data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and detect individual fish throughout the estuary.
Lidar digital elevation models and topographic change detection results from burned and unburned plots in the Sevilleta LTER.
These data were generated as part of a research project focused on montiroing sediment flux in dryland ecosystems following wildfire. In six separate small plots, three burned and three unburned, we conducted light detection and ranging (lidar) topographic surveys in 2016, 2017, and 2018 to document elevation changes and the volume of sediment deposition and erosion. At the down-wind edge of each plot, we used sediment catchers to trap sediment exiting the plots and thus estimate erosion volumes using in-situ equipment, which provided a secondary measurement of sediment efflux from all sites in addition to the lidar data. We used the geomorphic change detection software (https://gcd.riverscapes.xyz/) to produce maps of topographic change from the lidar digital elevation models for the 2016-2017 and 2017-2018 periods at all plots, burned and unburned. Results from this project may aid in understanding post-fire transport of sediment and nutrients from drylands following wildfire.
ICDAR 2019 Competition on Baseline Detection (cBAD)
<p>This dataset contains the training, evaluation, and test set for the ICDAR 2019 Competition on Baseline Detection (cBAD).</p> <p>A newly created freely available real world dataset consisting of 3021 annotated document page images that are collected from seven European archives and form the basis of cBAD. The baselines in all images were manually annotated. The training and the evaluation sets contain PAGE XMLs with annotated text regions and baselines.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/11/</p>
Versailles, Urban driving, Automated Driving and VRU detection
<p>Scenario description: cyclist and pedestrian detection during automated driving.</p> <p>Session description: Automated Driving part of the trip only. Two rounds: the first one without IoT (VRUs not connected) then with IoT (pedestrian with smartphone/watch and cyclist with connected bike).</p> <p>Datasets descriptions:</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_Vehicle: </strong>Data generated from the vehicle sensors</p> <p>Vehicle datasets generated by the vehicle sensors during urban driving at Versailles. This includes the data coming from the CAN bus and GPS. It includes following kind of datasets: Vehicle: general data (speed, battery), PositioningSystem: data from GPS, VehicleDynamics: data about dynamic (acceleration...), Accel: acceleration data, EnvironmentSensorsAbsolute: environment sensors in absolute coordinates</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_V2X: </strong>V2X messages during uraban driving sessions</p> <p>Data exchanged with other vehicles and pedestrian during urban driving. SortOfCam: messages sent or received from bicycles</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_IoT: </strong>Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by urban driving and car sharing application with vehicle, across oneM2M platform. oneM2M: car sharing status data</p> <p><strong>AUTOPILOT_Versailles_UrbanDriving_CAM: </strong>CAM messages</p> <p>This dataset refers to messages captured inside the vehicle during car sharing.</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.