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1,246 results for “vessel”
Aerial Vessels Detection Dataset
<p><strong>Aerial Vessels Detection Dataset</strong>: The dataset construction involved manually collecting all aerial images of vessels using UAV drones and manually annotated into three classes 'Person', 'Ship', and ''Boat'. The aerial images were collected through manual flights above Cyprus Coasts in Limassol, Famagusta and Larnaca areas. The main purpose of this dataset is to be used for marine monitoring. Capturing footage over large areas and localizing any unwanted vessels entering an area of interest, can aid in localizing refugees that illegally enter a country or manage marine traffic for commercial use.</p> <p>The images are collected in 720p and Full HD (1080p) but are usually resized before training.</p> <p>All images were manually annotated and inspected afterward with the vessels that indicate 'Person' for people detection, 'Boat' for small to medium-sized boats, and 'Ship' for large ships or commercial ships. All annotations were converted into VOC and COCO formats and initially labeled in YOLO, for training in numerous frameworks. The data collection took part in different periods.</p> <p>The dataset includes a total of 10252 images of which 1024 are split for validation, 1025 for testing, and the rest 8203 for training. </p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Images</strong></td> <td><strong>Person</strong></td> <td><strong>Boat</strong></td> <td><strong>Ship</strong></td> </tr> <tr> <td>Training</td> <td>8203</td> <td>219</td> <td>48550</td> <td>920</td> </tr> <tr> <td>Validation</td> <td>1024</td> <td>7</td> <td>5890</td> <td>143</td> </tr> <tr> <td>Testing</td> <td>1025</td> <td>13</td> <td>5247</td> <td>109</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Vessels Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7076145</p> </blockquote>
The Archaeological Ceramics from Mahurjhari: Vessel Forms by Stratigraphic Layer
<p>This spreadsheet contains the quantities of each type of vessel form found in each stratigraphic layer of each excavated trench at Mahurjhari, India. The data is arranged according to the excavated trenches. For each trench, the combined MNI count and number of bases are presented.</p> <p> </p>
Archaeological Ceramics from Mahurjhari: the Vessel Forms
<p>A descriptive and illustrated list of the vessel forms identified and defined during the analysis of archaeological ceramics in the excavated assemblage from the site at Mahurjhari, excavated by the Deccan College, Pune 2000-2003.</p>
Mass of wastes discharged directly from vessels to the water column
<p>Data set of mass discharge of selected pollutants from shipping in the European region. Created in the framework of the project Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE).</p> <p>To determine the mass of discharged pollutants, AIS-based ship emission modelling of discharge volumes is combined with results of water effluent analysis. For the discharge volumes, the Ship Traffic Emission Assessment Model (STEAM) is used. The total discharge volume of wastes is comprised of five waste streams: open/close scrubber, grey, black and ballast water. For the content of pollutants in the five waste streams, a bibliographic database of waste stream pollutant concentrations compiled during the project is used.</p>
Synthetic AIS Dataset of Vessel Proximity Events
<p>The Automatic Identification System (AIS) allows vessels to share identification, characteristics, and location data through self-reporting. This information is periodically broadcast and can be received by other vessels with AIS transceivers, as well as ground or satellite sensors. Since the International Maritime Organisation (IMO) mandated AIS for vessels above 300 gross tonnage, extensive datasets have emerged, becoming a valuable resource for maritime intelligence.</p> <p>Maritime collisions occur when two vessels collide or when a vessel collides with a floating or stationary object, such as an iceberg. Maritime collisions hold significant importance in the realm of marine accidents for several reasons:</p> <ol> <li>Injuries and fatalities of vessel crew members and passengers.</li> <li>Environmental effects, especially in cases involving large tanker ships and oil spills.</li> <li>Direct and indirect economic losses on local communities near the accident area.</li> <li>Adverse financial consequences for ship owners, insurance companies and cargo owners including vessel loss and penalties.</li> </ol> <p>As sea routes become more congested and vessel speeds increase, the likelihood of significant accidents during a ship's operational life rises. The increasing congestion on sea lanes elevates the probability of accidents and especially collisions between vessels.</p> <p>The development of solutions and models for the analysis, early detection and mitigation of vessel collision events is a significant step towards ensuring future maritime safety. In this context, a synthetic vessel proximity event dataset is created using real vessel AIS messages. The synthetic dataset of trajectories with reconstructed timestamps is generated so that a pair of trajectories reach simultaneously their intersection point, simulating an unintended proximity event (collision close call). The dataset aims to provide a basis for the development of methods for the detection and mitigation of maritime collisions and proximity events, as well as the study and training of vessel crews in simulator environments.</p> <p>The dataset consists of 4658 samples/AIS messages of 213 unique vessels from the Aegean Sea. The steps that were followed to create the collision dataset are:</p> <p>Given 2 vessels X (vessel_id1) and Y (vessel_id2) with their current known location (LATITUDE [lat], LONGITUDE [lon]): </p> <ol> <li>Check if the trajectories of vessels X and Y are spatially intersecting.</li> <li>If the trajectories of vessels X and Y are intersecting, then align temporally the timestamp of vessel Y at the intersect point according to X’s timestamp at the intersect point. The temporal alignment is performed so the spatial intersection (nearest proximity point) occurs at the same time for both vessels.</li> <li>Also for each vessel pair the timestamp of the proximity event is different from a proximity event that occurs later so that different vessel trajectory pairs do not overlap temporarily.</li> </ol> <p>Two csv files are provided. vessel_positions.csv includes the AIS positions vessel_id, t, lon, lat, heading, course, speed of all vessels. Simulated_vessel_proximity_events.csv includes the id, position and timestamp of each identified proximity event along with the vessel_id number of the associated vessels. The final sum of unintended proximity events in the dataset is 237. Examples of unintended vessel proximity events are visualized in the respective png and gif files.</p> <p>The research leading to these results has received funding from the European Union's Horizon Europe Programme under the CREXDATA Project, grant agreement n° 101092749. </p>
Archaeological Ceramics from Vidarbha: The Vessel Forms
<p>A descriptive and illustrated list of the vessel forms identified and defined during the analysis of archaeological ceramics collected during archaeological surveys of Vidarbha in 2016.</p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Dataset for marine vessel detection from Sentinel 2 images in the Finnish coast
<p>This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake, if present.</p> <h2>Source data</h2> <div> <div>Individual products used to generate annotations are shown in the following table:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> </tr> </tbody> </table> </div> <div> <div> </div> <div>Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.</div> <div> </div> <div>The corresponding L2A product identifiers are:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL2A_20220515T100031_N0400_R122_T34VEM_20220515T141508.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEM_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T34VEM_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEM_20240717T165127.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL2A_20220606T095029_N0510_R079_T35VLG_20240619T162121.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220626T095039_N0510_R079_T35VLG_20240620T063951.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220703T094039_N0510_R036_T35VLG_20240702T130032.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T35VLG_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220627T100611_N0510_R022_T34WFT_20240628T095704.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34WFT_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220828T095549_N0510_R122_T34WFT_20240708T091048.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20210714T100029_N0500_R122_T34VEN_20230224T182455.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEN_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220624T100041_N0510_R122_T34VEN_20240714T162313.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEN_20240717T165127.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220617T100611_N0510_R022_T34VER_20240627T130404.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34VER_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220826T100611_N0510_R022_T34VER_20240705T120522.SAFE</td> </tr> </tbody> </table> </div> <div><br> <div>The raw products can be acquired from <a href="https://dataspace.copernicus.eu" target="_blank" rel="noopener">Copernicus Data Space Ecosystem.</a> The products listed above can be unavailable due to e.g. processing level updates and old versions being deleted. In those cases, try searching with the tile identifier and acquisition date in order to get the correct product ID.</div> <br> <h2>Annotations</h2> <br> <div>The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:</div> <br> <blockquote> <div>T34VEM</div> <div>|-20220515</div> <div>|-20220619</div> <div>|-20220721</div> <div>|-20220813</div> </blockquote> <br> <div>All layers have a column <strong>id</strong>, which has the value <strong>b</strong><strong>oat</strong> for all annotations.</div> <br> <div>CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.</div> <br> <div>As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.</div> <br> <h3>Annotation process</h3> The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts. <div> </div> <table style="width: 63.327%; height: 391.876px;"> <tbody> <tr style="height: 39.1875px;"> <td style="width: 72.7285%; height: 39.1875px;"><strong>Product name</strong></td> <td style="width: 23.0224%; height: 39.1875px;"><strong>Number of annotations</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">183</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">519</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1518</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1371</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">277</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1205</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">746</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">971</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">122</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">162</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">98</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">450</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">66</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">424</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">399</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">83</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">184</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> <td style="width: 23.0224%; height: 19.5938px;">88</td> </tr> </tbody> </table> <br><br> <h3>Annotation statistics</h3> <br>Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) or 100 (area).</div> <div> <table> <tbody> <tr> <td> </td> <td><strong>mean</strong></td> <td><strong>min</strong></td> <td><strong>25%</strong></td> <td><strong>50%</strong></td> <td><strong>75%</strong></td> <td><strong>max</strong></td> </tr> <tr> <td><strong>Area (m²)</strong></td> <td>5305.7</td> <td>567.9</td> <td>1629.9</td> <td>2328.2</td> <td>5176.3</td> <td>414795.7</td> </tr> <tr> <td><strong>Diameter (m)</strong></td> <td>92.5</td> <td>33.9</td> <td>57.9</td> <td>69.4</td> <td>108.3</td> <td>913.9</td> </tr> </tbody> </table> <br><br> <div>As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.</div> <br> <div>Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.</div> <br> <h3>Train-test-split</h3> <br> <div>We used tiles 34VEN and 34VER as the test dataset. For validation, we split the other three tile areas into 5x5 equal sized grid, and used 20 % of the area (i.e 5 cells) for the validation. The same split also makes it possible to do cross-validation.</div> <div> </div> <div> </div> <div> </div> </div> <div> <h3>Post-processing</h3> </div> <div><br> <div>Before evaluating, the predictions for the test set are cleaned using the following steps:</div> <br> <div>1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers `jarvi` (Lakes), `meri` (Sea) and `virtavesialue` (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.</div> <div>2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer `vesikivikko` (Water rock areas) from the Topographical database.</div> <div>3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes `38511`, `38512`, `38513` from the layer `vesikivi` in the Topographical database.</div> <div>4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, `ty_njr` class ids are 1, 2, 3, 4, 5, 8</div> <div>5. All predictions that are wind turbines, found in Topographical database layer `tuulivoimalat`</div> <div>6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.</div> </div> <div> </div> <div>Model checkpoint for the best performing model is available on Hugging Face platform: <a href="https://huggingface.co/mayrajeo/marine-vessel-detection-yolov8">https://huggingface.co/mayrajeo/marine-vessel-detection-yolo</a><br> <h2>Usage</h2> The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use <a href="https://github.com/mayrajeo/geo2ml">geo2ml</a>. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets. <div> </div> To generate COCO format dataset run</div> <div> </div> <div> <pre><code>from geo2ml.scripts.data import create_coco_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_coco_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, dataset_name='<name_of_dataset>', gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> <div><br> <div>To generate YOLO format dataset run</div> <div> <pre><code>from geo2ml.scripts.data import create_yolo_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_yolo_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> </div>
Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system (dataset)
<p>Set of design maps for the EU DEMO VVPSS, for each:</p> <ul> <li>VVPSS size</li> <li>Number of VVPSS connections</li> <li>Initial VVPSS temperature</li> </ul> <p>reporting peak and equilibrium pressure in the VV following an in-vessel LOCA initiated by a double-ended guillotine break of the largest feeding pipe. Model details reported in publication A. Froio and I. Moscato, Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system, submitted to Fusion Engineering and Design.</p> <p>The README.txt file has been written according to the Dubline Core Standard for Metadata (https://www.dublincore.org/).</p>
Fishing activities and trajectories for 2 fishing vessels
<p>This data set provides the pseudo-positions in space and time of two fishing vessels and the associated activities (fishing, cruising, stopped, recorded by an on board observer). It supports the analyses provided in a paper published in Methods in Ecology and Evolution and the methods of the R package m2b (https://cran.r-project.org/package=m2b). For privacy concerns, original latitude, longitude, time and vessels id were modified. Spatial data were scaled and centred to a fictional position (R'lyeh position, Lovecraft 1928) keeping the relative geometry unchanged (acceleration, time between two positions....). Time and vessel id were modified in the same manner, keeping the relative properties of the tracks unchanged (time succession, different vessel id...). Vessel id and time are purely fictional and follow the historical context proposed by Lovecraft (1928) in the R'lyeh surroundings. Again, if the absolute spatial and temporal description of the fishing track were changed, their relative mathematical properties are conserved and can support behaviour detection based on relative movement analysis.</p> <p> </p> <p>For the data_vessel.csv file (csv file with header), the variables are</p> <p>x : pseudo longitude</p> <p>y : pseudo latitude</p> <p>t : pseudo time in year-month-day hour:minutes:second format</p> <p>b: fishing activity, namely "fishing", "cruising", "stopped"</p> <p>id: unique id by vessels (fictional names).</p> <p><br> Reference</p> <p>H. P. Lovecraft, "The Call of Cthulhu" (1928)</p> <p> </p>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</p>
Catalogue of Stone Vessels
<p>This open dataset lists, describes, and provides relevant bibliography to all known archaeological sites in Galilee with evidence for stone vessels. It forms part of the dataset used in the monograph <em>Being Jewish in Galilee, 100–200 CE: An Archaeological Study</em> (Brepols). The dataset is available in both PDF and CSV formats. The PDF file provides a detailed description of and bibliography for the evidence of stone vessels at each archaeological site. The CSV file contains the raw data that can be easily imported into spreadsheets and databases.</p>
Hydraulic burst pressure test of Type IV composite pressure vessel
<p>The present dataset belongs to a hydraulic burst pressure test of one Type IV vessel designed to burst at 200 bar. Before testing, the vessel was inspected by ultrasonic measurements. During burst pressure test, strain gauges at nine positions within the cylindrical part and on one dome of the tank recorded the deformation behavior of the vessel. The dataset provides information on the nominal tank design and winding layup, data of geometrical measurement of a nominal identical vessel, data of the ultrasonic inspection and the pressure and strain gauge data recorded during burst pressure test. Assembling this information, the data set provides an experimental validation basis for simulation methods aiming to predict deformation and damage behavior of composite pressure vessels.</p>
Aerosol particles observed onboard the research vessel Mirai over the Southern Ocean in the austral summer of 2017
<p>We have compiled a dataset of field observations to measure aerosol particle size distributions and to collect the aerosols for the following laboratory analyses to quantify the chemical composition and ice nucleating properties of aerosols over the Southern Ocean in the austral summer of 2017 as a part of the research cruise of Japanese research vessel (R/V) Mirai (Cruise number of MR16-09 leg3). The particle size distributions (PSDs) of the submicron aerosols (14–737 nm in the electrical mobility diameter) were measured using a scanning mobility particle sizer, SMPS, which is composed of a differential mobility analyzer, DMA (model 3081, TSI Inc., Minnesota, USA) and a condensation particle counter, CPC (model 3010, TSI Inc.). Since a custom-made inlet system was installed in front of the SMPS, the PSDs of total and non-volatile aerosols upon heating at the 300°C were alternatively measured every 5 min. The PSDs of the coarse fluorescent and non-fluorescent particles (700–3000 nm in the optical diameter) were measured using a waveband integrated bioaerosol sensor, WIBS (type 4A, Droplet Measurement Technologies Ltd., Colorado, USA). Hourly averaged PSDs for the diameter range of 14–3000 nm were analyzed in the associated paper in order to relate the wave breaking state derived from the hourly observations of significant wave height on the R/V. Chemical compositions were derived from the collected samples with the following techniques at the laboratory, ion chromatography for water soluble ions (chloride, nitrate, sulfate, ammonium, sodium, potassium, magnesium, calcium ions), thermal optical transmittance technique for carbonaceous aerosols (organic and elemental carbons), and inductively coupled plasma mass spectrometry for aluminum (Al). Ice nucleating properties of the aerosol particles were analyzed using a droplet freezing method (Cryogenic Refrigerator Applied to Freezing Test, CRAFT) at National Institute of Polar Research (Tobo, 2016 <a href="https://doi.org/10.1038/srep32930">https://doi.org/10.1038/srep32930</a>). All the data indicating the concentrations were reported at standard temperature and pressure (0°C and 1 atm).</p> <p>We prepared five files (comma-separated values) in total, which are hourly aerosol concentrations measured using the SMPS and WIBS, Particle size distributions measured using the SMPS, Particle size distributions measured using the WIBS, Aerosol chemical compositions, and Ice nucleating particle concentrations during the research cruise of MR16-09 leg3. Each file includes the header part to describe the aerosol data including the date and time in UTC, and the positions of the R/V.</p> <p>The associated paper discusses some aspects of data treatment and questions regarding to the methods employed in this study.</p>
Vector-LabPics dataset for images of materials in vessels in the chemistry lab
<p><strong>LabPics 2: A newer and larger a version (But harder to use) can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a></strong></p> <p>The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked.</p> <p>For more details see:</p> <p><a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a></p> <p> </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support.</p> <p>Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset.</p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.</p>
Residual stresses in clad pressure vessel steel measured by contour method
<p>Data from contour method cut surfaces of low alloy steel plates clad in stainless steel. Two plates, each measuring 300 mm (length) x 200 mm (width) x 20 mm (thickness), were extracted from the outer cylindrical structure of a nuclear steam generator. The material was forged 18MND5 (French designation equivalent to A 508 Gr.3 Cl. 1). Stainless steel beads were then deposited, by submerged arc strip cladding, on the plates. One plate was clad in a single layer of AISI 309L, the second one was clad with a double layer, 309L followed by 308L. The datasets are in the form of lists of x, y, z coordinates, with one point per line, whitespace delimited in millimetres. Each cut has four files associated to it, two for each cut surface. For each surface, there is an outline file identifying the cut surface periphery and a points file containing the points lying on the surface. Two .mat files have also been uploaded, with the results from the analyses on the single and double layer clad plates.</p> <p>These measurements are part of a broader experimental investigation to better understand the role of residual stresses in underclad cracking. The contour method was used to characterise residual stresses in conjunction with neutron diffraction measurements. The details of the experimental procedure and other information will be found in the paper “Internal stresses in a clad pressure vessel steel during post-weld heat treatment and their relevance to underclad cracking" Cattivelli et al., soon to be published.</p>
Design of perfused PTFE vessel-like constructs for in vitro applications
<p>The design of a vessel-like construct for further use in perfused tissue models using PTFE membranes and collagen is presented. Endothelial cells were able to adhere to the tube’s wall, resisted perfusion, and formed an endothelial barrier delaying diffusion of fluorescently labeled molecules.</p>
Validation data set for automatic blood vessel segmentation in colorectal cancer histology (IHC)
<p><strong>Content</strong></p> <p>This data set contains 100 histological image patches of 1000 * 1000 px size. The samples were immunostained for CD34 (3,3'-Diaminobenzidine, DAB [brown]) with hematoxylin (blue) counterstain.</p> <p>Furthermore, the data set contains a table of blood vessel counts in each image by three blinded observers as well as an automatic count with a method based on the following paper:</p> <p>Kather, Jakob Nikolas et al. "Continuous Representation Of Tumor Microvessel Density And Detection Of Angiogenic Hotspots In Histological Whole-Slide Images". <em>Oncotarget</em> 6.22 (2015): 19163-19176. http://dx.doi.org/10.18632/oncotarget.4383</p> <p><strong>Image format</strong></p> <p>All images are RGB, 0.50 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors and liver metastases) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the Declaration of Helsinki.</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Dr. Jakob Nikolas Kather<br> http://orcid.org/0000-0002-3730-5348<br> ResearcherID: D-4279-2015</p>
Moving vessel profiler (MVP) transects from PolarFront 2022-05 cruise
<p><strong>MVP transects 2022-05</strong></p> <p><span>The Moving Vessel Profiler (MVP) was deployed to collect data with high-spatial resolution along transects aiming to cross the front. Instruments contained in the profiling unit ("fish") of the winch were a CTD (AML µCTD), a fluorescence sensor (WETLABS) and a laser optical plankton counter (LOPC). </span></p> <p><span>Together the instruments yield data on the physical environment, phytoplankton, zooplankton and other particle distributions.</span></p> <p><span>Weather conditions during the cruise allowed sampling along two transects (S1 and parts of S2). However, S1 did not cross the polar front due to too thick ice cover in the North. The south (waves to high) and north (ice) of transect S2 were sampled by a different platform, data elsewhere, together resulting in a complete transect crossing the front.</span></p> <p><span>Coverage</span></p> <p><span>Transect S1 start: 75 N, 29 30' E, 20 May at 06:57 UTC<br></span><span>Transect S1 end: 75 55' N, 29 54' E, 20 May at 16:16 UTC</span></p> <p><span>Transect S2 start: 75 24' N, 29 E, 23 May at 16:03 UTC<br></span><span>Transect S2 end: 76 43' N, 29 30' E, 24 May at 03:43 UTC</span></p> <p>S1: 2022-05-20T06:57Z/2022-05-20T06:57Z<br>S2: 2022-05-23T16:16Z/2022-05-24T03:43Z </p>
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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.