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29 results for “yolo”
Estimated Inundation Periods in the Yolo Bypass, California, 1998 – 2024
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. The YBFMP operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. The YBFMP serves to fill information gaps regarding environmental conditions in the bypass that trigger migrations and enhanced survival and growth of native fishes, as well as provide data for IEP synthesis efforts. YBFMP staff also conduct analyses of YBFMP monitoring data to address pertinent management related questions as identified by IEP. The Yolo Bypass has been identified as a high restoration priority by the National Marine Fisheries Service and US Fish and Wildlife Service Biological Opinions for Delta Smelt, Winter and Spring-run Chinook salmon and by California EcoRestore. The YBFMP informs the restoration actions that are mandated or recommended in these plans and provides critical baseline data on the ecology of the bypass and how it interacts with the broader San Francisco Estuary. YBFMP’s data is often accompanied by information on whether the Yolo Bypass is inundated, as water quality, and species composition and abundance can be greatly altered during inundation. This dataset was created to consistently estimate inundation over time. Estimating
Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
Daily water temperature (C) in the Yolo Bypass and Sacramento River, 1998-2019
This data is an integration of raw logger data as well as relevant California Data Exchange Center (CDEC) data (Pien et al. 2020, hourly) and water quality data collected during the Yolo Bypass Fish Monitoring Program’s (YBFMP) fish collection (Pien and Kwan 2022) to produce a daily water temperature dataset for the Yolo Bypass and Sacramento River at Sherwood Harbor and Rio Vista Bridge. The raw YBFMP’s water temperature data was collected by loggers attached to the rotary screw trap (STTD) and Sherwood Harbor (SHR). Logger data ranged from daily means (1998) to a fifteen-minute collection interval (2013-2017 for the Yolo Bypass, 2009-2019 for Sherwood Harbor). A daily mean, maximum, minimum, standard deviation and coefficient of variation in water temperature was produced as well as columns for sample size (n, number of measurements per day), method (data collection or estimation), category, length (number of consecutive missing dates) and site. Daily water temperature data for the full extent of the YBFMP’s fish collection is valuable for a variety of purposes. For example, variation in water temperature during inundation and comparisons between temperatures in the Sacramento River and Yolo Bypass have been used as metrics of habitat complexity and linked to life history diversity in salmon (Goertler et al. 2017).
Interagency Ecological Program: Fish catch and water quality data from the Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2024.
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. The YBFMP operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. Only juvenile and adult fish catch with associated water quality are presented in this dataset. The rotary screw trap sampling objectives are to: (1) examine species abundance and life stage of juvenile outmigrants and resident small-bodied fishes, (2) identify temporal and spatial patterns in fish abundance and species composition, and (3) examine the effect of physical and environmental conditions on these patterns. The fyke trap sampling objectives are to: (1) examine abundance of migrating and resident adult fishes, (2) identify temporal and spatial patterns in fish abundance and species composition, especially with regard to anadromous species, (3) examine the effect of physical and environmental conditions on these patterns, and (4) provide data on the timing and duration of species captured in the Yolo Bypass for comparison to those captured in other Sacramento Valley tributaries. The beach seine surveys are conducted in the Yolo Bypass’s perennial channel (Toe Drain), inundated floodplain, disconnected inundated ponds, and perennial ponds. The objectives o
Interagency Ecological Program: Zooplankton catch and water quality data from the Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2018
Largely supported by the Interagency Ecological Program (IEP), the California Department of Water Resources (DWR) has operated a fisheries and invertebrate monitoring program in the Yolo Bypass since 1998. The main objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to collect baseline data on lower trophic levels (phytoplankton, zooplankton and insect drift), juvenile and adult fish, hydrology, and water quality parameters. As the Yolo Bypass has been identified as a high restoration priority by numerous regulatory agencies, these baseline data are critical for evaluating success of future restoration projects. In addition, the data have already served to increase our understanding of the role of the Yolo Bypass in the life history of native fishes, and its ecological function in the San Francisco Estuary. Zooplankton are an important component in the diet of larval, juvenile, and small adult fishes within the San Francisco Estuary, including Delta Smelt, juvenile Chinook Salmon, Striped Bass, and Sacramento Splittail. The YBFMP collects zooplankton year-round from two sites. Since 2011, samples have been collected biweekly (every other week) to weekly (during floodplain inundation) using 150- and 50- micrometer mesh plankton nets. Zooplankton are identified and enumerated by contractors (currently BSA Environmental Services). The goals of the zooplankton monitoring program are to compare the seasonal variation in species densities and trends between (1) the Sacramento River channel, and (2) the Yolo Bypass, the river’s seasonal floodplain. Data on zooplankton catch and associated water quality parameters are presented in this dataset.
Interagency Ecological Program: Drift invertebrate and ichthyoplankton catch and water quality from the Sacramento River channel, and Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2022
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. Aquatic and terrestrial insects are an important component in the diet of juvenile and adult fishes within the San Francisco Estuary, including two important native fishes: juvenile Chinook Salmon and Sacramento Splittail. The YBFMP collects drift invertebrates year-round from two sites. Currently, samples are collected biweekly (every other week) to weekly (during floodplain inundation) using a rectangular aquatic drift net that sits at the surface of the water. Invertebrates are identified and enumerated by contractors (currently EcoAnalysts, Inc.). The goals of the monitoring program are to compare the seasonal variations in densities and species trends of aquatic and terrestrial insects/non-insects within the Sacramento River channel and the Yolo Bypass, the river’s seasonal floodplain. Drift invertebrate Key findings to date include: (1) Chinook Salmon sampled in the floodplain had diets comprised of 90% Dipterans and zooplankton, with Chironomidae being the dominant Diptera family (Sommer et al., 2001), (2) The floodplain of the Yolo Bypass contains significantly higher densities of Diptera (Diptera densities being positively associated with flow) and terrestrial invertebrates than the adjacent Sacramento River (Sommer et al. 2001b: Sommer
Modeled daily Yolo Bypass inundation
Hydrology is one of the major disturbance regimes thought to shape aquatic habitat. However, the San Francisco Estuary (SFE) has been altered by urban and agricultural development. Approximately 95% of the estuary’s wetlands have been diked, channelization is pervasive, and the biological community and water quality have been changed by exotic species introductions, sediment inputs from mining, and pollution from agricultural and urban chemicals. The hydrography has also been altered by upstream dams, which reduce the magnitude of both winter precipitation pulses and spring snow melt pulses when filling reservoirs. In addition to reservoir storage, 35% to 65% of tributary inflow is diverted by large water diversions, as well as thousands of smaller agricultural pumps and siphons. Nevertheless, the SFE retains a substantial area of seasonal off-channel habitat in the Sacramento River, the Yolo Bypass. The Yolo Bypass is hydraulically dynamic and is strongly influenced by tides during low discharge periods and dominated by fluvial river dynamics during flood events. This river floodplain-tidal slough complex is, therefore, much more hydrodynamically variable than the adjacent mainstem Sacramento River channel and benefits from additional metrics than those typically measured for river systems. These data are the modeled duration of inundation in the Yolo Bypass, which was approximated as the number of days in which Yolo Bypass flow was greater than 113.27 m3/s following an inundation event from the Sacramento River (e.g., when the stage height of the Sacramento River exceeded the height of the Fremont Weir, 10.2 meters). Inundation in the Yolo Bypass creates a complex transition zone between the river floodplain and tidal slough habitat, which likely represents the historically dominant habitat in the North Delta. Therefore, inundation brings many advantages for native fish, such as increased growth opportunities on the floodplain, an alternative route into the estuar
Interagency Ecological Program: Discrete water quality and phytoplankton data from the Sacramento River floodplain and Yolo Bypass tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998 - 2022
The Yolo Bypass Fish Monitoring Program (YBFMP) operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. The YBFMP serves to fill information gaps regarding environmental conditions in the bypass that trigger migrations and enhanced survival and growth of native fishes, as well as provide data for IEP synthesis efforts. YBFMP staff also conduct analyses of YBFMP monitoring data to address pertinent management related questions as identified by IEP. The Yolo Bypass has been identified as a high restoration priority by the National Marine Fisheries Service and US Fish and Wildlife Service Biological Opinions for Delta Smelt, Winter and Spring-run Chinook salmon and by California EcoRestore. The YBFMP informs the restoration actions that are mandated or recommended in these plans and provides critical baseline data on the ecology of the bypass and how it interacts with the broader San Francisco Estuary. Program objectives include: Collecting baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance; Analyzing and communicating Yolo Bypass data with stakeholders and the scientific and management communities to address pertinent management related questions; Providing technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. We collect discrete water quality data using a YSI ProDSS and sample phytoplankton, chlorophyll and nutrients as discrete water grabs taken biweekly (or weekly during Yolo Bypass inundation) along with lower trophic tows. Water is sampled at three sites along the Yolo Bypass and Sacramento River, then processed and analyzed by an internal DWR laboratory.
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
YOLO-CIANNA: Galaxy detection with deep learning, predicted SDC1 source catalogs
<p>Set of detected source catalogs from the SKAO SDC1 560MHz - 1000h challenge image using the YOLO-CIANNA method.<br><br>This upload is made to accompany the publication of <a title="Cornu et al. (2024)" href="https://ui.adsabs.harvard.edu/abs/2024arXiv240205925C/abstract" target="_blank" rel="noopener">Cornu et al. (2024)</a> and contains catalogs that were produced with the <a title="CIANNA release" href="https://doi.org/10.5281/zenodo.12806325" target="_blank" rel="noopener">CIANNA</a> V-1.0 framework using trained models available at <a title="Network models" href="https://doi.org/10.5281/zenodo.12801421" target="_blank" rel="noopener">10.5281/zenodo.12801421</a></p>
Figs. 114–119. 114, 115. Zorocrates oaxaca, new species. 116, 117. Z. sotano, new species. 118, 119. Z. yolo, new species. 114, 116, 118. Epigynum, ventral view. 115, 117, 119 in A Revision of the Spider Genus Zorocrates Simon (Araneae, Zorocratidae)
Figs. 114–119. 114, 115. Zorocrates oaxaca, new species. 116, 117. Z. sotano, new species. 118, 119. Z. yolo, new species. 114, 116, 118. Epigynum, ventral view. 115, 117, 119. Same, dorsal view.
YOLO-Rip_dataset
<p>This dataset consists of two folders: <em>with_rips</em> and <em>without_rips</em>, where <em>with_rips</em> contains 2486 images and annotation information, and <em>without_rips</em> contains 1307 images. We used labelImg to label these images and converted the label files into YOLO format labels, and we saved them in the <em>data_labels</em> folder. Suppose you have any questions about the use of this dataset, please contact the responding author.</p>
YOLO-based Drone Navigation for Pallet Identification
<p>This upload contains the following:</p> <ul> <li>The training data of 40 pallets and the resulting weights for four YOLOv4-tiny models, two trained to detect pallets and two trained to detect pallet blocks</li> <li>The documentation of 280 flights using these four models as a means of navigation for a micro drone, in the form of logs, images and feature vectors</li> <li>The resultings evaluation and visualisation scripts</li> </ul> <p>Further details, documentation and information on the project can be found in the corresponding <a href="https://github.com/FLW-TUDO/ReID_Drone_Scripts">Github Repo</a> and <a href="https://www.logistics-journal.de/archive/proceedings/2023/5807/rutinowski_en_2023.pdf">publication</a>. If you have any questions concerning these datasets, feel free to contact the corresponding author, <a href="https://www.linkedin.com/in/jeromerutinowski/">Jérôme Rutinowski</a>.</p> <p>This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.</p>
Yolo object detector raw results for example manipulations
<p>There is one example of the direct object detection outputs from YOLO for each manipulation type. Should you sish for more datasets, please contact the authors</p>
An endothelial monolayer with leukocytes annotated for classification with bounding boxes for YOLO
<p>Movies of leukocyte TEM generated by Max Grönloh (Sanquin Research) were used as input for deep learning analysis with YOLO on the ZeroCostDL4Mic platform.</p> <p>Bounding boxes generated with Makesense.ai by Guusje Mouton.</p> <p>After training, an unseen dataset was analysed. For results see: 'Filmpje_Predictions_YOLO.gif'</p> <p>The result shows a large number of false negatives, which can potentially be improved by increasing the volume of training data.</p>
Annotated mouse intestinal organoid dataset (YOLO format)
<p>Dataset of 840 light-transmitted images taken with EVOS FL microscope (Thermo Fisher) at objective 4X of mouse intestinal organoids grown at different densities and from multiple stages of culture. The dataset has a total of 23066 annotations<strong>. </strong>The images were manually annotated using the python library labelImg and exported for Yolo format as text files. The four classes of organoids in the text files are coded with integers: 0 for organoid0, 1 for organoid1, 2 for organoid3, and 3 for spheroid. The dataset images were split into a training and validation set consisting of 756 and 84 images using the python library module splitfolders. See publication ( doi:<a href="https://doi.org/10.1242/dmm.049756">10.1242/dmm.049756</a>) for more details. </p> <p><strong>Annotations</strong></p> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong> Label</strong></p> </td> </tr> <tr> <td> <p>Cystic organoid</p> </td> <td> <p>Very early stage, small, cystic, non-budding organoids with thick walls</p> </td> <td> <p><em>Organoid0</em></p> </td> </tr> <tr> <td> <p>Early organoid</p> </td> <td> <p>Early, budding organoids with 1-2 crypts</p> </td> <td> <p><em>Organoid1</em></p> </td> </tr> <tr> <td> <p>Late organoid</p> </td> <td> <p>Large, budding organoids with 3 or more crypt units</p> </td> <td> <p><em>Organoid3</em></p> </td> </tr> <tr> <td> <p>Spheroid</p> </td> <td> <p>Large, circular, thin-walled organoids representing, e.g. fetal, regenerating, tumorigenic, or hyperstimulated organoids </p> </td> <td> <p><em>Spheroid</em></p> </td> </tr> </tbody> </table>
Deep Learning for Detecting Verticilium Fungus in Olive Trees Using YOLO in UAV Imagery Dataset
<p>Dataset used in the paper entitled "Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery".</p> <p>Contains UAV RGB images of three olive fields located in Greece.</p>
YOLO-Strings: A YOLO-compatible Dataset for Detecting Stringing during 3D Printing
<p>The root data directory consists of two subdirectories, called <em>sequences</em> and <em>yolo</em>. The former contains all the individual sequences that were collected, further organized into subdirectories based on whether they have stringing or not. Each of these subdirectories contains images captured by the camera and the microscope. The latter comprises the training and validation subsets for the detection model, along with YOLO-style annotations.</p> <p> </p>
Annotated dataset of HepG2 spheroids (YOLO format)
<p>Dataset of birghtfield images of HepG2 spheroids in SP5D culture plates (Kugelmeiers) after 5 days of culture using a custom made inverted microscope with 4X magnification. The dataset consists of 1,395 images with a total of 19,129 labels. Three classes were defined: 0 for 'None', 1 for 'Healthy', 2 for 'Unhealthy'. The labelling was partially manual and partially automated: some spheroids were exposed to a toxic compound and automatically labelled as 'Unhealthy'. The resulting dataset includes 12,599 spheroids labelled as 'Healthy' and 6,530 as 'Unhealthy'. The dataset was partitioned into three sets for the training of a YOLO network: 70% images for training, 15% for testing and 15% for validation. More details will be available in the corresponding publication.</p>
Annotated dataset of tri-cellular liver spheroids (YOLO format)
<p>Dataset of birghtfield images of tri-cellular liver spheroids in SP5D culture plates (Kugelmeiers) after 5 days of culture using a custom made inverted microscope with 4X magnification. The dataset consists of 516 images with a total of 4,370 labels. Two classes were defined: 0 for 'Healthy', 1 for 'Unhealthy'. Manual labelling of the images resulted in 4,176 spheroids labelled as 'Healthy' and 194 as 'Unhealthy'. The dataset was partitioned into three sets for the training of a YOLO network: 80% images for training, 10% for testing and 10% for validation. More details will be available in the corresponding publication.</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.