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369 results for “Datasets Benchmarking”
Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Structured noise Convallaria dataset for structured noise removal benchmark as used in StructN2V paper
<p>Structured Convallaria dataset for structured noise removal benchmark as used in StructN2V paper (https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full)</p>
Dataset from: A pragmatic benchmarking study of an evidence-based personalized approach in 1938 adolescents with high-risk idiopathic scoliosis
<p>Dataset from a still non published study titled "A pragmatic benchmarking study of an evidence-based personalized approach in 1938 adolescents with high-risk idiopathic scoliosis"</p>
CY-Bench: A comprehensive benchmark dataset for subnational crop yield forecasting
<h1>CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting</h1> <h2><br>Overview</h2> <p>CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the <a href="https://www.agml.org/">AgML Community</a>, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.</p> <p>* Crops : Wheat & Maize<br>* Spatial Coverage : Wheat (29 countries), Maize (38).<br> See <a href="https://github.com/WUR-AI/AgML-CY-Bench/blob/main/notebooks/cybench_summary.ipynb">CY-Bench Summary</a> for the list of countries.<br>* Temporal Coverage : Varies. See <a href="https://github.com/WUR-AI/AgML-CY-Bench/blob/main/notebooks/cybench_summary.ipynb">CY-Bench Summary</a>.</p> <h2>Data </h2> <h3>Data format</h3> <p><br>The benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.</p> <h3>Data content</h3> <p>All data files are provided as .csv.</p> <table style="width: 100%;"> <tbody> <tr> <td style="width: 10.3122%;">Data</td> <td style="width: 21.9489%;">Description</td> <td style="width: 18.069%;">Variables (units)</td> <td style="width: 13.8137%;">Temporal Resolution</td> <td style="width: 35.8562%;">Data Source (Reference)</td> </tr> <tr> <td style="width: 10.3122%;">crop_calendar</td> <td style="width: 21.9489%;">start and end of growing season</td> <td style="width: 18.069%;">sos (day of the year),<br>eos (day of the year)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">World Cereal (Franch et al, 2022)</td> </tr> <tr> <td style="width: 10.3122%;">crop_mask</td> <td style="width: 21.9489%;">crop area fraction</td> <td style="width: 18.069%;">crop_area (km2), crop_area_percentage (%)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">WorldCereal (Van Tricht et al., 2023; EC-JRC, 2024)</td> </tr> <tr> <td style="width: 10.3122%;">fpar</td> <td style="width: 21.9489%;">fraction of absorbed photosynthetically active radiation</td> <td style="width: 18.069%;">fpar (%)</td> <td style="width: 13.8137%;">Dekadal (3 times a month; 1-10, 11-20, 21-31)</td> <td style="width: 35.8562%;">European Commission's Joint Research Centre (EC-JRC, 2024)</td> </tr> <tr> <td style="width: 10.3122%;">ndvi</td> <td style="width: 21.9489%;">normalized difference vegetation index</td> <td style="width: 18.069%;">-</td> <td style="width: 13.8137%;">approximately weekly</td> <td style="width: 35.8562%;">MOD09CMG (Vermote, 2015)</td> </tr> <tr> <td style="width: 10.3122%;">meteo</td> <td style="width: 21.9489%;">temperature, precipitation (prec), radiation, potential evapotranspiration (et0), climatic water balance (= prec - et0) </td> <td style="width: 18.069%;">tmin (C), tmax (C), tavg (C), prec (mm0, et0 (mm), cwb (mm), rad (J m-2 day-1)</td> <td style="width: 13.8137%;">daily</td> <td style="width: 35.8562%;">AgERA5 (Boogaard et al, 2022)</td> </tr> <tr> <td style="width: 10.3122%;">soil_moisture</td> <td style="width: 21.9489%;">surface soil moisture, rootzone soil moisture</td> <td style="width: 18.069%;">ssm (kg m-2), rsm (kg m-2)</td> <td style="width: 13.8137%;">daily</td> <td style="width: 35.8562%;">GLDAS (Rodell et al, 2004)</td> </tr> <tr> <td style="width: 10.3122%;">soil</td> <td style="width: 21.9489%;">available water capacity, bulk density, drainage class</td> <td style="width: 18.069%;">awc (c m-1), bulk_density (kg dm-3), drainage class (category)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">WISE Soil database (Batjes, 2016)</td> </tr> <tr> <td style="width: 10.3122%;">location</td> <td style="width: 21.9489%;">centroid</td> <td style="width: 18.069%;">latitude, logitude, region_area (km2)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;"> </td> </tr> <tr> <td style="width: 10.3122%;">yield</td> <td style="width: 21.9489%;">end-of-season yield</td> <td style="width: 18.069%;">yield (t ha-1)</td> <td style="width: 13.8137%;">yearly</td> <td style="width: 35.8562%;">Various country or region specific sources (see crop_statistics_... in https://github.com/WUR-AI/AgML-CY-Bench/tree/main/data_preparation)</td> </tr> </tbody> </table> <h3> </h3> <h3>Folder structure</h3> <ol> <li>cybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**.<br>```<br>CY-Bench<br>│<br>└─── maize<br>│ │<br>│ └─── AO<br>│ │ -- crop_calendar_maize_AO.csv<br>│ │ -- crop_mask_maize_AO.csv<br>│ │ -- fpar_maize_AO.cs<br>│ │ -- location_maize_AO.csv<br>│ │ -- meteo_maize_AO.csv<br>│ │ -- ndvi_maize_AO.csv<br>│ │ -- soil_maize_AO.csv<br>│ │ -- soil_moisture_maize_AO.csv<br>│ │ -- yield_maize_AO.csv<br>│ │ <br>│ └─── AR<br>│ -- crop_calendar_maize_AR.csv<br>│ -- crop_mask_maize_AR.csv<br>│ -- fpar_maize_AR.csv<br>│ -- ...<br>│ <br>└─── wheat<br>│ │<br>│ └─── AR<br>│ │ -- crop_calendar_wheat_AR.csv<br>│ │ -- crop_mask_wheat_AR.csv<br>│ │ -- fpar_wheat_AR.csv<br>│ │ ...<br>``` <h3>Example : CSV data content for maize in country X</h3> <p>```<br>X<br>└─── crop_calendar_maize_X.csv<br>│ -- crop_name (name of the crop)<br>│ -- adm_id (unique identifier for a subnational unit)<br>│ -- sos (start of crop season)<br>│ -- eos (end of crop season)<br>│ <br>└─── crop_mask_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id <br>│ -- crop_area<br>│ -- crop_area_percentage<br>│ <br>└─── fpar_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id <br>│ -- date (in the format YYYYMMdd)<br>│ -- fpar<br>│<br>└─── location_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id <br>│ -- latitude<br>│ -- longitude<br>│ -- region_area<br>│<br>└─── meteo_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id <br>│ -- date (in the format YYYYMMdd)</p> <p>│ -- tmin (minimum temperature)<br>│ -- tmax (maximum temperature)<br>│ -- prec (precipitation)<br>│ -- rad (radiation)<br>│ -- tavg (average temperature)<br>│ -- et0 (evapotranspiration)<br>│ -- vpd (vapor pressure deficit)<br>│ -- cwb (crop water balance)<br>│ <br>└─── ndvi_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id<br>│ -- date (in the format YYYYMMdd)<br>│ -- ndvi <br>│ <br>└─── soil_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id<br>│ -- awc (available water capacity)<br>│ -- bulk_density<br>│ -- drainage_class<br>│ <br>└─── soil_moisture_maize_X.csv<br>│ -- crop_name<br>│ -- adm_id<br>│ -- date (in the format YYYYMMdd)<br>│ -- ssm (surface soil moisture)<br>│ -- rsm ()<br>│ <br>└─── yield_maize_X.csv<br>│ -- crop_name<br>│ -- country_code<br>│ -- adm_id<br>│ -- harvest_year<br>│ -- yield<br>│ -- harvest_area<br>│ -- production</p> </li> <li>centroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows: <p>centroids</p> <p>│ └─── AO<br>│ │ -- AO.cpg<br>│ │ -- AO.dbf<br>│ │ -- AO.prj<br>│ │ -- AO.shp<br>│ │ -- AO.shx<br>│ └─── AR<br>│ │ -- AR.cpg<br>│ │ -- AR.dbf<br>│ │ -- AR.prj<br>│ │ -- AR.shp<br>│ │ -- AR.shx</p> ... <p>polygons</p> <p>│ └─── AO<br>│ │ -- AO.cpg<br>│ │ -- AO.dbf<br>│ │ -- AO.prj<br>│ │ -- AO.shp<br>│ │ -- AO.shx<br>│ └─── AR<br>│ │ -- AR.cpg<br>│ │ -- AR.dbf<br>│ │ -- AR.prj<br>│ │ -- AR.shp<br>│ │ -- AR.shx</p> ...</li> </ol> <h3>Data access</h3> <p>The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library</p> <h2><br>License and citation</h2> <p><br>We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.</p> <p> </p> <h2>Version Notes</h2> <p>1.0 is the dataset submitted to NeurIPS Datasets and Benchmarks Track. The paper and discussions are here: https://openreview.net/forum?id=jkJDNG468g#discussion</p> <p>1.1 and 1.2 fix some issues with column names and mismatches in adm_id between yield data and input data.</p> <p>1.3 includes location information in the form of centroids and polygons of admin regions.</p> <p>1.4 updates the fpar data for 2023. fpar data was incomplete for 2023 in earlier versions (due to unavailability in the data source itself).</p> <p>1.5 fixes an issue in crop calendar</p> <p>1.6 fixes an issue in ndvi time series</p> <p>1.7 updates storage precision to 3 decimal places to reduce data size</p> <p>1.8 filter out invalid yield values</p> <p>1.9 Add vpd. Add location. ET0 obtained from AgERA5 (was AQUASTAT-FAO ). Use AgERA5 2.0 (was AgERA5 1.1)</p> <p>1.10 Add region_are to location*.csv. Add crop_mask_*.csv. Fix error in yield Australia. </p>
Experimental dataset referring to: 'Transient Freezing of Water in a Square Duct: An Experimental Benchmark"
<p>This data set corresponds to the paper This data set corresponds to the paper Transient Freezing of Water in a Square Duct: An Experimental Benchmark. Please cite this paper when using this data (for instance for validating numerical melting/solidification models)</p> <p>The following flow conditions are included (for both the inlet and the center of the channel):</p> <p>Re = 474, T_c,set = -5<br> Re = 474, T_c,set = -7.5<br> Re = 474, T_c,set = -10<br> Re = 474, T_c,set = -15<br> Re = 1118, T_c,set = -5<br> Re = 1118, T_c,set = -7.5<br> Re = 1118, T_c,set = -10<br> Re = 1118, T_c,set = -15</p> <p>Each folder includes the original PIV images, the PIV images after rotation correction, scaling and AOI selection, the post processed velocity data, the post processed ice-layer and the temperature recordings of the cold-plate as well as the inlet, outlet temperatures and the flow rate (TData). The header for the recordings is included in the main dataset which may be used to navigate the columns and select the relevant data.</p> <p>Finally, there is one folder containing the flow measurements without the ice-growth, such that the flowfield in the channel may be compared to the expected flow field from literature for laminar flow in a square duct.</p>
Benchmark dataset for verification and validation of elPaSo Core module
<p>This dataset contain the set of vibroacoustic benchmark problems for verification and validation of the FEM research code "elPaSo Core".</p>
Dataset of "Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques"
<p>Dataset of "Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques"</p>
EUPPBench postprocessing benchmark dataset - station data
<p>The EUMETNET EUPPBench postprocessing benchmark station data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the the dataset in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following command</p> <pre><code class="language-bash">unzip EUPPBench-stations.zip rm EUPPBench-stations.zip</code></pre> <p>This will unpack the dataset. You need at least 35Gb of free space on your disk to perform this operation.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <p>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</p> <p> </p> <p><strong>Remark</strong></p> <p>You might also be interested by the gridded data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7429236">https://doi.org/10.5281/zenodo.7429236</a> .</p>
Synthetic Datasets from the 2023 ECXAI Workshop Paper on Principled Benchmarking for Rule Set Learning Algorithms
<p>Synthetic datasets generated as part of the demonstration given in the paper <em>Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms</em> presented at the <em>Evolutionary Computing and Explainable Artificial Intelligence</em> (ECXAI) workshop taking place as part of the 2023 GECCO conference.</p>
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
input datasets for pangene benchmark at https://github.com/Ensembl/plant-scripts
<p>Gzipped GFF and FASTA files of genome assemblies of datasets ACK, rice3, wheatchr1 and barley used for the pangene benchmark described at https://www.biorxiv.org/content/10.1101/2023.01.03.520531v2 .</p> <p>The results of analyzing these files can be found at https://github.com/Ensembl/plant-scripts/releases/tag/Apr2023</p> <p> </p> <p> </p> <p> </p>
Benchmark datasets for "Detecting T-cell expansion and quantifying clone survival from deep profiling of immune repertoires"
<p>T-cell receptor repertoire sequencing datasets describing time courses obtained for vaccination, normal aging and blood transplant cases. Datasets reported here were previously published (except for Tem/Tcm data), this is just a compendium of selected samples that is properly pre-processed and formatted.</p>
DATASET - Automated grain sizing from UAV imagery of a gravel-bed river: benchmarking of three object-based methods and analysis of particle-size clustering
<p>Dataset used to compute the grain size distributions from in-field line sampling and digitally on orthoimages with automated methodologies and by manual labelling. It also contains the data used to produce spatial statistics.</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.