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5,155 results for “resolution”
Forest cover, age and densification rate for Southern China at 30 m resolution
<p>The dataset includes Landsat based forest cover maps from 1986-2020 that were used to calculate forest age and densification rate.</p> <p>Forest probability as the output from a Random Forest model was used to reflect forest cover, and shows how likely an area resembles a dense forest. We set a threshold of 50% probability to define an area as dense forest, and the number of years from the year the threshold is crossed until 2018 as the forest age. Note that the area must remain above 50% until 2018 to qualify as forest. The years before the threshold is crossed are used to calculate the densification rate. It is defined as the mean probability change per year in the period where an area is between a probability of 20% and 50%. If the area falls below 20% the count is reset.</p>
High-resolution throughfall measurement design, Hainich, Germany, project AquaDiva
<p>This dataset contains the sampling design for throughfall data used for the analysis published in Metzger et al. (2017) and Fischer et al. (2023). It gives spatially distributed throughfall measurement points and their forest structural properties. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design.</p> <p>The field site and sampling design are described in Metzger et al. (2017). The throughfall data is given in an associated published dataset (Metzger and Hildebrandt, 2023).</p>
ValEqt: A high-resolution Earthquake and Repeating earthquakes catalog of the 2017 Valparaiso sequence
<p><strong>The ValEqt earthquake catalog</strong></p> <p>Description:</p> <p>Catalog of earthquakes detected near the 2017 Mw=6.9 Valparaiso (Chile) earthquake from 01/01/2016 to 01/01/2021. We also include a catalog of ValEqt's repeating earthquakes.</p> <p>Methods used to build this dataset are extensively described in this paper : <em>Upcomming paper Doi</em></p> <p>Files:</p> <ol> <li><em>ValEqt.txt </em>: Earthquake catalog</li> <li><em>Repeater.txt</em> : Repeating earthquake catalog</li> </ol> <p> </p>
Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)
<p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p> <p> </p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p>Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p>If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn.</p> <p>If you want to use <strong>monthly</strong> estimates from 2000 to 2022, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p> </p> <p> </p>
Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets
<p>Sample datasets for the <strong>Case Studies</strong> section of the <em> Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets </em>web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</a>)</p>
Dataset of AI4ER MRes titled "Improving Urban Tree Management Using High-Resolution Satellite Data"
<p>This repository contains the data used in the Master's thesis titled "Improving Urban Tree Management Using High-Resolution Satellite Data" by Andrés C. Zúñiga-González as part of the AI4ER MRes 1st year project at the University of Cambridge.</p> <p>The folders are split into large and small training and testing datasets. These folders contain the tiles (in png and tif formats) used in the models. In addition, it includes the crowns in ESRI Shapefile format for the training and testing datasets. Finally, it contains the best model from the project (named urban_trees_Cambridge_20230630.pth).</p>
High-resolution global map of closed-canopy coconut palm
<p>The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: <br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.</p> <p>The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel. </p> <p>The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files:<br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.<br> The attribute ‘Class’ contains the following values: <br> [0] Land cover could not be determined because sub-meter resolution data was not available.<br> [1] Other land covers that are not coconut palm.<br> [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel.<br> [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure. <br> [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground.<br> [5] Palm species that are not coconut palm.</p> <p> </p> <p>Changelog v1-2:</p> <p>- Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.</p>
Spatial organization of the mouse retina at single cell resolution by MERFISH
<p><strong>Abstract:</strong><br> <br> The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina1, little is known about their spatial organization. Here, we generated the first single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the first connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.<br> <br> <strong>Data description:</strong><br> 1. VZA105a_integrated_368genes.h5ad<br> This file contains the raw MERFISH count matrix for four samples with 368 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.</p> <p><br> 2. VA45_integrated.h5ad<br> This file contains the raw MERFISH count matrix for six samples with 500 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> <br> 3. merfish_impute.h5ad<br> This file contains the imputed count matrix for ten samples. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> </p>
Dataset for the high-resolution NEMO4 model of Cumberland Bay, South Georgia
<p>This data-set consist of the required model set up files to reproduce a NEMO4 high-resolution model of Cumberland Bay, South Georgia, for the years 2001 to 2010 as described in the thesis 'Oceanographic Variability in Cumberland Bay, South Georgia: Implications for Glacier Retreat and Fisheries Management' Joanna Zanker. Files required to reproduce the process tests detailed in the thesis are also provided with the relevant names and the scripts for running the individual-based model with the model flow fields parameterised for mackerel icefish.</p>
A 500m-Resolution Dataset of Agricultural Drought Areas in the North China Plain During 2006–2019
<p>Agricultural drought stands as a dominant natural hazard in the North China Plain, showing increasing frequency and severity over recent decades. However, limitations posed by the coarser spatiotemporal resolution of accessible agricultural drought data archives impede the practical application of regional-scale agricultural drought simulation and monitoring studies. Hence, we generated a 500 m-resolution agricultural drought areas dataset of summer-harvest crops and autumn-harvest crops spanning across the North China Plain (NCP) from 2006 to 2019, encompassing three degrees of agricultural drought area: drought-covered area, drought-damaged area, and crop failure area. The generated dataset holds the potential for effective application in generating synthetic regional drought information, support impact-based agricultural drought monitoring and prediction, and subsequently could greatly assist in optimizing agricultural water management and ensuring global food security.</p>
Supplemental material of "An annotated whole-genome multilocus sequence typing schema for scalable high resolution typing of Streptococcus pyogenes"
<p>This supplemental material includes the genome assemblies, associated metadata and analysis results for five datasets used to define a publicly available annotated wgMLST schema for <em>S. pyogenes</em> and to evaluate its suitability for high resolution typing. A brief description for each file in the dataset is available in the included README file. Raw sequencing data and sample metadata for the 265 isolates included in Dataset1 have been deposited in the European Nucleotide Archive (ENA) under Project <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49967?show=reads">PRJEB49967</a>.</p> <p>The wgMLST schema was created with <a href="https://github.com/B-UMMI/chewBBACA">chewBBACA</a> and is publicly available at <a href="https://chewbbaca.online/species/1/schemas/1">chewie-NS</a>, where a more detailed description of schema creation, annotation and curation can be found.</p>
Moderate Spatial Resolution Burned Area Atlas for the Mau Forest Reserve in Kenya (2012 - 2017)
<p>This dataset contains moderate spatial resolution annual burned area records over the Mau Forest Reserve in Kenya. The dataset was derived from VIIRS Active Fire Detections (AFDs) and Landsat 7 & 8 Atmospherically Corrected Surface Reflectance, applying the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm and the differenced Normalized Burn Ratio (dNBR) for burned area identification.</p> <p>Please consider that the dataset is still subject to revision to address inaccuracies associated with the spatial extent of derived burned areas.</p> <p>Further documentation and related resources can be found at: https://github.com/StefMeh/Burned-Area-Estimation.git</p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Dataset for Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery
<p>This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). </p> <p>The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. </p> <p>The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. </p> <p>Both file configurations are processed by the code linked in the paper. </p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p>
High spatial resolution dataset of grapevine yield components at the within-field level
<p>This dataset comprises a comprehensive mapping of vine yield at the plant scale over two vine fields located in the southern region of France. Both vine fields were planted with the Vitis vinifera : cv. Syrah. The first field (Field 1) occupies 0.8 ha and data were collected in 2022, while the second field (Field 2) has an area of 0.5 ha and data were collected in 2008. Throughout the growing season, information regarding unproductive vines, inflorescence number, and bunch weight was collected for both vine fields. For both fields, at the flowering stage, the location of each productive and unproductive vines (dead and missing vines) was georeferenced, and the number of inflorescences was manually counted for all productive vines. For Field 1, at harvest, all bunches of the field were manually weighed with an accuracy of ±1 gram and georeferenced precisely (one point per vine). For each vine, total yield (grams per vine) was then computed as as the sum of the weight of its bunches. For Field 2, at harvest, the total yield per vine was estimated based on the weighing of representative bunches obtained from several regularly spaced set of 5 vines. In addition to the yield data, two ancillary data, including soil apparent resistivity measurements and common vegetative index derived from remote sensed imagery, are provided for both vine fields. Overall, the dataset consists of 3644 vines, with 2151 being productive, along with a total count of 33354 inflorescences and 19635 manually weighed bunches at harvest.</p> <p>Raw data includes 9 shapefiles (.shp), one per data type and per field.</p> <ul> <li>“Field1_Dead_Missing_Vines.shp” (Figure 1.A) contains the location of missing and dead vine identified in Field 1;</li> <li>“Field1_Inflorescences.shp” and “Field2_Inflorescences.shp” (Figure 1.B and Figure 1.C) both contain the location and the number of inflorescences per vine counted during flowering;</li> <li>“Field1_Final_Yield.shp” and “Field2_ Final_Yield.shp” (Figure 1.D and Figure 1.E) both contain location and measured values of yield weight per vine at harvest. For Field 1, the list of the bunch weight is also available;</li> <li>“Field1_Soil_Resistivity.shp” and “Field2_ Soil_Resistivity.shp” (Figure 1.F and Figure 1.G) contain the electrical resistivity measurements of the soil on each field;</li> <li>“Field1_Vegetation_Index.shp” and “Field2_ Vegetation_Index.shp” (Figure 1.H and Figure 1.I) contain vegetation index values, NDVI (Normalized Difference Vegetation Index, without unit) for Field 1 and FCover (Fraction of vegetation Cover, in %) for Field 2.</li> </ul> <p>Aggregated data are composed of three csv files, two for Field 1 and one for Field 2. “Field1_Yield.csv” and “Field2_Yield.csv” aggregate all available yield data for each vine plant (either productive or other types). In both these csv files, each line represents a planted vine. Another file named "Field1_Bunches.csv" contains another representation of the data for Field 1. In this file, each row corresponds to a weighed bunch.</p> <p>A Data in Brief article is associated to this dataset.</p>
High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)
<p>This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution.</p> <p>During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural.</p> <p>The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.</p>
Dataset of "High-resolution MHz trARPES based on a tunable VUV source"
<p>This upload includes the experimentally measured data on Au111, Bi2Se3, and TaTe2 by time- and angle-resolved photoemission spectroscopy, as well as a laser spectrum of the Y-Fi VUV laser system.</p>
Global Phytoplankton Phenological Indices - 9km resolution
<p>Bloom phenology metrics calculated from OC-CCI v6.0 data at a spatial resolution of 9km. </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.