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265 results for “terrain”
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog GeoTIFF
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon GeoTIFF
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon ASCII
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms
<p>This is a set of elevation models of archetypal landforms: </p> <ul> <li>volcanic caldera (Crater Lake, Oregon, USA),</li> <li>active sand dunes (Great Sand Dunes, Colorado, USA),</li> <li>a braided riverbed (Jackson Hole, Wyoming, USA),</li> <li>folded ridges (Massanutten Mountain, Virginia, USA),</li> <li>stabilized sand dunes (Sandhills, Nebraska, USA),</li> <li>crater of a shield volcano (Kilauea, Hawaii, USA),</li> <li>karst plateau (Kočevje Rog, Slovenia),</li> <li>narrow rock formations, aka hoodoos (Bryce Canyon, USA)</li> </ul> <p>All elevation models were derived from NED LiDAR sources with cell sizes ranging from 1 to 10 meters. The size of the models varies between approximately 4,000 × 4,000 and 5,500 × 5,500 height samples. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats.</p> <p>Version 2 adds models of Kilauea, Hawaii, USA, Kočevje Rog, Slovenia, and Bryce Canyon, USA.</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea GeoTIFF
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: GeoTIFFI</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea ASCII
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing
<p><strong>Files and Descriptions:</strong></p> <p>1. <strong>TP_Lake.csv</strong>: This CSV file contains identified lakes in the Tibetan Plateau.</p> <p>2. <strong>TP_River_Monthly_Statistics.csv</strong>: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.</p> <p>3. <strong>S2RiverWidth.py</strong>: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.</p> <p>4. <strong>best_model_vCloud10.pth</strong>: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).</p> <p>5. <strong>Example.tif</strong>: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.</p> <p><strong>Model Input Requirements:</strong><br>The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.</p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</p>
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
Code and data for "Computational morphology of debris and alluvial fans on irregular terrain using the visibility polygon"
<p>This code is for simulating conical fan morphologies. </p> <p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (FieldCase1Input.mat, FieldCase2Input.mat) provides the input data needed to simulate the two field cases. The simulations can be run using the code Case4_FieldCase1.m and Case5_FieldCase2.m. The results can be compared with the provided output data (FieldCase1Output.mat, FieldCase2Output.mat).</p> <p>2. Code for three idealized cases, including the comparison of analytical solutions and model simulations are provided.</p> <p>3. Function VisiPolygon is the algorithm for calculating point visibility polygon. Function FanTopo_slope is the model for simulating fan morphology. </p> <p><br> </p>
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
<p><em>Aim:</em> Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.</p> <p><em>Location: </em>Seamounts around the Cabo Verde Archipelago (NW Africa).</p> <p><em>Methods:</em> We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.</p> <p><em>Results</em>: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.</p> <p><em>Main conclusions:</em> Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.</p>
Dataset for "Finding order in chaos: Quantitative predictors of chaos terrain morphology on Europa"
<p>Dataset S1 contains two shapefiles containing the chaos borders (ChaosBordersShapefiles.zip) and each block mapped (AllBlocksShapefiles.zip), and a text file that contains the total area (km<sup>2</sup>) of each mapped chaos terrain and the area (km<sup>2</sup>) of every block we mapped within each chaos terrain in the manuscript "Finding order in chaos: Quantitative predictors of chaos terrain morphology on Europa".</p>
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms
<p><strong>Synthetic Lunar Terrain (SLT) </strong>is a dataset based on a reconstruction of a typical <strong>cratered lunar surface landscape </strong>at the <a href="https://set.adelaide.edu.au/atcsr/space-research/exterres-laboratory" target="_blank" rel="noopener">EXTERRES Laboratory</a> at University of Adelaide, Roseworthy Campus. On a surface area of <strong>3.6m x 4.8m</strong>, multiple synthetic craters with different sizes and geometries were sculpted into<strong> lunar regolith simulant</strong>. A <strong>9kW metal-halide lamp</strong> illuminated the scene, providing high contrast drop-shadows from the rims of craters and similar surface features that are characteristic for the Earth's moon.</p> <p>The purpose of this dataset is to provide multimodal recordings of visual information to develop and test algorithms on a hardware analogue of the moon rather than relying on computer simulations. In particular, comparisons between <strong>neuromorphic vision sensors</strong> like <strong>event-based cameras</strong> and imaging with <strong>conventional monocular cameras</strong> are at the core of this work. For this purpose, an event-based camera (Gen4 Prophesee with Prophesee-Sony IMX636 sensor) was mounted downward-pointing next to a optical camera (Basler a2A1920-160ucPRO with Sony IMX392 sensor) on an extendable rod which was moved above the surface in a slow and continuous sweep. In total, SLT consists of camera recordings from 21 different positions/settings, with clockwise and anti-clockwise motions under varying, extreme lighting conditions.</p> <p>The event-stream and grayscale image data can be further referenced via a detailed <strong>3D point cloud</strong> obtained by a FARO Focus S70 3D Scanner. This 3D Scan was post-processed, realigned and resampled into a 3D point cloud of <strong>~6.25M points, </strong>with a surface density of <strong>1.862 p/mm²</strong>, providing a ground-truth for the crater geometries.</p> <p>In detail, SLT contains the following:</p> <ul> <li>eventbased.zip: <ul> <li><strong>42 camera orbits</strong> in the binary <strong>EVT 3.0</strong> format (<a href="https://docs.prophesee.ai/stable/data/encoding_formats/evt3.html" target="_blank" rel="noopener">Prophesee docs</a>) </li> <li>corresponding <strong>.mp4 </strong>event-frame video rendering for visualization purposes (33.333ms accumulation time at 30FPS)</li> <li>corresponding<strong> .bias</strong> file containing settings used during recording</li> </ul> </li> <li>code.zip: <ul> <li>Standalone C++ code of the <strong>metavision EVT3-to-RAW file decoder</strong>, allowing to convert the binary EVT 3.0 format into a plaintext <strong>.csv </strong>that includes <ul> <li>the coordinates of the event-pixel,</li> <li>the polarity change,</li> <li>and the time-stamp of the event.</li> </ul> </li> <li>This code is an unmodified redistribution from the <a href="https://www.prophesee.ai/metavision-intelligence/" target="_blank" rel="noopener">Metavision SDK</a>, version 4.6.0, released by Prophesee under Apache License 2.0.</li> </ul> </li> <li> optical.zip: <ul> <li><strong>42 image sequences</strong> in <strong>.tif</strong> format (LZW, 1920x1200px, 8bit, grayscale) <ul> <li>Length of image sequences varies between about 300 to 700 images per sequence</li> </ul> </li> </ul> </li> <li>3d_scan.zip: <ul> <li><strong>SLT3d_scan.ply:</strong> 3D point cloud of the scene Stanford Polygon File Format</li> <li><strong>SLT3d_scan.xyz:</strong> 3D point cloud with plaintext x y z coordinates, white-space separated</li> </ul> </li> <li>cratermap.png: <ul> <li>Annotations of <strong>130 different surface features</strong> that have been manually identified as crater-like with approximate x,y-coordinates.</li> </ul> </li> <li>positionmap.png: <ul> <li>Illustration of the different positions from which the rod was moved over the scene (not to scale).</li> </ul> </li> <li>sample.zip: <ul> <li>A sample containing 1 event-camera orbit with the corresponding image sequence (for convenience only, to test the dataset without the need to download it's entirety)</li> </ul> </li> </ul> <p>The global coordinate frame of this dataset puts the origin at the centre of the scene. The shorter side of the terrain is roughly aligned with the x-axis, the longer side with the y-axis. The z-axis represents height/depth (compare with <strong>cratermap.png</strong>). The different conditions (compare with <strong>positionmap.png</strong>) from which the data was taken are encoded as follows:</p> <ul> <li><strong>A1, ..., A9</strong> refer to the left side of the scene (negative x)</li> <li><strong>B1, ..., B9 </strong>refer to the right side of the scene (positive x)</li> <li><strong>S1, S2, S3</strong> and <strong>S4 </strong>describe special lighting conditions and/or parameter settings</li> <li><strong>CW </strong>refers to a "clockwise" sweeping of the camera-rod, relative to the position</li> <li><strong>ACW</strong> refers to an "anti-clockwise" sweeping of the camera-rod, relative to the position</li> </ul> <p>The light from the metal-halide lamp was directed through a small opening, shining along the positive y-axis. In some of the setups, an obstacle was placed between the surface and the opening, blocking out part of the light to create a light-dark separator on the surface, emulating the <strong>terminator</strong> on the Moon between it's day and night side, resulting in highly contrastive images.</p> <p>We encourage you to consult and cite our related publication, should you find SLT useful.</p> <ul> <li>Märtens, M., Farries, K., Culton, J. and Chin, TJ. "<strong>Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms</strong>", Proceedings of "<em>International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS), 2024</em>", pp. 609-614</li> </ul> <p> </p>
D-PLACE dataset derived from 'Global Multi-resolution Terrain Elevation Data 2010'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Global Multi-resolution Terrain Elevation Data courtesy of the U.S. Geological Survey (Downloaded 14 Jul 2014)</p> </blockquote>
Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.</p>
Repos mérité sur la terrasse du Centre Pro Natura Lucomagno après une journée de terrain. (Photo Anne Freitag) in Société Vaudoise D'Entomologie (Sve)
Repos mérité sur la terrasse du Centre Pro Natura Lucomagno après une journée de terrain. (Photo Anne Freitag)
Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
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Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry
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ScienceDex guides
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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.