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2,113 results for “High resolution”
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
High-resolution harvester data for estimating rolling resistance and forest trafficability
<p>This study describes an extensive, high-resolution data on rolling resistance coefficient collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50~km of harvester routes and used in the following study:</p> <p>Salmivaara, A., Holmström, E., Kulju, S., Ala-Ilomäki, J., Virjonen, P., Nevalainen, P., Heikkonen, J., and Launiainen, S. 2024.High-resolution harvester data for estimating rolling resistance and forest trafficability. European Journal of Forest Reseach. doi: 10.1007/s10342-024-01717-6</p> <p>The coordinates are in metric system, and the relative position between sites are maintained. However, they have been converted and do not refer to actual locations.</p> <p>Two datasets collected by a Ponsse Ergo harvester are available: </p> <p>1) All non-frost period of 2021, with 8m*8m grid means of rolling resistance ('grid mean rr'), along with grid mean longitudinal inclination of the machine ('grid mean pitch'), time, topographic wetness index ('twi') and binary indication whether a road is within 4m radius from the grid centerpoint ('roads'). </p> <p>2) Data including all 11 test sites with all first pass harvester machine data ('time', 'E_conv', 'N_conv', 'dtorq', 'drpm', 'peng', 'cooling', 'phydr', 'pwheels', 'v_ms', 'fmotive', 'slopeforce', 'normalforce', 'pitch', 'rr') and grid means of spatial data ('grid', 'grid mean rr', 'grid mean pitch', 'roads', 'site', 'slope_rad_terrain', 'bmroot_all'. 'c_bmroot_ratio', 'broadlv_rootbm_ratio', 'p_bmroot_ratio','s_bmroot_ratio', 'age', 'diameter', 'standheight', 'b_vol', 'b_pulp_vol', 'b_timber_vol', 's_vol', 's_pulp_vol', 's_timber_vol', 'p_vol', 'p_pulp_vol', 'p_timber_vol', 'broadlv_vol','broadlv_pulp_vol', 'broadlv_timber_vol', 'canopy_cov', 'broadlv_canopy_cov', 'ba', 'volume', 'dem', 'devMeanElev16', 'devMeanElev8', 'diffMeanElev8', 'erosion risk', 'tri', 'roughness_mag', 'roughness_scale', 'dtw00_5ha', 'dtw_01_0ha', 'dtw_04_0ha', 'dtw_10_0ha', 'twi', 'sat_deficit_doy', vol_moisture_doy, 'bare_rock_areas', 'lakes', 'streams', 'peat', 'topsoil', 'aspect', 'aspect_sin', 'aspect_cos', 'main_class_fertility_class_comb')</p> <p> </p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data
<p><span>We</span> have developed a tree-based approach to create spatially continuous forest stand mean height maps across China through integrating high-<span>point</span> density, high-precision close-range LiDAR data and multisource remote sensing data. The accuracy analysis of the arithmetic mean height (Ha) and the weighted mean height (Hw) demonstrates the feasibility of the proposed method. A practical framework for forestry investigation based on close-range LiDAR was proposed. The mean values of Ha and Hw are 13.3 ± 3.3 m 11.3 ± 2.9 m on pixel level, respectively. Validation based on LiDAR and field sample data shows that the RMSE values, range from 2.6 to 4.1 m for Ha and 2.9 to 4.3 m for Hw, respectively, indicating that our approach outperforms existing forest canopy height maps derived from area-based approaches. Hopefully, our methods and maps will serve as a foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. </p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
High-resolution topography data and fault trace along the Southern Riyueshan fault, NE margin of Tibetan Plateau, China
<p>High-resolution digital elevation models (DEM) topography data extracted from the uncrewed aerial vehicle (UAV) of a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK, and GF-7 satellite stereo imagery. The trace of the Riyueshan fault is interpreted based on these high-resolution topography data.</p>
High temporal resolution microclimate records
<b>Description: </b><p>Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/111"><b>Microclimate stratification in modified forests</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=52">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>High temporal resolution microclimate records</b> (Worksheet Data)</p><p>Dimensions: 229330 rows by 7 columns</p><p>Description: Microclimate records collected at very high temporal resolution (10 sec) at a small number of sites</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>RH</b>: Relative humidity. (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2015-12-11 to 2016-12-07</p><p><b>Latitudinal extent: </b>4.7104 to 4.7523</p><p><b>Longitudinal extent: </b>116.9484 to 117.6278</p>
Data and scripts for the paper: Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations
<p>This is a collection of the processed data, plotting scripts, and processing scrips used in the making of the publication: "Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations" By Griewank, Schemann, and Neggers. </p> <p>WARNING: These files were used exclusively used by Philipp Griewank on his local work station, and the main purpose of uploading them is for reasons of scientific transparency. They are not highly optimized tools intended for general usage, so comments are hit and miss. Feel free to contact Philipp Griewank (currently philipp.griewank@uni-koeln.de) with any questions. </p>
High resolution annual average air pollution concentration maps for the Netherlands
<p>Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB.</p> <p>A description of concepts, datasets and scripts is given in the manuscript "High resolution annual average air pollution concentration maps for the Netherlands" by Oliver Schmitz, Rob Beelen, Maciej Strak, Gerard Hoek, Ivan Soenario, Bert Brunekreef, Ilonca Vaartjes, Martin J. Dijst, Diederick E. Grobbee, and Derek Karssenberg. <em>Scientific Data</em> 6:190035 (2019). <a href="https://doi.org/10.1038/sdata.2019.35">https://doi.org/10.1038/sdata.2019.35</a></p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). The Python scripts are licensed under the MIT License.</p> <p>Contact: o.schmitz@uu.nl</p>
High-resolution cone-beam scan of an apple and pebbles with two dosage levels
<p>We release two tomographic scans with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The objects are referred to as apple and pebbles (more specific, hydrograins), respectively. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. </p> <p> </p> <p>Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p> </p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>
High-resolution cone-beam scan of two pomegranates with two dosage levels
<p>We release tomographic scans of two pomegranates with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. </p> <p> </p> <p>Both dataset were collected over a 360 degrees in circular and continuous motion with 2001 projections distributed evenly over the full circle for the good dataset and 501 projections distributed evenly over the full circle for the noisy dataset. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p> </p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl or</li> <li>m.j.lagerwerf [at] cwi.nl</li> </ul>
3-km high resolution model outputs using the WRF and WRF-Hydro model for HRB
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Heihe river basin (HRB). Model results are used for investigating the effect of lateral terrestrial water flow on regional climate modeling. The analysis results were published in a peer-reviewed journal at https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JD030174</p> <p>Two experiments use the following model configuration: 3km horizontal resolution with 350*350 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Leibniz Supercomputing Center (LRZ) SuperMUC. </p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrfout_selvar_P_ET_R_DRA" provides P, ET, surface runoff, drainage, and filename with "wrfout_selvar_T_Q2_SM_SH2O_AWS_CONV" provides T, specific humidity, soil moisture, and liquid water, atmosphere water storage and convergence. Tagged precipitation from upper HRB is also provided.</p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
Assessing the peatland hummock-hollow classification framework using high-resolution elevation models: Implications for appropriate complexity ecosystem modelling
<p>The hummock-hollow classification framework used to categorize peatland ecosystem microtopography is pervasive throughout peatland experimental designs and current peatland ecosystem modelling approaches. However, identifying what constitutes a representative hummock-hollow pair within a site and characterizing hummock-hollow variability within or between peatlands remains largely unassessed. Using structure-from-motion (SfM), high resolution digital elevation models (DEM) of hummock-hollow microtopography were used to: 1) examine how much area needs to be sampled to characterize site-level microtopographic variation; and 2) examine the potential role of microtopographic shape/structure on biogeochemical fluxes using data from 9 northern peatlands. This data set is comprised of plot DEMs, supporting data, and the script used to analyze data and produce figures presented in the manuscript submitted to Biogeosciences Discussion "ASSESSING THE PEATLAND HUMMOCK-HOLLOW CLASSIFICATION FRAMEWORK USING HIGH-RESOLUTION ELEVATION MODELS: IMPLICATIONS FOR APPROPRIATE COMPLEXITY ECOSYSTEM MODELLING".</p>
A high-resolution speleothem record of Marine Isotope Stage 11 as a natural analog to Holocene Asian summer monsoon variations
<p>A full-spectrum characterization of past interglacial climate is a necessary prerequisite for the detection and attribution of climate changes during the current interglacial. Here we present a speleothem record of Asian summer monsoon (ASM) during Marine Isotope Stage (MIS) 11 interglacial (MIS 11c), from Yongxing cave, China. The record’s unprecedented chronologic constraints and decadal-scale temporal resolution allow a precise and direct comparison of ASM between the MIS 11c and the Holocene. Our data suggest that orbital–centennial patterns of ASM were remarkably similar during both interglacial, including their pacing and structure. Notably, a multi-millennial stronger monsoon late in MIS 11c, the ‘Late-MIS 11c shift’, is similar to the Late Holocene strengthening of the ASM, the ‘2-kyr shift’. Thus the multi-centennial ASM weakening at the end of the ‘Late-MIS 11c shift’ could imply that the current century-long ASM waning trend may persist into the future, if only natural forcings are considered.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part III)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part III of the dataset which contains Nhek.tgz and Data.tgz.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part I)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part I of the dataset which contains Gm12878.tgz and Hmec.tgz.</p>
In silico prediction of high-resolution Hi-C interaction matrices (part II)
<p>The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line.</p> <p>This is part II of the dataset which contains K562.tgz and Huvec.tgz.</p>
High-resolution ptychotomography dataset of nanoporous glass
<p>This repository contains nanoporous glass tomography dataset, acquired at the cSAXS beamline at the Swiss Light Source at the Paul Scherrer Institute in Villigen (Switzerland). Details about the data acquisition can be found in Ref. [1].</p> <p><strong>Citation and acknowledgements</strong></p> <p>For use of the ptychographic X-ray computed tomography dataset on nanoporous glass:</p> <p><em>[1] M. Holler, A. Diaz, M. Guizar-Sicairos, P. Karvinen, E. Färm, E. Härkönen, M. Ritala, A. Menzel, J. Raabe and O. Bunk. Scientific Reports, 4, 3857 (2014).</em></p> <p>Alignment and tomographic reconstruction provided in "<em>aligned_phase_sinogram" and "tomogram_delta" </em> were performed by a method described in</p> <p><em>[2] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</em></p> <p><strong>Dataset description</strong></p> <p>The provided dataset is saved in Matlab MAT v7.3 format.</p> <p><em>stack_object</em> - complex valued unaligned projections with dimensions [Npix_vertical, Npix_horizontal, number_of_angles]</p> <p><em>rotation_angle</em> - vector of corresponding projection angles in degrees</p> <p><em>lambda</em> - wavelength of the illumination beam [m]</p> <p><em>pixel_size</em> - size of the reconstruction pixel [m]</p> <p><em>probe_size</em> - size of the reconstruction probe in pixels</p> <p><em>aligned_phase_sinogram</em> - aligned and unwrapped phase projections</p> <p><em>tomogram_delta</em> - reconstructed tomogram of real part of refractive index delta, n = 1-delta-i*beta</p>
High-resolution Canopy Height Model of Hawaii Island 2018-2020
<p>Forest canopy height model for Hawaii Island using lairborne lidar data collected by NOAA in 2018, 2019 and 2020. The maps are produced by year at the resolution of 1 m. The raw point cloud data had am average point cloud density of 8 pulses per squre m. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/9635/index.html</p> <p>ALS 2018 data was reprocessed using Lastools software to reclassify ground class (2)</p> <p>ALS 2019_20 was also reprocessed using Lastools software to reclassify unclassified (1) points to vegetation (5)</p> <p>The CHM’s generation procedure is composed by four steps. It starts by the creation of 500m x 500m tiles using a 50m buffer, resorting to the lastile function, followed by the lasheight function that is used to compute the elevation of each point above the ground. Then, the lastile function is used again to remove the buffer from the normalised point clouds. These first three steps resort to the LASTools software. The fourth, and final step, consists in the generation of the CHM with a 1 m resolution resorting to the pit-free algorithm implemented in the rasterize_canopy function from the lidR package.</p> <p>The file is a GeoTIFF with LZW compression in ArcGIS pro 3.3 </p> <p>EPSG:6635</p> <p>Use of these data requires citation of this dataset </p>
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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.