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35 results for “field boundaries”
El Verde Field Station (EVFS) Area Boundary KML shapefile and coordinates
The EVFS Area Boundary shapefile (.kml) and coordinates Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
EU field boundaries
<p><em>A vector dataset of Field Boundaries, automatically delineated from Sentinel-2 satellite imagery from May-June 2022.</em></p> <p>Automatic field delineation refers to the process of automatically tracing the boundaries of agricultural parcels from satellite or aerial imagery. We consider an agricultural parcel as a spatially homogeneous land unit used for agricultural purposes, where a single crop is grown. The result of the FD is a set of closed vector polygons marking the extent of each agricultural parcel. Such polygons are the input to a multitude of applications, ranging from the management of agricultural resources, such as the Area Monitoring for the Common Agricultural Policy, to precision farming, to the estimation of damages to crop yield due to natural (e.g. drought, floods), and human-made disasters (e.g. war). Automatic estimation of parcels with high fidelity in a timely manner allows therefore to characterize the changes of agricultural landscapes due to anthropogenic activities, agricultural practices, and climate change consequences.</p> <p><strong>Additional links</strong></p> <ul> <li>Automatic Field Delineation <a href="https://medium.com/sentinel-hub/automatic-field-delineation-new-release-1c2938399f0">Blog post</a></li> <li>Dataset description on <a href="https://github.com/fiboa/data-survey/blob/main/data/Planet.md">Fiboa</a></li> </ul>
Phase Field Simulation of Morphotropic phase boundary of relaxor ferroelectrics
<p>Phase-field simulation of a mixed system of Tetragonal and Rhombohedral phases representing the morphotropic phase boundary of relaxor ferroelectrics</p>
Satellite-derived crop field boundaries in heterogeneous smallholder-dominated regions in the North of Mozambique
<p>Satellite-based field delineation has rapidly evolved due to recent advances in machine learning for computer vision. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck for operational field delineation and downstream applications, particularly in Sub-Saharan Africa. We here provide reference field boundaries collected in the scope of a research project funded by the F.R.S.-FNRS. A detailed description of the data can be found in our corresponding <a href="https://doi.org/10.48550/arXiv.2312.08384">pre-print</a>.</p> <p>The dataset contains multiple files:</p> <ul> <li>sites.gpkg: Vector dataset containing the sampling sites covered by the dataset. We covered 513 sites of 600x600m, or 36 ha each. </li> <li>human_fields_train.gpkg: 1,518 field delineations distributed across 200 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for model training in the paper. </li> <li>human_fields_test.gpkg: 2,199 field delineations distributed across 313 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for evaluation of all experiments described in the paper. </li> <li>pseudo_fields_train.gpkg: 766 pseudo labels obtained from predictions using a <a href="../doi/10.5281/zenodo.7315089">pre-trained FracTAL ResUNet model</a>. The pseudo-labels correspond to the selection using P99(SemCN). For other sets of pseudo-labels please contact us.</li> </ul> <p><strong>Brief description of methods</strong></p> <p>For <strong>site selection</strong>, we developed a stratified random sampling scheme to sample from regions with actively used cropland. To identify these regions, we used an existing map of active and fallow cropland for the growing season of September 2020 through August 2021 (<a href="https://doi.org/10.1016/j.jag.2022.102937">Rufin et al., 2022</a>). We aggregated the map to a 1 ha grid and calculated the proportions of active cropland. We sampled 1,000 sites from regions mapped as containing at least 50% of active cropland within a one-hectare grid cell. We defined a site extent of 600 by 600 meters, or 36 ha, in order to assure that a sufficient number of fields can be delineated, even in regions with comparatively large field size. The selected sites were screened for VHR image quality and acceptable visibility of at least five fields, resulting in 513 sample sites. </p> <p>For <strong>human labels</strong>, we tasked human annotators to collect sparse labels (i.e. at least five fields) per site. We tasked the interpreters to collect only fields containing non-tree crops by systematically excluding tree crop plantations from our data. While individual trees in the field interior were included in our labels, trees overlapping with the field boundaries were avoided and the tree canopy was considered as the field boundary for completing the labels. All field delineations underwent an iterative quality assessment, where 18% of the initial field delineations and 7% of the field delineations in a second iteration were discarded. </p> <p>The <strong>pseudo labels </strong>provided here were selected using the 1% most confident predictions from the pre-trained model. The confidence scores were computed as the median of all pixel-level field extent probabilities for each field instance. </p> <p>For more details please read the corresponding paper:</p> <p>Rufin, Wang, Lisboa, Hemmerling, Tulbure & Meyfroidt, P. (2023). <em>Taking it further: Leveraging pseudo labels for field delineation across label-scarce smallholder regions.</em> <a href="https://doi.org/10.48550/ARXIV.2312.08384">https://doi.org/10.48550/ARXIV.2312.08384</a></p>
Fig. 1 in Diversity of carabids (Coleoptera: Carabidae) within two Dutch cereal fields and their boundaries
Fig. 1. Positions of sampling locations (squares) within two fields of winter wheat, Droevendaal farm, Wageningen. A – field 1; B – field 2. dd – dry ditch; gr – 2 m high grassy ridge; gs – grassy strip; h – hedge; r – tarred road; t – paved track; tr – tree row; wd – water ditch; ws – weedy strip.
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Magnetic Field Boundaries in Cassini Plasma Spectrometer Data
<p>This dataset consists of:</p> <ul> <li><em>get_files.sh</em> - a bash script to download ELS data (.DAT and .LBL) files.</li> <li><em>crossing_events_urls.txt</em> - a text file containing URLs for each of the ELS<br> data files. <em>get_files.sh</em> reads this file directly.</li> <li><em>crossing_events.txt</em> - a text file containing the list of crossing events<br> with associated data, obtained by processing Table S1 in [1].</li> <li><em>labels.zip</em> - a zip file containing directory with events for each DAT file,<br> in YAML format.</li> <li><em>plotter.py</em> - a plotting script in Python.</li> <li><em>requirements.txt</em> - a file indicating dependencies for the plotting script.</li> </ul> <p><strong>Data</strong></p> <p>This dataset spans 1259 observations from CAPS ELS, each with a .LBL file and a<br> .DAT file. Together, these take 128 GB of space. To download these files, do:</p> <pre>./get_files.sh</pre> <p>in a Bash shell/terminal. The files are stored in the 'data/' folder (created if<br> not present) in the current directory.</p> <p>ELS data files are obtained from NASA's Planetary Data System at<br> <a href="https://pds-ppi.igpp.ucla.edu/search/view/?f=yes&id=pds://PPI/CO-E_J_S_SW-CAPS-3-CALIBRATED-V1.0/DATA/CALIBRATED">https://pds-ppi.igpp.ucla.edu/search/view/?f=yes&id=pds://PPI/CO-E_J_S_SW-CAPS-3-CALIBRATED-V1.0/DATA/CALIBRATED</a></p> <p>For help in understanding the ELS data files, see the CAPS User Guide at<br> <a href="https://pds-ppi.igpp.ucla.edu/ditdos/download?id=pds://PPI/COCAPS_1SAT/DOCUMENT/CAPS_USER_GUIDE/CAPS_PDS_USER_GUIDE_V1_00.PDF">https://pds-ppi.igpp.ucla.edu/ditdos/download?id=pds://PPI/COCAPS_1SAT/DOCUMENT/CAPS_USER_GUIDE/CAPS_PDS_USER_GUIDE_V1_00.PDF</a>.</p> <p><strong>Labels</strong></p> <p>Unzipping the <em>labels.zip</em> file will create a <em>labels/</em> folder in the current<br> directory, requiring 28MB of space.</p> <p>Within <em>labels/</em>:</p> <ul> <li><em>bs/</em> - includes bow shock crossing events.</li> <li><em>mp/</em> - includes magnetopause crossing events.</li> <li><em>dg/</em> - includes known data-gap events. See [1].</li> <li><em>sc/</em> - includes unreliable data events. See [1].</li> <li><em>valid/</em> - union of all files in <em>bs/</em> and <em>mp/</em>.</li> <li><em>all/</em> - union of all files in <em>bs/</em>, <em>mp/</em>, <em>dg/</em> and <em>sc/</em>.</li> </ul> <p>Within the <em>labels/bs/</em> and <em>labels/mp/</em> folders:</p> <ul> <li><em>in/</em> - crossing events with the transition direction as inward.</li> <li><em>out/</em> - crossing events with the transition direction as outward.</li> <li><em>all/</em> - union of all files in in/ and out/.</li> </ul> <p>Each label file is a YAML file containing a 'change_points' field. The entries<br> under this field each indicate the time of a transition event. The other fields<br> ('bimodality' and 'negative_ions') are not relevant for this dataset.</p> <p><strong>Plotting the Data (and Labels)</strong></p> <p>To help visualize the data and labels, we supply a plotting script<br> <em>plotter.py</em>, in Python (version 2.7). First, install dependencies with:</p> <pre>pip install -r requirements.txt</pre> <p>and then run:</p> <pre>./plotter.py -h</pre> <p>to see the available options. Example usage (after downloading data and<br> unzipping labels):</p> <pre>./plotter.py data/ELS_200418018_V01.DAT -l labels/mp/all/ELS_200418018_V01.yaml --interpolated -f max_filter -fsize 100 --title "An Observation from CAPS ELS" </pre> <p>will open up a new window with a plot of the data.<br> To directly save to a file, use the '-o <em>savefilename</em>' option. For example,</p> <pre>./plotter.py data/ELS_200418018_V01.DAT -l labels/mp/all/ELS_200418018_V01.yaml --interpolated -f max_filter -fsize 100 --title "An Observation from CAPS ELS" -o ELS_200418018_V01.png </pre> <p><strong>References</strong></p> <p>[1] Jackman, C. M., Thomsen, M. F., & Dougherty, M. K. (2019). Survey of<br> Saturn's magnetopause and bow shock positions over the entire Cassini mission:<br> Boundary statistical properties and exploration of associated upstream<br> conditions. Journal of Geophysical Research: Space Physics, 124, 8865– 8883.<br> <a href="https://doi.org/10.1029/2019JA026628">https://doi.org/10.1029/2019JA026628</a></p> <p>The original table of magnetopause and bow shock crossing events can be found at<br> <a href="https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1029%2F2019JA026628&file=jgra55251-sup-0001-Table_SI-S01.txt">https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1029%2F2019JA026628&file=jgra55251-sup-0001-Table_SI-S01.txt</a></p> <p><strong>Grants</strong></p> <ul> <li>NASA Contract through JPL with South West Research Institute. Grant Number: 1243218</li> <li>Science and Technology Facilities Council. Grant Number: ST/L004399/1</li> <li>NASA. Grant Number: 1243218</li> <li>Diamond Jubilee Fellowship</li> <li>STFC. Grant Number: ST/L004399/1</li> </ul>
Prehistoric field boundary TCL 1, Tresco, Scilly
Prehistoric field boundary, found in 1978. The longest exposed in 2019, it has possible branches running off under seaweed and beach boulders, hinting of further discoveries to be made. Source: Objaverse 1.0 / Sketchfab
Oregon hydrologic area agricultural field boundaries and field level and hydrologic unit water use data
<p>The data was developed for the USGS Water-Use and Data Research program grant opportunities G20AS00053 and G21AS00258, combined with fundnig from Oregon Water Resources Department to improve estimates of water use from irrigated lands in Oregon. These data contain attributes of irrigation status, irrigation source type, crop type, irrigation method, assumed irrigation efficiency, irrigation water source, evapotranspiration (ET) data from OpenET, and effective precipitation developed using the USBR ET Demands model. Thee data were aggregated in order to further the development of estimates of applied water at the field-scale.</p>
Raw data and MTEX script for the article : "Quantification of grain boundary mobilities in natural olivine by annealing experiments and full-field modelling"
<p>This dataset contains the EBSD (.ctf) files of the different sample areas analyzed in the article : "Quantification of grain boundary mobilities in natural olivine by annealing experiments and full-field modelling" by J. Furstoss, S. Demouchy, A. Tommasi, E. Gardès, F. Barou and N. Marino, which is expected to be published in Tectonophysics. This repository also contains the Analyze.m MTEX script which has been used for the post-processing of the EBSD files. This script has to be run in matlab and outputs different figures and .csv files containing the raw data used in the article. For any questions please contact jfurstoss@orange.fr </p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
A region-wide, multi-year set of crop field boundary labels for Africa
<div> <div> <p>Data resulting from a project undertaken to generate a comprehensive set of crop field boundary labels throughout the continent of Africa, representing the years 2017-2023. The project was funded by the <a title="" href="https://lacunafund.org/">Lacuna Fund</a>, and led by <a title="" href="https://farmerline.co/">Farmerline</a>, in collaboration with <a title="" href="https://spatialcollective.com/">Spatial Collective</a> and the <a title="" href="agroimpacts.info">Agricultural Impacts Research Group</a> at <a title="" href="https://www.clarku.edu/departments/geography/">Clark University</a>.</p> <p>Please refer to the technical report in the accompanying repository for more details on the methods used to develop the dataset, an analysis of label quality, and usage guidelines.</p> </div> </div>
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Ukraine Field Boundary Delineation 2022
<h2><strong>Authors:</strong></h2> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Oleksandra Oliinyk<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <h2><strong>Information:</strong></h2> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
Ukraine Field Boundary Delineation 2021
<div><strong>Authors:</strong></div> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Alex Olin<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <div><strong>Information:</strong></div> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></p>
10,000 Crop Field Boundaries across India
<p>Release of dataset and neural network weights accompanying the paper "Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision" (forthcoming in <em>Remote Sensing)</em>. Ten thousand crop fields in India were delineated manually through inspection of high-resolution satellite imagery (Airbus SPOT). We also provide the weights of the highest performing neural network (FracTAL ResUNet architecture) pre-trained in France and fine-tuned on Airbus SPOT images in India. The model was trained in MXNet 1.6.0 and can be loaded with the "model.load_parameters()" function.</p>
Simulation performance of different planetary boundary layer schemes in WRF V4.3.1 on wind field over Sichuan Basin within "Gray zone" resolution
<p>Weather Research and Forecasting (WRF) model was <span>used</span> to <span>investigate the performance of different planetary </span>boundary layer <span>(PBL) </span>parameteri<span>z</span>ation schemes <span>on </span>simulat<span>ing</span> surface wind <span>fields over Sichuan Basin</span> at a spatial resolution of <span>0.33</span>km<span>.</span> <span>T</span>he <span>experiment</span> is based on <span>multi-</span>case stud<span>ies, so</span> 2<span>8 near-surface </span>wind events from 2021 to 2022 <span>were selected, and</span> <span>a</span> total of 112 <span>sensitivity </span>simulations were <span>carried out by</span> employing four common<span>ly used</span> <span>PBL </span>schemes: YSU, MYJ, MYNN2, and QNSE<span>, and</span> compared to observations.</p> <p>The mean 10 minutes observations of wind speed and direction during the study period from the Guanghan Airport are stored in one seperate txt file named OBS-28cases-every10min-10m-wind-Guanghan-Airoprt.txt.</p> <p>Wind field (U10, V10) at the central point from the WRF simulations of inner domain from all the PBL simulations are investigated, which are stored in txt format (.txt), and all the results are available from the first author(yuet@mail.iap.ac.cn).</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.