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5,155 results for “resolution”
Numerical simulation of supercell thunderstorms (at 50 meter resolution) associated with above anvil cirrus plumes
<p>Four-dimensional data from high-resolution simulations (50 meter grid spacing) of supercell thunderstorms conducted on the Frontera supercomputer are contained in this archive. Output from two simulations is included, named "Strong" and "Weak". In the Strong simulation, data from parcel trajectories is also included. </p> <p>The four-dimensional (time, and three dimensions of space) data and parcel data is saved in Network Common Data Format (NetCDF), version 4, an open-source self-describing scientific data format commonly used in the atmospheric sciences. </p>
Dataset for "Effective super-resolution method for paired electron microscopic images"
<p>This is the image set used in the paper, Qian, Xu, Drummy, and Ding, 2020, “Effective super-resolution method for paired electron microscopic images,” <em>IEEE Transactions on Image Processing</em>, Vol. 29, pp. 7317–7330.</p>
Dataset for "Bayesian hierarchical models for combining misaligned two-resolution metrology data"
<p>This file contains the datasets used in the paper, Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” <em>IIE Transactions</em>, Vol. 43, pp. 242 – 258.</p>
Event-based hyperspectral EELS: towards nanosecond temporal resolution
<p>Here we present the two data sets presented in the work <a href="https://arxiv.org/abs/2110.01706">Event-based hyperspectral EELS: towards nanosecond temporal resolution</a>. Data was processed using Rust.</p> <p>In ASI Cheetah Timepix3, we have two different kinds of events: electron and TDC events (little-endian). Data chunk package is a 8-byte data that begins with "TPX3". Electron hit is a 8-byte packet that contains "0xb" in 60-63 bits. TDC data packet is a 8-byte packet that contains "0x6" in 60-63 bits. 56-59 bits identify if it is comes from TDC Line 1 or 2 and also identifies if it is a falling or a rising edge.</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Molecular dynamics simulations in: High-resolution structures with bound Mn2+ and Cd2+ map the metal import pathway in an Nramp transporter
<p>Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of <em>Deinococcus radiodurans</em> Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.</p>
Stellar chromospheric activity database of solar-like stars based on the LAMOST Low-Resolution Spectroscopic Survey
<p>A stellar chromospheric activity database of solar-like stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains spectral bandpass fluxes and indexes of Ca II H and K lines derived from 1,330,654 high-quality LRS spectra of solar-like stars. We measure the mean fluxes at line cores of the Ca II H and K lines using a 1 Å rectangular bandpass and a 1.09 Å FWHM triangular bandpass, as well as the mean fluxes of two 20 Å wide pseudocontinuum bands on the two sides of the lines. Three activity indexes of Ca II H and K lines, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass and <em>S</em><sub>tri</sub> and <em>S<sub>L</sub></em> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes to quantitatively indicate the chromospheric activity level. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H and K lines for all the spectra in the database. This database, with more than one million high-quality LAMOST LRS spectra of Ca II H and K lines and basal chromospheric activity parameters, can be further used for investigating activity characteristics of solar-like stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams of Ca II H and K lines.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv<br> (see Table3 in the paper 2022_ApJS_263_12 for description of the columns)</p> <p>(2) Library of Spectrum Diagrams of Ca II H and K lines<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-174.zip (25 subfolders)<br> spectrum_diagrams_175-199.zip (24 subfolders)<br> spectrum_diagrams_200-224.zip (24 subfolders)<br> spectrum_diagrams_225-249.zip (23 subfolders)<br> spectrum_diagrams_250-259.zip (10 subfolders)<br> spectrum_diagrams_260-274.zip (12 subfolders)<br> spectrum_diagrams_275-299.zip (23 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-374.zip (20 subfolders)<br> spectrum_diagrams_375-399.zip (19 subfolders)<br> spectrum_diagrams_400-424.zip (23 subfolders)<br> spectrum_diagrams_425-449.zip (22 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Supplementary Dataset for "A new mechanical perspective on a shallow megathrust near-trench slip from the high- resolution fault model of the 2011 Tohoku-Oki earthquake"
<p>This dataset contains the results obtained by the analysis in this study, such as the digital data of the slip distribution and stress drop estimated by Kubota et al.(2022)</p>
Data from: DNA metabarcoding improves the taxonomical resolution of visually determined diet composition of beaked redfish (Sebastes sp.)
<p class="pf0"><span>Beaked r</span><span>edfish, dominated by <em>Sebastes mentella</em>, have recently reached record abundance levels in the Gulf of St. Lawrence (GSL) and knowledge of their diet composition is essential to understand the trophic role that these groundfish play in the ecosystem. The objective of the present study was to compare the performance of the visual examination and DNA metabarcoding of stomach contents of the same individual redfish caught in the estuary and northern Gulf of St. Lawrence. Using a universal metazoan mitochondrial cytochrome c oxidase subunit I (COI) marker, a total of 27 taxonomic sequence matches, 16 at the species level considered as primary prey, were obtained from 185 stomachs with DNA metabarcoding and compared to </span><span>the</span> <span>26 prey types, 16 at genus or species level, obtained with stomach content analysis (SCA). While both techniques pointed to a similar definition of diet composition, our results</span><span> also revealed that the SCA and DNA metabarcoding perform differently among prey categories, both in terms of detectability and taxonomical resolution, as well as in estimated contribution to diet. </span><span>The use of DNA metabarcoding along with SCA improves the taxonomical resolution of visually determined prey,</span><span> which supports the concept that both techniques provide useful complementary information that is best used together to gain a maximum level of information on the predator's diet.</span></p>
Tracing pathways from high-resolution tractography, transcription, and temporal dimensions
<p>The neural circuits supporting human cognition are topics of enduring interest. The lack of tools available to map circuits has precluded our ability to trace the evolution of the human connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data to develop enhanced tools to test for modifications in developmental programs across species. We found corresponding ages across species and transcriptionally define neurons with stereotypical projections in humans and macaques. We used these data to test for modifications in frontal cortex circuit. Frontal cortex circuitry development is extended in primates, which is concomitant with an expansion in cortico-cortical pathways compared with mice in adulthood. These parameters varied little across humans and macaques. We identify a collection of conserved features in frontal cortex circuits in studied primates. We demonstrate that the integration of transcriptional and connectomic data across temporal dimensions is a robust approach to trace the evolution of connections in primates. This dataset contains scripts as well as diffusion MR scans of mouse brains.</p>
Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )
<p>This dataset contains the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case.</p> <p>It contains the validation exercice developped at ENAC using real flown data from 2016, and modifying it to respect our constraints.<br> <br> Inside, one can find:</p> <p>-The file "exo_artimation.txt" a file containing the whole scenario, that can be played using REJEU platform.</p> <p>-10 individual files, extracted from "exo_artimation.txt" containing conflicting aircrafts. Those files where used in other steps of the ARTIMATION project, notably in the solution dataset, the heatmatrix, heatmap, storyboard developped (all linked in related identifiers).</p>
High resolution solar irradiance variability climatology dataset part 2: classifications, supplementary data, and statistics
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a></p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance series classification, cloud shadow and enhancement statistics, and satellite observations for studying intra-day surface solar irradiance variability.</p> <p><strong>Part 2 of 2</strong></p> <p>This dataset is the derived from the <a href="https://doi.org/10.5281/zenodo.7093164">1 Hz observational record of direct, diffuse, and global horizontal irradiance</a> measured by the Baseline Surface Radiation Network station at Cabauw, the Netherlands. More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>.</p> <p><strong>Methodology</strong></p> <p>An extensive dataset description is currently being written for Earth System Science Data. In the mean time, a more condensed description is available in preprint at <a href="https://arxiv.org/abs/2209.10284">Arxiv</a>.</p> <p>Processing scripts are published at this <a href="https://zenodo.org/record/7099491">Zenodo release</a>.</p> <p><strong>Dataset contents</strong></p> <p>This dataset contains daily time series with the following data, from 2011-02 until 2020-12-31:</p> <ol> <li>Cloud shadow and cloud enhancement time series classifications (see methodology)</li> <li>Overcast, clear-sky and variable time series classifications (see methodology)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> for clear-sky global horizontal irradiance (version 3.5)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> atmospheric composition input (aerosols, ozone, and total column water vapour)</li> <li>Solar elevation and azimuth angles (calculated using <a href="https://github.com/pingswept/pysolar/releases/tag/0.10">PySolar</a>)</li> <li>Quality flags: non-official 1 Hz and official 1-minute (from <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">BSRN at PANGAEA</a>)</li> <li>Cabauw observatory<a href="https://dataplatform.knmi.nl/dataset/cesar-tower-meteo-lb1-t10-v1-2"> tower wind speed and direction</a></li> </ol> <p>Additional satellite data time series from 2014-01 until 2016-12:</p> <ol> <li>MSGCPP satellite data for an area over central Netherlands (<a href="https://essd.copernicus.org/articles/9/415/2017/">CLAAS2 source</a>)</li> <li>Post processed timeseries of cloud types over Cabauw derived from this MSGCPP satellite data</li> <li>A nubiscope + satellite derived validation dataset for overcast and clear-sky classifications</li> </ol> <p>Statistics files:</p> <ol> <li>Cloud shadow and cloud enhancement event detection and event statistics based on the time series for 2011-2020</li> <li>Daily radiation statistics for 2011-2020</li> </ol> <p>And finally, for all days there are quicklooks available that visualize the irradiance time series, classification, and if available satellite data.</p> <p><strong>Version History</strong></p> <p><em>New in v1.1</em><strong> </strong></p> <ul> <li>CAMS McClear updated from v3.1 to v3.5 (2011-2020)</li> <li>Fix incorrect dominant cloud type in CLAAS2 timeseries (`claas2_processed.zip`, 2014-2016)</li> <li>Update timeseries statistics and quicklooks with new clear-sky data (2011-2020)</li> <li>Added official quality flags (2011-2020)</li> <li>Added preprocessed validation dataset (2014-2016)</li> <li>Added nubiscope to quicklooks (2014-2016)</li> </ul>
Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root
<p>Supplemental datasets for "Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root". </p>
High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula
<p><strong>Abstract:</strong></p> <p>Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017.</p> <p><strong>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</strong></p> <p>This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below:</p> <p><strong>Raster value Land cover/use category</strong></p> <p>1 Forest</p> <p>2 Marsh</p> <p>3 Salt patch</p> <p>4 Built</p> <p>5 Open water</p> <p>6 Farmland</p> <p>7 Bare soil</p> <p>8 Other vegetation</p> <p> </p> <p><strong>Input Data:</strong></p> <p>These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE.</p> <p><strong>Methods:</strong></p> <p>NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel.</p> <p>NDVI = (Near-infrared – Red) / (Near-infrared + Red)</p> <p>NDWI = (Green – Near-infrared) / (Green + Near-infrared)</p> <p>SI = (256 – Blue) * (256 + Blue)</p> <p>In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel.</p> <p>EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1)</p> <p>A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands.</p> <p>Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits.</p> <p><strong>Accuracy assessment:</strong></p> <p>A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below.</p> <table> <tbody> <tr> <td> <p><strong>Delaware 2013</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.46%</p> </td> <td> <p>90.89%</p> </td> <td> <p>0.90</p> </td> <td> <p>86.37%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>84.03%</p> </td> <td> <p>80.13%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.02%</p> </td> <td> <p>71.18%</p> </td> <td> <p>0.82</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>94.56%</p> </td> <td> <p>95.06%</p> </td> <td> <p>0.95</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>91.01%</p> </td> <td> <p>96.63%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.16%</p> </td> <td> <p>86.19%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.70%</p> </td> <td> <p>87.30%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>82.46%</p> </td> <td> <p>84.20%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Delaware 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>95.07%</p> </td> <td> <p>90.30%</p> </td> <td> <p>0.93</p> </td> <td> <p>91.37%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>88.86%</p> </td> <td> <p>92.44%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>91.82%</p> </td> <td> <p>85.59%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>87.58%</p> </td> <td> <p>93.54%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.68%</p> </td> <td> <p>90.48%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>91.61%</p> </td> <td> <p>93.61%</p> </td> <td> <p>0.93</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>95.67%</p> </td> <td> <p>87.67%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>91.16%</p> </td> <td> <p>90.24%</p> </td> <td> <p>0.91</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2011</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.32%</p> </td> <td> <p>90.97%</p> </td> <td> <p>0.90</p> </td> <td> <p>87.20%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.14%</p> </td> <td> <p>82.08%</p> </td> <td> <p>0.85</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>96.74%</p> </td> <td> <p>78.76%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>89.02%</p> </td> <td> <p>88.50%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.74%</p> </td> <td> <p>96.10%</p> </td> <td> <p>0.94</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>84.31%</p> </td> <td> <p>89.83%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>87.53%</p> </td> <td> <p>90.05%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.11%</p> </td> <td> <p>80.57%</p> </td> <td> <p>0.83</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Maryland 2017</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>88.66%</p> </td> <td> <p>88.66%</p> </td> <td> <p>0.89</p> </td> <td> <p>87.34%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.65%</p> </td> <td> <p>88.35%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>93.29%</p> </td> <td> <p>68.30%</p> </td> <td> <p>0.79</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>93.44%</p> </td> <td> <p>86.92%</p> </td> <td> <p>0.90</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.36%</p> </td> <td> <p>92.36%</p> </td> <td> <p>0.92</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>83.84%</p> </td> <td> <p>94.55%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>92.86%</p> </td> <td> <p>82.61%</p> </td> <td> <p>0.87</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>86.56%</p> </td> <td> <p>76.00%</p> </td> <td> <p>0.81</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2012</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>91.18%</p> </td> <td> <p>0.88</p> </td> <td> <p>86.88%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>87.03%</p> </td> <td> <p>84.39%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>97.67%</p> </td> <td> <p>72.41%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>90.97%</p> </td> <td> <p>86.24%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>94.17%</p> </td> <td> <p>87.39%</p> </td> <td> <p>0.91</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>86.87%</p> </td> <td> <p>91.81%</p> </td> <td> <p>0.89</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>85.82%</p> </td> <td> <p>83.04%</p> </td> <td> <p>0.84</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>83.60%</p> </td> <td> <p>80.59%</p> </td> <td> <p>0.82</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Virginia 2016</strong></p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>User’s accuracy</p> </td> <td> <p>Producer’s accuracy</p> </td> <td> <p>F-score</p> </td> <td> <p>Overall</p> </td> <td> <p>Kappa</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p>84.65%</p> </td> <td> <p>86.00%</p> </td> <td> <p>0.85</p> </td> <td> <p>85.83%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Marsh</p> </td> <td> <p>86.25%</p> </td> <td> <p>90.72%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Salt patch</p> </td> <td> <p>90.61%</p> </td> <td> <p>62.12%</p> </td> <td> <p>0.74</p> </td> </tr> <tr> <td> <p>Built</p> </td> <td> <p>88.70%</p> </td> <td> <p>77.72%</p> </td> <td> <p>0.83</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>92.12%</p> </td> <td> <p>84.41%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Farmland</p> </td> <td> <p>85.17%</p> </td> <td> <p>90.36%</p> </td> <td> <p>0.88</p> </td> </tr> <tr> <td> <p>Bare Soil</p> </td> <td> <p>83.30%</p> </td> <td> <p>88.54%</p> </td> <td> <p>0.86</p> </td> </tr> <tr> <td> <p>Other Vegetation</p> </td> <td> <p>84.49%</p> </td> <td> <p>84.17%</p> </td> <td> <p>0.84</p> </td> </tr> </tbody> </table> <p>While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.</p> <p> </p> <p><strong>Data format:</strong></p> <p>The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Code:</strong> Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes.</p> <p><strong>Datasets for download:</strong></p> <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> <p>These data layers cover 3 counties: Kent, New Castle, Sussex.</p> <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> <p>These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester.</p> <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> <p>These data layers cover 2 counties: Accomack, Northampton.</p> <p>We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files. </p> <p><strong>Data citation:</strong></p> <p>Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Daily-resolution pan-Arctic Radar and Laser Freeboard
<p>This dataset contains daily-resolution pan-Arctic laser / radar freeboard and uncertainty estimates, created following the methodology outlined in <a href="https://doi.org/10.1029/2022GL100696"><strong>Nab et al. (2023): "Synoptic variability in satellite altimeter-derived radar freeboard of Arctic sea ice"</strong></a></p> <p>Please refer to this publication for a full description of the data and cite this publication when using these data.</p> <p>Variables: Laser / Radar Freeboard (m), Uncertainty (m), Longitude, Latitude, Day</p> <p>Data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years</p> <p>Days where no data is available are filled with NaNs</p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
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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.