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56 results for “Earth surface”
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.
<p>Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the <strong>land surface temperature (heatmaps)</strong> of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.</p> <p>There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health. </p> <p>The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.</p> <p>- 2013-2017<br>- 2014-2018<br>- 2015-2019<br>- 2016-2020<br>- 2017-2021<br>- 2018-2022<br>- 2019-2023</p> <p>The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>.</p> <p>The format of this dataset is organized in two ZIP format files:</p> <p>- LANDSAT_8_L2SP_000000-milan_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-athens_LST_peak.zip</p> <p>Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.</p> <p>The values of the images are in degree Celsius and nodata value is -9999.</p> <p> </p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
The data for Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to Spectral Taxonomy and Surface Processes
<p>README</p> <p>% =========================================================================<br>% Project: "Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to <br>% Spectral Taxonomy and Surface Processes"<br>% Author: Edgard G. Rivera-Valentín<br>% Institution: Johns Hopkins University Applied Physics Laboratory<br>% Email: edgard.rivera-valentin@jhuapl.edu<br>% ORCID: 0000-0002-0786-7307<br>% Date: 18 September 2024<br>% =========================================================================</p> <p>% =========================================================================<br>% Licenses:<br>% Any software provided in this repository is licenced under the MIT License, detailed below and within <br>% this archive. <br>% Any data provided in this repository is licenced under Creative Commons Attribution 4.0 International, <br>% detailed within this archive. <br>%<br>% MIT License<br>%<br>% Copyright (c) 2024 The Johns Hopkins University Applied Physics Laboratory LLC<br>%<br>% Permission is hereby granted, free of charge, to any person obtaining a copy<br>% of this software, data, and associated documentation files (the "Software"), to deal<br>% in the Software without restriction, including without limitation the rights<br>% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell<br>% copies of the Software, and to permit persons to whom the Software is<br>% furnished to do so, subject to the following conditions:<br>%<br>% The above copyright notice and this permission notice shall be included in<br>% all copies or substantial portions of the Software.<br>%<br>% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR<br>% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br>% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE<br>% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER<br>% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br>% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN<br>% THE SOFTWARE.<br>%<br>% ADDITIONAL LICENSING INFORMATION:<br>% Any reuse of the figures and data provided in this repository must abide by <br>% the Copyright policy of the American Astronomical Society Journals:<br>% https://journals.aas.org/article-charges-and-copyright/#AAS_material<br>% =========================================================================</p> <p>% =========================================================================<br>% Description:<br>% This is a data repository for the manuscript published in the Planetary<br>% Science Journal:<br>% Title = Radar Circular Polarization Ratio of Near-Earth Asteroids: Links <br>% to Spectral Taxonomy and Surface Processes<br>% Please see the Zenodo metadata for detailed publicatoin information.<br>% This repository includes:<br>% (1) A csv file that has the table of data used in the published work. The<br>% table includes the data for each near-Earth asteroid used in the<br>% anlaysis. The header information includes: Asteroid Number, Designation<br>% or Name, Taxonomic Type (Following the Bus-DeMeo classification system),<br>% CPR (where CPR is circular polarization ratio), CPR uncertainty (where<br>% the uncertainty is the 1-sigma value), a (where a is semi-major axis in<br>% au), q (where q is the perihelion distance in au), Q (where Q is the<br>% aphelion distance in au), P (where P is the rotational period in hours),<br>% Hmag (where Hmag is the absolute magnitude), and Orbital Class. <br>% (2) A .mrt file that contains the same data as the csv file, but formatted<br>% according to the specifications of the machine-readable table format<br>% used by the AAS Journals. The .mrt version will also be published with<br>% the final PSJ article.<br>% (3) .mat files containing the output from the statistical modeling<br>% presented in the paper. These files are required to run the .m file<br>% included in this repository.<br>% (4) Make Figures.m, which is a Matlab code that remakes all the figures<br>% presented in the paper given the data in each of the .mat files. <br>% (5) The .png files for each of the figures presented in the paper. <br>% =========================================================================</p> <p>% =========================================================================<br>% File Formats:<br>% This archive includes various file formats. <br>% .csv file is a text file format that uses commas to separate values, and<br>% newlines to separate records. <br>% .mrt file is an ASCII byte-by-byte format that is documented here:<br>% https://journals.aas.org/mrt-overview/<br>% and readable by tools such as astropy, TOPCACT, etc. <br>% .m file is a simple text file used by Matlab, it can be opened by any<br>% text editor and executed by Matlab. <br>% .mat file is the file format used by MATLAB for saving data. It can be<br>% ready by other software, such as python, e.g., <br>% https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html<br>% .png file is short for Portable Network Graphic, which is a type of<br>% raster image file. <br>% =========================================================================</p>
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
The Earth Surface System Scientific Data Thesaurus
<p>The Earth Surface System Scientific Data Thesaurus</p>
Response to Sea Surface Temperature and Primary Productivity to change in Earth's Orbit Eccentricity - Simulations
<p>This dataset contains ocean and ocean biogeochemistry outputs from modeling experiments with present-day geography and various Earth's orbit confiurations. The set of simulation targets the role of Eccentricity on the tropical ocean sea surface temperature and primary productivity (Beaufort & Sarr, 2024) . The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed formulti-millennial climate simulations, GMD) and offline version of PISCESv2 model (Aumont et al., 2015 - PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, GMD). It includes 4 simulations. Data are monthly averages over the last 100 years of the simulations.</p> <p>Complementary outputs (4 simulations) can be found at https://www.seanoe.org/data/00728/84031/ (Beaufort et al., 2022)</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
<p>The datasets used in this study are provided in 7 folders:</p> <ol> <li> <p>"dataset A", containing data from Sandwich Town Neck Beach on Cape Cod, Massachusetts. These images are published as a USGS data series (Sherwood et al., 2021) are publicly available at <a href="https://doi.org/10.5066/P9BFD3YH">https://doi.org/10.5066/P9BFD3YH</a></p> </li> <li> <p>"dataset B", containing data from North and South Carolina collected immediately after Hurricane Florence in October 2018. National Geodetic Survey emergency response imagery courtesy of the National Oceanic and Atmospheric Administration, available at <a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov</a></p> </li> <li> <p>"dataset C", containing some examples of shoreline environments captured by a low-altitude aircraft. These images are published as a USGS data series (Kranenburg et al., 2020) are publicly available at <a href="https://doi.org/10.5066/P9CA3D8P">https://doi.org/10.5066/P9CA3D8P</a></p> </li> <li> <p>"dataset D", containing data collected from the Pearl River and its tributary the Bogue Chitto, and from the Chickasawhay, Buoy and Leaf tributaries of the Pascagoula River, in spring 2021. Used with permission from U.S. Fish and Wildlife Service</p> </li> <li> <p>"dataset E", containing Sentinel-2 satellite images of coastal lagoon environments in Salinas Rivermouth Natural Preserve and National Wildlife Refuge in Monterey, California. Sentinel-2 imagery courtesy of European Space Agency (ESA)</p> </li> <li> <p>"dataset F", containing Landsat-8 of Cape Hatteras, Cape Hatteras National Seashore, North Carolina. Landsat-8 imagery is courtesy of U.S. Geological Surve</p> </li> <li> <p>"code", containing a version of the code used to generate the results contained in this data repository. Full details about this code can be obtained from the github code repository (<a href="https://github.com/dbuscombe-usgs/dash_doodler">https://github.com/dbuscombe-usgs/dash_doodler</a>) and website (<a href="https://dbuscombe-usgs.github.io/dash_doodler/">https://dbuscombe-usgs.github.io/dash_doodler/</a>).</p> </li> </ol>
Antarctic surface climate and surface mass balance in the Community Earth System Model version 2 (1850-2100) - AWS data
<p>This Antarctica AWS temperature and wind speed dataset was compiled by Alexandra Gossart and Niels Souverijns (<a href="https://doi.org/10.1175/JCLI-D-19-0030.1">https://doi.org/10.1175/JCLI-D-19-0030.1</a>).</p>
Supplementary Dataset for "Most Earth-surface calcites precipitate out of isotopic equilibrium", by M. Daëron, R.N. Drysdale, M. Peral, D. Huyghe, D. Blamart, T.B. Coplen, F. Lartaud, and G. Zanchetta
<p>This repository contains the raw data files and Python code used to generate all figures and equations included in the article "Most Earth-surface calcites precipitate out of isotopic equilibrium", by Daëron et al. (https://doi.org/10.1038/s41467-019-08336-5).</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.