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365 results for “Spatial modeling”
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption
<p>Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis. </p>
Data supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
<p>Spatial autocorrelation in machine learning for modelling soil organic carbon: Data supplement</p> <p><br>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment
<p>The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019.</p> <p>Data deposit in preparation for article:</p> <p>Effect of spatial input data quality on the uncertainty of the<br> SWAT model, submitted 2022</p> <p>Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn<br> Uuemaa</p> <p>contact: alexander.kmoch@ut.ee</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
3D models (NXS): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p><span>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</span></p>
3D models (true color, TIF): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Spatial clustering of Neobuccinum eatoni occurrence data for potential distribution modeling
<p>The occurrence dataset for <em>Neobuccinum eatoni</em> was compiled through filtration process, starting with records from the Global Biodiversity Information Facility (GBIF) and supplemented by museum specimens and additional sources like SOMBASE, iBOL, NIWA, ANTABIF, and SCAR-AntOBIS. Further data were sourced from the National Museum of Natural History in Paris, the University of Vigo, and recent fieldwork in Antarctica, Heard Island, and Kerguelen Island. Records were meticulously screened to remove misidentified specimens, inaccurate locations, duplicates, and outdated entries, ensuring accuracy and relevance. To address spatial autocorrelation, clustering methods divided the data into distinct geographic clusters, producing a refined dataset used to model <em>N. eatoni</em>'s potential distribution with enhanced predictive reliability by reducing spatial autocorrelation effects.</p>
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Spatial models of topsoil properties in Romania using digital soil mapping techniques
<p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:</p> <p>Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, <em>Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning</em><strong>, PLOS ONE</strong>, 2023</p> <p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).</p> <p>The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).</p> <p>The soil variables are classified as follows:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Classes</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>2</strong></p> </td> <td> <p><strong>3</strong></p> </td> <td> <p><strong>4</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><em>pH</em></p> </td> <td> <p>≤ 5</p> <p>(strongly acid)</p> </td> <td> <p>5.1 – 5.8 (moderately acid)</p> </td> <td> <p>5.9 – 6.8</p> <p>(weakly acid)</p> </td> <td> <p>6.9 – 7.2</p> <p>(neutral)</p> </td> <td> <p>7.3 – 8.4</p> <p>(weakly alkaline)</p> </td> <td> <p>8.5 – 8.8 (moderately alkaline)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>EC (mS m<sup>-1</sup>)</em></p> </td> <td> <p>≤ 12.75</p> </td> <td> <p>12.76 – 16.49</p> </td> <td> <p>16.50 – 20.04</p> </td> <td> <p>20.05 – 24.18</p> </td> <td> <p>24.19 – 29.11</p> </td> <td> <p>29.12 – 35.23</p> </td> <td> <p>≤ 35.24</p> </td> </tr> <tr> <td> <p><em>OC (g kg<sup>-1</sup>)</em></p> </td> <td> <p>< 7.5</p> <p>(very low)</p> </td> <td> <p>7.5 – 17.4</p> <p>(low)</p> </td> <td> <p>17.4 – 37.8 (moderate)</p> </td> <td> <p>37.8 – 61.0</p> <p>(high)</p> </td> <td> <p>> 61</p> <p>(very high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>CaCO<sub>3</sub></em></p> <p><em>(g kg<sup>-1</sup>)</em></p> </td> <td> <p>0</p> <p>(no carbonates)</p> </td> <td> <p>1 – 10</p> <p>(low)</p> </td> <td> <p>11 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 80</p> <p>(medium 2)</p> </td> <td> <p>81 – 107</p> <p>(medium 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>P (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>< 4</p> <p>(extremely low)</p> </td> <td> <p>4 – 8</p> <p>(very low)</p> </td> <td> <p>8 – 18</p> <p>(low)</p> </td> <td> <p>18 – 36</p> <p>(medium)</p> </td> <td> <p>36 – 72</p> <p>(high)</p> </td> <td> <p>> 72</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>N (g kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 1</p> <p>(very low)</p> </td> <td> <p>1.1 – 1.4</p> <p>(low)</p> </td> <td> <p>1.5 – 2.0</p> <p>(medium 1)</p> </td> <td> <p>2.1 – 2.7</p> <p>(medium 2)</p> </td> <td> <p>2.8 – 6.0</p> <p>(high)</p> </td> <td> <p>> 6</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>K (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 40 *</p> <p>(extremely low)</p> </td> <td> <p>41 – 65 *</p> <p>(very low)</p> </td> <td> <p>66 – 130</p> <p>(low)</p> </td> <td> <p>131 – 200 (medium)</p> </td> <td> <p>201 – 300</p> <p>(high)</p> </td> <td> <p>> 300</p> <p> (very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Clay (%)</em></p> </td> <td> <p>≤ 25</p> <p> (low 1)</p> </td> <td> <p>26 – 32</p> <p>(low 2)</p> </td> <td> <p>33 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 45</p> <p>(medium 2)</p> </td> <td> <p>≥ 46</p> <p> (high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Silt (%)</em></p> </td> <td> <p>< 25</p> <p>(medium 1)</p> </td> <td> <p>25 – 32</p> <p>(medium 2)</p> </td> <td> <p>33 – 40</p> <p>(high 1)</p> </td> <td> <p>41 – 50</p> <p>(high 2)</p> </td> <td> <p>> 50</p> <p>(high 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Sand (%)</em></p> </td> <td> <p>< 15</p> <p>(low 1)</p> </td> <td> <p>15 – 25</p> <p>(low 2)</p> </td> <td> <p>26 – 35</p> <p>(low 3)</p> </td> <td> <p>36 – 56</p> <p>(medium)</p> </td> <td> <p>> 56</p> <p>(high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>* classes not present on the Romanian territory</p> <p> </p> <p> </p> <p> </p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Lake browning generates a spatiotemporal mismatch between DOC and limiting nutrients, 2018 spatial survey, modeled light limitation and whole-lake productivity changes in long-term Adirondack lake survey 1994-2012
This data set contains information on a spatial survey of dissolved organic matter (DOM) across lakes and wetlands in the Northeast and Midwest, USA and modeled long-term changes in light limitation and whole-lake productivity in a suite of lakes in the Adirondack State Park, New York, USA. Widespread long-term increases in DOM have been observed in many lakes in a process known as browning. This data set enables the assessment of potential changes in dissolved absorbance and dissolved organic nutrients associated with browning. This data set accompanies a manuscript in review at Limnology and Oceanography: Letters.
Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin , Alaska, 1921 to 2100.
Output data set of the MBL-GEM III model run for tussock tundra in the Kuparuk River Basin, Alaska, described in detail in Le Dizès, S., B. L. Kwiatkowski, E. B. Rastetter, A. Hope, J. E. Hobbie, D. Stow, and S. Daeschner, Modeling biogeochemical responses of tundra ecosystems to temporal and spatial variations in climate in the Kuparuk River Basin (Alaska), J. Geophys. Res., 108(D2), 8165, doi:10.1029/2001JD000960, 2003. We ran the model at a 10 km x 10 km resolution for 123 cells at a yearly time step for 180 years, from 1921 to 2100. Two scenarios enabled the investigation of the effects of two opposing climate change scenarios for the 2001-2100 future period: warmer and wetter ("wet scenario" or Scenario 1) and warmer and drier ("dry scenario" or Scenario 2). These 246 files contain all simulation results for each scenario for individual cells in the Kuparuk River basin.
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.