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41 results for “Model America”
Modeling Range Expansion of Hemlock Woolly Adelgid in Eastern North America 1951-2009
Range expansion by native and non-native species will continue to be a major component of global change. Anticipating the potential effects of changes in species distributions requires models capable of forecasting population spread across realistic, heterogeneous landscapes and subject to spatiotemporal variability in habitat suitability. Several decades of theory and model development, as well as increased computing power and availability of fine-resolution GIS data, now make such models possible. This R code allows simulation of the spread of the hemlock woolly adelgid (HWA, Adelges tsugae) under climatic conditions experienced from December of 1951 until March of 2009. The code describes a spatially explicit stochastic model that combines dynamic dispersal and population processes with fine-resolution maps characterizing spatiotemporal heterogeneity in winter temperature and hemlock abundance to model range expansion of HWA. The model is parameterized using multi-year datasets describing population and dispersal dynamics of HWA and is applied to eastern North America.
ConFire Model input/output for South America
<p>ConFIRE input and output used in the submission of Kelley et al. "Low Climatic Influence found in 2019 Amazonia Fires".</p> <p>"input" dir including "amazon_inference_data-2002-MCDBA_obs-TERRA_M__T.csv", just contains a csv file of all common grid data in other "input" subdirectories, which can be used in "optimise_run_model/bayesian_inference.ipynb" notebook. The files in the subdirectories are then used to make gridded model output using "optimise_run_model/make_model_output.ipynb".</p> <p>Output, also provided, is summerized in the files in "outputs/sampled_posterior_ConFire_solutions/constant_post_2018_full_2002_BG2020/"</p> <p>The three files contain:</p> <ul> <li>fire_summary_frequancy_of_counts.nc - Contains one spatial variable (long name "firecount frequency of occurrence") is the probability of a fire count (along the model_level_number dimension) at a particular month (time dimension) according to the models full posterior, <span class="math-tex">\(P(y_j)\)</span>(see papers supplementary). All months start on Jan 2001. </li> <li>fire_summary_precentile.nc - Contains one spatial variable (long name "firecount at percentile"), the fire count at each percentile of the full posterior in 1% increments from 1-99% (along the model_level_number dimension). Note, by definition, the 0a nd 100% percentile is 0 and <span class="math-tex">\(\infty \)</span> .</li> <li>fire_summary_observed_liklihood.nc - the position ( "variable_0") and p-value ("variable") of MCD64A1 in the model posterior.</li> <li>model_summary.nc - Summary of each variable of ConFire: <ul> <li>"burnt_area": burnt area or fire count summary, described as percentiles as per "fire_summary_precentile", but this time just assessing parameter uncertainty, i.e <span class="math-tex">\(P(\beta | Y_s)\)</span> in supplementary of paper.</li> <li>All other variables describe different model controls in the same "burnt_area". Full information on how controls are constructed can be found in Kelley et al. (2019) with updates listed in supplementary of this paper. <ul> <li>"fuel_continuity", "moisture_content", "ignitions", "suppression" are the actual values of the control</li> <li>"standard_<<control>>" is the standard limitation imposed by the control.</li> <li>"potential_<<control>> is the potential limitation</li> <li>"sensitivity_<<control>> is the senstivity of fire to a particular control.<br> <br> See Kelley et al. 2019 for the definition of limitation types and sensitivity</li> </ul> </li> </ul> </li> </ul> <p>Kelley, D.I., Bistinas, I., Whitley, R. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9, </strong>690–696 (2019) doi:10.1038/s41558-019-0540-7</p> <p> </p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Model America - Chicago Archetype extract from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p><strong>Critical note: Building multipliers and models will be updated soon.</strong></p> <p>Archetype metadata, models, and multipliers are provided for 93 building <strong>archetypes</strong> located within the city of Chicago (United States):</p> <p> </p> <ol> <li><strong>Data (12KB *.csv) - minimalist list of each building (rows) for the following fields (columns)</strong> <ol> <li>ID - unique building ID</li> <li>Area - estimate of total conditioned floor area (ft<sup>2</sup>)</li> <li>CZ - ASHRAE Climate Zone designation</li> <li>Height - building height (ft)</li> <li>NumFloors - number of floors (above-grade) (IECC = Residential)</li> <li>BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards</li> <li>Standard - building vintage</li> <li>WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)</li> <li>Area2D - footprint area (ft<sup>2</sup>)</li> <li>Num_build_per_zone - Number of this building type/vintage in WRF zone</li> <li>Total_zone_area - Total area of this building type/vintage in WRF zone (ft<sup>2</sup>)</li> <li>Area_multiplier - Scaling factor for building type/vintage for building in WRF zone</li> </ol> </li> <li><strong>Models (7.69MB *.zip) - EnergyPlus building energy models named according to ID</strong> <ul> <li>Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page <a href="https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf">Input/Output Reference Guide </a>for everything that can be retrieved or simulated from these models.</li> </ul> </li> </ol>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
Model America - Arizona extract from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <ol> <li><strong>Data (884.3MB *.csv) - minimalist list of each building (rows) for the following fields (columns)</strong> <ol> <li>ID - unique building ID</li> <li>Centroid - building center location in latitude/longitude (from Footprint2D)</li> <li>Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...)</li> <li>State_abbr - state name</li> <li>Area - estimate of total conditioned floor area (ft<sup>2</sup>)</li> <li>Area2D - footprint area (ft<sup>2</sup>)</li> <li>Height - building height (ft)</li> <li>NumFloors - number of floors (above-grade)</li> <li>WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)</li> <li>CZ - ASHRAE Climate Zone designation</li> <li>BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards</li> <li>Standard - building vintage</li> </ol> </li> <li><strong>Sample Models (21.74GB*.zip by county) – OpenStudio and EnergyPlus building energy models named according to ID</strong></li> </ol> <p><span><span>ESS-Dive Revised dataset: New </span><span>J ;</span><span> Stubbings </span><span>A ;</span><span> Li </span><span>F ;</span><span> Bass </span><span>B ;</span><span> Adams </span><span>M ;</span><span> Berres A (2023): Model America - Arizona extract from ORNL's </span><span>AutoBEM</span><span> v1.1. Southwest Urban Corridor Integrated Field Laboratory (SW-IFL), ESS-DIVE repository. Dataset. </span></span><a href="https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2212792" target="_blank" rel="noreferrer noopener"><span><span>doi:10.15485/2212792</span></span></a><span><span> accessed via </span></span><a href="https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2212792" target="_blank" rel="noreferrer noopener"><span><span>https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2212792</span></span></a><span><span> </span></span></p> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Input data of GridPath model for South America's MERCOSUR sub-region
<p><strong>This repository contains input data of the GridPath model for South America’s MERCOSUR sub-region, presented in the forthcoming paper entitled “Exploring sustainable electricity system development pathways in South America’s MERCOSUR sub-region” by the same authors. These data can be used in conjunction with the GridPath model (version v0.8.0), available at <a href="https://github.com/blue-marble/gridpath](https://github.com/blue-marble/gridpath)">https://github.com/blue-marble/gridpath</a>. Description of the key input data and the scenarios presented in the paper are provided in the <a href="https://docs.google.com/spreadsheets/d/1l7ImQtKHpYTPp9cMmTGHCdJB-wJvq7klwrwKmrtCrqw/edit?usp=sharing">Readme file</a>.</strong></p>
Climate model and proxy input data for PaleoDA South America reconstruction
<p>This repository contains input data needed to run the paleoclimate reconstruction code for "A continental reconstruction of hydroclimatic variability in South America during the past 2000 years", submitted to Climate of the Past in February 2024 [https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/]. The Github repository is located here: https://github.com/mchoblet/paleoda_sa/tree/main</p> <p><strong>Structure:</strong></p> <p>model_data: One File for each Model (GISS, CCSM (isoGSM), CESM, ECHAM5, iHADCM3) and variable (prec,tsurf,d18O, SPEI). Monthly resolution.</p> <p>proxy_data: One File for each proxy record type (Trees and corals contain a separate file for annual and djf linear regression parameters, the proxy data as such is the same). The data has yearly resolution, and thus also contains NaNs for when a year is not covered by a proxy. Note, that these time series are resampled to a regular resolution in the multi-time scale PaleoDA code.</p> <p><strong>Climate Model Data:</strong></p> <p>The original data can be found in https://zenodo.org/records/6610684. The data in this repository here has been slightly modified and regridded for easier processing by the reconstruction algorithm. When using the data here, please also cite https://zenodo.org/records/6610684 and the publication </p> <p>"Investigating stable oxygen and carbon isotopic variability in speleothem records over the last millennium using multiple isotope-enabled climate models", by </p> <div>Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld (https://cp.copernicus.org/articles/18/1625/2022/cp-18-1625-2022.html)</div> <p><strong>Climate Proxy Data:</strong></p> <p>A regional proxy record subselection for South America. See References in Appendix A Choblet et al. (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/). The DOI of each record is stored as Metadata.</p> <p><strong>How were these files created?</strong></p> <p>The steps are documented in the the Github repository https://github.com/mchoblet/paleoda_sa/tree/main (data_preprocessing). The SPEI drought index has ben computed from modeled precipitation and temperature using Thornthwaite's method (using the Climate Indices package, https://github.com/monocongo/climate_indices).</p> <p><strong>Manuscript revision in July 2024:</strong></p> <ul> <li>Added historical documentary indices time series and the Puyehue lake record. For technical reasons in the PaleoDA algorithm, it is kept apart from the other lake records. The reconstruction code on Github has been updated for including these datasets.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <div> </div>
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
An Optimized North America MODIS Leaf Area Index (LAI) Dataset for Air Quality Modeling
<p>Air Quality Research Division, Environment and Climate Change Canada,</p> <p>4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada</p> <p>Email: Junhua.zhang@ec.gc.ca</p> <p> </p> <p>Leaf Area Index (LAI) is used in air quality models for land surface processes and for calculating biogenic emissions. MODIS LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used in the air quality modeling community for such purposes. However, limitations of MODIS LAI product have been seen for some geographic areas, particularly unreasonably low LAI over the evergreen needleleaf boreal forests in the northern hemisphere during wintertime due to snow cover and low sun angle. Missing retrievals over urban areas and areas with persistent cloud cover are also seen. Considerable efforts have been made to improve the MODIS LAI product. However, some issues are still persistent, such as the very low LAI over boreal forests during wintertime. In order to solve these issues for supporting regional air quality modelling, the 8-day MODIS Collection 6 (C6) LAI product at 500m resolution (MCD15A2H) was examined for North America. Statistics were calculated by month and by land cover type defined in the “Land Cover Type 1” science data set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) product. Comparisons with LAI calculated from the EPA’s Biogenic Emissions Landuse Database, version 4 (BELD4, https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources) were also done (Zhang et al., 2020). Based on the analysis, an updated monthly LAI dataset was calculated based on 1) 17-year (2003-2019) average of MODIS summer-time peak LAI, 2) fraction of evergreen and deciduous for each pixel from BELD4, and 3) monthly profiles of LAI for evergreen and deciduous vegetation species from MODIS LAI (Zhang et al., 2021). This is the final LAI dataset for North America compiled using the 17 years of MODIS LAI product complemented by information from BELD4.</p> <p> </p> <p>REFERENCES:</p> <p>Zhang, J., M. D. Moran, P. A. Makar, and S. Kharol, 2020. Examination of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 19th CMAS Conference, 26-30 Oct., Virtual [see https://www.cmascenter.org/conference/2020/slides/ZhangJ_MODIS_LAI_CMAS_2020.pdf].</p> <p>Zhang, J., P. A. Makar, S. Kharol, M. D. Moran, and C. McLinden, 2021. Examination and Processing of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 2021 Meteorology and Climate - Modeling for Air Quality Conference, Sep 14-17, 2021, Virtual</p> <p> </p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America
<p>The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021). We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper. </p> <p>Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. <em>The Cryosphere, 15</em>(2), 771-791.</p> <p>Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)</p>
Model America - Arizona extract from ORNL's AutoBEM - (Archetypes with simulation results)
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Two sets of data are provided for 2,555,153 buildings located within the boundary of Arizona in the United States:</p> <ol> <li><strong>Data (420KB *.csv) - Arizona 527 building archetypes based on different Climate Zones (2B-136, 3B-133, 4B-128, 5B-130) with simulation results.</strong></li> <li><strong>Models (38.8MB *.zip by Climate Zone) – OpenStudio and EnergyPlus building energy models named according to ID.</strong></li> </ol> <p> </p> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <p><strong>Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)<br></strong></p> <p><strong>Building Information Data Fields:</strong></p> <ul> <li>ID</li> <li>CZ</li> <li>Centroid</li> <li>State_Abbr</li> <li>Footprint2D</li> <li>Height,Area2D</li> <li>BuildingType</li> <li>NumFloors</li> <li>Area</li> <li>Standard</li> <li>NumWalls</li> <li>WWR_surfaces</li> </ul> <p><strong>Energy Simulation Data Fields:</strong></p> <ul> <li>Electricity_Facility[kBTU]</li> <li>NaturalGas_Facility[kBTU]</li> <li>Heating_Electricity[kBTU]</li> <li>Cooling_Electricity[kBTU]</li> <li>Heating_NaturalGas[kBTU]</li> <li>Heating_Total[kBTU]</li> <li>WaterSystems_Electricity[kBTU]</li> <li>Lighting_Electricity[kBTU]</li> <li>Equipment_Electricity[kBTU]</li> <li>Fans_Electricity[kBTU]</li> <li>Pumps_Electricity[kBTU]</li> <li>HeatRejection_Electricity[kBTU]</li> <li>HeatRecovery_Electricity[kBTU]</li> <li>Surface_Outside_Face_Heat_Emission[GJ]</li> <li>Zone_Exfiltration_Heat_Loss[GJ]</li> <li>Zone_Exhaust_Air_Heat_Loss[GJ]</li> <li>Heat_Rejection_Energy[GJ]</li> <li>Anthropogenic_Emissions[GJ]</li> </ul> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Data from: Abundance models of endemic birds of the Sierra Nevada de Santa Marta, northern South America, suggest small population sizes and dependence on montane elevations
<p>Abundance measures are almost non-existent for several bird species threatened with extinction, particularly range-restricted Neotropical taxa, for which estimating population sizes can be challenging. Here we use data collected over nine years to explore the abundance of 11 endemic birds from the Sierra Nevada de Santa Marta (SNSM), one of Earth's most irreplaceable ecosystems. We established 99 transects in the "Cuchilla de San Lorenzo" Important Bird Area within native forest, early successional vegetation, and areas of transformed vegetation by human activities. A total of 763 bird counts were carried out covering the entire elevation range in the study area (~175–2650 m). We applied hierarchical distance-sampling models to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size. Most species were more abundant in the montane elevational range (1800–2650 m). Habitat-related differences in abundance were only detected for five species, which were more numerous in either early succession, secondary forest, or transformed areas. Inferences of effective population size indicated that at least four endemics likely maintain populations no larger than 15,000–20,000 mature individuals. Estimates of species' area of occupancy and effective population size were lower than most values previously described, a possible consequence of increasing anthropogenic threats. At least four of the endemics exceeded criteria for threatened species listing and a thorough evaluation of their extinction risk should be conducted. Population strongholds for most of the study species were located on the northern and western slopes of the SNSM between 1500–2700 m. We highlight the urgent need for facilitating effective protection of native vegetation in premontane and montane ecosystems to safeguard critical habitats for the SNSM's endemic avifauna. Follow-up studies collecting abundance data across the SNSM are needed to obtain precise range-wide density estimations for all species.</p>
Computational model results for "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure"
<p>The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America. </p>
Potential distributional shifts in North America of allelopathic invasive plant species under climate change models
<p>Occurrence data for invaive species used in ecological niche modeling for predictive studies. These data are cleaned to removed data with duplicates, incomplete coordinates, unlikely coordinates (e.g., 0,0), or those lacking environmental data were removed using the scrubr v.0.1.1 package in R (Chamberlain, 2016). Points falling outside of the respective training region for each species were also removed. These data represent downloads from iDigBio and GBIF.</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.