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136 results for “Model Versioning”
Spartina alterniflora marsh vegetation data along the Georgia coast used in the Belowground Ecosystem Resiliency Model version 2.0
Study plots (1-m2) were established in eight Spartina alterniflora-dominated marshes (7 on Sapelo Island, Georgia, and 1 on Skidaway Island, Georgia). At three sites, plots were sampled once each during May, July, August, September, and October of 2016. At all sites, plots were sampled once each during June, August, and November of 2021, February, May, August, and November of 2022, and February of 2023. One long-term (quarterly 2013 to 2023) GCE LTER sampling site is also included. Nine replicate plots were placed in vegetated marsh along transects that spanned 3 Landsat-8 and -9 pixel footprints, with 3 plots per pixel footprint. In each plot, measurements included plant biomass, plant species, stem density, and height. Aboveground biomass was calculated using allometric relationships between plant height and mass from plant clipping studies. During these surveys, destructive core sampling was also performed in the proximity of the plots (n = 1 per plot) to measure above and below ground biomass. Chlorophyll, foliar N, and Leaf Area Index measurements were taken in the proximity of the plots. This dataset reflects an update to the "PLT-GCED-2106" dataset (doi: 10.6073/pasta/03f4f78c6498aecca34faf4339591129). This project also utilized data from the "PLT-GCEM-1610" dataset doi: 10.6073/pasta/9746c71b35e9f8c544ea12c601c33949). Those data utilized in this project are duplicated here for completeness.
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
A high-resolution, multi-decadal, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 3.0, 1994-2019)
<p>The data is from a Regional Ocean Modelling System free-running, hydrodynamic simulation of the East Australian Current System. The model has a horizontal resolution of 2.5-6 km in the cross-shore direction and 5 km in the alongshore direction, and 30 vertical s-levels. The model domain covers the southeastern Australia oceanic region from 25.1-41.5°S and 147.1-162.2°E, and the grid is orientated 20 degrees clockwise to be predominantly orientated alongshore. The time period covered is 02 Jan 1994 to 28 Feb 2019. The model outputs provided are daily averages of the following variables: Two-dimensional variables: Sea surface height (zeta), barotropic cross-grid velocity (u) and barotropic along-grid velocity (v). Three-dimensional variables: Temperature (temp), salinity (salt), density (rho), cross-grid velocity (u), along-grid velocity (v) and vertical velocity (w), temperature time rate of change (temp_rate), temperature horizontal advection term (temp_hadv), temperature vertical advection term (temp_vadv), temperature horizontal diffusion term (temp_hdiff), temperature vertical diffusion term (temp_vdiff). In this version, the heat budget terms (temp_rate, temp_hadv, temp_vadv, temp_hdiff and temp_vdiff) are set to be zeros on the land.</p> <p> </p> <p>This model is part of the <a href="../records/8294716"><strong>South East Australian Coastal Ocean Forecast System (SEA-COFS)</strong></a> suite of models.</p>
Monthly climate variables of isotope-enabled climate model simulations over the last millennium (850-1849CE) version 2
<p>Here we provide climate fields in monthly resolution for five isotope-enabled model: ECHAM5-wiso (Sjolte et al. 2018, Werner et al. 2016), GISS-E2-R (Lewis and Legrande 2015, Colose et al 2016), iCESM (Brady et al. 2019, Stevenson et al 2019), iHadCM3 (Bühler et al. 2021, Tindall et al. 2009), and isoGSM (Yoshimura et al. 2008) in supplement to Buehler et al. (2021, submitted to Clim. Past. Discuss.). The model simulations were performed with different sets of boundary conditions as described in Bühler et al. (2021, submitted to Clim. Past. Discuss.) in line with the PMIP3 protocoll (Schmidt et al. 2012). We provide output for surface temperature (in K), total precipitation amount (in mm month^-1), evaporation (in mm month^-1), latent heat (in W m^-2), and oxygen isotope ratios of precipitation (in permil).<br> Additionally, we provide simulation output extracted at cave locations within the SISAL v.2. database (https://researchdata.reading.ac.uk/256/, Comas-Bru et al. (2020)). We include output for sites that pass the resolution and dating screening, meaning that have at least 2 radiometric dates (or are lamina-counted) and provide 36 oxygen isotope ratio measurements within the last millennium.</p> <p>For version 2, we updated the damaged ECHAM5 precipitation file and the time axis to the iCESM precipitation and tsurf files.</p>
RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)
<p>RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p>
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
BAM Generalized National Models Documentation, Version 4.0
<h2>A generalized modeling framework for spatially extensive species abundance prediction and population estimation</h2> <p><span>In the face of rapid environmental change, spatially explicit estimates of species abundance and distribution are needed to inform conservation planning and management decisions across a range of spatial scales. We present a generalized modeling framework bridging the gap between local studies and regional to national management needs by compiling and harmonizing data from many sources to predict avian abundance at a fine resolution and broad extent. We first applied detectability offsets to integrate avian point-count data from a large collection of research and monitoring projects across the entire breadth of subarctic Canada (>250,000 unique sampling locations). We then subsampled the data by two time periods and sixteen geographic regions and developed boosted regression trees to model the density of 143 boreal landbird species as a function of environmental covariates representing climate, local- (250 m) and landscape-level (up to ~1.5 km) vegetation composition, land cover, and topography. Finally, bootstrapped model predictions for each region were combined to generate predictive density maps, habitat- and region-specific density estimates, and Canada-wide population estimates. Our models estimated a total of approximately 3.56 billion breeding males (7.13 billion individuals) across subarctic Canada, with the majority breeding in boreal and hemi-boreal regions. Forest generalist species made up nearly half of this estimate (1.57 billion breeding males), followed by boreal forest specialist species (1.05 billion), habitat generalists (350 million), and species associated with eastern forests (274 million), grasslands (124 million), western forests (74.7 million), wetlands (63.5 million), and Arctic tundra (17.7 million). Introduced species comprised 48.9 million breeding males. An analysis of variable importance showed that, across species, most of the variation in bird abundance was explained by landscape-level vegetation composition, suggesting that the effect of climate on bird abundance is mostly indirect, via vegetation, but that landscape-level variables are needed to capture this variation. Model classification accuracy was highest from a habitat perspective for forest- and grassland-associated species (lowest for mountain- and urban-associated species); and for Regulidae and Phasianidae from a taxonomic perspective (lowest for Bombycillidae and Paridae). In developing these models, we created a standardized, updatable, and reproducible workflow that can be used to update these analytical products and improve their utility for conservation and management planning.</span></p> <p>This data set contains:</p> <ul> <li>Reproducible code for the modeling approach based on <https://github.com/borealbirds/LandbirdModelsV4></li> <li>Source code for the website at <https://borealbirds.github.io/> based on <https://github.com/borealbirds/borealbirds.github.io></li> <li>Data and image assets for the website based on <https://github.com/borealbirds/api></li> </ul> <p>Please note, in late March 2025, we discovered a systematic error in the offsets used in these models, and have since updated the products to correct that error. For more information, please see the <https://github.com/borealbirds/QPAD-offsets-correction> repository for further details or email <bamp@ualberta.ca> for assistance.</p>
Deliverable 2.1 Aero-hydro-elastic model definition - SOFTWIND 10 MW FOWT (wave-tank SIL version)
<p>For the detailed validation and verification of the capabilities of QBladeOcean in work package 2 of FLOATECH, a detailed definition of the models is needed. This database presents the QBladeOcean model of the DTU 10MW Reference Wind Turbine mounted on the SOFTWIND floater.</p> <p>Update V2.0.0: <br>Structure files are modified according to the requirements of the QBladeCE version</p> <p>Update V3.0.0:<br>- Added controller from SOFTWIND experiments (Modified from DTU 10MW to have oO star controller parameters)<br>- Modified mooring line length<br>- Shifted platform COG slightly towards centerline<br>- Modified blade definition to AD14 blade def.<br>- Included STATICBUOYANCY flag</p> <p>Update V3.1.0:<br>- Included ADVANCEDBUOYANCY flag<br>- Corrected excitation file (.3), previously: incorrect assignment of wave headings and excitation force coefficients<br>- Addition of mean drift file (.8)<br>- Corrected error in added mass matrix entry [4,2] (sway-roll coupling)</p> <p>Update V3.2.0:<br>- DELTA_DIR_DIFF 1-->20<br>- STATICBUOYANCY --> true</p> <p>Update V3.3.0:<br>- updated Substructure .txt file to format compatible with new QBlade version 2.0.6.4+<br>- extrapolation stretching activated<br>- depth dependent drag coefficient of 0.6 until z = -4m<br>- adjusted "DAMP_[-]" paremeter in the "MOORELEMENTS" table of ths Substructure .dat file to be zero due to numerical instabilities</p>
Global soil type dataset for WRF-ARW model, based on HWSD version 2
<p>Global soil type dataset, based on HWSD ("Harmonized World Soil Database", version 2.0), suitable for meteorological model WRF-ARW.</p> <ul> <li>spatial resolution: 30 arc seconds by 30 arc seconds (about 1km)</li> <li>original data (HWSD 2.0) <ul> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip</a></li> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip</a></li> <li>https://gaez.fao.org/pages/hwsd</li> <li>documentation: Nachtergaele, Freddy, et al. Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations, 2023. https://www.fao.org/3/cc3823en/cc3823en.pdf</li> </ul> </li> <li>the original 7 soil layers (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–150 cm and 150–200 cm) have been remapped to the 2 layers required by WRF (topsoil 0-30 cm, botsoil 30-200 cm)</li> <li>the original Soil Mapping Units (SMU) have been remapped to the 16 soil categories used by WRF: <ul> <li>the depth-weighted averages of the content of clay, silt and sand lead to 12 texture-based categories (Sand, Loamy sand, Sandy loam, Silt loam, Silt, Loam, Sandy clay loam, Silty clay loam, Clay loam, Sandy clay, Silty clay, Clay), as defined by USDA;</li> <li>category "Organic material" is assigned where the average content of organic carbon exceeds the threshold of 25%;</li> <li>where the content of clay, silt and sand is not defined, HWSD special categories are mapped to the WRF last 3 categories, as follows: <ul> <li>"Water bodies" to "Water", </li> <li>"Rock outcrops" and "Rocky sublayers" to "Bedrock", </li> <li>"Land ice and glaciers", "Dunes/shifting sands", "Salt flats", and "Other" to "Other"</li> </ul> </li> </ul> </li> </ul> <p>The dataset is provided in three ways:</p> <ol> <li>two global files (SoilType_depth<T>to<B>cm.tif), one for each layer; format is GeoTIFF, compatible with <a href="https://github.com/openwfm/convert_geotiff" target="_blank" rel="noopener"><em>convert_geotiff</em></a>, a commandline utility for converting data from GeoTIFF to geogrid format used by WRF;</li> <li>16 tiles, 8 for each layer, each covering 90 degrees by 90 degrees (SoilType_depth<T>to<B>cm_lon<W>to<E>deg_lat<S>to<N>deg.tif); format is GeoTIFF;</li> <li>two compressed folders, hwsd_toplayer.zip and hwsd_bottomlayer.zip, each including 648 tiles in binary format and an "index" ASCII file, following the Geogrid data format and naming convention, as described <a href="https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/duda_wps_advanced.pdf">here</a>.</li> </ol> <p>Soil categories are coded as follows</p> <table> <tbody> <tr> <td><strong>code</strong></td> <td><strong>category</strong></td> </tr> <tr> <td>1</td> <td>sand</td> </tr> <tr> <td>2</td> <td>loamy sand</td> </tr> <tr> <td>3</td> <td>sandy loam</td> </tr> <tr> <td>4</td> <td>silt loam</td> </tr> <tr> <td>5</td> <td>silt</td> </tr> <tr> <td>6</td> <td>loam</td> </tr> <tr> <td>7</td> <td>sandy clay loam</td> </tr> <tr> <td>8</td> <td>silty clay loam</td> </tr> <tr> <td>9</td> <td>clay loam</td> </tr> <tr> <td>10</td> <td>sandy clay</td> </tr> <tr> <td>11</td> <td>silty clay</td> </tr> <tr> <td>12</td> <td>clay</td> </tr> <tr> <td>13</td> <td>organic material</td> </tr> <tr> <td>14</td> <td>water</td> </tr> <tr> <td>15</td> <td>bedrock</td> </tr> <tr> <td>16</td> <td>other</td> </tr> </tbody> </table> <p> </p>
NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024
<p><strong>Repository updates</strong></p> <p><em>Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024 </em></p> <p><em>Update on Sep 5th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022</p> <p><em>Update on June 7th 2022: </em>The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022</p> <p><em>Update on March 8th 2021: </em>The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code. </p> <p><em>Update on March 8th 2021: </em>The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths. </p> <p><em>Update on January 30th 2021: </em>The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020.</p> <p><strong>Overview</strong></p> <p>NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018).</p> <p>This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links.</p> <p>A preprint is now available in <em>The Cryosphere Discuss</em> explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022). </p> <p><strong>Data production:</strong></p> <p>Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas.</p> <p><strong>Forcings:</strong></p> <p><em>NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below:</em></p> <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> <p><em>The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file. </em></p> <p><strong>Operation IceBridge snow depths:</strong></p> <p>The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within <em>gridded_oib_snowdepths.zip</em> includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD. </p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.</p>
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"
<p>This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.</p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
<p>Human development at the foot of the mountains faces sediment-laden flood hazards characterized by high-velocity, erosive flows carrying mud and debris, and when flood control infrastructure that protects communities fills with sediment, it loses capacity. The estimation and management of sediment-laden floods have proven challenging because cycles of wildfire, precipitation, and infrastructure sedimentation are still poorly understood. Efforts to model compound hazards such as post-fire floods are relatively new, and existing models do not consider the role of flood control infrastructure, such as debris retention basins and flood channels, in the development of post-fire floods. Here we present data sources and calibration methods to estimate sediment-laden flood hazards downstream of infrastructure on a catchment-by-catchment basis using the Post-Fire Flood Hazard Model (PF2HazMo), a stochastic modeling approach that utilizes continuous simulation to resolve the effects of antecedent conditions and system memory. Data sources provide parameter ranges needed for stochastic modeling, and several performance measures are considered for model calibration. With application to three catchments in Southern California, we show that PF2HazMo predicts the median of the simulated distribution of peak bulked flows within the 95% confidence interval of observed flows, with an order of magnitude range in bulked flow estimates depending on the performance measure used for calibration. Using infrastructure overtopping data from a post-fire wet season, we show that PF2HazMo accurately predicts the number of flood channel exceedances. Model applications to individual watersheds reveal whether existing infrastructure is undersized to contain present-day and future overtopping hazards based on current design standards.</p>
PACT-1D model version including polar halogen emissions - output files
<p>This dataset includes all output files created from the PACT-1D model (v1.0) developed with Arctic chlorine and bromine emission parameterizations. The PACT-1D source code used to create these output files is available at: https://doi.org/10.5281/zenodo.5654589.</p> <p> </p>
Model results based on COSMOS climate model (old version MPI-ESM1)
<p>Nino 3.4 SST of individual models (COSMOS-Nordemg and COSMOS-Tiedtke) and supermodel</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</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)
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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.