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695 results for “model output”
Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage - MODEL OUTPUT
<p>This is the repository for the model output and controls pertaining to the simulation experiments performed in "Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage" by Nathaniel Fillman, Andreas Schmittner, and Karin Kvale. Citation and DOI for parent publication will be updated here when available.<br> See https://github.com/fillmann/variable-stoichiometry for model code.</p>
RAP-Chem Model Output for the Beckwourth Complex Fire
<p>RAP-Chem data for the Beckwourth Complex Fire between 7/7/2021 and 7/14/2021.</p>
Model output for the effects of topography and orbital forcing
<p>modeling data for the Asia</p>
Basal Area Estimates for DBH Classes from ARRI Modelled Outputs
<p><strong>Summary</strong></p> <p>Basal area per acre (BAA) for ‘All’ trees, ‘Pine’ trees, and ‘Non-Pine’ trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>Three modelling method raster outputs of basal area per acre across three diameter at breast height (DBH) size classes are contained here. Single model raster outputs are linear regression models, ensemble linear models (LM) are mean estimates from 50 linear models, and ensemble generalized additive models (GAM) are mean estimates from 50 GAMs. The units for these single band rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'Non-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive regression models (GAM) and Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The ensemble LM models were created using a modification of the Hogland, 2021 script that uses the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM, and LM, models for each cell were averaged to produce the mean estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the models of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated basal area per acre as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p><strong>Reference</strong></p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114 </p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"
<p>Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity”.</p>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality".V2
<p>A minor coding error was found in the pre-formatted data of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16630">Barrere et al. (2023)</a>. This error did not affect the main results of the paper, but led to minor change in the value of the posterior estimates, stored in data/sensitivity/jags_dominance.Rdata. This repository contains the new version of the parameters. </p>
Output datasets from model for the paper "On the potential role of viscoelasticity in fluid-induced seismicity"
<p>Output datasets from model for "On the potential role of viscoelasticity in fluid-induced seismicity" </p>
LarsAckermann GMD Iceberg 2023 - model output ICBHF
<p>This dataset contains model output from the experiment ID "icbHF" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency</p>
LarsAckermann GMD Iceberg 2023 - model output ICBFW
<p>This dataset contains model output from the experiment ID "icbFW" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency</p>
LarsAckermann GMD Iceberg 2023 - model output CTL
<p>This dataset contains model output from the experiment ID "icb" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency<br> </p>
LarsAckermann GMD Iceberg 2023 - model output ICB
<p>This dataset contains model output from the experiment ID "icb" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061</p><p>---------------------------------------------------------</p><p>It contains:</p><p>mean output over the last 100 model years of:<br> - 3D ocean temperature<br> - 3D salinity<br> - 2D sea-ice height<br> - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins<br> - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basins</p><p>timeseries over 700 years of:<br> - global ocean temperature<br> - AABW for the IndoPacific sector<br> - global AABW</p><p>yearly data of:<br> - 3D ocean temperature<br> - 3D vertical velocity<br> - 3D vertical bolus velocity<br> - 3D Väisälä frequency<br> - 2D iceberg freshwater and heat fluxes<br> - buoys files with information about iceberg velocity, location, and dimension<br> </p>
Evaluation of the Accuracy, Safety and Robustness of a Single-input-single-output (SISO) Model-based Predictive Closed-loop System to Guide Patient-individualized ICU Sedation
ClinicalTrials.gov study NCT00735631. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data for: Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence
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Bayesian models' outputs for: A new method to explicitly estimate the shift of optimum along gradients in multispecies studies
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GEOS-Chem-TOMAS model output for 2019-2020
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Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"
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CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
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Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
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Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
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GEOS-Chem output for "Lightning NOx Emissions: Reconciling measured and modeled emissions estimates with updated NOx chemistry"
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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.