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27,923 results for “Models”
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. A 100 yr old thermal erosion event response to N fertilization.
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for a 100yr old TEF response to N fertilization over 25 years.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra control simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under control conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra fertilized greenhouse simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under fertilized greenhouse conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra greenhouse simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under greenhouse conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen fertilized simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen and phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen and phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra shade house simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under shade conditions.
Model simulated hydrological estimates for the North Slope drainage basin, Alaska, 1980-2010
Estimates of runoff, river discharge, snow water equivalent (SWE), subsurface runoff, and soil temperatures are drawn from the Permafrost Water Balance Model (PWBM). The simulation and derived data span the period 1980-2010. The model was forced with daily gridded meteorological data obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis (version 5.2.0). The estimates of total runoff (daily), soil temperature (daily), subsurface runoff (monthly), and SWE (monthly) are expressed on a spatial grid (N=312; 25x25 km EASE-Grid version 1, Northern Hemisphere) over the North Slope drainage basin, with the coastline extending from Utqiagvik (formerly Barrow) to just west of the Mackenzie River delta. River discharge, calculated as a volume flux of runoff at each grid cell, was routed through the river network defined on a simulated topological network (STN). Archived files contain discharge flux through the grid cell representing the outlet of each of forty-two basins defined across the region on the 25 km resolution EASE-Grid. Details of the PWBM, forcing variables, model validation and results of analysis are described in Rawlins et al. (2019).
Modeling Foundation Species in Food Webs
Foundation species are basal species that play an important role in determining community composition by physically structuring ecosystems and modulating ecosystem processes. Foundation species largely operate via non-trophic interactions, presenting a challenge to incorporating them into food-web models. Here, we used non-linear, bioenergetic predator-prey models to explore the role of foundation species and their non-trophic effects. We explored four types of models in which the foundation species reduced the metabolic rates of species in a specific trophic position. We examined the outcomes of each of these models for six metabolic rate “treatments” in which the foundation species altered the metabolic rates of associated species by one-tenth to ten times their allometric baseline metabolic rates. For each model simulation, we looked at how foundation species influenced food-web structure during community assembly and the subsequent change in food-web structure when the foundation species was removed. When a foundation species lowered the metabolic rate of only basal species the resultant webs were complex, species-rich, and robust to foundation species removals. On the other hand, when a foundation species lowered the metabolic rate of only consumer species, all species, or no species the resultant webs were species poor and the subsequent removal of the foundation species webs resulted in the further loss of species and complexity. This suggests that in nature we should look for foundation species to predominantly facilitate basal species.
EXPOS Model for Estimating Topographic Exposure to Wind
EXPOS is a simple model of topographic exposure to wind that utilizes digital elevation data to predict which points on a landscape are exposed to or protected from a given wind direction. The model was developed to approximate the effects of topography on hurricane wind flow at a landscape scale (~ 10 km). The model requires an accurate digital elevation map and a specified wind direction. Each point on the elevation map is then classified as protected or exposed, depending on whether or not it falls within the wind shadow cast by points upwind. The wind shadow is estimated by assuming that the wind bends downward no more than a fixed inflection angle from the horizontal as it passes over a height of land. The effects of topographic features beyond the margins of the input elevation map are of course not predicted by the model. For a given landscape and wind direction, predicted protected areas decrease in size as the inflection angle increases. Application of the model in studies of two recent hurricanes suggests that the probability of wind damage in protected areas also decreases as the inflection angle increases. An inflection angle of about 5 to 10 degrees was found to give good results. For more information on the EXPOS model, please see the published paper (Boose, E. R., D. R. Foster, and M. Fluet. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecological Monographs 64: 369-400). Additional information is contained in the documentation that accompanies the program. For an updated version of the EXPOS model in R and Python, please see HF447.
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
Modeling Impacts of Hurricanes on Current Aboveground Forest Carbon in New England 2020-2120
Nature-based climate solutions are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (over 75% forested by land area), and forest carbon is a significant component of regional climate mitigation strategies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies that rely on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and geographic extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here we show that a single hurricane in the region can down between 121-250 MMTCO2e or 4.6-9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England’s forests (16 MMTCO2e yr-1). However, the emissions from the storms are not instantaneous; it takes approximately 19 years for the downed carbon to become a net emission, and 100 years for 90% of the downed carbon to be emitted. Using the HURRECON and EXPOS models to reconstruct hurricanes across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7 and 24.8 fold increase in the extent of high-severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage; both inland and northward into heavily forested regions traditionally unaffected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from large infrequent disturbances is necessary for decision-makers relying on forests as a nature-based climate solution. This data set
MCR LTER: Coral Reef: Modeling the effects of selectively fishing key functional groups of herbivores on coral resilience; data for Cook et al., 2023 Ecosphere
These data and code were generated in support of the manuscript: Cook DT, Schmitt RJ, Holbrook SJ, and HV Moeller, Ecosphere. To investigate the impacts of selectively harvesting functional groups of herbivorous fishes on coral resilience, we used a dynamic model that is grounded by the coral reef system in Moorea, French Polynesia. Our model simulates the fraction of a reef occupied through time by classes of key benthic spaceholders (coral, two stages of macroalgae, and turf). Benthic and fishing dynamics are linked through the harvesting of two functional groups of herbivorous fishes. We utilize data collected on the abundance of fishes on the reef and in the catch in Moorea, French Polynesia to inform our model and to empirically explore patterns of fishing selectivity. These data and code were published in Ecosphere and were a part of the thesis of D. Cook (2023). This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
North Temperate Lakes LTER General Lake Model Parameter Set for Lake Mendota, Summer 2016 Calibration
The General Lake Model (GLM), an open source, one-dimensional hydrodynamic model, was used to simulate various physical, chemical, and biological variables on Lake Mendota between 15 April 2016 and 11 November 2016. GLM (v.2.1.8) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires four major “scripts†to run the model. First, the glm2.nml file configures lake metadata, meteorological driver data, stream inflow and outflow driver data, and physical response variables. Second, the aed2.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, phosphorus, and nitrogen, among others. Third, aed2_phyto_pars.nml configures all parameters pertaining to phytoplankton dynamics. And fourth, aed2_zoop_pars.nml configures all parameters pertaining to zooplankton dynamics. This dataset contains parameter descriptions and values as they were used to simulate organic carbon and greenhouse gas production on Lake Mendota in summer 2016. Meteorological data and stream files used in this calibration are also included in this dataset. Additional methods and model descriptions can be found in J.A. hart’s Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017. Readers are referred to the GLM (Hipsey et al. 2014) and AED (Hipsey et al. 2013) science manuals for further details on model configuration.
Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019
This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.
Modeling an auditory stimulated brain under altered states of consciousness using the generalized ising model
Open the record for dataset details and reuse information.
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Drainage reorganisation and species evolution: model sensitivity analysis data
<p>Data description:</p> <ul> <li><strong>‘trial_factor_values.csv’:</strong> The factor values for experiment trials were generated using a quasi-random Sobol sequence (Sobol, 1967). The table field, ‘initial_landscape_id’ is the identifier for unique combinations of the following factor values that controlled the landscape elevation in the initial conditions phase of the model: initial elevation seed, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, and <span class="math-tex">\(k_d\)</span>. The factors, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, <span class="math-tex">\(k_d\)</span>, <span class="math-tex">\(P_m\)</span>, and allopatric wait time varied logarithmically. The values of these factors in the file are the exponent of base 10.</li> <li><strong>‘trial_response_values_initial_conditions_phase.csv’:</strong> Topographic relief at steady state along with the model time to initial steady state are the trial model responses included in the file. Values are listed for each initial landscape ID rather than trial because many trials had the same combinations of the factors that controlled the topography of the initial landscape. </li> <li><strong>‘trial_response_values_perturb_phase_base_level_fall_scenario.csv’ and ‘trial_response_values_perturb_phase_fault_throw_scenario.csv’:</strong> Model responses of the perturb phase for base level fall and fault throw scenario along with the initial landscape ID, species count values, and the model time back to steady state.</li> <li><strong>The files beginning with `sobol`</strong>: the sensitivity analysis results output by the software, ‘SALib’ (Herman and Usher, 2017). ‘S1’, ‘S2’, and ‘ST’ in the file name indicates if the file contains data of the Sobol first, second, or total order effect, respectively.</li> </ul>
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.