Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12,170
datasets available to search
ShareScore release 0.7.1
Dataset results
12,170 results for “simulations”
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).
Litterfall in Simulated Hurricane Experiment at Harvard Forest since 1989
The hurricane site contains two plots - the blowdown (treatment) and the control - TSB and TSC respectively. The control plot contains 12 baskets and the blowdown 13. Litter baskets were placed in both the blowdown and control plots in Sept 1989. TSB-13 was originally part of an AVIRIS overflight study and was not included in the data collections until 1990. The litterfall year begins approximately September 20 each year. Years are designated with the fall date, e.g., Sept 1989-Sept 1990 is the 1989 litter year. Litter was collected 2x/year (Nov and the following Sep) in 1989 and 1990 and 3x/year in all subsequent years (Nov, June and Sep). Baskets were removed from the blowdown plot in fall 1990 while the experimental manipulation was underway. Only control plot baskets were collected for the 1990-1991 litterfall year. Nov. 2005 and June 2006 are combined as early snow in Nov. 2005 precluded collection. Litter was not sorted by species in all years and in most cases only the Nov collections were sorted from 1989 through 1993, 2000 and 2006. In other years, a total unsorted litter weight for each basket was recorded. Unsorted litter is indicated as unsorted species type.
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Simulated NGS read datasets for bacterial pathogenic potential prediction
<p>## Predicting pathogenic potentials from NGS reads: novel bacterial species</p> <p>This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus.</p> <p>## Predicting pathogenic potentials from NGS reads: novel strains of known species</p> <p>The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).</p>
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Explicit FE simulation results for orthopedic screw-bone interaction, for different screw geometries and bone quality
<p>The dataset disclosed herein was employed to train artificial neural network surrogate models, specifically for tasks related to screw optimization. Please read "_readMe.txt" before using.</p>
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
BLASTNet Simulation Dataset
<p>Go to <a href="https://blastnet.github.io/">https://blastnet.github.io/</a> to access and download this reacting and non-reacting flow physics simulations.</p> <p><strong>Mission</strong></p> <p>BLASTNet 2.1 was developed to provide the researchers in reacting and non-reacting flow physics communities with high-fidelity simulation datasets in a convenient format for ML applications. With ~5 TB, 765 full-domain samples, and 36 configurations, BLASTNet can effectively address these gaps and aid in fostering open/fair ML development within reacting and non-reacting flow physics communities.</p> <p><strong>Application</strong></p> <p>This data is useful for fluid flows in a wide range of ML applications tied to automotive, propulsion, energy, and the environment. Specifically, scientific engineering tasks related to these domains may include turbulent closure modeling, spatio-temporal modeling, and inverse modeling.<br> </p>
Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"
<p>3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Dataset of "Neutron imaging and molecular simulation of systems from methane and p‑xylene"
<p>The dataset contains parameterizations, and input files for molecular dynamics simulations used in the study of methane dissolution in p-xylene. For selected conditions, full simulation data, i.e., trajectories and energetics are provided. All used simulation results data are provided in the table, along with the measured experimental data.</p>
Microbial narrow-escape is facilitated by wall interactions: Simulation Supplementary material
<p>Simulation codes and simulation results for the paper "Microbial narrow-escape is facilitated by wall interactions".</p>
Forest expansion for different warming scenarios simulated for 2010 to 3000 CE with LAVESI for Siberia
<p>Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century.</p> <p>During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values.</p> <p>Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005).</p> <ol> <li>Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka</li> <li>Column 3: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 4: Forest line in m</li> <li>Column 5: Treeline in m</li> <li>Column 6: Single-tree line in m</li> </ul> </li> <li>Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 3: Tundra area at region Taimyr Peninsula in km²</li> <li>Column 4: Tundra area at region Buor Khaya Peninsula in km²</li> <li>Column 5: Tundra area at region Kolyma River Basin in km²</li> <li>Column 6: Tundra area at region Chukotka in km²</li> </ul> </li> <li>The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE <ul> <li>projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")</li> </ul> </li> </ol> <p>This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).</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.