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1,028 results for “simulation model”
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications
GEO Series GSE242456. Mus musculus; Homo sapiens. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications [ATAC-Seq]
GEO Series GSE242166. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Beaufort Gyre westward expansion simulation using a two-layer idelized model
<p>This data set supports the analysis presented in the manuscript: "Beta-drift of eddies drove Beaufort Gyre westward expansion during 2003-2014" (2023GL107142), which is under review for publication by Geophysical Research Letters. The model output contains 100 years of simulation for the nine models.The surface stress and deta effect are active, and their grid is configured with a 8 km horizontal resolution. </p><p>Simulation length: 100 years<br>Simulation machine: ubuntu on Desktop computer<br>Case name: ctrl, fast moving, slow moving, large area(large tau), stress asymmetry, largeneta,smallbeta, stress-constant,f-planet<br>Time range: 1-5182561 (corresponding to 1 to 100 model year);a model year=360 day;output frequency= 10 day</p><p> </p><p>model output:</p><p>eta:sea surface height; h1:halocline layer depth;tau: surface stress;u1/v1: halocline layer horizonal velocity</p><p>The mat file is named after the corresponding case. However, it is important to note that the data names inside the mat file are not the actual case names, but rather the names used for initial records.</p><p> </p><p>The model code modified from<br> aronnax (https://github.com/edoddridge/aronnax)</p><p>please refer to:</p><p>Doddridge, E. W., & Radul, A. (2018). Aronnax: An idealised isopycnal ocean model. <i>Journal of Open Source Software</i>, 3(26), 592. <a href="http://doi.org/10.21105/joss.00592">http://doi.org/10.21105/joss.00592</a></p><p>for details of this code</p>
Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)
<p>Parameters and Sets files for an updated model built for Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)</p> <p>* Data are designed as an input for Hypatia Modelling Framework</p> <p>** Model was develeped for master thesis study " Investigating the regional contributions to the Italian decarbonization: an Energy Modelling multi-regional approach." C. Lo Guidice, F. Cruz, K. Gad, E. Colombo</p>
model output used for Paper "Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types"
<p>This dataset contains the model output from CESM2.2-8p4z, used in Yu et al., 2024 in Journal of Advances in Modeling Earth Systems (JAMES). </p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
Open the record for dataset details and reuse information.
Run input files for the simulation of a C. jejuni inner membrane model
<p>Run input files for the simulation of a C. jejuni inner membrane model (Chapter 5 of Kahlan Newman's Doctoral Thesis). Full trajectories can be shared on request. </p>
Global SPEI over the last millennium calculated from monthly climate variables of isotope-enabled climate model simulations
<p>This data is Standardized precipitation evapotranspiration index (SPEI) for different time scales from 851 to 2000. The time scale is partially omitted due to the upload capacity, but it is from 1 month to 48 months at maximum.</p> <p>[Structure]</p> <p>Spatial resolution: 1.9(Treated the earth as a 94x192 grid)</p> <p>Time resolution: 1 month(1150 year = 13800 month)</p> <p>This data is one-dimensional. When using, please slice to (13800,94,192) using python, etc.</p> <p>The method for obtaining the grid for the survey area is as follows.</p> <p>north latitude: (94/180)*(90+lat)</p> <p>south latitude: (94/180)*(90-lat)</p> <p>east longitude: (192/360)*longitude</p> <p>west longitude: (192/360)*(360-longitude)</p> <p> </p> <p>This SPEI is calculated using the SPEI package in R. The settings for this package are as follows.</p> <p>Setting</p> <p>kernel: type = rectangular, shift =0</p> <p>distribution: log-Logistic</p> <p>fit: ub-pwm</p> <p> </p> <p>The data used for this SPEI calculation are climate data reconstructed by data assimilation using isotope ratios. Please refer to the following page for details of the data.</p> <p>Details</p> <p>[Title]</p> <p>Data assimilation products by using multiple climate model simulations and different combinations of proxies</p> <p>[url]</p> <p>https://zenodo.org/record/5760209#.ZAs2qxXP1D9</p>
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications [cfChIPseq]
GEO Series GSE242168. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications [RNA-Seq]
GEO Series GSE242454. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications [cfMNase]
GEO Series GSE242169. Homo sapiens. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications [xenograft plasma WGS]
GEO Series GSE294649. Mus musculus. 2 samples. Type: Other.
An improved IBIS model for simulating NPP dynamics in alpine mountain ecosystems: a case study in the eastern Qilian Mountains, northeastern Tibetan Plateau
<p>The data set include figures and tables. They provide supports for an improved IBIS model for simulating NPP dynamics in alpine mountain ecosystem.</p> <p>Fig. 1 Study area</p> <p>Fig. 2 Framework of the IBIS<sub>i</sub> model</p> <p>Fig. 3 Spatialization of soil thickness</p> <p>Fig. 4 Nonlinear relation between soil thickness and slope.</p> <p>Fig. 5 Sample areas for validation</p> <p>Fig. 6 A comparison of NPP monitored by remote sensing, NPP simulated by the IBIS model and NPP simulated by IBIS<sub>i</sub> model in the EQM from 2000 to 2015.</p> <p>Table 1. Vegetation parameters regionalization</p> <p>Table 2. Initialization of total LAI</p>
Time series of fluxes, biochemical and spectral variables simulated SCOPE model
<p>The dataset contains:</p> <ul> <li>Time series of fluxes, biochemical and spectral variables simulated with Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model, parameterized using structural vegetation parameters as well as meteorological data from the research station of Majadas de Tiétar (39°56′24.68″N, 5°45′50.27″W) (Cáceres, Spain)</li> <li>Time series of decomposed Photochemical Reflectance Index, far-red solar-induced chlorophyll fluorescence and far-red fluorescence yield into seasonal, diurnal and sub-diurnal components with Singular Spectrum Analysis</li> </ul>
Model simulation of the effect of ENSO on the winter aerosol over China
<p>The dataset includes three files "CLI_run", "EL_run", "LA_run". "CLI_run" used climatological mean SST as the boundary condition. "EL_run" and "LA_run" used climatological mean SST plus El Niño and La Niña perturbations, respectively.</p>
Climate models simulations archive for analysis of ENSO influence on Arctic stratosphere
<p>CMIP5 model (CCSM4, CMCC-CMS, CNRM-CM5, IPSL-CM5B-LR, MRI-CGCM3, MIROC5) data of historical simulation (1950-2005): daily zonal mean temperature averaged over 70-90N, zonal mean zonal wind averaged over 60-62N, and geopotential height averaged over 50-70N at hPa levels from 1000 to 10 hPa (29 February removed)</p>
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></p>
Causal Modeling Using Network Ensemble Simulations Predicts Novel Lipid Metabolism Genes
GEO Series GSE15226. Mus musculus. 120 samples. Type: Expression profiling by array.
A soil moisture-dependent model to simulate water table depth and proportions of surface and subsurface runoff and its validation at basin scale
<p>The data is the simulations of the SMD-model, Sy-mdoel, adn Noah-MP in three basins in China and the USA. The vaiables are monthly water table depth, soil moisture, subsurface and total runoff. </p>
Representation error in global model CO2 simulations over India
<p>The data provides the possible representation error in the model simulations of CO<sub>2 </sub>concentrations over India, with a spatial resolution of one degree. Representation error is is estimated following Pillai et al. (2010) using WRF-Chem-GHG simulations at 10 km x 10 km spatial resolution.</p> <p>Reference.</p> <p>Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmospheric Chemistry and Physics, 10, 83–94, https://doi.org/10.5194/acp-10-83-2010, 2010.</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)
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.