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5,805 results for “Data model”
Codes and data set for Cryoconite hole model (CryHo)
<p><strong>Codes and data set for cryoconite hole model (CryHo)</strong> (Onuma et al., 2023, <em>The Cryosphere</em>). The content is as below.</p> <p><strong>- cryho_disclose_v1</strong>: readme, the model codes, parameter files for the model, model outputs and shell scripts for sensitivity tests <br> <strong>- data</strong>: model input data (meteorological conditions), model data of extinction coefficient for ice* and observational data of cryoconite hole depths<br> <strong>- python</strong>: Python scripts for the visualization<br> <strong>- figure</strong>: png files created by the Python scripts<br> <br> *If you have any questions about extinction coefficients for ice, please contact Dr. Teruo Aoki, a co-author in this dataset.</p>
Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system
<p>The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings.</p> <p><br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows:</p> <p>c#k#_VARIABLE</p> <p>c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided.</p> <p><br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows:</p> <p>c#k#_hyst_o2thermo.nc</p> <p>where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3].</p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Field and Model Data for Bottom Trawling Impacts in the North Sea
<p>This dataset includes both field measurement and modeled results of bottom trawling in the North Sea for the period of 1950-2020.</p>
Ice Throw from Wind Turbines: Experimental Data, 6DOF Model, CFD results, 3D Scans
<p>Compiled data and code from the Eisball Project (funded by the Austrian Research Promotion Agency FFG, project number 865060)</p> <p> </p> <p> </p> <p>6DOF_model_octave.zip - reference implementation of the six-degree-of-freedom model in MathML (Octave or MATLAB)</p> <p>experimental_data.csv - Experimental Data from dropping artificial ice fragments from wind turbines, recording drop distance and direction, details in experimental_data_column_description.txt</p> <p>???_forces_and_moments.csv - forces and moments tables for the use in the 6DOF model, specific per specimen type</p> <p> </p> <p>Data was first published in Nov 2021 at https://boku.ac.at/wau/risk/abgeschlossene-projekte/eisball-1 (may not persist)</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
HITS Inc.'s models and data for Dacon challenge, Jump AI 2023
<p>Here deposits model and data files developed during HITS Inc.'s participation in the Dacon challenge, Jump AI 2023:</p> <p><a href="https://dacon.io/competitions/official/236127/overview/description">https://dacon.io/competitions/official/236127/overview/description</a></p> <p>Note that the files here alone are less useful unless appropriate codes are employed.</p> <p><strong>File description:</strong></p> <ul> <li>pred_model_AutoGluon.tar.xz: AutoGluon model parameters for prediction.</li> <li>valid_model_AutoGluon.tar.xz: AutoGluon model parameters for validation.</li> <li>ckpts_original.tar.xz: fine-tuned <a href="https://github.com/yuyangw/MolCLR">MolCLR</a> model parameters.</li> <li>qc_out.tar.xz: molecular electronic structure files (.wfn).</li> <li>sdf_optimized.tar.xz: molecular structure files (.sdf).</li> <li>atomwfn.tar.xz: atomic electronic structure files (.wfn).</li> </ul>
Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1−J2 Heisenberg Model Data
<p>This repository contains raw data for the article "Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1-J2 Heisenberg Model", Olivier Gauthé and Frédéric Mila, 2023.</p><p>Raw data is provided as json files into the archive data_PEPS_ferroJ1-J2/ subdirectory.zip. The file "data_mswt_ferroJ1-J2.json" contains modified spin wave theory data.</p><p><br>The jupyter notebook "plot_ferroJ1-J2.ipynb" provides scripts to load and visualize data, as well as reproducing figures from the paper.<br>It can be executed using<br>python version 3.9.17<br>numpy version 1.24.3<br>scipy version 1.10.1</p><p>All the data was generated using finite temperature PEPS. Refer to the paper for a complete methodological discussion. The source code to produce PEPS data is available upon reasonable request.</p><p>Olivier Gauthé<br>October 2023</p>
DATA and CARE model for the segmentation of bacterial nucleoids in SMLM data
<p>This dataset and CARE model is part of the publication "<strong>Transertion and cell geometry organize the <em>Escherichia coli</em> nucleoid during rapid growth</strong>".</p> <p>Data represents PAINT images of fixed <em>Escherichia coli </em>cells grown in LB Lennox to exponential phase. Cultures were treated with different antibiotics and fixed various treatment times (0 - 60 min).</p> <p>The strain (NO34) expresses a MreB<sup>sw</sup>-sfGFP fusion protein from the native chromosomal locus. It was a kind gift from Zemer Gitai (<a href="http://doi:10.1016/j.bpj.2016.07.017">Ouzounov et al., 2016</a>).</p> <p>The CARE 2D model was trained from scratch for 100 epochs (332 steps/epoch) on 736 paired image patches (image dimensions: (1024,1024), patch size: (256,256)) with a batch size of 4, an initial learning rate of 0.0004, 10% validation data and a laplace loss function, using the CARE 2D ZeroCostDL4Micnotebook. Data was augmented by a factor of two involving rotation and flipping and random zoom magnification.</p> <p>The model can currently not be used in CSBDeep Fiji plugin, but in the <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCostDL4Mic platform</a>.</p> <p> </p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
Delta smelt (Hypomesus transpacificus) life cycle model input data.
Synthesized data used for fitting delta smelt population dynamics models, essentially consisting of predictor variables (environmental conditions and indices of prey and predators) and response variables (abundance indices). Input data is sourced from a variety of both federal and California state government monitoring programs taking place within the San Francisco Estuary, California. These include California Department of Fish and Wildlife fish surveys, Interagency Ecological Program's Environmental Monitoring Program for zooplankton, California Department of Water Resources' Dayflow, and United States Geological Survey water monitoring data. The sourced data are recorded from sub-hourly to monthly time scales and at various spatial scales, aggregated at monthly or greater time scales using summary statistics (e.g. means) and are not spatially explicit but use spatial stratification approaches for statistic calculation as appropriate.
Caribou Poker Creek Research Watershed GIS Data: Digital Elevation Model (DEM)
This file contains one of many raster grids of the Elevation Derivatives for National Applications (EDNA), a multi-layered database that provides systematic and consistent topographically-derived hydrologic derivatives. The filled DEM grid was created from the original elevation data by filling all of the depressions, or sinks, in the original DEM. To create this grid, an algorithm was used to loacted and fill all depressions or sinks where there was no flow from pixel to pixel. During this process, efforts were made to maintain natural sink features. Originator: U.S. Geological Survey. Publication_Date: 2006. Title: cpcrw_dem.tif. Edition: Stage I Data. Geospatial_Data_Presentation_Form: Remote-sensing image. Series_Information: Series_Name: Elevation Derivatives for National Applications (EDNA). Publication_Information: Publication_Place: USGS EROS, Sioux Falls, South Dakota. Publisher: U.S. Geological Survey.
Bonanza Creek Experimental Forest GIS Data: Digital Elevation Model (DEM)
This file contains one of many raster grids of the Elevation Derivatives for National Applications (EDNA), a multi-layered database that provides systematic and consistent topographically-derived hydrologic derivatives. The filled DEM grid was created from the original elevation data by filling all of the depressions, or sinks, in the original DEM. To create this grid, an algorithm was used to loacted and fill all depressions or sinks where there was no flow from pixel to pixel. During this process, efforts were made to maintain natural sink features. Originator: U.S. Geological Survey. Publication_Date: 2006. Title: bcef_dem.tif. Edition: Stage I Data. Geospatial_Data_Presentation_Form: Remote-sensing image. Series_Information: Series_Name: Elevation Derivatives for National Applications (EDNA). Publication_Information: Publication_Place: USGS EROS, Sioux Falls, South Dakota. Publisher: U.S. Geological Survey.
Modeled flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly modeled ecosystem flux measurements were calculated from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Throw trap and Electrofishing Data from Water Conservation Area 3B, Florida, USA, 2019-2022 for the Decompartmentalization Physical Model Project
This dataset includes densities and biomass of fishes and macroinvertebrates collected using throw traps or an airboat-mounted electrofisher in the study region of the Decompartmentalization Physical Model (DPM) located in Water Conservation Area (WCA) 3B. Some sites in this region experienced seasonal increases in water flow due to the operations of the S-152 structure. The sites sampled for this dataset were either located along a gradient of water flow (downstream the S-152) or were in a reference area that had ambient flow conditions. The purpose of this dataset was to quantify community responses of consumers groups to flowing water and how it may interact with local nutrient conditions at the site level. Hydrological, floc nutrient and periphyton volume data used in the analyses are included. This data package includes the R script that was used to run the statistical models for the manuscript titled "Discharge and nutrients interact to determine trophic structure in a wetland: evidence from a landscape-scale manipulation". The data collection for this data package is complete.
Chlorophyll and phytoplankton composition climatological data on the Northwest Atlantic Shelf from 1978 to 2014: post-processed model data
This dataset includes 8-day composite of surface chlorophyll and bimonthly phytoplankton size composition climatological results on the Northwest Atlantic Shelf from the Gulf of Maine to the Mid-Atlantic Bight based on the physical-biological coupled model results from 1978 to 2014. Two size classes, small phytoplankton (SP) and large phytoplankton (LP), are provided. For more details please see: Zhengchen Zang, Rubao Ji, Zhixuan Feng, Changsheng Chen, Siqi Li, and Cabell S Davis (2021) Spatially varying phytoplankton seasonality on the Northwest Atlantic Shelf: a model-based assessment of patterns, drivers, and implications. ICES Journal of Marine Science, Volume 78, Issue 5, 1920-1934, https://doi.org/10.1093/icesjms/fsab102.
Water Balance Modeling Project at the Sevilleta National Wildlife Refuge, New Mexico: Vegetation Plot Data (1995-1998)
The water balance vegetation plots were part of a larger water balance monitoring project at the Sevilleta LTER. The plots were designed to measure the percent cover of photosynthetic/transpiring (green) plant species at specific sites where time domain reflectometry (TDR) probes and weather stations were already installed. In 1995, there were three sites (Field Station, Deep Well and Rio Salado). A 30m x 30m plot was installed at each site, and collection of vegetation data commenced in July 1995. Percent cover (green) and species identities were recorded monthly at a representative sample of 1m square quadrats within each plot.
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p> </p>
Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set
<p>For a better understanding of magnetic field in the solar corona and dynamic activities such as flares and coronal mass ejections, it is crucial to measure the time-evolving coronal field and accurately estimate the magnetic energy. Recently, a new modeling technique called the data-driven coronal field model, in which the time evolution of magnetic field is driven by a sequence of photospheric magnetic and velocity field maps, has been developed and revealed the dynamics of flare-productive active regions. Here we report on the first qualitative and quantitative assessment of different data-driven models using a magnetic flux emergence simulation as a ground-truth (GT) data set. We compare the GT field with those reconstructed from the GT photospheric field by four data-driven algorithms. It is found that, at least, the flux rope structure is reproduced in all coronal field models. Quantitatively, however, the results show a certain degree of model dependence. In most cases, the magnetic energies and relative magnetic helicity are comparable to or at most twice of the GT values. The reproduced flux ropes have a sigmoidal shape (consistent with GT) of various sizes, a vertically-standing magnetic torus, or a packed structure with curled field lines. The observed discrepancies can be attributed to the highly non-force-free input photospheric field, from which the coronal field is reconstructed, and to the modeling constraints such as the treatment of background atmosphere, the bottom boundary setting, and the spatial resolution.</p>
Simulation data and software scripts used in calculus of ∆36 signature from EMAC clumped O2 isotope-inclusive model
<p>This publication contains simulation data and software scripts for calculating quantities related to clumped oxygen isotope signature (∆<sub>36</sub>) derivation, as described in the "static" framework of Yeung‍ et‍ al. (2016), hereinafter "Y16") and subsequently used in Yeung‍ et‍ al.‍ (2019) analysis. We provide the output of the 1950–2011 transient simulation with EMAC model with explicit "dynamic" simulation of ∆<sub>36</sub> (i.e. <sup>18</sup>O<sup>18</sup>O isotopologues undergoing transport, mixing and O(<sup>3</sup>P)-mediated isotope equilibration) to demonstrate the importance of several assumptions/simplifications involved in the static calculus.</p> <p> </p> <p>Please refer to .README.pdf for details.</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.