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104 results for “Inverse Modelling”
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Inverse Model Results for WAIS
<div> <p>This page contains the results of a basal drag inversion performed for the West Antarctic Ice Sheet (WAIS). We provide the results in NetCDF files of our six conducted experiments in the associated (not yet published) study, showing the basal drag and basal drag coefficient, as well as the outputs of the 1D steady-state thermal model. In addition, the Matlab scripts used to generate the figures in our manuscript and the finite element mesh are uploaded. </p> <p>To perform the inversion and L-curve analysis, we rely on the inversion model presented in Wolovick et al. (2023) (available at https://doi.org/10.5281/zenodo.7798650). All parameters and the added code are included in an uploaded spreadsheet (Parameters_ISSMInversion.xlsx). </p> <p><strong>Name conventions </strong></p> <ul> <li>m#: Describes the exponent in the sliding law. The values can bet set to m1 (linear sliding) or m3 (non-linear sliding).</li> <li>Ntype: Describes the effective pressure source in the sliding law. Values are "noN" (i.e., Weertman sliding), "Nop" (i.e., parameterized effective pressure) and "Ncuas" (i.e., effective pressure used from subglacial hydrology CUAS-MPI).</li> <li>Budd, Weertman: Describes the two different sliding laws we use in this manuscript. Budd uses an effective pressure source and Weertman does not use an effective pressure field. </li> </ul> <p><strong>Figure scripts</strong></p> <p>The following scripts generate all figures shown in the manuscript: </p> <ul> <li> <p>AllLcurveFigures_v1.m: Creates Figure 6 in the manuscript. </p> </li> <li> <p>BestDragFigure_v1.m: Creates Figure 13, Figure 14 and Figure 16 in the manuscript. </p> </li> <li> <p>BestInversionComparisonFigure_v1.m: Creates Figure 15 in the manuscript. </p> </li> <li> <p>ConvergenceFigure_v1.m: Creates Figure 7 in the manuscript. </p> </li> <li> <p>DragCoeffComparisonFigure_N_m_v1.m: Creates Figure 11 in the manuscript. </p> </li> <li> <p>DragLakeCandidatesFigure_v1.m: Creates Figure 17 and 18 in the manuscript. </p> </li> <li> <p>EffectivePressureFigure_v1.m: Creates Figure 5 in the manuscript. </p> </li> <li> <p>LcurveComparisonFigure_m_Ncuas_v1.m: Creates Figure 12 in the manuscript. </p> </li> <li> <p>LcurveComparisonFigure_N_m_v1.m: Creates Figure 9 and Figure 10, as well as the Table 1 in the manuscript. </p> </li> <li> <p>MeshFigure_v1.m: Creates Figure 3 in the manuscript. </p> </li> <li> <p>ModelSetupFigure_v1.m: Creates Figure 2 in the manuscript. </p> </li> <li> <p>SubdomainLcurveFigure_v1.m: Creates the subfigures of Figure 8 in the manuscript. </p> </li> <li> <p>ThermalFigure_v1.m: Creates Figure 4 in the manuscript. </p> </li> </ul> <p><strong>Input</strong> </p> <ul> <li> <p>Mesh_WAIS_500m-19km.mat: mat-file of finite-element mesh for WAIS study domain. </p> </li> </ul> <p><strong>Inversion results </strong></p> <ul> <li> <p>BestInversionResult_WAIS_m3_Ncuas_bestlambda-0.5.nc: Best basal drag and squared drag coefficient result for the experiment m=3, Ncuas evaluated at the best lambda value 0.5 determined with the L-curve analysis. </p> </li> <li> <p>BestInversionResult_WAIS_m3_Ncuas_lambda-0.562.nc: Best basal drag and squared drag coefficient result for the experiment m=3, Ncuas evaluated at the lambda value 0.562 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_Ncuas_lambda-1.nc: Basal drag and squared drag coefficient result for the experiment m=1, Ncuas evaluated at the lambda value 1 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m3_Nop_lambda-0.1.nc: Basal drag and squared drag coefficient result for the experiment m=3, Nop evaluated at the lambda value 0.1 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_Nop_lambda-3.16.nc: Basal drag and squared drag coefficient result for the experiment m=1, Nop evaluated at the lambda value 3.16 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m3_noN_lambda-3.16.nc: Basal drag and squared drag coefficient result for the experiment m=3, using a Weertman sliding-law evaluated at the lambda value 3.16 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_noN_lambda-0.316.nc: Basal drag and squared drag coefficient result for the experiment m=1, using a Weertman sliding-law evaluated at the lambda value 0.316 determined with the L-curve analysis. </p> </li> <li> <p>ThermalOutput_WAIS.nc: Basal temperature, basal melt rates and depth-averaged rheology output from the used 1D thermal model, as well as the parameterized effective pressure Nop and the effective pressure Ncuas determined from a subglacial hydrology model. </p> </li> </ul> <p> </p> </div>
Datasets associated with "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model"
<p>Datasets that accompany "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model". These datasets include the debris thickness estimates derived including and excluding ponds (denoted as wponds and noponds, respectively), flux divergences, the shapefile of the 600 m boxes, the master 10 m DEM, the median x and y velocities, and the change in elevation for each pair of DEMs used in the study for Ngozumpa, Khumbu, and Imja-Lhotse Shar Glaciers. Debris thickness is in units of meters. Flux divergence is in units of meters per year. A negative flux divergence means the box is gaining mass and is called the emergence velocity; while positive flux divergence means the box is losing mass and is called the submergence velocity.</p>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
Application Cases of Inverse Modelling with the PROPTI Framework - Data Set
<p><strong>Contents</strong></p> <p>Set of simulation data, supplementary for a paper submitted to (published: 15 June 2019) the Fire Safety Journal, with the title <a href="https://www.sciencedirect.com/science/article/pii/S0379711219300438">"Application Cases of Inverse Modelling with the PROPTI Framework"</a>. See also our project at <a href="https://www.researchgate.net/project/PROPTI-An-Generalised-Inverse-Modelling-Framework">ResearchGate</a>.</p> <p>This repository contains the complete input data for each IMP run of the mass loss calorimeter, shown in this paper. This comprises of the experimental data files, the templates for the simulation models and the input file for PROPTI.</p> <p>The data base files are provided. This includes the original ones created by PROPTI during the run, as well as the cleaned data base files, used to create the plots, and the extracted best parameter sets per generation. Plots, created during the IMP runs as means of monitoring the progress are also included.</p> <p>Furthermore, the repository contains a small collection of Jupyter notebooks which have been used to process the data base files and create the plots presented in this paper.</p> <p>The full factorial simulations were set up from within a Jupyter notebook. This notebook and the conducted simulations are also part of this repository.</p> <p>Data of the various TGA simulations are provided within a very <a href="https://zenodo.org/record/2538851#.XSXfAXtCSUk">similar repository</a>, linked to a <a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">conference paper</a> (ESFSS 2018, Nancy, France).</p> <p>Finally, the simulation input files, PROPTI input, as well as the custom script for file handling in concert with OpenFOAM, are provided.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis scripts, the Jupyter notebooks, to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 4.1 GB. Version 2 adds about 5.1 GB.</p> <p> </p> <p><strong>Version 2:</strong></p> <p>Version 2 contains new IMP runs that address an error in determining the normalised residual mass, see Jupyter Notebook "RevisedTargetAssessment.ipynb", as well as input from the reviewers. The IMP runs are denoted by "08" after the optimisation algorithm label, e.g. "MLC_FSCABC_08_new_75kw_Ins".</p>
Yerrida Basin 3D geological model and gravity inversion results
<p>This dataset contains an archive for an implicit 3D geological model of the Yerrida Basin, southern Capricorn region, Western Australia.</p> <p><strong><em>Yerrida_Basin_3D.zip </em></strong>is a GeoModeller three dimensional geological model. Also included are 2D and 3D voxets resulting from inversion of gravity data using the geological model as a constraint. Geomodeller software is available from here: <a href="https://www.intrepid-geophysics.com/ig/index.php?page=downloads">https://www.intrepid-geophysics.com/ig/index.php?page=downloads</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p> <p> </p>
A model of P-wave velocity beneath the greater Alpine region from teleseismic full P-waveform inversion
<p>The dataset provides values of P-wave velocity in a 3D spherical chunk beneath the greater Alpine region as they resulted from a teleseismic full waveform inversion of AlpArray data. </p> <p>Please find a description of the dataset in the accompanying README file.</p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Data for: Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction (Part 1/2)
<p>Magnetic Resonance Imaging measurement data used in our paper about "Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>)</p>
Magnetotelluric data from Santos basin (SE Brazil) and inversion resistivity models exploring basin wedge and deep crustal structure beneath.
<p><strong>Magnetotelluric data</strong></p> <p>Processed data from 90 magnetotelluric broadband stations acquired in are available in Electrical Data Interchange (EDI) and ModEM format.</p> <p>The MMT data were recorded in 2007 by WesternGeco Electromagnetics as part of the National Observatory Rio de Janeiro project funded by Petrobras. The campaign comprised a total of 92 sites from shallow water (about 50 m depth) to deep water (about 1600 m depth). The stations are placed along three NW-SE parallel profiles in the northwest part of Santos basin. The central profile is approximately 160 km long and consists of 56 stations, while the west profile and east profile extend about 55 km each and contain 18 and 16 stations, respectively.</p> <p> </p> <p><strong>Models</strong></p> <p>Inversion models and predicted data are present for two different starting resistivity model testes 10 and 1 Ohm.m. The inversion models were estimated using ModEM - modular system for inversion of electromagnetic geophysical data.</p>
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
Air-Sea fluxes of CO2 in the Indian Ocean between 1985 and 2018: A synthesis based on Observation-based surface CO2, hindcast and atmospheric inversion models.
<p>This data set contains 14 hindcast models (CCSM-WHOI.nc, CESC_ETHZ.nc, CNRM-ESM2-1.nc, EC_Earth3.nc, FESOM_REcoM_LR.nc, MOM6_Princeton.nc, MPIOM_HAMOCC.nc; MRI-ESM2-1.nc, NorESM-OC1.2.nc, ORCA1-LIM3-PISCES.nc, ORCA025-EOMAR.nc, Plankotom12, INCOIS-BIO-ROMS.nc, ROMS-NYUAD.nc), nine empirical models (CMEMS-LSCE-FFNN.nc, CSIRML6.nc, Jena-MLS.nc, JMAMLR.nc, Spco2_LDEO_HPD.nc, SOMFNN.nc, NIES-MLR3.nc, UOEX-WAT20.nc, OceanSODAETHZ.nc) and CO2 flux climatology data (CO2_Climatology.nc). This data set also has two atmospheric inversion models output and those are - MACTM (MACTM.nc) and CAMSv20r1 (CMSv20r1.zip format and inside the zip folder files are .nc format).</p>
Martian crustal models at the InSight landing site from joint inversion of ellipticity, P-to-s RFs and autocorrelations times
<p>- Preferred models resulting from the joint inversion of three local-scale measurements: Rayleigh-wave ellipticity, P-to-s Receiver functions (Joshi et al., 2023) and P-wave lag times from autocorrelation (Schimmel et al., 2021).</p> <p>- Scripts and observables to perform joint inversion.</p>
Slip deficit rate realizations for 2023 New Zealand National Seismic Hazard Model geodetic inversions
<p>This data set contains inversion results presented in Johnson et al. (2023) and also Johnson et al. (2022). All calculations involving slip deficit rates in those papers were conducted using the results in the files provided in this data set. </p>
CarbonWatch-NZ: National Scale Inverse Modelling of New Zealand's Carbon Balance
<p>New Zealand flux estimates derived from the CarbonWatch-NZ national scale inverse modelling system (Bukosa<br> et al., 2023; Steinkamp et al., 2017), prepared for the publication "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2). </p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
Open the record for dataset details and reuse information.
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
Data regarding the application of the inverse modeling (TsuSedMod) to the sediment deposits of CE 1755 Lisbon tsunami at Salgados, Algarve (Portugal)
<p>This dataset contains data used to run TsuSedMod model. It contains the vertical textural distribution of 4 sediment samples (LV09, LV11,LV11a and LV13) retrieved at Salgados, southern Portugal<br> and respective results.</p>
Underlying data for Slimani et al. Identification of dominant hydrogeochemical processes for groundwaters in the Algerian Sahara supported by inverse modeling of chemical and isotopic data
<p>The data hereafter underlie the paper by Slimani et al. doi:10.5194/hess-20-1-2016,</p> <p>appeared to Hydrol. Earth Syst. Sci., 20, 1-23, 2016.</p> <p> </p> <p> </p> <p>1. File Tableaux_data.xls</p> <p> </p> <p>This is an Excel sheet file. It contains:</p> <p>- raw analytical data, mostly in mg/L;</p> <p>- data converted in mmol/L;</p> <p>- data corrected from the defect of cations - anions balance; the correction is made proportionally.</p> <p>- the previous data completed with logarithms of activities, computed by Phreeqc, </p> <p>for calcium, sulfate, carbonate and water; those data are used to plot equilibrium diagrams for calcite and gypsum (figure 6);</p> <p>- for Phreatic aquifer only, saturation indexes for halite, anhydrite, calcite, dolomite and gypsum, along with distance from south to north, used in figure 7.</p> <p>2. Directory Phreeqc_res</p> <p>Contains the input file with all samples from CI, CT and Phr in a single file, and the selected output file.</p> <p>All calculations were made with version phreeqc-3.1.2 and database sit.dat.</p> <p>3. Directory Inverse models</p> <p>This directory contains inverse models for computing transformations:</p> <p>- from CI (average) to CT (average):</p> <p>- from CT (average) to Phr (pole I, average);</p> <p>- from pure water to Phr (pole II, sample P036);</p> <p>- for mixing Phr pole I and II, and try to explain a sample typical of medium mixing ratio, sample P068.</p>
Crustal thicknesses, Moho depths and 3-D density anomaly model for GJI paper: Crustal structure of onshore-offshore Atlantic Canada and environs from constrained 3-D gravity inversion using variable mesh depths by J. Kim Welford
<p>The files are provided as ascii text files in terms of both latitudes/longitudes and eastings/northings. For the 3-D density anomaly model, it is provided with columns of x, y, z, and absolute density. The conversions from latitudes/longitudes to eastings/northings for all of the models and maps in this work are computed with ellipsoid WGS-84 and UTM zone 19 using Generic Mapping Tools.</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.