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677 results for “Inversion”
Atmospheric inversion results: sources of atmospheric halocarbons in the Eastern Mediterranean
<p>Settings, run script, observations and results from all inverse modelling runs used in Schönenberger et al., APC, 2018, for European sources of halocarbons for the year 2013.</p> <p>Settings and run script can be found in <a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/inversion_run_scripts.tar.gz">inversion_run_scripts.tar.gz</a>. In order to reprocess the results the R package 'Rinversion' for atmospheric inversion (<a href="https://doi.org/10.5281/zenodo.1194641">https://doi.org/10.5281/zenodo.1194641</a>) has to be installed and the simulated source sensitivities (<a href="https://doi.org/10.5281/zenodo.1194037">https://doi.org/10.5281/zenodo.1194037</a>) have to be downloaded. Observations (<a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/halocarbon_observations.tar.gz">halocarbon_observations.tar.gz</a>) are post-processed observations of the AGAGE network (<a href="http://agage.mit.edu/">http://agage.mit.edu/</a>) plus those gathered at the Finokalia site (<a href="https://doi.org/10.5281/zenodo.1186221">https://doi.org/10.5281/zenodo.1186221</a>).</p> <p>Inversion results (<a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/inversion_results.tar">inversion_results.tar</a>) are contained as separate packages for each individual sensitivity experiment described in the publication. The sensitivity experiments are listed in inversion_results/InversionRuns.xlsx and agree with those listed in Table 1 of the publication. Subfolders are organised by halocarbon species: HCFC_142b, HCFC_22, HFC_125, HFC_152a, HFC_134, HFC_143a. For each halocarbon results are stored in form of plots, ASCII spread sheets (csv, dat) or R data objects (.rda). Time series and distribution plots are available in separate subfolders. Intermediate data used by the inversion code are stored in 'intermediates', whereas the final results of each inversion run are stored as a single R object (inversion_results_XXX.rda).</p> <p> </p> <p> </p>
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>
Suspended sediment concentration inversion from acoustic backscatter in rivers
<p>This repository contains scripts and data related to the article entitled <em>Using a down-looking multi-frequency Acoustic Backscatter System (ABS) for measuring suspended sediments in rivers </em>published in Water Resources Research. The scripts are provided through Python Jupyter Notebooks. Instructions for running the scripts are given in readme.txt file.</p>
Global nitrous oxide fluxes estimated using atmospheric inversions
<p>Nitrous oxide emissions are presented from three independent atmospheric inversion frameworks. The frameworks are: 1) INVICAT: an inversion using the atmospheric transport model, TOMCAT and a 4D-Var optimisation method; 2) JAMSTEC: an inversion using the MIROC4-ACTM atmospheric transport model and a Bayesian analytical optimisation method; and 3) PYVAR: an inversion using the LMDZ5 atmospheric transport model and a 4D-var optimisation method. The emissions were optimised monthly and have been re-gridded from the model native resolution to 1.0 by 1.0 degrees. The files for TOMCAT and LMDZ5 (i.e. the inversion frameworks INVICAT and PYVAR, respectively) contain two flux variables: 1) the prior fluxes as estimated a priori, and 2) the posterior fluxes as estimated by the inversion. The file for the JAMSTEC inversion, contains five flux variables: 1) flux_apri_land: the prior fluxes over land, 2) flux_apri_ocean: the prior fluxes over ocean, 3) flux_apri_fossil: the prior estimate of emissions from combustion, 4) flux_apos_land: posterior fluxes over land estimated by the inversion, and 5) flux_apos_ocean: the posterior fluxes over ocean estimated by the inversion. Note that flux_apri_fossil was not optimised in the inversion but for the total posterior N<sub>2</sub>O emission, needs to be added to the flux_apos_ocean and flux_apos_land variables.</p>
Data for [Inverse magnetic susceptibility fabrics in pelagic sediment: Implications for magnetofossil abundance and alignment]
<p>Data for [Inverse magnetic susceptibility fabrics in pelagic sediment: Implications for magnetofossil abundance and alignment]</p>
Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset
<p>This dataset contains supplementary files uploaded as part of the above journal article.</p> <p>5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few<br> records contain negative peak delay times due to change in waveform shape from filtering, these<br> were excluded during further calculations. The other 4 sub-datasets contain files as well as the script<br> to generate the results of Δlog <em>t<sub>p</sub></em> , κ, ε<sub>param</sub> and P(<em>m<sub>l </sub></em>) as shown in figures 7, 8, 9 and 10 respectively<br> of the main article.</p> <p>Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (<em>t<sub>p</sub> </em>) in 2-4 Hz, 4-8 Hz, 8-16 Hz<br> and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent<br> origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude,<br> event longitude, event depth, station code, station latitude, station longitude, <em>t<sub>p</sub></em> in 2-4 Hz, <em>t<sub>p</sub></em> in 4-<br> 8 Hz, <em>t<sub>p</sub></em> in 8-16 Hz and <em>t<sub>p</sub></em> in 16-32 Hz in a sequential manner.</p> <p><br> Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and<br> nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)<br> written in BASH. The text files contain Δlog <em>t<sub>p</sub></em> values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands<br> respectively. The columns in the text files represent node index, node latitude, node longitude,<br> node depth and Δlog <em>t<sub>p</sub></em> value in a sequential manner. The GMT script uses GSHHG coastline<br> data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .<br> Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of<br> the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5<br> (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt,<br> kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The<br> text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.<br> The columns in the text files represent node latitude, node longitude and κ value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of κ using GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,<br> aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain<br> ε<sub>param</sub> values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in<br> the text files represent node latitude, node longitude and ε<sub>param</sub> value of the node sequentially.<br> This GMT script also uses GSHHG coastline data whose path can be added to the script, same as<br> in data set S2 case. The GMT script file can be run to see the spatial distribution of ε<sub>param</sub> using<br> GMT-5 (Wessel et al., 2013) and above.</p> <p>Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,<br> psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain<br> psdf (P(<em>m<sub>l</sub></em><sub> </sub>)) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The<br> columns in the text files represent node latitude, node longitude and P(<em>m<sub>l</sub></em> ) value of the node<br> sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the<br> script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution<br> of P(<em>m</em><sub><em>l </em></sub>) using GMT-5 (Wessel et al., 2013) and above.</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>
New advance in land controlled-source audio-frenquency magnetotellurics exploration: measurement at a shortened transmitter-receiver offset and three-dimensional inversion
<p><span>Ambient noise significantly affects the quality of land controlled-source audio-frequency magnetotellurics (CSAMT) data. Shortening the transmitter–receiver offset can enhance the raw data signal-to-noise ratios (S/N). However, most of CSAMT explorations still collect field data using a large separation. To enhance the S/N of the raw electric and magnetic field and the corresponding Cagniard apparent resistivity, we propose shortening offset to 2–4 km during CSAMT fieldwork and inverting apparent resistivity rather than only electric field. We tested this approach with three experimental models with varying offsets, simulating 3D responses. Result showed that longer offsets predominantly produce data anomalies in the far-field zone, whereas shorter offsets shift these anomalies toward the transition zone with some in the far-field or near-field zones. Shortened-offset responses exhibit stronger electric and magnetic fields. Synthetic datasets with different noise levels were generated for inversion analysis. The results indicate that shortening the offset, particularly in noisy environments, improves data S/N. Inversion results reveal that a shortened CSAMT transmitter–receiver offset allows for data collection with most anomalies in the transition-field zone, facilitating accurate prediction of the true model in the 3D inversion. In low-noise environments, inversion results from data with a shortened offset are comparable to those with a long offset. However, in high-noise environments, the shortened-offset approach yields more accurate results due to improved data S/N. Field data inversion from Yanqing, China, further confirms the effectiveness of our scheme. The shortened-offset approach achieves higher S/N datasets and inversion results that align with actual geological structures.</span></p> <p> </p>
Datasets used in a Transformer network for image inversion of multi-dimensional nonuniform aperture synthesis radiometers
<p><span>该数据集于 2023 年 11 月在中南大学生成,并通过 matlab 仿真软件进行仿真和收集。主要用于图像重建网络的训练和测试。</span>该数据集由原始场景亮度数据、能见度数据和一维、二维和三维非均匀天线阵列图像重建的能见度函数对应的频域采样点位置数据,以及使用其他一些常规方法进行图像重建获得的亮度数据组成。此外,为了验证所提方法的有效性,在工作频率为 33.5 Ghz 的原型 8 元一维非均匀天线阵列上进行了室内实验,并生成了测量数据集。</p> <p>具体来说,名为 Tb_in、V2_noise、T3_AAF 和 T2_idft 的四个仿真数据集存储在名为 1d 的 zip 包中。</p> <p>Tb_IN_1d存储了一维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。</p> <p>V2_noise存储了包含各种误差的能见度函数值,主要是通过将原始场景亮温图像输入到运行在接收频率为 33.5 GHz 的非均匀积分孔径辐射计模拟程序中得到的。</p> <p>T2_idft 和 T3_AAF 分别是使用逆离散傅里叶变换和阵列因子形成方法进行图像重建获得的明亮温度数据。这两组数据都可用于后续的比较实验。</p> <p>名为 Tb_IN_2d、Tb_out_2d、VS_2d 和 VS_P_2d 的四个数据集存储在名为 2d 的 zip 包中。</p> <p>Tb_IN_2d内部存储的是二维非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感影像场景分类公共数据集。</p> <p>VS_2d为二维非均匀天线阵列对应的能见度函数值,主要是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀集成孔径辐射计模拟程序中得到的。</p> <p>VS_P_2d存储了二维非均匀天线阵列的能见度函数对应的频域采样点位置,该值主要通过计算能见度函数值得到。</p> <p>Tb_out_2d文件存储了使用传统方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。</p> <p>名为 Tb_IN_3d、Tb_out_3d、VS_3d 和 VS_P_3d 的四个数据集存储在名为 3d 的 zip 包中。</p> <p>Tb_IN_3d内部存储的是 3D 非均匀天线对应的原始场景亮温数据,该数据选自西北工业大学制作的遥感图像场景分类公共数据集。</p> <p>VS_3d是三维非均匀天线阵列对应的能见度函数值,是将原始场景亮温图像输入到接收频率为 33.5 GHz 的非均匀积分孔径辐射计仿真程序中得到的。</p> <p>VS_P_3d存储了三维非均匀天线阵列的能见度函数对应的频域采样点位置,该位置是通过计算能见度函数值得到的。</p> <p>Tb_out_3d文件存储了使用常规方法进行图像重建得到的亮温值,该数据也用于后续与所提方法获得的数据的比较实验。</p> <p>名为 Array2_R、Array3_R、V2_noise 和 Tb_out 的四个数据集存储在名为 8mm8 channel 的 zip 包中。</p> <p>存储在 Array2_R 和 Array3_R 中的是系统在不同位置测量的目标点源的相关矩阵,矩阵中元素的值反映了测试点对目标点源的检测能力。根据此相关矩阵,可以计算可见性值。</p> <p>存储在 V2_noise 内部的是包含各种误差的测量可见性函数的样本。对这些数据进行实验主要是为了验证所提方法的有效性。</p> <p>存储在 Tb_out 中的是使用测试数据集获得的亮温结果数据,用于测试训练的网络。</p>
Datasets and Codes for "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc"
<p>This Zenodo record contains the supplementary datasets and code for the paper titled "Relative Moment Tensor Inversion for Microseismicity: Application to Clustered Earthquakes in the Cascadia Forearc."<br><br></p> <p><strong>Datasets</strong></p> <ul> <li>phases.txt<br> Contains phases used for the event location and moment tensor inversion.<br> Format: ID, station, phase, year, month, day, secday<br> ID: Event identifier (same for all files)<br> station: Station name<br> phase: 1 for P-wave or 2 for S-wave<br> secday: Seconds in the day</li> <li>polarity.txt<br> Contains first motion P polarity used in the study.<br> Format: ID, station, polarity, trust, type<br> polarity: 1 for up or -1 for down<br> trust: Value between 0 and 1, indicating confidence level.<br> type: E for emergent or I for impulsive<br> Note that the arrival type has been automatically assigned and not double-checked.</li> <li>relocation.txt<br> HypoDD relocation file (see hypoDD manual for full description).<br> Format: ID, LAT, LON, DEPTH, X, Y, Z, EX, EY, EZ, YR, MO, DY, HR, MI, SC, MAG, NCCP, NCCS, NCTP, NCTS, RCC, RCT, CID</li> <li>MT_soluton.txt<br> Contains all double-couple moment tensor solutions.<br> Format: ID, strike, dip, rake, mag, kagan_std<br> mag: Moment magnitude (Mw); "None" if the event is not considered stable<br> kagan_std: Quality interpretation of the moment tensors using Kagan angle standard deviation, as described in the main paper.</li> </ul> <p> </p> <p><strong>Codes</strong></p> <p>Future development of the relative moment tensor algorithm will be conducted on GitHub as part of the Marie-Sklodowska-Curie Action relMT funded by the European Union (https://github.com/wasjabloch/relMT)</p> <p>Here are the files in Codes.zip:</p> <ul> <li>synthetics.zip<br> Contains codes for performing and testing synthetic moment tensor inversion.</li> <li>relMT.zip<br> Contains the code for performing moment tensor inversion on real data.</li> <li>intrustion.txt<br> Contains instructions for setting up and running the codes.</li> <li>environment_MAC.yml<br> File to create the python environment on a MAC or LINUX machine.</li> <li>environment_WINDOWS.yml<br> File to create the python environment on a WINDOWS machine.</li> </ul>
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>
A niching particle swarm optimization strategy combined with cluster analysis for the multimodal inversion of surface waves
<p>The data include two study cases used for multimodal surface wave inversion.</p> <p>For case 1, the data present a combination of active and passive surface wave methods.</p> <p>For case 3, we use Rayleigh waves to detect a low-velocity soft interlayer underneath the road.</p> <p>Detailed description can be found in the data description document.</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>
Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"
<p>Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".</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>
Centroid values of aerosol optical properties for 8 sub-types based in AERONET inversion data (1993–2018)
<p>In this project, we adapted our previously defined 5 aerosol optical typology scheme (Hamill et al. 2016) to result in a more discriminating 8 aerosol typology scheme (Giordano 2019). Previously we presented an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phases of this research, we opto-physically identified five major types of <strong>Bulk Columnar Aerosol</strong> (BCA) based solely upon intensive optical properties of spectral Single Scattering Albedo (<strong>SSA</strong>), spectral Indices of Refraction (real – <strong>RRI</strong> and imaginary – <strong>IRI</strong>), and two Angstrom Exponents (extinction – <strong>EAE</strong> and absorption – <strong>AAE</strong>). These BCA were classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that were found to lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD440nm < 0.4 as per AERONET criteria for almucantar scan inversion). </p> <p>The classifications obtained are made available to be used in interpreting aerosol retrievals from satellite-borne instruments and as input for regional climate models. A major result of this aerosol typology is a dataset describing the types of aerosol particles that are distinct from one another in optical properties and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data (Giordano 2022). An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a>]. Each of these original five aerosol types (Hamill et al. 2016, Giordano 2019) was further discriminated into specific sub-types by this same scheme to achieve an 8-aerosol typology (Giordano 2019 Chapter 2). For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. Here we segmented the Biomass Burning Aerosol type into the sub-types of <em>Flaming</em> (<strong>BMF</strong>) and <em>Smoldering</em> (<strong>BMS</strong>) using the centroid separation method and the MD criteria for in-class inclusion was adjusted to 1.5 MD. Similarly, we found great confidence in discriminating the MIXED aerosol type into two distinct regimes which we simply labeled as <em>MIXEDtype1</em> (<strong>MIXED1</strong>) and <em>MIXEDtype2</em> (<strong>MIXED2</strong>). These can be visually verified by examining any one of many possible renditions of 3-D optical spaces noting their 5-D centroids are separated by a distance of 3.47-3.85 MD [Giordano 2019 Chapter 2]. Likewise, the Urban Industrial Aerosol class was further discriminated into European Urban Industrial (<strong>EURO UI</strong>) and North American (<strong>NA UI</strong>), whose 5-D centroids are separated by a distance of 2.60–3.08 MD. We then used the previously employed mathematical strategies to sort the global AERONET data retrievals into the aerosol types classified against their reference standards. We believe the strategies regarding aerosol differentiation using polarization data (Hamill, Piedra and Giordano 2020) are an additional method useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based 8-aerosol optical typology, in a 5-D basis is useful for applications in aerosol optics, including direct forward modeling of radiative transfer to determine the effects of aerosol absorption and/or scattering on vertical heating profiles and ground received irradiance quantities, for input into more complicated remote sensing algorithms, used as calibration/validation values for in-situ and laboratory experimental studies, and evaluating radiative forcing calculations in atmospheric models.</p> <p>[Work related to an 8-aerosol typology in 6-D, 8-D, 9-D and 10-D optical property bases, and their files, are to be published subsequently as a different database project in 2023.]</p>
Data for: Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
<p>Magnetic Resonance Imaging measurement data used in our work about "Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations". 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> <p><br> Further information about the individual datasets:</p> <p>data_GSM_t1<br> Type: Gold-Standard T1 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|15<br> FOV [mm]: 200<br> T_INV [ms]: 30:250:2530</p> <p>data_GSM_t2<br> Type: Gold-Standard T2 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|(15:40:455)<br> FOV [mm]: 200</p> <p>data_05b_b1map<br> Type: B1 Map<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_05b_kspace<br> Type: Radial Single-Shot Dataset<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_06_irbssfp_long<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 10.8|5.4<br> FA [deg]: 45<br> T_RF [ms]: 2.5<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irbssfp_short<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 4.1|2.58<br> FA [deg]: 6<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_s03_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_s03_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.75|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_5ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 6.14|3.07<br> FA [deg]: 35<br> T_RF [ms]: 2.5<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_1ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.5|2.75<br> FA [deg]: 35<br> T_RF [ms]: 2.1<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.0|2.5<br> FA [deg]: 35<br> T_RF [ms]: 1.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_2ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.6|2.3<br> FA [deg]: 35<br> T_RF [ms]: 1.2<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4|2<br> FA [deg]: 35<br> T_RF [ms]: 0.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_4ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 3.8|1.9<br> FA [deg]: 35<br> T_RF [ms]: 0.4<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p> </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.