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677 results for “Inversion”
MIROC4-ACTM CO2 Inversion flux (2001-2020; case s050_ux4_gvjf)
<p>Details in :</p> <p>Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M.: Estimated regional CO<sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO<sub>2</sub> inversions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1039, in review, 2021.</p>
ZnO-Promoted Inverse ZrO2–Cu Catalysts for CO2-Based Methanol Synthesis under Mild Conditions
<p>Datasets supporting the publication 'ZnO-Promoted Inverse ZrO<sub>2</sub>–Cu Catalysts for CO<sub>2</sub>-Based Methanol Synthesis under Mild Conditions': catalyst evaluation data (Excel), XRD (Origin), crystallite sizes, Cu surface area, CO<sub>2</sub> uptake, H<sub>2</sub>-TPR, CO<sub>2</sub>-TPD, CO-DRIFTS profiles (CSV)</p>
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </p>
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Experiments regarding the inverse method extended with the integer hull ("RITPS")
<p>This archive contains the benchmarks regarding the evaluation of the inverse method extended with integer hull ("RITPS"):</p> <ul> <li>the version of IMITATOR: <span><a href="https://github.com/imitator-model-checker/imitator/releases/tag/v3.4.0-alpha2">v3.4.0-alpha2</a></span> (Cheese Durian)</li> <li>the source models</li> <li>the script to run all versions of CSMA/CD</li> <li>the expected results (in subdirectory "res")</li> </ul>
HR-GNSS data used in Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
<p>Here are the high-rate GNSS data we used to infer the low-frequency components of seismic source radiation within the M 6.2, 24/August/2016, Amatrice Earthquake. In particular, the traces are used to constrain frequencies between 0.03-0.06 Hz. This data has been used to evaluate the performance of the method, in a train/test split procedure, described in the manuscript. We upload data here to comply with AGU Fair data policy (https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Policies/Data-policy)</p> <p>Please find the pre-print of the manuscript from the ESSOAR (<a href="https://doi.org/10.1002/essoar.10504341.1">https://doi.org/10.1002/essoar.10504341.1</a>).</p> <p>Notice that the complete set of data are reposited on INGV FTP server: ftp://gpsfree.gm.ingv.it/amatrice2016/</p> <p>The data is originally processed by Avallone et al. (2016), and the detailed analysis procedure has been explained there. In the case where you used this data, please cite the original articles: </p> <p>Avallone, A., Latorre, D., Serpelloni, E., Cavaliere, A., Herrero, A., Cecere, G., ... & Selvaggi, G. (2016). Coseismic displacement waveforms for the 2016 August 24 Mw 6.0 Amatrice earthquake (central Italy) carried out from High-Rate GPS data. Annals of Geophysics, 59. (<a href="https://doi.org/10.4401/ag-7275">https://doi.org/10.4401/ag-7275</a>)</p> <p>Avallone, A., Selvaggi, G., D'Anastasio, E., D'Agostino, N., Pietrantonio, G., Riguzzi, F., ... & Zarrilli, L. (2010). The RING network: improvement of a GPS velocity field in the central Mediterranean. Annals of Geophysics, 53(2), 39-54. (<a href="https://doi.org/10.4401/ag-4549">https://doi.org/10.4401/ag-4549</a>)</p> <p> </p>
Marine time domain electromagnetic data and true model for 2.5D inversion
<p>Dataset contains the description of complex 3D geoelectric model (with bathymetry, curved surfaces of geoelectric layers, target bodies simulated HC deposits, and background inhomogeneities) and marine time domain electromagnetic data calculated via finite element modeling. Noised data sets have been used for geometric 2.5D inversion.</p>
Dataset for "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory"
<pre>This dataset gathers all cross-spectral, spectral and bispectral data produced and used in the manuscript accepted for publication in Geophysical Research Letters: "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory", by K. Martins, P. Bonneton, O. de Viron, I. L. Turner, M. D. Harley and K. Splinter The sharing of the processed data is motivated by research reproductibility purposes and with the hope that it will foster efforts in improving the newly-proposed depth-inversion procedure. Three laboratory experiments are considered, namely the experiments reported in van Noorloos (2003), Michallet et al. (2011) and GLOBEX (e.g., see Ruessink et al., 2013). The processed data is organised in separate self-explanatory .mat files (generated with MATLAB software), gathering: - "cel_data" : wave phase velocities obtained from cross-spectral and cross-correlation analyses between adjacent wave gauges - "bulk_data" : range of bulk wave parameters computed across the different wave flumes - "bispectrum_data" : spectral and bispectral estimates computed across the different wave flumes The scripts for performing the depth-inversion as well as a for plotting the results are provided. The new Boussinesq depth-inversion procedure relies on the function "fun_compute_krms_terms.m", which is provided and originates from the bispectral analysis library accessible from the first author GitHub repository at https://github.com/ke-martins/bispectral-analysis. Acknowledgements: Kévin Martins greatly acknowledges the financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement 887867 (lidBathy). We warmly thank Ap van Dongeren and Hervé Michallet for providing the raw data for the experiments described in van Noorloos (2003) and Michallet et al. (2011), respectively. The raw data from GLOBEX used in this research can be accessed on Zenodo at https://zenodo.org/record/4009405 and can be used under the Creative Commons Attribution 4.0 International license. The GLOBEX project was supported by the European Community’s Seventh Framework Programme through the Hydralab IV project, EC Contract 261520. References: Michallet, H., Cienfuegos, R., Barthélemy, E., & Grasso, F. (2011). Kinematics of waves propagating and breaking on a barred beach. European Journal of Mechanics - B/Fluids, 30 (6), 624 – 634. doi: 10.1016/j.euromechflu.2010.12.004 Ruessink, G. B., Michallet, H., Bonneton, P., Mouazé, D., Lara, J. L., Silva, P. A., & Wellens, P. (2013). GLOBEX: Wave dynamics on a gently sloping laboratory beach. In Coastal Dynamics ’13: Proceedings of the Seventh Conference on Coastal Dynamics, Arcachon, France. van Noorloos, J. C. (2003). Energy transfer between short wave groups and bound long waves on a plane slope (Master’s thesis, Delft University of Technology, Delft, The Netherlands). Retrieved from http://resolver.tudelft.nl/uuid:13616ff0-407d-43de-9954-ba707cd40d27</pre>
Synthetic proton radiographs for testing direct inversion algorithms
<p>Proton radiographs generated by particle tracing in specified radial force profiles in cylinders and spheres saved in pradformat (github.com/phyzicist/pradformat) in a zipped folder intended as tests for direct inversion algorithms. For details see: J. R. Davies, and P. V. Heuer, https://arxiv.org/abs/2203.00495</p> <p>Version 2 includes 3 additional radiographs for a spherical Gaussian potential with a reduced bin width and more bins (0.02R and 200x200 bins)</p> <p>Version 3 corrects an error in the x values given for the original spherical Gaussian potentials with negative mu values sphGauss_mum0p25 and sphGauss_mum0p5. The bin widths were half that of the spherical Gaussian results with positive mu values. </p> <p>Version 4 corrects an error in the x values given for the mesh run and adds a smoothed version of the source intensity (mu0)</p>
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
<p>The set consists of 5 files that constitute the main calculations of ammonia emissions over Europe for the years 2013-2020. <br> The detailed description of variables follows:</p> <p>1) PriorEmission.nc<br> - Pall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia prior emissions used in the study [ng/m2/s]</p> <p>2) PosteriorEmission.nc<br> - Xall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia posterior emissions [ng/m2/s]</p> <p>3) UncertaintyEmission.nc<br> - Uall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with uncertainty of posterior emissions [ng/m2/s]</p> <p>4) stations_vmodVSobs.mat <br> - st_list: list of stations identifiers, 53 stations in total<br> - st_coord: stations coordinates [lot,lat]<br> - st_OBSdays: matrix of the size 53 x (366*8) with observations in daily resolution [ug/m3]<br> - st_ind_obs: logical matrix of the size 53 x (366*8) with indicators when each station provides observation (1) and when not (0)<br> - st_prior_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with prior emission [ug/m3]- st_post_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with posterior emission [ug/m3]<br> - st_prior1_vmod_days: same as st_prior_vmod_days for EC6G4 prior<br> - st_prior2_vmod_days: same as st_prior_vmod_days for EGG prior<br> - st_prior3_vmod_days: same as st_prior_vmod_days for NE prior<br> - st_prior4_vmod_days: same as st_prior_vmod_days for VD prior</p>
Global net ecosystem exchange of CO2 inferred from the OCO-2 XCO2 retrievals (GCAS OCO-2 inversion)
<p>Here is a dataset of global carbon flux estimates over 2015-2019 using the OCO-2 column-averaged dry-air mole fraction (XCO<sub>2</sub>) retrievals (ACOS XCO<sub>2</sub> v10) by the global carbon assimilation system (GCAS v2) (Jiang et al., 2021). </p> <p> </p> <p><strong>Citations:</strong></p> <p>Jiang, F. et al., 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmos. Chem. Phys., 21(3): 1963-1985.</p> <p>Jiang, F. et al., 2022. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data., 14, 3013–3037.</p> <p>He, W., Jiang, F., Ju, W., et al. Improved constraints on the recent terrestrial carbon sink over China by assimilating OCO-2 XCO<sub>2 </sub>retrievals, JGR-Atmopsheres, 2022, under review.</p> <p><strong>Contacts: </strong></p> <p>Wei He (weihe@nju.edu.cn); Fei Jiang (jiangf@nju.edu.cn)</p> <p>Note: <strong>If you want to use this dataset for your researches, please contact us in advances. </strong>Thank you!</p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Dataset of backprojection and MT inversion results for 2023 Türkiye earthquake sequence
<p>This dataset contains the results of the back-projection analysis of the two Mw 7.7 and 7.6 earthquakes on February 6, 2023 in south-eastern Türkiye and the centroid moment tensor inversion results for one foreshock and 221 aftershocks. All methods and results are described in detail in the publication: Petersen et al., (2023): The 2023 SE Türkiye seismic sequence: Rupture of a complex fault network. The Seismic Record, 3 (2): 134-143. <a href="https://doi.org/10.1785/0320230008">https://doi.org/10.1785/0320230008</a></p>
Evolution of model and geological inconsistencies during inversion
<p>Supplementary material to: </p> <p>Giraud, J., Caumon, G., Grose, L., Ogarko, V., and Cupillard, P.: Integration of automatic implicit geological modelling in deterministic geophysical inversion, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-129, 2023</p> <p>The GIF shows a 3D view of the inverted model and its geological inconsistencies during inversion when geological correction is applied at each iteration. </p>
Statistical blending of global-gridded climatological products: an approach to inverse hydrological model
<p>The growing use of global-scale environmental products in hydro-climatic modeling (with different assumptions, resolutions, and precisions) has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending (fusion) of these products to achieve optimal modeling while avoiding difficulties. The proposed statistical blending in this study includes five large-scale and satellite precipitation (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA5-Land of ECMWF (ERA), Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM), and Terra) and evapotranspiration (Global Land Evaporation Amsterdam Model (GLEAM), SSEBop, Moderate Resolution Imaging Spectroradiometer (MODIS), Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on <em>in situ</em> values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well<strong>.</strong></p>
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International Brain Laboratory public data
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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.