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677 results for “Inversion”
Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach
<p>Abstract:</p> <p>A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results.</p> <p>Datasets:</p> <p>The original geochemical and mineralogical data for soils of the conterminous United States (A and C horizon datasets) were downloaded from <a href="https://mrdata.usgs.gov/ds-801/">https://mrdata.usgs.gov/ds-801/</a>.</p> <p>The ‘Shacklette and Boerngen’ dataset was downloaded from <a href="https://mrdata.usgs.gov/ussoils/">https://mrdata.usgs.gov/ussoils/</a>.</p> <p>A worked example for the five selected samples of Figure 5 is available as a Microsoft Excel spreadsheet (NALG_Ch_oxides_with_estimated_SiO2_LOI_worked example.xlsx) on Zenodo.org.</p> <p>The new datasets including sample identification, coordinates, converted major oxide concentrations, and the concentration estimates for SiO<sub>2</sub> and LOI in wt% for the A and C horizon datasets from the North American Soil Geochemical Landscapes (NASGL) project are available as comma separated value files (NALG_Ah_oxides_with_estimated_SiO2_LOI.csv and NALG_Ch_oxides_with_estimated_SiO2_LOI.csv) on Zenodo.org.</p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
List of VLFEs obtained in the paper "Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis"
<p>List of VLFEs obtained in Toh et al., (2020, GRL).</p> <p>"Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis" by Akiko Toh, Wan-Jou Chen, Nozomu Takeuchi, Douglas Dreger, Wu-Cheng Chi, and Satoshi Ide. </p> <p> </p>
Files and plotting scripts for "Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method"
<p>This archive contains the files required to reproduce the results and figures presented in <em>Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method</em>, J. T. Parker, P. A. Hill, D. Dickinson and B. D. Dudson.</p> <p>Also available at the repository: https://gitlab.com/JosephThomasParker/files-and-plotting-scripts-for-parallel-tridiagonal-matrix-inversion-with-a-hybrid-multigrid-thomas-algorithm-method/</p> <p>Questions to joseph.parker@ukaea.uk.</p> <p>This version is before submission to journal.</p>
New fault slip distribution for the 2010 Mw 7.2 El Mayor Cucapah earthquake based on realistic 3D finite element inversions of coseismic displacements using space geodetic data
<p>The .csv files included in this repository contain the data used in the numerical model as input, while the .txt file is the output (slip on a regular grid of points on the fault planes from the joint inversion of the geodetic datasets.</p>
Dome A Inverse Model
<p>This is the result of a geophysical inversion for the ice sheet and basal hydrological state around Dome A, East Antarctica. The datasets used to constrain the inversion are observations of basal water, basal freeze-on, internal layers, and a geothermal flux prior. The inversion solved for best-fit geothermal flux and accumulation rate fields, along with their respective uncertainty and skewness, and also partitioned the fractional contribution of each individual data type towards constraining the final answer. Note that skewness fields are not statistically significant, but they are provided here for completeness anyway.<br><br>Also included is the ice sheet state produced by the best-fit forward model, including: englacial and basal temperatures, basal melt/freeze rate and water flux, strain heating, hydraulic heating (ie, the combined thermal effect of PMP changes and viscous dissipation in the water system), ice velocity, strain rate, viscosity, and shape function; plus post-processing variables like ice age, best-fit H* in a D-J model, freeze-on thickness, model echo-free-zone thickness, isotopic smoothing due to diffusion, the oldest useful ice for ice coring, and the normalized elevation at which the oldest useful ice is found.</p> <p> </p> <p>Parameters included in the inversion results for both geothermal flux and accumulation rate: output of evolutionary algorithm, local optimization correction, best-fit fields, uncertainty estimate, skewness estimate, and fractional constraints contributed by the five constraints used in the inversion (water observations, freeze-on observations, internal layer observations, GHF prior, and smoothness contraint). Also contains an estimate of the bias in geothermal flux induced by the use of smoothed gridded topography that does not fully capture the deep narrow valleys where water is present. All inversion results variables are 2D.<br><br>Best-fit model results include: englacial temperature (3D), basal temperature (2D), basal logical state (wet/dry; 2D), basal melt rate (2D), basal water flux (2 components plus magnitude, 2D), hydraulic heating (sum of viscous dissipation and supercooling in basal hydrological system, 2D), ice velocity (3 components, 3D), vertically averaged ice velocity (2 components plus magnitude, 2D), effective strain rate (3D), effective viscosity (3D), horizontal velocity shape function (3D), strain heating (3D), corner elevation in best-fit D-J model (2D), freeze-on thickness (2D), ice age (3D), spreading length from isotopic diffusion (3D), echo-free-zone thickness (2D), oldest useful ice for ice coring (2D), and the normalized elevation of the oldest useful ice (2D). Best-fit model also includes a misfits structure describing the misfit with the observational constraints.<br><br>Units: all velocities (including accumulation rate and basal melt rate) are in m/yr. Water flux is in m^2/yr. Strain rate is in 1/yr. Ice age is in yr. All other variables are in MKS units (temperature is in K, geothermal heat flux and hydraulic heating are in W/m^2, strain heating is in W/m^3, viscosity is in Pa*s, etc).</p> <p>Files are provided in both .mat format and netcdf format. The mat-files have slightly more information, such as the model parameters and the data constraints. The netcdf files have 2D and 3D grids only. The inversion was run twice, once with BedMachine as the basal topography input and once with Bedmap2 as the basal topography input. Both versions use Martos et al. (2017) as the GHF prior. The version with BedMachine is considered the preferred version.</p> <p>Full explanation given in a pair of papers publishd in JGR: Earth Surface:</p> <div> <div>Wolovick, M. J., Moore, J. C., & Zhao, L. (2021). Joint Inversion for Surface Accumulation Rate and Geothermal Heat Flow From Ice-Penetrating Radar Observations at Dome A, East Antarctica. Part I: Model Description, Data Constraints, and Inversion Results. Journal of Geophysical Research: Earth Surface, 126(5), e2020JF005937. https://doi.org/10.1029/2020JF005937</div> <div>Wolovick, M. J., Moore, J. C., & Zhao, L. (2021). Joint Inversion for Surface Accumulation Rate and Geothermal Heat Flow From Ice-Penetrating Radar Observations at Dome A, East Antarctica. Part II: Ice Sheet State and Geophysical Analysis. Journal of Geophysical Research: Earth Surface, 126(5), e2020JF005936. https://doi.org/10.1029/2020JF005936</div> <div> </div> <div>Edit for Version 3, October 7, 2024: I added the actual scripts for the model itself, along with the input files used to run the model. I also updated the desciption with the references to the actual published papers.</div> <div> </div> <div>Edit for Version 4, May 20, 2025: I forgot to include one script (makerednoise2.m), so I uploaded that script.</div> </div>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
pyMelt_MultiNest inversion files for Nekrylov et al. "Magmatic evolution along the Emperor-Hawaiian seamount chain revealed by olivine-hosted melt inclusions"
<p>pyMelt_MultiNest inversion data collected for Nekrylov et al. "Magmatic evolution along the Emperor-Hawaiian seamount chain revealed by olivine-hosted melt inclusions"</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores
<p>This repository contains the accompanying inlists and run_star_extras files for Buchele et al. (2024): Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores.</p> <p> </p> <p>The MESA version used is r22.05.1. We have modified the /star/private/pulse_fgong.f90 file in the MESA source code to output the Gamma1 derivatives that are necessary to compute u-Y kernels by replacing the file with the version from <a href="https://github.com/MESAHub/mesa/pull/431">https://github.com/MESAHub/mesa/pull/431</a>. Additionally, we use the MESA SDK versions 22.6.1 and the GYRE version 6.0 that is included with the r22.05.1 release of MESA. </p> <p> </p> <p>The directory grid_base contains the inlist which set the parameters that do not change across the tracks of our grid</p> <p> </p> <ul> <li> <p>Inlist_all includes parameters that are constant across all tracks and evolutionary stages </p> </li> <li> <p>Inlist_prems includes parameters for the pre-main sequence evolution of all tracks </p> </li> <li> <p>Inlist_ms includes parameters for the main sequence evolution of all tracks </p> </li> <li> <p>run_star_extras.f90 includes the code which stops the evolution of a track when the central hydrogen abundance reaches the target value </p> </li> <li> <p>gyre.in includes parameters for calculating of frequencies using GYRE</p> </li> </ul> <p> </p> <p>The directory reference_models provides the inlists to generate our reference model for each star in our sample. This directory also includes the FGONG structure files of the computed reference models. Each file is labeled with the identifier used in the text, typically the KIC number. </p> <p> </p> <p>The physics_tweaks directory provides the inlists to generate the models with modified input physics. The suffixes refer to the physics that was changed in each model, as follows: </p> <ul> <li> <p>core_kap: Core opacity</p> </li> <li> <p>pp2: change to the He3+He4->Be7 reaction rate</p> </li> <li> <p>cno: change to the N14 + p -> O15. </p> </li> </ul>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"
Open the record for dataset details and reuse information.
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
<p>Human activity is omnipresent in our landscapes. Animals can perceive risk from humans similar to predation risk, which could affect their fitness. We assessed the influence of the relative intensity of recreational activities on body weight and pregnancy rates of red deer (<em>Cervus elaphus</em>) between 1985 and 2015. We hypothesized that stress, as a result of recreational activities, affects pregnancy rates of red deer directly and indirectly via a reduction in body weight. Furthermore, we expected non-motorized recreational activities to have a larger negative effect on both body weight and fecundity, compared to motorized recreational activities. The intensity of recreational activities was recorded through visual observations. We obtained pregnancy data from female red deer that were shot during the regular hunting season. Additionally, age and body weight were determined through post-mortem examination. We used two generalized linear mixed models (GLMM) to test the effect of different types of recreation on 1) pregnancy rates and 2) body weight of red deer. Recreation had a direct negative correlation with the fecundity of red deer, with body weight as a mediator as expected. Besides, we found a negative effect of non-motorized recreation on fecundity and body weight and no significant effect of motorized recreation. Our results support the concept of humans as an important stressor affecting wild animal populations at a population level and plead to regulate recreational activities in protected areas that are sensitive. The fear humans induce in large-bodied herbivores and its consequences for fitness may have strong implications for animal populations.</p>
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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
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