Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
677
datasets available to search
ShareScore release 0.9.0
Dataset results
677 results for “Inversion”
Global biogeography of ant social parasites: Exploring patterns and mechanisms of an inverse latitudinal diversity gradient
<p><strong>Aim</strong>: One of the most consistent global biogeographic patterns is the latitudinal diversity gradient where species richness peaks within the equatorial tropics and decreases towards the poles. Here, we explore the global biogeography of socially parasitic ants, which comprises the most diverse group of social parasites in the Hymenoptera. We test the biogeographic hypothesis that ant social parasites are distributed along an inverse latitudinal diversity gradient by peaking in diversity outside of the equatorial tropics.</p> <p><strong>Location</strong>: Global</p> <p><strong>Taxon</strong>: Ants (Hymenoptera: Formicidae)</p> <p><strong>Methods</strong>: We assembled a comprehensive biogeographic dataset for all 371 taxonomically described socially parasitic ant species. We used phylogenetic and taxonomic studies to compare species richness with the number of species representing independent evolutionary origins of social parasitism across a latitudinal gradient. In addition, we compared ant social parasite diversity across biogeographic regions using rarefaction to account for different sampling efforts. Finally, we tested for a correlation between latitude and the proportion of ant social parasite species within regional ant faunae.</p> <p><strong>Results</strong>: The geographic distribution records and the inferred 91 origins of socially parasitic life histories show that both species richness and the number of species representing independent evolutionary origins of social parasitism peak in the northern hemisphere outside of the equatorial tropics. Based on rarefaction curves, northern latitude regions harbour the most ant social parasite species, but the diversity of independent evolutionary origins is not significantly different between northern and southern hemispheres. The proportion of ant social parasite species within regional faunae is correlated with latitude only in the northern hemisphere.</p> <p><strong>Main conclusions</strong>: The inverse latitudinal diversity gradient of ant social parasites contrasts with the biogeographic pattern observed in free-living, non-parasitic ant species and appears to be driven by large species radiations as well as by the presence of specialized life histories exclusive to the northern hemisphere.</p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
Temperature inversion (TI) and humidity inversion (HI) collected at Xiaomai Island, Qingdao, Shandong, China, from December 30, 2021 to March 1, 2022.
<p>This dataset presents the parameters of temperature inversion (TI) and humidity inversion (HI) calculated using a microwave radiometer. The data were collected at Xiaomai Island, Qingdao, Shandong, China, from December 30, 2021 to March 1, 2022. The parameters include the moment of occurrence, strength, base height, depth and intensity of TI and HI.</p>
Inverse Model Results for Filchner-Ronne Catchment
<p>This page contains the results of the inversions for basal drag and drag coefficient in the Filchner-Ronne catchment presented in <a href="https://doi.org/10.5194/tc-17-5027-2023">Wolovick et al., (2023)</a>, along with the code used to perform the inversions and L-curves, analyze the results, and produce the figures presented in that paper. </p><blockquote><p>This all looks very complicated. There's so many files here. The description is so long. I just want to know the basal drag!</p></blockquote><p>If you don't want to get into the weeds of inverse modeling and L-curve analysis, or if you are uninterested in wading through our collection of model structures and scripts, then you should use the file <strong>BestCombinedDragEstimate.nc</strong>. That file contains our best weighted mean estimate of the ice sheet basal drag in our domain, along with the weighted standard deviation of the scatter of the different models about the mean. As discussed in the paper, this combined estimate is constructed from the weighted mean of 24 individual inversions, representing 8 separate L-curve experiments on our highest-resolution mesh, with three regularization values per L-curve (best estimate regularization, along with minimum and maximum acceptable regularization levels). Each inversion is weighted according to the inverse of its total variance ratio, which is a quality metric incorporating both observational misfit and inverted structure. For ease of use, these results have been interpolated from the unstructured model mesh onto a 250 m regular grid. <i><strong>If you only want to know the basal drag in the Filchner-Ronne region, that is the only file you should use</strong></i><strong>.</strong></p><p> </p><p>For users who want to go further, we will now explain the remaining files in this release. First we give a brief summary of all of the scripts included here and their functions, and then we will give an explanation of the matfiles that contain the actual inversion and L-curve results. Note that the scripts presented here are the matlab scripts used to organize and set up model runs for ISSM. The Ice-Sheet and Sea-level System Model (ISSM) is a highly versatile parallelized finite-element ice sheet model run in C but controlled using Matlab or Python front-ends. We do not include the underlying code for ISSM here; users who are interested in installing ISSM should go to <a href="https://issm.jpl.nasa.gov/">the ISSM home page</a>. We merely include the Matlab scripts we used to organize our ISSM front-end, set up model structures, and then analyze and visualize results.</p><p><strong>Main Matlab scripts:</strong></p><p>These are the main functional scripts used to set up and run the model.</p><ul><li>ISSMInversion_v3.m. This is the primary script we used to set up the inversions and perform L-curve analysis. It requires a model mesh as input along with some gridded data. It also produces an L-curve figure (figure 3) after performing the L-curve analysis. This script can be run in two modes: "setupandsend", which prepares model structures and sends them to the cluster to be solved, and "loadandanalyze", which loads the solutions from the cluster, saves them to matfiles, and performs analysis and visualization. In addition to L-curve analysis and the L-curve figure, this script can also produce a variety of additional figures of model output that we did not show in the paper.</li><li>MakeISSMMesh_v4.m. This is the script we used to make our model meshes. It requires a domain boundary as input along with some gridded data.</li><li>ModelBoundaryPicker_v1.m. This script opens a crude graphical interface for picking the domain outline.</li><li>OrganizeInversionsForRelease_v2.m. This script assembles L-curve and inverse model results and organizes them into the data release you see here. Note that it doesn't compute the combined drag estimate itself, (that is done by CombinedDragFigure_v1.m), but it does interpolate the combined drag estimate from the model mesh to the grid, and it produces the output netcdf file.</li></ul><p>Note that the gridded data files needed by some of the above scripts are not included in our release here. Users interested in using these scripts for their own projects will need to provide their own gridded inputs, for instance from BedMachine or Measures. </p><p><strong>Figure-making scripts:</strong></p><p>These scripts produced almost all of the figures we presented in the paper, and also computed the statistics we presented in the tables in the paper.</p><ul><li>CombinedDragFigure_v1.m. This script computes the combined drag estimate on the highest-resolution mesh, and makes a figure displaying it (Figure 12 in the paper).</li><li>InversionComparisonFigure_HOSSA_v1.m. This makes figure 11 in the paper and also computes the statistics shown in table 3.</li><li>InversionComparisonFigure_m_v1.m This makes figures 9 and 10, and also computes the statistics shown in table 2.</li><li>InversionComparisonFigure_N_v1.m. This makes figure 8, and also computes the statistics shown in table 1.</li><li>InversionComparisonFigure_v1a.m. This makes figure 4 in the paper.</li><li>InversionResConvergenceFigure_v2.m. This makes figure 6.</li><li>InversionResMisfitFigure_v1.m. This makes figure 7.</li><li>InversionSettingFigure_v1.m. This makes figure 1.</li><li>InversionSpectrumFigure_v1.m. This performs spectral analysis and makes figure 5.</li><li>InversionThermalSettingFigure_v1.m. This makes figure A1.</li><li>MeshSizeFigure_v1.m. This makes figure A2.</li><li>NComparisonFigure_v1.m. This makes figure 2.</li></ul><p><strong>Other utility Matlab functions:</strong></p><p>These miscellaneous function do various tasks. Many of them are called as subroutines of the scripts above. Additionally, many of them are generally useful in contexts beyond the inverse modeling presented here.</p><ul><li>FlattenModelStructure.m. ISSM has the unfortunate convention of saving every variable in 3D meshes on every single 3D mesh node, which is quire wasteful for variables that are actually 2D (ie, most of the model variables). This function flattens all uneccessarily 3D information, but unlike the built-in ISSM function flatten.m, this script preserves the 3D geometry of the mesh, along with 3D variables that actually are 3D (such as englacial temperature, for example). This function can also be run in reverse to expand variables back to full 3D before calling solve().</li><li>intuitive_lowpass.m. This function low-pass filters a 1D dataset using a gaussian filter. It has several options for handling boundary conditions at the end points.</li><li>LaplacianInterpolation.m. This function fills in missing data values for gridded data products by solving Poisson's equation (Laplacian=0).</li><li>LaplacianInterpolation_mesh.m. This function does the same thing but on an unstructured mesh.</li><li>loadnetcdf.m. This function loads variables from netcdf files into the Matlab workspace using a similar syntax as load() for matfiles.</li><li>MultiWavelengthInterpolator.m. This function interpolates gridded data onto an unstructured mesh using a multi-grid approach. The grid is smoothed at multiple wavelengths and each mesh element interpolates from the wavelength that is appropriate for its size. This functionality is useful for preventing aliasing in coarse-resolution areas when interpolating onto a mesh with variable mesh size. It also produces results that are approximately (but not precisely) conservative.</li><li>ThreeByThree.m. This function iteratively performs a 3x3 smoothing on gridded data.</li><li>unpack.m. This function takes a structure and "unpacks" it by making every field into a variable in the workspace.</li></ul><p> </p><p><strong>Matfiles with L-curve data and model structures.</strong></p><p>The results of our L-curve analyses and our actual inversion results are stored in matfiles. We performed 21 experiments shown in the paper; for each one we performed an independent L-curve analysis using 25 individual inversions, for a total of 525 inversions. However, for this data release we simplify matters by only presenting 3 inversions per experiment, corresponding to the best regularization value (LambdaBest) and the maximum and minimum acceptable regularization values (LambdaMax and LambdaMin). In addition, for each experiment we also provide an LCurveFile that summarizes the L-curve analysis but does not contain any actual model results. In total, we present 84 matfiles in this data release.</p><p><strong>Naming convention:</strong></p><p>All matfiles presented here have the following naming convention:</p><p>Mesh#_eqn_m#_Ntype_LambdaType.mat</p><ul><li>Mesh#: this represents the mesh on which the inversions were performed, ranging from Mesh1 (highest resolution) to Mesh10 (lowest resolution).</li><li>eqn: this represents the type of equations solved in the inversion. Values are "SSA" or "HO".</li><li>m#: exponent in the sliding law. Values are m1, m3, and m5.</li><li>Ntype: effective pressure source in the sliding law. Values are "noN" (ie, Weertman sliding), "Nop", "Nopc", and "Ncuas".</li><li>LambdaType: values of this string are "LCurveFile" (for the file summarizing the whole L-curve experiment), "LambdaMin", "LambdaBest", and "LambdaMax".</li></ul><p><strong>Variables in the model files:</strong></p><p>Every file ending with "LambdaMin", "LambdaBest", or "LambdaMax" is a model file containing the same set of variables. Those variables are:</p><ul><li>md. This is a a model structure variable usable by any ISSM installation. Note that if you do not have ISSM installed on your machine, Matlab will not recognize class "model" and you will not be able to load this variable. The results of the inversion are stored in md.results.StressbalanceSolution. Other important things for the inversion, such as cost functions, cost function coefficients, and velocity observations, are stored in md.inversion. Note that the process of normalizing cost function components described in the paper was performed in practice by manipulating values of md.inversion.cost_function_coefficients. Other important fields of the model structure that might be relevant are md.mesh (describing the numerical mesh), md.geometry (ice sheet geometry), md.mask (ice and ocean masks), md.friction (containing the basal sliding law, including the slip exponent and effective pressure field), md.materials (containing material properties, including rheology), md.stressbalance (controlling the stress balance solution), and md.solvers and md.toolkits (numerical solvers). In addition, users should note that md.cluster is class "ollie", which is a custom class made for AWI's old HPC setup. This will produce a warning that the class of md.cluster is unrecognized if you load md on another computer, and you will need to replace md.cluster with something appropriate to your own setup if you want to use md in the "solve()" command. Also note that md.miscellaneous.dummy contains a copy of the variable "GoodData_obs_vertices", a data quality metric produced by the script ISSMInversion_v3.m. Finally, users should note that md.miscellaneous.name is still set to the original file name we used internally, not the cleaner names we have used for this release.</li><li>MyInversionParameters. This is a structure containing the parameters used by ISSMInversion_v3.m to set up and L-curve and inverse model.</li><li>oldfilename. This is the original file name that we used when we generated these results internally. The cleaner file names you see here were created by the script OrganizeInversionsForRelease_v2.m</li><li>thislambda. The regularization lambda value used for this inversion.</li></ul><p><strong>Variables in the L-curve files:</strong></p><p>The files ending in "LCurveFiles" contain data summarizing the results of each L-curve experiment. Those variables are:</p><ul><li>Lambda. This variable contains the 25 values of the regularization parameter lambda tested in the L-curve. Size: [25,1].</li><li>DataCost, RegularizationCost. These two variables contain the values of J_obs and J_reg for each of the 25 inversions in the L-curve. Size: [25,1].</li><li>IsOutlier. This variable identifies whether any of the 25 inversions have been identified as an outlier and excluded from the curve-fitting process. Size: [25,1].</li><li>LambdaWavelengths. This variable contains all 50 of the smoothing wavelengths tested during the curve-fitting process. Note that these are wavelengths in log(lambda) space. Size: [1,50].</li><li>LogLambda_curves. This variable contains log(lambda), interpolated to fine spacing. All variables on the smooth curves are defined on a sampling defined by this variable. Size: [1000,1].</li><li>LogDataCost_curves, LogRegCost_curves. These variables contain all of the candidate smoothed curves of log(J_obs) and log(J_reg). Size: [1000,50].</li><li>CurvatureVariance_curves, ScatterVariance_curves. Variance measures (Eqs A1 and A2 in the paper) used to quantify how close the smooth curves are to fitting the 25 inversion data points (ScatterVariance) and how much variability the smooth curves have (CurvatureVariance). Size: [1,50].</li><li>TotalVariance_curves. Total variance metric used to select optimal smoothing wavelength. The optimal smoothing wavelength is defined by the minimum of this variable. Size: [1,50].</li><li>LogDataCost_bestcurve, LogRegCost_bestcurve. These variables contain the final best smoothed curve of log(J_obs) and log(J_reg). Size: [1000,1].</li><li>LogDataGradient_bestcurve, LogRegGradient_bestcurve. These variables contain the first derivatives, d(log(J))/d(log(lambda)), for both J_obs and J_reg, computed for the best smoothed curve. Note that their size has been reduced by 1 relative to the original curves (these values should be placed at the midpoints between adjacent samples in LogLambda_curves). Size: [999,1].</li><li>LogDataCurvature_bestcurve, LogRegCurvature_bestcurve. These variables contain the second derivatives, d^2(log(J))/d(log(lambda))^2, for both J_obs and J_reg, computed for the best smoothed curve. Note that their size has been reduced by 2 relative to the original curves (these values should be placed at the samples in LogLambda_curves, excluding the first and last samples). Size: [998,1].</li><li>TotalCurvature_bestcurve. This variable corresponds to the sum LogDataCurvature_bestcurve+LogRegCurvature_bestcurve. The best lambda value is the peak of this variable and the min/max acceptable values are defined where this variable drops to half of its maximum value. Note that, while the fine lambda sampling of the smoothed curve allows us to define these lambda values with some precision, we still need to select models from the original 25 lambda values, which have much coarser resolution in log(lambda) space. The "LambdaBest" model is selected to be the model that is closest to the fine-resolution lambdabest in log(lambda) space, while the LambdaMin and LambdaMax models are selected to be the first and last models inside those thresholds. Size: [998,1].</li><li>bestwavelength, bestwavelengthind. These variables describe the best smoothing wavelength (wrt log(lambda)) used to generate the best smooth curve. bestwavelengthind is the index of this wavelength within LambdaWavelengths. Size: [1,1].</li><li>bestlambda, bestlambda_minallowable, bestlambda_maxallowable. These three variables represent the continuously determined best, min, and max lambda values. Size: [1,1].</li><li>bestlambdaind, bestlambdaind_minallowable, bestlambdaind_maxallowable. As above, but these represent the index of the lambda values within LogLambda_curves. Size: [1,1].</li><li>bestmodelind, bestmodelind_minallowable, bestmodelind_maxallowable. The index of the models selected to represent the corresponding best lambda values. Note that for the bestlambda, this is the model with log(lambda) closest to the best value, but for the min/max acceptable, this is the first or last model within the relevant bound. Indices refer to the list of 25 models in each L-curve. Size: [1,1].</li><li>bestlambda_loguncertainty. The error bar around the best lambda value in log(lambda) space. This is equal to half of bestwavelength. The corresponding uncertainty ratio on a linear scale is exp(bestlambda_loguncertainty). Note that this same uncertainty value also applies to minimum and maximum acceptable lambda. Size: [1,1].</li><li>MyInversionParameters. A structure containing the parameters used by ISSMInversion_v3.m to set up and the L-curve and inversions.</li><li>LCurveFittingParameters. A structure containing the parameters used by ISSMInversion_v3.m to perform the curve-fitting analysis.</li><li>oldfilename. The original file name of this L-curve in our internal system before we gave the files neater names for public release.</li></ul><p> </p>
Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem
<p>It contains the simulation scripts and the data acquired and analyzed to produce the manuscript "Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem" (DOI: <a href="https://doi.org/10.1088/1361-6420/accdc5"> 10.1088/1361-6420/accdc5</a>).</p>
Lacustrine speciation associated with chromosomal inversion in a lineage of riverine fishes
<p>Geographic isolation is the primary driver of speciation in many vertebrate lineages. This trend is exemplified by North American darters, a clade of freshwater fishes where nearly all sister species pairs are allopatric and separated by millions of years of divergence. One of the only exceptions is the Lake Waccamaw endemic <em>Etheostoma perlongum</em> and its riverine sister species <em>E. maculaticeps</em>, which have no physical barriers to gene flow. Here we show that lacustrine speciation of <em>E. perlongum</em> is characterized by morphological and ecological divergence likely facilitated by a large chromosomal inversion. While <em>Etheostoma perlongum</em> is phylogenetically nested within the geographically widespread <em>E. maculaticeps</em>, there is a sharp genetic and morphological break coinciding with the lake-river boundary in the Waccamaw River system. Despite recent divergence, an active hybrid zone, and ongoing gene flow, analyses using a de novo reference genome reveal a 9 Mb chromosomal inversion with elevated divergence between <em>E. perlongum</em> and <em>E. maculaticeps</em>. This region exhibits striking synteny with known inversion supergenes in two distantly related fish lineages, suggesting deep evolutionary convergence of genomic architecture. Our results illustrate that rapid, ecological speciation with gene flow is possible even in lineages where geographic isolation is the dominant mechanism of speciation.</p>
Training data and test data sets for simultaneous inversion of velocity density based on U-T
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Marmousi model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
Data from: Complex evolutionary processes maintain an ancient chromosomal inversion
<p>Genome re-arrangements such as chromosomal inversions are often involved in adaptation. As such, they experience natural selection, which can erode genetic variation. Thus, whether and how inversions can remain polymorphic for extended periods of time remains debated. Here we combine genomics, experiments, and evolutionary modeling to elucidate the processes maintaining an inversion polymorphism associated with the use of a challenging host plant (Redwood trees) in <em>Timema </em>stick insects. We show that the inversion is maintained by a combination of processes, finding roles for life-history trade-offs, heterozygote advantage, local adaptation to different hosts, and gene flow. We use models to show how such multi-layered regimes of balancing selection and gene flow provide resilience to help buffer populations against the loss of genetic variation, maintaining the potential for future evolution. We further show that the inversion polymorphism has persisted for millions of years and is not a result of recent introgression. We thus find that rather than being a nuisance, the complex interplay of evolutionary processes provides a mechanism for the long-term maintenance of genetic variation.</p>
Dataset for the SIAM MPI23 project "Model inversion for complex physical systems using low-dimensional surrogates"
<p>This dataset contains 20,000 synthetic simulations of a simplified two-dimensional confined aquifer model of the Hanford Site. The inputs are the Kosambi-Karhunen-Loève expansion (KKLE) coefficients of the input log-transmissivity field. The outputs are the corresponding stationary pressure responses observed at 323 observation wells. Also included are the arrays necessary to reconstruct the log-transmissivity inputs from the KKLE coefficients.</p>
Data files for 'Tan et al., (2023). Structural heterogeneity-controlled rupture process of the 2021 Mw 7.1 Fukushima, Japan earthquake revealed by joint inversion of seismic and geodetic data'
<p>slip model.dat: rupture model of the 2021 Mw 7.1 Fukushima earthquake</p> <p>In 'slip model.dat', each row contains the moment rate function of each sub-fault. The numbers of the sub-faults are given in the first two columns.</p>
Inverse folding for antibody sequence design using deep learning
<p>Model weights of the <a href="https://arxiv.org/abs/2310.19513">AbMPNN model (arXiv:2310.19513)</a> presented at the <a href="https://icml-compbio.github.io/">2023 ICML Workshop on Computational Biology</a>, and csv files with the split between train, test and validation across the <a href="https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab/">SAbDab</a> and <a href="https://zenodo.org/record/7258553">ImmuneBuilder</a> datasets.</p><p>This model is based on <a href="https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1">ProteinMPNN</a> and can be run using the corresponding code: <a href="https://github.com/dauparas/ProteinMPNN">https://github.com/dauparas/ProteinMPNN</a>.</p>
Slip model of the 2022 Mw 6.6 Luding earthquake from inversion of GNSS and Sentinel-1A satellites
<p>2022年Mw 6.6泸定地震的滑移模型,来自GNSS和Sentinel-1A卫星的反演“2022年Mw 6.6泸定地震滑移模型,GNSS和Sentinel-1A卫星反演”中使用的GNSS数据</p>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.1.0 - June 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (<code>v2.1.0</code>)</strong></p> <p><strong>Build Type: </strong><em>Instance-Inverse Relations-OWL</em></p> <p><strong>Build Date: </strong><code>June 01, 2021</code></p> <blockquote> <p>Please note that all resources linked below redirect to a publicly Google Cloud Storage bucket where all data are publicly accessible. Routing users from this wiki page is perfectly safe and allows us to avoid requiring users to have a Google account and login to download data. If you have any questions or concerns, please email the project maintainer at <a href="https://github.com/callahantiff/PheKnowLator/wiki/callahantiff@gmail.com">callahantiff@gmail.com</a>.</p> </blockquote> <p>If you have a Google account you can access the data directly via 👉 <a href="https://console.cloud.google.com/storage/browser/pheknowlator">here</a></p> <p> </p> <p>📚 For additional information on the builds please see the following <a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">README</a><br> 🗂 For additional information on the KG file types please see the following <a href="https://github.com/callahantiff/PheKnowLator/wiki/KG-Construction#table-knowledge-graph-build-output">Wiki page</a></p> <p> </p> <p> </p> <p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p> <p>Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/June-01%2C-2021">here</a>.</p>
Cormorant Oceanography Bathymetry Inversion
<p>This dataset includes observations and H(x) outputs as well as some processing and post-processing scripts used for the journal article titled:<br> <br> <strong>Multivariate Data Assimilation at a Partially-mixed Estuary</strong><br> <br> <strong>DOI:</strong> https://doi.org/10.1175/JTECH-D-22-0101.1</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Constructing a 3-D radially anisotropic crustal velocity model for Oklahoma by using full waveform inversion
<p>The OK3D_Vp_Ani.csv and OK3D_Vs_Ani.csv is inverted 3-D compressive and shear velocity model proposed in the publication.</p> <p>Each file contains horizontally and vertically polarized velocity components and their relative perturbation with respect to the averaged 1-D velocity profile, as well as the RA defined in the paper, at each location (longitude, latitude, depth).</p> <p>These two files are stored in CSV format, and can be easily readed by pandas module in python environment.</p>
Data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"
<p>Main data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"</p>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
Dataset for: A marine cryptochrome with an inverse photo-oligomerization mechanism
<p>Raw data for all sequence analysis, SEC runs, MD simulations, and UV/Vis absorbance readings supporting the findings of the study 'A marine cryptochrome with an inverse photo-oligomerization mechanism'</p>
Submesoscaledynamics in the Bay of Bengal: Inversions and instabilities
<p>High-resolution observations reveal the complex processes controlling the evolution and subduction of a cold and salty, dense filament in the Bay of Bengal. The filament, likely formed through coastal upwelling, was advected offshore by a mesoscale strain field and brought adjacent to fresher water from runoff and rain. The front on an edge of the dense filament is observed to undergo restratification and steepening, responding to evolving mesoscale and submesoscale convergence and divergences. Measurements and analyses indicate the development of both small-scale instabilities (such as SI) and slightly larger-scale ageostrophic secondary circulation, acting in concert to subduct and stir surface heat into the interior. Our results highlight the importance of small-scale three-dimensional dynamics in setting upper ocean properties in the Bay of Bengal.</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.