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677 results for “Inversion”
Data of FigS3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS3, “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_FigS3.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3_M .txt), six files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-1-6 .csv) and one file in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-4 .sps). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M 1.pdf).</p>
Data of FigS5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS5, “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_FigS5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M.txt).</p> <p> </p>
Data of FigS6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS6, “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_FigS6.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains two files in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1-2_M.txt). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf).</p> <p> </p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
Exceptions to the rule: Relative roles of time, diversification rates and regional energy in shaping the inverse latitudinal diversity gradient
<p><strong>Aim</strong>: Inverse latitudinal diversity gradients (i-LDG), whereby regional richness peaks outside the tropics, have rarely been investigated and their causes remain unclear. Here, we investigate three prominent explanations, postulating that species-rich regions have had (1) longer time to accumulate species, (2) faster diversification, and (3) more energy to support more diverse communities. These mechanisms have been shown to explain the tropical megadiversity, and we examine whether they can also explain i-LDG.</p> <p><strong>Location</strong>: Global</p> <p><strong>Time period</strong>: Contemporary</p> <p><strong>Major taxa studied</strong>: Amphibians, birds, mammals </p> <p><strong>Methods</strong>: We estimated the time for species accumulation, regional diversification rates, and regional energy for six tetrapod taxa (≈ 800 species). Then, we quantified the relative effects and interactions among these three classes of variables, using variance partitioning, and confirmed the results across alternative metrics for time (community phylometrics and BioGeoBEARS), diversification rates (BAMM and DR), and regional energy (past and current temperature, productivity).</p> <p><strong>Results</strong>: While regional richness across each of the six taxa peaked in the temperate region, it varied markedly across hemispheres and continents. The effects of time, diversification rates, and regional energy varied greatly from one taxon to another, but high diversification rates generally emerged as the best predictor of high regional richness. The effects of time and regional energy were limited, with the exception of salamanders and cetaceans. </p> <p><strong>Main conclusions</strong>: Together, our results indicate that the causes of i-LDG are highly taxon-specific. Consequently, large-scale richness gradients might not have a universal explanation and different causal pathways might converge on similar gradients. Moreover, regional diversification rates might vary dramatically between similar environments and, depending on the taxon, regional richness might or might not depend on the time for species accumulation. Together, these results underscore the complexity behind the formation of richness gradients, which might involve a symphony of variations on the interplay of time, diversification rates, and regional energy.</p>
Nicaragua Upper-Plate Earthquake Inversion and Static Stress Change
<p>This data set contains GPS time series and displacements (in tabular form) for the April 10 2014 and Sept 15 & 28 2016 M>5 upper-plate earthquakes in Nicaragua. The data set also contains configuration files for GBIS code, used in determining fault kinematics and Coulomb 3.3, used for calculating static stress change following the earthquakes.</p> <p>A README.txt file is in each folder and details what configuration files do and format of data.</p>
Full Inverse Velocity Fields for "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model"
<p>Velocity fields on all approximate neutral surfaces from the inverse model presented in "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model". The pressure of the approximate neutral surface is contoured in the background. The number in the title represents the pressure of the approximate neutral surface at the reference station in the Hunter Channel.</p>
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
Nonvolatile Electric-Field Control of Inversion Symmetry: Manuscript Data
<ul> <li>Relevant Raw Data files for Main Text Figures of "Nonvolatile Electric-Field Control of Inversion Symmetry."</li> <li> <p>Relaxation input and output files of the polar & antipolar phases (including structure .cif files) and the input and output files of the DOS calculation from Main Text Fig. 3</p> </li> </ul>
Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event
<p>We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions. </p> <p>The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p>
Figure1. Three-layer image decomposition with content protection-Access Management in Medical Image Databases Based on New Format and Contents Protection with Inverse Pyramid Decomposition
<p>The image preparation for the image database with layered access is shown on Fig. 1. The<br> image is archived layer by layer and the watermarks are inserted together with the image<br> processing. The ROI (if there is one in the image) is processed in such a way, that to permit direct<br> access for authorized users (separate pyramid is developed for the ROI representation).</p>
Raw images and processed datasets related to the journal article Robust Assessment of Post-Localisation Hardening Behaviour in Eurofer97 using Inverse Finite Element Methods
Open the record for dataset details and reuse information.
Chromosomal inversions from an initial ecotypic divergence drive a gradual repeated radiation of Galápagos beetles
<p>Island faunas exhibit some of the most iconic examples where similar forms repeatedly evolve within different islands. Yet, whether these deterministic evolutionary trajectories within islands are driven by an initial, singular divergence and the subsequent exchange of individuals and adaptive genetic variation between islands remains unclear. Here, we address this issue using a gradual, repeated evolution of low-dispersive highland ecotypes from a dispersive lowland ecotype of <em>Calosoma</em> beetles along the island progression of the Galápagos. We show that repeated highland adaptation involved selection on multiple shared alleles within extensive chromosomal inversions that originated from an initial adaptation event on the oldest island. These highland inversions first spread through dispersal of highland individuals. Subsequent admixture with the widely distributed lowland ecotype resulted in polymorphic dispersive populations from which the highland populations evolved on the youngest islands. Our findings emphasize the significance of an ancient divergence in driving repeated evolution and highlight how a mixed contribution of inter-island colonization and within-island evolution can shape parallel species communities on islands.</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Dataset: T-Rex 2X Inverse Tesla Daily Target ETF (TSLZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Tuttle Capital Daily 2X Inverse Regional Banks ETF (SKRE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Roundhill Daily Inverse Magnificent Seven ETF (MAGQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Inverse Model Results for WAIS
<div> <p>This page contains the results of a basal drag inversion performed for the West Antarctic Ice Sheet (WAIS). We provide the results in NetCDF files of our six conducted experiments in the associated (not yet published) study, showing the basal drag and basal drag coefficient, as well as the outputs of the 1D steady-state thermal model. In addition, the Matlab scripts used to generate the figures in our manuscript and the finite element mesh are uploaded. </p> <p>To perform the inversion and L-curve analysis, we rely on the inversion model presented in Wolovick et al. (2023) (available at https://doi.org/10.5281/zenodo.7798650). All parameters and the added code are included in an uploaded spreadsheet (Parameters_ISSMInversion.xlsx). </p> <p><strong>Name conventions </strong></p> <ul> <li>m#: Describes the exponent in the sliding law. The values can bet set to m1 (linear sliding) or m3 (non-linear sliding).</li> <li>Ntype: Describes the effective pressure source in the sliding law. Values are "noN" (i.e., Weertman sliding), "Nop" (i.e., parameterized effective pressure) and "Ncuas" (i.e., effective pressure used from subglacial hydrology CUAS-MPI).</li> <li>Budd, Weertman: Describes the two different sliding laws we use in this manuscript. Budd uses an effective pressure source and Weertman does not use an effective pressure field. </li> </ul> <p><strong>Figure scripts</strong></p> <p>The following scripts generate all figures shown in the manuscript: </p> <ul> <li> <p>AllLcurveFigures_v1.m: Creates Figure 6 in the manuscript. </p> </li> <li> <p>BestDragFigure_v1.m: Creates Figure 13, Figure 14 and Figure 16 in the manuscript. </p> </li> <li> <p>BestInversionComparisonFigure_v1.m: Creates Figure 15 in the manuscript. </p> </li> <li> <p>ConvergenceFigure_v1.m: Creates Figure 7 in the manuscript. </p> </li> <li> <p>DragCoeffComparisonFigure_N_m_v1.m: Creates Figure 11 in the manuscript. </p> </li> <li> <p>DragLakeCandidatesFigure_v1.m: Creates Figure 17 and 18 in the manuscript. </p> </li> <li> <p>EffectivePressureFigure_v1.m: Creates Figure 5 in the manuscript. </p> </li> <li> <p>LcurveComparisonFigure_m_Ncuas_v1.m: Creates Figure 12 in the manuscript. </p> </li> <li> <p>LcurveComparisonFigure_N_m_v1.m: Creates Figure 9 and Figure 10, as well as the Table 1 in the manuscript. </p> </li> <li> <p>MeshFigure_v1.m: Creates Figure 3 in the manuscript. </p> </li> <li> <p>ModelSetupFigure_v1.m: Creates Figure 2 in the manuscript. </p> </li> <li> <p>SubdomainLcurveFigure_v1.m: Creates the subfigures of Figure 8 in the manuscript. </p> </li> <li> <p>ThermalFigure_v1.m: Creates Figure 4 in the manuscript. </p> </li> </ul> <p><strong>Input</strong> </p> <ul> <li> <p>Mesh_WAIS_500m-19km.mat: mat-file of finite-element mesh for WAIS study domain. </p> </li> </ul> <p><strong>Inversion results </strong></p> <ul> <li> <p>BestInversionResult_WAIS_m3_Ncuas_bestlambda-0.5.nc: Best basal drag and squared drag coefficient result for the experiment m=3, Ncuas evaluated at the best lambda value 0.5 determined with the L-curve analysis. </p> </li> <li> <p>BestInversionResult_WAIS_m3_Ncuas_lambda-0.562.nc: Best basal drag and squared drag coefficient result for the experiment m=3, Ncuas evaluated at the lambda value 0.562 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_Ncuas_lambda-1.nc: Basal drag and squared drag coefficient result for the experiment m=1, Ncuas evaluated at the lambda value 1 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m3_Nop_lambda-0.1.nc: Basal drag and squared drag coefficient result for the experiment m=3, Nop evaluated at the lambda value 0.1 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_Nop_lambda-3.16.nc: Basal drag and squared drag coefficient result for the experiment m=1, Nop evaluated at the lambda value 3.16 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m3_noN_lambda-3.16.nc: Basal drag and squared drag coefficient result for the experiment m=3, using a Weertman sliding-law evaluated at the lambda value 3.16 determined with the L-curve analysis. </p> </li> <li> <p>InversionResult_WAIS_m1_noN_lambda-0.316.nc: Basal drag and squared drag coefficient result for the experiment m=1, using a Weertman sliding-law evaluated at the lambda value 0.316 determined with the L-curve analysis. </p> </li> <li> <p>ThermalOutput_WAIS.nc: Basal temperature, basal melt rates and depth-averaged rheology output from the used 1D thermal model, as well as the parameterized effective pressure Nop and the effective pressure Ncuas determined from a subglacial hydrology model. </p> </li> </ul> <p> </p> </div>
Figure 2. (a) Representation of laser servo-driver for inverse kinematics analysis; (b) Representation of the lag angle, B, of the internal servomechanism (magnified version of the chin-rest).-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>As the servo-driver will be attached in the chin-rest in a non-central area with respect to the<br> semispherical structure shown in Figure 1(a), it is necessary to calculate a lag angle, according to<br> the measurements from the chin-rest, see Figure 2(b). This was done using a hybrid formula based<br> on the law of cosines,</p>
Dataset from the inversion of methane emissions in France (ESPiGRAD project)
<p>This dataset contains the results from two inversions of methane fluxes in main-land France in 2012. The inversions assimilate surface data from the ICOS European network in an analytical framework. This study is part of the ESPiGRAD project and is described in: Isabelle Pison, Antoine Berchet, Marielle Saunois, Philippe Bousquet, Grégoire Broquet, Sébastien Conil, Marc Delmotte, Anita L. Ganesan, Olivier Laurent, Damien Martin, Simon O'Doherty, Michel Ramonet, T. Gerard Spain, Alex Vermeulen, and Camille Yver Kwok, How a European network may help with estimating methane emissions on the French national scale, Atmos. Chem. Phys., 18, 1–20, 2018, https://doi.org/10.5194/acp-18-1-2018.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.