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45 results for “high dimensional data”
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Research data supporting: "TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra"
<p>This repository contains the set of data shown in the paper <strong>"<em>Time</em>SOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra"</strong>, published on The Journal of Chemical Physics (DOI: 10.1063/5.0147025).</p>
Data and Results for: Cost-effective, high-throughput fruit phenotyping system for three-dimensional reconstruction of fruit form
<p>The dataset contains the raw data used for generating the results in the paper, and the reconstructed results at 1mm and 2mm voxel resolution.</p> <p><strong>Raw data</strong>: Samples are gathered into groups of five, DataObjects1-5.zip, ... , DataObjects56-59.zip.</p> <p>Once uncompressed, each directory holds the images and configuration information used for each sample. For instance, '1_obj' is for the first sample, and the 'data' folder has the image files by camera. In this case one camera was used, 'cam2', and there are 62 images. 'pattern_square_mm_external.txt', 'pattern_square_mm_internal.txt', and 'rotate_specification_file.txt' are all configuration files needed for the calibration step.</p> <p> </p> <p><strong>Results</strong>: Reconstructions results are in compressed folders 'reconstruction-results-1mm.zip' and 'reconstruction-results-2mm.zip' for 1 mm and 2 mm voxel resolutions, respectively. These results are three-dimensional model files than can be viewed with a variety of software, we have used the free MeshLab: https://www.meshlab.net/ .</p> <p> </p> <p> </p> <p> </p>
Raw data of high-dimensional aptamer selection generated by ProSELEX
<p>This is the raw selection dataset generated by ProSELEX pipeline (https://www.nature.com/articles/s41557-023-01207-z). The target of selection is human myeloperoxidase (MPO). See https://github.com/dwangnu/AptaZ for instructions.</p>
Data from: Female mate preferences on high dimensional shape variation for male species recognition traits
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Data from: High-dimensional imaging of vestibular schwannoma reveals distinctive immunological networks across histomorphic niches in NF2-related schwannomatosis
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Data from: A method for analysis of phenotypic change for phenotypes described by high-dimensional data
The analysis of phenotypic change is important for several evolutionary biology disciplines, including phenotypic plasticity, evolutionary developmental biology, morphological evolution, physiological evolution, evolutionary ecology and behavioral evolution. It is common for researchers in these disciplines to work with multivariate phenotypic data. When phenotypic variables exceed the number of research subjects—data called 'high-dimensional data'—researchers are confronted with analytical challenges. Parametric tests that require high observation to variable ratios present a paradox for researchers, as eliminating variables potentially reduces effect sizes for comparative analyses, yet test statistics require more observations than variables. This problem is exacerbated with data that describe 'multidimensional' phenotypes, whereby a description of phenotype requires high-dimensional data. For example, landmark-based geometric morphometric data use the Cartesian coordinates of (potentially) many anatomical landmarks to describe organismal shape. Collectively such shape variables describe organism shape, although the analysis of each variable, independently, offers little benefit for addressing biological questions. Here we present a nonparametric method of evaluating effect size that is not constrained by the number of phenotypic variables, and motivate its use with example analyses of phenotypic change using geometric morphometric data. Our examples contrast different characterizations of body shape for a desert fish species, associated with measuring and comparing sexual dimorphism between two populations. We demonstrate that using more phenotypic variables can increase effect sizes, and allow for stronger inferences.
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Source data for "High-κ perovskite membranes as insulators for two-dimensional transistors"
<p>IMPORTANT: The data must not be reused in other studies or publications without the approval of the corresponding authors. Additional data related to this paper may be requested from the corresponding authors upon reasonable request.</p>
Raw data: Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides
<p>Raw data for the manuscript titled:</p> <p><strong>Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides</strong></p> <p> </p>
(simulated Data:) Detecting interactions in high dimensional data using Cross Leverage Scores
<p>We upload the data according to the paper "Detecting interactions in high dimensional data using Cross Leverage Scores" (Teschke et al., 2024) which we submitted at the Biometrical Journal. The data is also being uploaded on behalf of the paper's co-authors, Katja Ickstadt and Alexander Munteanu.</p> <p>Furthermore we upload some Intermediate results, because calculations takes very long time and need lots of memory. You can therefore use them for further insights.</p> <p>For details of the simulation, we refer to Teschke et al. (2024).</p>
Research data supporting "Surface Enhanced Raman Scattering Artificial Nose for High Dimensionality Fingerprinting"
<p>Experimental raw research data supporting the publication: Kim N., Thomas M.R. et al., 2019, Nature Communications.</p>
Data from: Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data
1. Spatial patterns of community composition turnover (beta diversity) may be mapped through Generalised Dissimilarity Modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional datasets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing datasets, including a time series of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional datasets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single date multispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional datasets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.
High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
<p>Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of<br> human electrophysiological data using fMRI priors"</p> <p>https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1</p>
Data from: A method for analysis of phenotypic change for phenotypes described by high-dimensional data
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Data from: A method for assessing phylogenetic least squares models for shape and other high-dimensional multivariate data
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Data from: Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data
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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.