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252 results for “Synthetic data”
Synthetic building performance data
<p>A dataset of uncertainty-infused synthetic building performance profiles as hallucinated by a generative adversarial network (GAN). This dataset includes both the original building performance data, as well as 50 synthetic performance scenarios that are generated by a GAN. Further details on the method and codes can be found here: https://github.com/Khayatian/seer</p>
NGS Data Accompanying "Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics"
<p>NGS Data Accompanying "Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics", currently in review.</p>
SASC: A Simple Approach to Synthetic Cohorts. Applying COVID-19 clinical data to generate longitudinal observational patient cohorts and comparison with alternative synthetic cohort approaches as well as real patient data
<p>Subset from COVID-19 Dataset from https://zenodo.org/record/3766350#.YVcfyTFBxgA. Used as reference for a publication dealing with synthetic patient cohort generation.</p>
RD-Connect GPAP synthetic data spiked-in variant data
<p>This data is a subset of Rare Disease Synthetic Dataset (<a href="https://ega-archive.org/datasets/EGAD00001008392">EGAD00001008392</a>) dataset. The subset only contains the chromosomal regions with the spiked-in causative variants. The associated study is the Human genomic and phenotypic synthetic data for the study of rare diseases (<a href="https://ega-archive.org/studies/EGAS00001005702">EGAS00001005702</a>) study. For more info go to <a href="https://ega-archive.org/">https://ega-archive.org/</a>.</p> <p>All this data was created with the support of the RD-Connect GPAP (<a href="https://platform.rd-connect.eu/">https://platform.rd-connect.eu/</a>), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).</p>
Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"
<p>Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.</p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Synthetic gene expression data with underlying gene network
<p>This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest.</p> <p>In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023).</p> <p>The data is generated by the function <em>gen_data</em> in R package <em>networkRF</em> which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the <em>num.var</em> argument of the function <em>gen_data.</em> More descriptions on the implementation and the format of the output can be found in the help page of the R package.</p>
Synthetic in-situ T/S data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project
<p>"Synthetic observations" of in-situ Temperature and Salinity profiles as a function of depth have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and location (in x,y,z dimensions) where a true in-situ profile exists in the ENACT-4 database (Good et al 2013) over the simulation period: 1980-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal) based on the ISBA-CTRIP hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. The synthetic in-situ T/S dataset from the model is available over the period 1993-2018.</p><p>See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html</p>
Microscopy and biophysical data for: Synthetic control of actin polymerization and symmetry breaking in active protocells
Open the record for dataset details and reuse information.
3D Cortical Bone and Trabecular Bone Structure [synthetic data, simple, capsule shell model]
<p>Trabecular bone patterns are mimicked by generating and arranging "capsule shells" in a three-dimensional voxel by following probability distribution. Ground truth (gt) contains 4 labels (Background: 0, Cortical Bone: 11, Trabecular Bone: 21, Cavity: 31).</p>
Synthetic data for the solution of geophysical inverse problems
<p>The synthetic LAB used to define an inverse problem is discretized in different number of parameters. Each "LAB" file is one specific discretization and contains the coordinatates X and Y and the value of the LAB at each position</p> <p>The reference velocity fields obtained from solving the Stokes equation with the synthetic LABs are found in "velo" files. They contain, X, Y, and Z and the field Vx, Vy and Vz.</p> <p>Another synthetic data set represents the african lithosphere. The first two columns of its LAB file contain the longitude and latitude of each point and the third column is the value of the LAB. The reference velocity field obtained from solving Stokes is found in the corresponding "velo" file.</p>
Data from: Genome duplication effects on functional traits and fitness are genetic context and species dependent: studies of synthetic polyploid Fragaria
PREMISE OF THE STUDY Divergence in functional traits and adaptive responses to environmental change underlies the ecological advantage of polyploid plants in the wild. While established polyploids may benefit from combined outcomes of genome doubling, hybridization and polyploidy-enabled adaptive evolution, it remains less clear whether genome doubling alone can drive ecological divergence or whether the outcome is genetically variable.METHODS Using synthetic, colchicine-induced, autotetraploid (4x) plants derived from self-pollinated diploid (2x) seeds, and their colchicine-treated but unconverted diploid (2x.nc) full sibs from two diploid wild strawberry taxa (Fragaria vesca ssp. vesca and F. vesca ssp. bracteata), we examined the effects of genome doubling on functional traits, heat stress tolerance and fitness components across taxa and maternal families (i.e. genetic families) within taxa.KEY RESULTS Comparisons between 2x and 2x.nc plants indicated a negligible effect of colchicine treatment on functional traits. Genome doubling increased stomatal length, and decreased stomatal density, specific leaf area and leaf vein density, recapitulating patterns observed in wild polyploid Fragaria. Trichome density, heat stress tolerance and relative growth rate were not significantly affected by genome doubling. Although a reduction in clonal reproduction was observed in response to genome doubling, this effect was strongly genetic family dependent.CONCLUSIONS The results suggest that genome doubling during incipient speciation alone can generate ecological divergence and variation among genetic lineages. This response potentially allows for rapid short-term evolutionary adaptation and fuels genomic diversity and independent origins of polyploidy.
leonardlab/Source Data for "Elucidation and refinement of synthetic receptor mechanisms"
<p>This source data accompanies the manuscript "Elucidation and refinement of synthetic receptor mechanisms".</p>
Synthetic Molecules and Egghunt Data
<p>Data needed to reproduce the synthetic molecule analysis from SPLOC paper and the Egghunt also described in that work. Scripts to run this analysis can be found here https://github.com/BioMolecularPhysicsGroup-UNCC/Publications</p>
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
<p>3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)</p> <p>M. Jessell<sup>1,2</sup>, J. Giraud<sup>1,2</sup>, M. Lindsay<sup>1,2 </sup></p> <p><sup>1</sup>Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia</p> <p><sup>2</sup>Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia</p> <p>Contact author: Jeremie Giraud (jeremie.giraud@uwa.edu.au)</p> <p>Companion dataset to the paper:</p> <p>Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.</p> <p>This dataset contains models and data shown in the paper, in both 2D and 3D:</p> <p>1. Geological model</p> <ul> <li>Reference lithology voxet:</li> </ul> <p>The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.</p> <ul> <li>Probability voxet<br> The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.</li> </ul> <p>2. True and inverted models for density and magnetic susceptibility</p> <p>Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.</p> <p>3. Bouguer and total magnetic field anomaly</p> <p>Calculation is detailed in the paper.</p> <p>The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.</p> <p>It is a companion dataset to: <br> Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4452620">http://doi.org/10.5281/zenodo.4452620</a></p>
The input sounding and synthetic AMV data for the idealized case study
<p>This is a data repository in support of the article "Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part I: Observing System Simulation Experiment (OSSE)Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part I: Observing System Simulation Experiment (OSSE)" submitted to AGU <em>J. of Advances in Modeling of Earth Systems. </em></p> <p>The data set consists of</p> <ul> <li>The input sounding data for the truth simulation of an idealized supercell strom (input_sounding).</li> <li>The simulated AMV observations with the resolutions of 10km, 20km, 30km and 40km are contained in the zipped files.</li> </ul>
Data set to article "Synthetic inversions for density using seismic and gravity data" by Blom, Boehm and Fichtner
<p><strong>Data set to “Synthetic inversions for density using seismic and gravity data” by Nienke Blom, Christian Boehm and Andreas Fichtner</strong></p> <p>This data set relates to our paper <em>“Synthetic inversions for density using seismic and gravity data”</em><em>, </em><em>in which we discuss the imaging of density variations inside the Earth as a separate, independent parameter using seismic waveform tomography and gravity measurements</em>. The research consists of synthetic experiments conducted using a home-written MATLAB wave propagation code. The data set contains the code itself, the input files and output files for each of the experiments described in the manuscript and its supplementary material, all the figures, some extra material (such as a video of Figure 1 in the manuscript) and some scripts.</p> <p>Below I’ll give a description of the contents of this data set and how they are structured, followed by an overview of the experiments conducted for the paper.</p> <p>In this data set, the following things can be found:</p> <ul> <li> <p>There is a directory with all the figures: FIGURES. This contains the figures in *.pdf, *.eps and *.png formats.</p> </li> <li> <p>There is a directory FD2D_ADJOINT_CODE with in it the MATLAB code fd2d-adjoint. If you plan on using our code, it would be awfully kind if you'd make a reference both to the code and to this paper. It was a lot of work to develop the code and the experiments. NOTE: the code supplied here is a snapshot of the code taken in February 2017. A more up-to-date version might be found on github (www.github.com/Phlos/fd2d-adjoint)</p> </li> <li> <p>For each (series of) experiment(s) described in the paper, there is a directory T1, T2, …, Tn. This also holds for the supplementary tests, the folders for which are designated with the suffix .SUPPLEMENTARY.</p> </li> <li> <p>For Figure 1 in the manuscript, there is a directory Fig1.snapshots. In this directory, everything pertaining to the snapshots figure and its corresponding video can be found.</p> </li> <li> <p>There is a separate directory SCRIPTS with a couple of useful scripts that might be used in addition to the ones in the fd2d-adjoint code.</p> </li> </ul> <p><br> In each of the test directories T1...Tn, there are subdirectories for each experiment conducted within that test framework. Each of the subdirectories has a name Systematic.test-[xxx]. Within those Systematic.. directories, the following can be found:</p> <ul> <li> <p>an input file Systematic….input_parameters.m that can be copied to [fd2d-adjoint]/input/input_parameters.m in order to re-run the experiment. As the code has been under development while the tests were run, it may be that some input parameters are missing from the earlier experiments.</p> </li> <li> <p>A mat-file obs.all-vars.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recalculation of the ‘obs’ data when the code is run.</p> </li> <li> <p>A mat-file initial_misfits.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recomputation of the initial misfits with respect to the obs data when the code is run.</p> </li> <li> <p>A file lbfgs_output_log.txt which monitors the misfit and gradient development across the iterations. If the inversion was restarted a couple of times, all of this remains in the logfile.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], an iter[xxx].all-vars.mat file, which contains most of the matlab output files for this iteration.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], some figures:</p> <ul> <li> <p>a model plot of the current model anomalies with respect to the background model iter[xxx].model-diff.rhovsvp.png.</p> </li> <li> <p>a gravity plot of the gravity vector difference between the current model and the background model iter[xxx].gravity_difference.png.</p> </li> <li> <p>a kernel plot of the total relative kernels (whether seis only or seis+grav) of the current model in rho-mu-lambda parametrisation: iter[xxx].rho-mu-lambda.png.</p> </li> </ul> </li> </ul> <p><br> </p> <p>Now follows a brief description of each of the (series of) tests conducted for the paper. The test numbers are mostly chronological, and so are the Systematic.test… subdirectories.</p> <ul> <li> <p><strong>Figure 1</strong>: shows snapshots of wave propagation past a density anomaly. The full data for this and the full video are given in the Fig1.snapshots. <em>Discussed in: Figure </em><em>1 of the manuscript.</em></p> </li> <li> <p><strong>T1: </strong><strong>reference.</strong> A reference test in which we assess to which density can be recovered as an independent parameter. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Reference experiment: Systematic.test-033</p> </li> </ul> </li> <li> <p><strong>T2: </strong><strong>ignored density.</strong> A test in which the effect is explored if density is ignored, i.e. if it is kept fixed to the starting model. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Fixing density: Systematic.test-040</p> </li> </ul> </li> <li> <p><strong>T3: </strong><strong>starting model</strong>. A series of test in which is explored to what extent the starting models of P and S seismic velocity influence the recovery of density. In the different sub-tests, different levels of information on P and S velocity are already present. <em>Discussed in: Figure </em><em>6</em></p> <ul> <li> <p>vs, vp 100% correct: Systematic.test-029</p> </li> <li> <p>vs, vp 75% correct: Systematic.test-037</p> </li> <li> <p>vs,vp 50% correct: Systematic.test-036</p> </li> </ul> </li> <li> <p><strong>T4: </strong><strong>fixed velocities</strong>. A series of tests in which is explored to what extent one can “get away with” only updating density, assuming that the models for P and S velocity are already sufficiently accurate. <em>Discussed in: Figure </em><em>7</em></p> <ul> <li> <p>vs,vp fixed at 50% correct: Systematic.test-038</p> </li> <li> <p>vs, vp fixed at 75% correct: Systematic.test-041</p> </li> <li> <p>vs, vp fixed at 100% correct: Systematic.test-039</p> </li> </ul> </li> <li> <p><strong>T5: </strong><strong>gravity</strong>. A set of tests in which the addition of gravity data to the (up until here purely) seismic inversion. Both the full gravity vector and its potential are used as gravity data. <em>Discussed in: Figure </em><em>8</em></p> <ul> <li> <p>seismic + full gravity vector (x,z) data: Systematic.test-045</p> </li> <li> <p>seismic + gravity potential data (‘geoid’): Systematic.test-046</p> </li> </ul> </li> <li> <p><strong>T6: noise</strong>. A series of tests in which the addition of noise to the seismic data is explored. Both correlated and uncorrelated noise are explored. Noise levels vary across frequencies. <em>Discussed in: Figure </em><em>9</em></p> <ul> <li> <p>correlated noise: Systematic.test-050</p> </li> <li> <p>uncorrelated noise: Systematic.test-052</p> </li> </ul> </li> <li> <p><strong>T7: impedance</strong>. A test in which the impedance contrast across anomaly boundaries are set to zero. It is explored to what extent the recovery of density relies on the presence of an impedance contrast. <em>Discussed in: Figure </em><em>10</em></p> <ul> <li> <p>no impedance contrast: Systematic.test-055</p> </li> </ul> </li> <li> <p><strong>T8: parametrisation (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A test in which it is explored to what extent the inversion is affected if an inversion parametrisation using density and the elastic parameters mu and lambda is used, instead of the otherwise used parametrisation density-S velocity-P velocity. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>1,2 @ </em><em>Supplementary_material.pdf</em></p> <ul> <li> <p>inversion parametrisation rho-mu-lambda (reference target model): Systematic.test-032</p> </li> <li> <p>inversion parametrisation rho-mu-lambda with ‘scaling’ target model: Systematic.test-062a</p> </li> </ul> </li> <li> <p><strong>T9: scaling relations</strong>. A set of tests in which it is explored to what extent the recovery of density and seismic velocities is influenced if density is scaled to S velocity using a fixed scaling. <em>Discussed in: Figure </em><em>5</em></p> <ul> <li> <p>target model with density scaled to S velocity in different ways; all parameters free: Systematic.test-063</p> </li> <li> <p>same target model, but now density is scaled to S velocity with a fixed relationship: Systematic.test-067</p> </li> </ul> </li> <li> <p><strong>T10: anomaly strength (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A set of tests in which the effect of the strength of the anomalies on the recovery of density and the other parameters is investigated. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>3-5 @ </em><em>Supplementary_material.pdf</em><em> </em></p> <ul> <li> <p>target model like reference case, but the anomalies 10% of PREM instead of 1%: Systematic.test-065</p> </li> <li> <p>target model like reference case, but the anomalies <em>in the upper mantle only</em> 10% of PREM instead of 1%: Systematic.test-064</p> </li> </ul> </li> </ul> <p><br> </p> <p>If you have any further questions, feel free to contact me.</p> <p>All the best,</p> <p>Nienke Blom, Utrecht University<br> n.a.blom@uu.nl<br> nienke.blom@posteo.net</p> <p> </p>
data for publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies"
<p>Dataset for the scientific publication "Benefits of biobased fertilizers as substitutes for synthetic nitrogen fertilizers: Field assessment combining minirhizotron and UAV-based spectrum sensing technologies" in the Journal Frontiers of Environmental Science. </p><p><a href="https://doi.org/10.3389/fenvs.2022.988932">https://doi.org/10.3389/fenvs.2022.988932</a></p>
Synthetic phenopacket data from HPOA from 2023-12-22 using phenotype2phenopacket
<p>Generated from phenotype2phenopacket commit 37f45b6 on or around Dec 22, 2023 like so:</p> <p>git clone https://github.com/yaseminbridges/phenotype2phenopacket.git</p> <p>git checkout 37f45b6</p> <p>wget http://purl.obolibrary.org/obo/hp/hpoa/phenotype.hpoa</p> <p>poetry install </p> <div> <div> <div> <div>p2p create --phenotype-annotation ./phenotype.hpoa --output-dir test_ppk</div> </div> </div> </div> <p> </p>
Podcast: Synthetic 3D data and archeology. Régine Hunziker and Andrei Aioanei, Université de Strasbourg
<p>Prof. Régine Hunziker-Rodewaldt und Andrei Aioanei sprechen über die Bedeutung von komplexen interoperablen Forschungsdaten. Insbesondere 3D-Datensätze werden immer wichtiger. Die beiden Forscher sprechen über Data Science mit 3D, aber auch über die Bedeutung von künstlicher Intelligenz.</p> <p>Prof. Régine Hunziker-Rodewaldt and Andrei Aioanei are speaking about the importance of complex interoperable research data. Especially 3D data sets become more important. Both researches speak about data science with 3D, and also the importance of artificial intelligence.</p> <p>Prof. Régine Hunziker-Rodewaldt e Andrei Aioanei parlano dell'importanza di dati di ricerca complessi e interoperabili. In particolare, i set di dati 3D diventano sempre più importanti. Entrambi i ricercatori parlano della scienza dei dati in 3D e dell'importanza dell'intelligenza artificiale.</p> <p>Prof. Régine Hunziker-Rodewaldt et Andrei Aioanei parlent de l'importance des données de recherche complexes et interopérables. Les ensembles de données en 3D deviennent particulièrement importants. Les deux chercheurs parlent de la science des données en 3D et de l'importance de l'intelligence artificielle.</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.