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82 results for “Denoising”
studyforrest_movie_denoised
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TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
<p>TIDMAD is the first dataset and benchmark from a dark matter physics experiment, providing ultra-long time series data and comprehensive tools that enable machine learning models to directly advance the fundamental physics search for dark matter.</p> <p>This data is availble for download via <code>download_data.py</code>. Metadata for this dataset is specified in <code>TIDMAD_croissant.json</code>. The file names are listed in <code>filelist.dat</code>. For furhter information and publically available code, please see the associated <a href="https://github.com/jessicafry/TIDMAD" target="_blank" rel="noopener">GitHub repository</a>. For more information on this dataset and benchmark, please reference our TIDMAD paper.</p>
Efficient PCA denoising of spatially correlated redundant MRI data
<p>MRI data used for the study: "Henriques, Ianus, Novello, Jovicich, Jespersen, Shemesh. Efficient PCA denoising of spatially correlated redundant MRI data. Imaging Neuroscience (In Press)."</p><p><strong>Preclinical scanner data</strong></p><p>All animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.</p><p>A mouse brain (C57BL/6J) was extracted via transcardial perfusion with 4% Paraformaldehyde (PFA), immersed in 4% PFA solution for 24 h, washed in Phosphate-Buffered Saline (PBS) solution for at least 24 h, and then placed on a 10 mm NMR tube filled with Flourinert (Sigma Aldrich, Lisbon, PT), which was sealed using paraffin film. </p><p>The MRI experiments were performed on a 16.4 T Bruker Aeon Ascend scanner (Bruker, Karlsruhe, Germany), interfaced with an Avance IIIHD console, and equipped with a gradient system capable of producing up to 3000 mT/m in all directions. A constant temperature of 37oC was maintained throughout the experiments using the probe's variable temperature capability. </p><p>Two distinct diffusion-weighted datasets were then acquired using Bruker's standard "Diffusion Tensor Imaging EPI":</p><ul><li><i>Dataset1 </i>(<strong>MB_exp1.nii</strong> and its brain mask<strong> MB_exp1_mask.nii</strong>): For this dataset, we modulated the amount of spatial correlations by acquiring EPI datasets with parameters optimized to mitigate noise spatial correlations, particularly avoiding k-space undersampling acquisition during EPI's gradient ramps and without using partial Fourier, which minimize regridding.</li><li><i>Dataset2 </i>(<strong>MB_exp2.nii</strong> and its brain mask<strong> MB_exp2_mask.nii</strong>): The second dataset was acquired with identical resolution, number of acquisitions, etc., but with large factors inducing spatial correlations, including k-space sampling during gradient ramps (default Bruker's acquisition and reconstruction procedures for acquisition speed) and with a significant phase partial Fourier factor of 6/8 (note for partial Fourier acquisitions, EPI data is reconstructed with zero-padding, according to the default reconstruction procedures by Bruker's pre-clinical reconstruction software Paravision 6.0.1).</li></ul><p>All datasets are acquired for the following diffusion-weighted parameters: 30 gradient directions for b-values 1, 2 and 3 ms/μm2 (Δ = 15 ms, δ = 1.5 ms), and 20 consecutive b-value=0 acquisitions - b-values and diffusion gradient directions are saved in files: <strong>MB.bval</strong> / <strong>MB.bvec</strong>.</p><p>Other acquisition parameters: TR/TE = 3000/50 ms, 9 coronal slices, Field of View = 12×12 mm2, matrix size 80×80, in-plane voxel resolution of 150×150 μm2, slice thickness = 0.7 mm, number of averages = 2, number of segments = 1, double sampling acquisition.</p><ul><li><i>Gold standard acquisitions for dataset 2 </i>(<strong>MB_exp2_20averages.nii</strong>): For a gold standard reference, the second dataset was also repeated for 20 averages. Note, since this dataset is aligned to <strong>MB_exp2.nii</strong> you can use <strong>MB_exp2_mask.nii </strong>for its brain mask.</li></ul><p>For all datasets, Spatial drifts in the image domain were first corrected using a sub-pixel registration technique (Guizar-Sicairos et al., 2008).</p><p> </p><p><strong>Clinical scanner data</strong></p><p>Experiments were approved by the Ethical Committee of the University of Trento and the participant signed an informed consent. </p><p>MRI data was a acquired for a healthy control (male, 54 years) using a 3T MAGNETOM PRISMA scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head-neck RF receive coil. </p><p>Diffusion MRI data was acquired using a monopolar single diffusion encoding EPI PGSE (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) along 30 diffusion gradient directions for five non-zero b-values = 1, 2, 3, 4.5 and 6 ms/μm2 (Δ = 39.1 ms, δ = 26.3 ms) and 17 interspersed b-value=0 acquisitions. b-values and diffusion gradient directions are saved in files: <strong>HB.bval</strong> / <strong>HB.bvec</strong>. Note, only the masked version of these dataset (<strong>HB_masked.nii</strong> and its brain mask <strong>HB_mask.nii</strong>) is provided to guarantee that data privacy standards are met. For noise maps covering all FOV, the noise maps computed as the std of the 5 first repeating unmasked b = 0 acquisitions are provided in file <strong>stdS0i.nii.</strong></p><p>Other acquisition parameters were the following: TR/TE = 4000/80 ms, 63 axial slices, Field of View = 220×220 mm2, matrix size 110×110, isotropic resolution of 2 mm, 6/8 phase partial Fourier, parallel imaging with GRAPPA 2, simultaneous multi-slice factor 3. All diffusion MRI data was reconstructed using zero-padding, which is the default procedure for data acquired with partial Fourier above 70%. </p>
Investigating Self-Supervised Image Denoising with Denaturation
<p>The attached zip file conatains image data and Python codes for reproducing the partial results shown in Expt.1, Expt.2, Expt.3, and Expt.4 of the arXiv paper [1]. <br>First, check "readme" file in the zip file for the reproduction. </p>
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
<p>Supporting data for AFProfile</p> <p>casp15.tar.zst - predicted structures and MSAs for the CASP15 set<br>native_afm_2_6_bench.tar.zst - native cif files for all complexes with ranking confidence <0.75 in the AFM 2-6 chains benchmark ( https://doi.org/10.1093/bioinformatics/btad424)<br>pred_top_models_afm_2_6_bench.tar.zst - predicted top ranked models and scores for all 100 samples.<br>afm_opt_metrics.csv - the best models, confidences and MMscores for the AFProfile run on the AFM 2-6 chains benchmark (n=427 structures)<br>msa_shapes.csv - the shape of the MSA as input to AFM for each structure<br>directed.tar.zst - contains all models for the AFProfile run on the AFM 2-6 chains benchmark (n=42700 samples)</p> <p>The directories are compressed with zstd: https://github.com/facebook/zstd<br>Uncompress:<br>tar --use-compress-program full/path/to/zstd -xvf file.tar.zst</p>
Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
<p>Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video.</p>
DeepBacs – Escherichia coli nucleoid denoising dataset and CARE model
<p>Training and test images of H-NS-mScarlet-I expressing <em>E. coli </em>cells for image denoising, as well as a trained CARE model.</p> <p>Additional information can be found on our <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example images show confocal images of labelled <em>E. coli</em> nucleoids at low and high SNR.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Confocal fluorescence images</p> <p><strong>Microscope</strong>: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective </p> <p><strong>Cell type</strong>: <em>E. coli</em> strain CS01 expressing H-NS-mScarlet-I fusion protein (H-NS-mScarlet-I) in NO34 parental strain (MreB-sfGFPsw, kindly provided by Zemer Gitai) </p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512 x 512 px<sup>2</sup> (Pixel size: 45 nm)</p> <p> </p> <p><strong>CARE model</strong>:</p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1400 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.2), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
DATASET of Large-scale Neural Recordings for DENOISING Engine
<p><span>30 sec raw data (.brw) was recorded with BrainWave SW and detected LFP events and spikes were stored in (.bxr). These extracellular recordings were obtained from acute hippocampal-cortical slices and were collected at 14KHz/electrode sampling frequency.</span></p>
Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"
<p>The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers.<br>This dataset provides the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.</p> <p> </p> <p>Abstract: </p> <p>One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.</p>
Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."
<p>Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data." Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.</p>
Simulated brainweb PET/MR data sets for denoising and deblurring
<p>The data set consists of 20 subjects based on the normal anatomical models from the brainweb phantom. See <a href="https://brainweb.bic.mni.mcgill.ca/">https://brainweb.bic.mni.mcgill.ca/</a>.</p> <p>For each subjectXX the data is organized as follows:</p> <ol> <li>image_0.nii.gz -> (1st simulated "random" PET contrast)</li> <li>image_1.nii.gz -> (2nd simulated "random" PET contrast)</li> <li>image_2.nii.gz -> (3rd simulated "random" PET contrast)</li> <li>attenuation_image.nii.gz -> (attenuation image)</li> <li>t1.nii.gz -> (high resolution T1 MR)</li> </ol> <p>All data sets have a shape of (220,220,184) and a voxel size of 1mm x 1mm x 1mm and are provided in nifti format.<br> The T1 MR scans and antomical models used to create the ground truth PET images are taken from the BrainWeb: Simulated Brain Database.</p> <p>See:</p> <p> http://www.bic.mni.mcgill.ca/brainweb/<br> C.A. Cocosco, V. Kollokian, R.K.-S. Kwan, A.C. Evans : "BrainWeb: Online Interface to a 3D MRI Simulated Brain Database"<br> NeuroImage, vol.5, no.4, part 2/4, S425, 1997 -- Proceedings of 3-rd International Conference on Functional Mapping of the Human Brain, Copenhagen, May 1997.<br> R.K.-S. Kwan, A.C. Evans, G.B. Pike : "MRI simulation-based evaluation of image-processing and classification methods"<br> IEEE Transactions on Medical Imaging. 18(11):1085-97, Nov 1999.<br> R.K.-S. Kwan, A.C. Evans, G.B. Pike : "An Extensible MRI Simulator for Post-Processing Evaluation"<br> Visualization in Biomedical Computing (VBC'96). Lecture Notes in Computer Science, vol. 1131. Springer-Verlag, 1996. 135-140.<br> D.L. Collins, A.P. Zijdenbos, V. Kollokian, J.G. Sled, N.J. Kabani, C.J. Holmes, A.C. Evans : "Design and Construction of a Realistic Digital Brain Phantom"<br> IEEE Transactions on Medical Imaging, vol.17, No.3, p.463--468, June 1998</p>
Epitranscriptomic subtyping, visualization, and denoising by global motif visualization
<p>The Analysis of large clusters with density histogram based approximate clustering. </p>
Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery
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Multiplane microscopy dataset for benchmarking denoising methods
<p>The dataset consists of 810 microscopy images collected from CHO, U2OS, and RPE1 cell lines in fluorescent and brightfield modalities. Cell lines were grown in standard medium, seeded on plates, fixed with formaldehyde, stained with a fluorescent dye (Hoechst33342) and imaged with PerkinElmer Phenix high-throughput confocal microscope in fluorescence and brightfield modalities using a 20x objective. For the fluorescent modality, images were acquired in low-exposure (20 ms) and high-exposure (100 ms) modes. For the brightfield, we used a single exposure (100 ms). Higher exposure time usually translates into better image quality, alleviating Poisson noise, however, it leads to sample degradation, and lower measurement speed.</p> <p>In total, three wells on a plate were imaged, one well per cell line, each with nine fields of view. Each field of view was imaged across ten focal planes separated by 2 micrometers in z-stack, resulting in 270 microscopy images of size 2160 x 2160 pixels per exposure time per modality.</p> <p>We used the following folder structure: /{well}_{cell line}/{modality}_{exposure time}/fov_{field of view}_plane_{plane}.png</p> <p>We recommend to use the first two wells (CHO and U2OS, 180 images) as a training set, the first three fields of view from the last well (RPE1, 30 images) as a validation set, and the last six fields of view (RPE1, 60 images) as a test set.</p>
Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
<p><strong>Overview </strong></p> <p>This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.</p> <p> </p> <p><strong>Database</strong></p> <p>The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: <a href="https://pubmed.ncbi.nlm.nih.gov/30094756/">https://pubmed.ncbi.nlm.nih.gov/30094756/</a></p> <p>In addition, PPG signals corrupted by the noise generator at different levels are also included in the database.</p> <p> </p> <p><strong>Realistic noise generator</strong></p> <p>Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper.</p> <p> </p> <p><strong>Data Files</strong></p> <p>The reference PPG signal can be found in <em>Datasets\GoodSignals\PPG</em> and the simultaneously acquired ECG in <em>Datasets\GoodSignals\ECG</em>. The folder <em>Datasets\NoisySignals</em> contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation).</p> <p>Name convention: <em>ppg_stdx_y</em> denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x.</p> <p><em>Datasets\BPMs</em> contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s.</p> <p><strong>Code</strong></p> <p>The folder <em>Code </em>contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator.</p> <p><strong>When referencing this material, please cite:</strong></p> <p>Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 2725. <a href="https://doi.org/10.3390/s21082725">https://doi.org/10.3390/s21082725</a></p>
Dataset of "Denoising Image-based Experimental Data without Clean Targets based on Deep Autoencoders"
<p>Dataset of the paper "Denoising Image-based Experimental Data without Clean Targets based on Deep Autoencoders", published in Experimental Thermal and Fluid Science (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.expthermflusci.2024.111195" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.expthermflusci.2024.111195</a>)</p> <p>The project received funding from: the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085); the National Natural Science Foundation of China (NSFC No 12227803 and No 12372276).</p>
DeepBacs – Bacillus subtilis denoising dataset
<p>Live-cell time series of vertically aligned <em>B. subtilis</em> cells expressing FtsZ-GFP protein fusion.</p> <p>Additional information can be found in this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows raw and denoised images (Noise2Void 2D) of vertically aligned <em>B. subtilis</em> cells (VerCINI)</p> <p> </p> <p><strong>Data type</strong>: Fluorescence images of vertically oriented <em>B. subtilis </em>cells</p> <p><strong>Microscopy data type</strong>: 2D widefield images (fluorescence)</p> <p><strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)</p> <p><strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads, Cells were imaged at 1 frame/second with continuous exposure for 2 minutes at 1-8 W/cm2</p> <p><strong>File format</strong>: .tiff (16-bit) </p> <p>One frame was selected from each time series</p> <p><strong>Image size</strong>: 1024x1024 px² (Pixel size: 65 nm)</p> <p><strong>Author(s)</strong>: Mia Conduit<sup>1</sup>, Séamus Holden<sup>1,2</sup></p> <p><strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk</p> <p> </p> <p><strong>Affiliation</strong>:</p> <p>1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK</p> <p>2) ORCID: 0000-0002-7169-907X</p> <p> </p> <p><strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235</p>
Raman Signal Denoising using Fully Convolutional Encoder Decoder
<p>Test dataset used in the manuscript 'Raman Signal Denoising using Fully Convolutional Encoder Decoder'.</p>
Denoised ECGs from The PhysioNet/Computing in Cardiology Challenge 2020
<p>This dataset is a denoised version of the CPSC dataset presented in <em>Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020<sup>1</sup></em>. This dataset was used as part of the article <em>A Lightweight and Interpretable Model to classify Bundle Branch Blocks from ECG Signals.</em></p> <p>1)Perez Alday, E. A., Gu, A., Shah, A., Liu, C., Sharma, A., Seyedi, S., Bahrami Rad, A., Reyna, M., & Clifford, G. D. (2020). Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020 (version 1.0.1). <em>PhysioNet</em>. <a href="https://doi.org/10.13026/f4ab-0814">https://doi.org/10.13026/f4ab-0814</a>.</p>
Bioacoustic data for evaluating wind denoising and detection methods
<p>Acoustic survey data accompanying publication "Wind-robust sound event detection and denoising for bioacoustics", by Julius Juodakis and Stephen Marsland.</p> <p>Contents (see the publication for details):</p> <ul> <li>pilotdata/: short audio files used for selecting the best-fit wind model</li> <li>surveys/LSK_additional_wavs/: remaining recordings from the little spotted kiwi survey that have not been deposited previously</li> <li>surveys/rawannots/: annotations produced by the unadjusted, OLS-adjusted and QR-adjusted detectors, for two bird surveys</li> <li>surveys/reviewed/: annotations passing human review for the unadjusted and OLS-adjusted detectors, for two bird surveys</li> <li>denoising/: examples of noise and signal which were mixed to evaluate the denoising.</li> </ul> <p>Audio files are provided in WAV PCM format. Annotations and filters are intended for use with AviaNZ software (https://www.avianz.net/), and provided in AviaNZ-compatible JSON format.</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.