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Dataset results
40 results for “pretrained model”
3DUNetCNN Pretrained Models
<p>Pre-trained model and configuration files designed to work with the <a href="https://github.com/ellisdg/3DUnetCNN">3DUNetCNN GitHub project</a>.</p> <p>The model files are named: <dataset>_<receptive_field>[_<version>][_<fold_number>].h5</p> <p>The corresponding configuration files are also given. These files include important details regarding data preprocessing, model architecture, and model training. The easiest way to train one of these models to on a new application is to modify the given configuration file to suit your needs and then to feed configuration file along with the pre-trained model to the "train.py" script from the GitHub repository. The receptive field is given for reference for those looking to implement a model. However, taking a pre-trained model at one receptive field and then training at another receptive field is perfectly reasonable, and will like reduce training time compared to training model from scratch. It is also worth noting that the project employs flexible model loading that allows for the weights from pretrained UNet models to be used as weights for models with slightly different UNet architectures. Therefore, even if you change architecture (using the configuration file), you should still be able to load weights from a pretrained model with a different architecture to reduce the training time needed.</p> <p>When using the AutoImplant models, please cite the following paper:</p> <ul> <li>Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-64327-0_6">https://doi.org/10.1007/978-3-030-64327-0_6</a></li> </ul> <p>The augmented AutoImplant data used to train the models can be found <a href="https://zenodo.org/record/4270278">here</a>.</p>
Pretrained Spec2Vec model (case study for MS2Query)
<p>The model was trained for 20 iterations on a subset of the "AllPositive" dataset, created from GNPS positive ionmode mass spectra (subset of 91320 spectra out of 95320 spectra after filtering). It is an example model to run the MS2query app: https://github.com/iomega/ms2query.</p>
PGL-SUM pretrained models
<p>This dataset contains pretrained models of the PGL-SUM network architecture for video summarization, that is presented in our work titled “Combining Global and Local Attention with Positional Encoding for Video Summarization”, in Proc. IEEE ISM 2021. </p> <p>This work introduces a new method for supervised video summarization, which aims to overcome drawbacks of existing RNN-based summarization architectures that relate to the modeling of long-range frames' dependencies and the ability to parallelize the training process. The proposed PGL-SUM network architecture relies on the use of self-attention mechanisms to estimate the importance of video frames. Contrary to previous attention-based summarization approaches that model the frames' dependencies by observing the entire frame sequence, our method combines global and local multi-head attention mechanisms to discover different modelings of the frames' dependencies at different levels of granularity. Moreover, the utilized attention mechanisms integrate a component that encodes the temporal position of video frames - this is of major importance when producing a video summary. Experiments on two benchmarking datasets (SumMe and TVSum) demonstrate the effectiveness of the proposed model compared to existing attention-based methods, and its competitiveness against other state-of-the-art supervised summarization approaches.</p> <p><strong>File format</strong></p> <p>The provided “pretrained_models.zip“ file contains two sets of pretrained models of the PGL-SUM network architecture. After downloading and unpacking this file, in the created “pretrained_models” folder you will find the following sub-directories:</p> <ul> <li> <p>table3_models,</p> </li> <li> <p>table4_models</p> </li> </ul> <p>The sub-directory “table3_models“ contains models of the PGL-SUM network architecture that have been trained in a single-batch mode and were manually selected based on the observed summarization performance on the videos of the test set. The average performance of these models (over the five utilized data splits) is reported in Table III of [1].</p> <p>The sub-directory “table4_models“ contains models of the PGL-SUM network architecture that have been trained in a full-batch mode and were automatically selected after the end of the training process based on the recorded training losses and the application of the designed model selection criterion (described in Section IV.B of our paper). The average performance of these models (over the five utilized data splits) is reported in Table IV of [1].</p> <p>Each of these sub-directories contains the pretrained model (.pt file), for:</p> <ul> <li> <p>Each utilized benchmarking dataset: {SumMe, TVSum}</p> </li> <li> <p>And each utilized data-split: {0, 1, 2, 3, 4}</p> </li> </ul> <p>The naming of each of the provided .pt files indicates the training epoch associated with the selected pretrained model.</p> <p>Finally, the data-splits we used for performing inference on the provided pretrained models, and the source code that can be used for training your own models of the proposed PGL-SUM network architecture, can be found at: <a href="https://github.com/e-apostolidis/PGL-SUM">https://github.com/e-apostolidis/PGL-SUM</a>.</p> <p><strong>License and Citation</strong></p> <p>This dataset is provided for academic, non-commercial use only. If you find this dataset useful in your work, please cite the following publication where it is introduced:</p> <p>[1] E. Apostolidis, G. Balaouras, V. Mezaris, I. Patras, "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. 23rd IEEE Int. Symposium on Multimedia (ISM), Dec. 2021. Software available at: https://github.com/e-apostolidis/PGL-SUM</p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the EU Horizon 2020 programme under grant agreement H2020-832921 MIRROR, and by EPSRC under grant No. EP/R026424/1.</p> <p> </p>
ZeroCostDL4Mic / DeepBacs - Multi-label U-Net training dataset (Bacillus subtilis) and pretrained model
<p>Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP, the manually annotated instance segmentation mask and the corresponding 2-label semantic segmentation mask used for model training.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired fluorescence and segmented mask images</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</p> <p><strong>File format</strong>: .tiff (8-bit)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm)</p> <p><strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin and mask images with labeled cytosol and cell boundaries were created using a custom Fiji macro (see our <a href="https://github.com/HenriquesLab/DeepBacs/tree/main/ImageJ-macros">github repository</a>).</p> <p> </p> <p><strong>Multi-label U-Net model</strong>:</p> <p>The U-Net (2D) multilabel model was generated using the ZeroCostDL4Mic platform (Chamier & Laine et al., 2021). It was trained from scratch for 200 epochs on 733 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (256 x 256 px²)) with a batch size of 8 and a categorical_crossentrop loss function, using the U-Net (2D) multilabel ZeroCostDL4Mic notebook (v 1) (Chamier & Laine et al., 2021). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.1.105). The training was accelerated using a Tesla P100GPU.</p> <p> </p> <p><strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup></p> <p><strong>Contact email</strong>: <a href="mailto:Seamus.Holden@newcastle.ac.uk">Seamus.Holden@newcastle.ac.uk</a></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>
Trained Models from "General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings"
<p>Trained models from the paper:</p> <p>Lukas Galke, Isabell Cuber, Christoph Meyer, Henrik Ferdinand Noelscher, Angelina Sonderecker, and Ansgar Scherp: <strong>General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings</strong>, in: <em>International Joint Conference on Neural Networks (IJCNN), </em>2022.</p> <ul> <li>File seq2mat_hybrid_bidirectional_sbertlike-100p-bsz512 holds the model from pretraining</li> <li>File ws2020_transformer_final_models holds the fine-tuned models for each task of the GLUE benchmark</li> </ul>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Pretraining Datasets raw audios from CORAA
<p>This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech.</p> <p>The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.</p>
Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
<p>This repository contains materials for the paper,<em> Exploiting Pretrained Biochemical Language Models for Targeted Drug Design, </em>which<em> </em>has been accepted for publication in <em>Bioinformatics</em> Published by Oxford University Press.</p> <p><em>data.zip</em> contains vocabulary files for the pretrained models, additional information regarding proteins (PFAM family, protein similarity) and interactions filtered from <a href="https://www.bindingdb.org/bind/index.jsp">BindingDB</a> which are further split into train, validation and test sets and used to train target specific molecule generation models. </p> <p><em>models.zip </em>includes files for the models trained in this study. </p> <p><em>predictions.zip </em>comprises the compounds generated with the targeted models and the result of their evaluation with respect to benchmarking metrics. </p> <p><em>docking.zip </em>contains <em>targets/ </em>including PDB files of the test proteins selected for docking evaluation, <em>ligands/ </em>including SDF files for molecules generated with the targeted models and two decoding strategies (i.e. beam search and sampling) and <em>complex/ </em>including docking outputs. </p> <p> </p> <p> </p>
Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks
<p>Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.</p>
Pretrained models for multiobject tracker for mice
<p>Noninvasive tracking of all individuals in unmarked mouse group using multicamera fusion and deep learning</p>
Hacker News lda2vec model pretrained word vectors
<p>See also: https://zenodo.org/record/45901 and https://zenodo.org/record/49899 and https://zenodo.org/record/49900</p>
S3GM: Learning spatiotemporal dynamics with a pretrained generative model
<h1>Datasets of Kuramoto-Sivashinsky equation (KSE) and Kolmogorov flow.</h1> <h2>Description of KSE data:</h2> <p>Each file for KSE datasets contains 4 dimensions in the following order: (B*V)*T*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>V</td> <td>number of varying parameters</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 1 for KSE)</td> </tr> <tr> <td>values of parameter used to generate <strong>training </strong>dataset</td> <td>1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0</td> </tr> <tr> <td>values of parameter used to generate <strong>test </strong>dataset</td> <td>1.1, 2.5, 3.2</td> </tr> </tbody> </table> <h2>Description of Kolmogorov flow data:</h2> <p>Each file for Kolmogorov flow contains 5 dimensions inthe following order: (B*Re*K)*T*X*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>Re</td> <td>number of varying Reynolds numbers</td> </tr> <tr> <td>K</td> <td>number of varying source terms (controled by the value of k)</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 2 for Kolmogorov flow)</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>training </strong>dataset</td> <td>100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>test </strong>dataset</td> <td> <div> <div>50, 125, 575, 1100, 1500</div> </div> </td> </tr> <tr> <td>values of k used to generate <strong>training </strong>dataset</td> <td>2, 3, 4, 5, 6, 7, 8</td> </tr> <tr> <td>values of k used to generate <strong>test </strong>dataset</td> <td> <div> <div>2, 4, 6, 8</div> </div> </td> </tr> </tbody> </table> <h2>Description of ERA5 data:</h2> <p>Training and testing dataset for ERA5 contains 5 dimensions inthe following order: 1*T*X*X*C, which is manually collected from <a href="https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download">https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download</a>. <strong>Note that the quantities in the datasets are already rescaled</strong> (the scale factors are saved in the scalar_era5.npy file, which is a 4x2 array recording the means and stds for the 4 quantities we used). Details are listed in the following table:</p> <table> <tbody> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 4 for ERA5)</td> </tr> <tr> <td>time span for <strong>training </strong>dataset</td> <td>1979-2022</td> </tr> <tr> <td>time span for <strong>test </strong>dataset</td> <td> <div> <div>2023</div> </div> </td> </tr> </tbody> </table> <h2>Pretrained checkpoints:</h2> <p>The .zip file contains the pretrained checkpoints for KSE, Kolmogorov flow and ERA5. Within the .zip file, the folder'kse_v0' is the checkpoint for KSE, 'kol_v0' is the checkpoint for Kolmogorov flow, and 'era5_v0' is the checkpoint for ERA5.</p> <h1><em>Source code:</em></h1> <p>The source code is upload as Github repository in <a href="https://github.com/lzy12301/S3GM">https://github.com/lzy12301/S3GM</a></p>
Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
<p>This is preprocessed data and pretrained models from two of our papers:</p> <p>"Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020)<br> <a href="https://arxiv.org/abs/1910.10838">https://arxiv.org/abs/1910.10838</a></p> <p> "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) <a href="https://arxiv.org/abs/2011.04839">https://arxiv.org/abs/2011.04839</a></p> <p>This data is meant to be used with our open-source implementation, which can be found here: https://github.com/nii-yamagishilab/multi-speaker-tacotron</p> <p>More information about the directory structure and how to use the data can be found in the READMEs on GitHub.</p>
peaks2maps pretrained model: OHBM2018 release v1.0.0
<p>See: https://github.com/chrisfilo/peaks2maps</p>
Pretrained models and results for "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model"
<p>This dataset contains the pretrained model weights and precomputed results for the paper "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model" submitted to <em>Geophysical Research Letters</em>. A preprint of the paper can be found at <a href="https://arxiv.org/abs/2211.01001">https://arxiv.org/abs/2211.01001</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-multi">https://github.com/MeteoSwiss/c4dl-multi</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> <li>If you want to train models, data (<a href="https://zenodo.org/api/files/0cfc0cf4-755a-4618-8341-39b107a3901c/c4dl-patches-2020-additional.zip">c4dl-patches-2020-additional.zip</a>) -> data/2020/</li> </ul> <p>Additionally, you will need the datasets from <a href="https://zenodo.org/deposit/6802292">this Zenodo archive</a>. Follow the instructions there for downloading.</p>
PopV pretrained models for Tabula sapiens reference and helper files
<p>This repository contains PopV pretrained models for all organs published in Tabula sapiens <a href="https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5">https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5</a>. Datasets are available from <a href="https://zenodo.org/record/7587774">https://zenodo.org/record/7587774</a>.</p> <p>The code to train all models will be released in a Github reproducibility repository.</p>
Pretrained models and test examples of live cell anatomy segmentation
<p>Pretrained models of live cell anatomy segmentation and test examples of the segmentation of 15 structures</p>
Python Annotated Code Search (PACS) Datasets & Pretrained Models
<p>This upload contains datasets and pre-trained models used for the paper <em>Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent. </em>The code for easily loading these datasets and models will be made available here: <a href="http://github.com/nokia/codesearch">http://github.com/nokia/codesearch</a> </p> <p><strong>Datasets</strong><br> There are three types of datasets:</p> <ul> <li>snippet collections (code snippets + natural language descriptions): so-ds-feb20, staqc-py-cleaned, conala-curated</li> <li>code search evaluation data (queries linked to relevant snippets of one of the snippet collections): so-ds-feb20-{valid|test}, staqc-py-raw-{valid|test}, conala-curated-0.5-test</li> <li>training data (datasets used to train code retrieval models): so-duplicates-pacs-train, so-python-question-titles-feb20</li> </ul> <p>The staqc-py-cleaned snippet collection, and the conala-curated datasets were derived from existing corpora:</p> <ul> <li>staqc-py-cleaned was derived from the Python StaQC snippet collection. See <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset">https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset</a>, <a href="https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset/blob/master/LICENSE.txt">LICENSE</a>. </li> <li>conala-curated was derived from the conala corpus. See <a href="https://conala-corpus.github.io/">https://conala-corpus.github.io/</a> , <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a></li> </ul> <p>The other datasets were mined directly from a recent Stack Overflow dump (https://archive.org/details/stackexchange, <a href="https://creativecommons.org/licenses/by-sa/4.0/">LICENSE</a>). <br> <br> <strong>Pre-trained models</strong><br> Each model can embed queries and (annotated) code snippets in the same space. The models are released under a BSD 3-Clause License.</p> <ul> <li>ncs-embedder-so-ds-feb20</li> <li>ncs-embedder-staqc-py</li> <li>tnbow-embedder-so-ds-feb20</li> <li>use-embedder-pacs</li> <li>ensemble-embedder-pacs</li> </ul>
Dynamical Variational Autoencoders (DVAE) pretrained models
<p>The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In recent years, a series of papers have presented different extensions of the VAE to process sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompasses a large subset of these temporal VAE extensions. Then we present in detail seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate these models with existing classical temporal models. We reimplemented those seven DVAE models and we present the results of an experimental benchmark conducted on the speech analysis-resynthesis task (the PyTorch code is made publicly available). The paper is concluded with an extensive discussion on important issues concerning the DVAE class of models and future research guidelines.</p>
Pretrained Models for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all trained models which were used to compute the results presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Pretrained Model of Channel Mixer Layer for Spatiotemporal Predictive Learning
<p>Model supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</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.