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46 results for “network visualization”
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
DATA SUPPORTING RESEARCH ON THE DEVELOPMENT OF PALEONTOLOGY IN BRAZIL (NETWORK VISUALIZATIONS)
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
Comparing the representation of a simple visual stimulus across the cerebellar network [dataset]
<p>This repository contains all the raw data that has been used to generate the figures in the paper "Comparing the representation of a simple visual stimulus across the cerebellar network".<br> To use the dataset, select all 10 .zip files provided here and unpack them simultaneously.</p> <p> </p> <p>The dataset contains the following folders:</p> <ul> <li>freely_swimming_beh folder contains the data for the freely swimming behavior experiments. Inside, subfolders for each behavioral experiment run can be found, containing the experiment metadata and the behavioral log.</li> <li>flashes folder contains the data for the imaging experiments with the FLASHES protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>steps folder contains the data for the imaging experiments with the STEPS protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>fitting folder contains data dictionaries with the results from the model fitting needed to reproduce the manuscript figures.</li> <li>data_dict4decoding_complete.h5 is a dictionary file containing all necessary data to reproduce the decoding experiments.</li> </ul>
Visualizing linguistic variation in a network of Latin documents and scribes
<p>Gephi project files (for Gephi 0.8.x) and Sigma.JS -visualizations.</p>
Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"
<p>This data archive contains all data newly created in the following publication:</p> <p>Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-<br>Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and<br>Controlling Chemical Reaction Explorations and Networks", in preparation.</p> <p>The directory contents are as follows:</p> <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>
Visualizations of the Network Analysis of Königsfelden Abbey
<p>Visualizations produced from networks regarding the production of charters in and for Königsfelden Abbey.</p> <p>A commented version of all networks is added as PDF file: modularity-and-networks.pdf.</p> <p>The referenced numbers of people can be found as PDF file: kf-personenliste.pdf.</p> <p>The data used, is published online: https://doi.org/10.5281/zenodo.632560</p>
Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
Visual Genome - Visual Relationship Detection - Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks
<p>This repository contains a processed version of <strong>Visual Genome</strong> for <em>Visual Relationship Detection</em>, from the Diploma (MSc) thesis <strong>Scene Graph Generation using Message Passing Neural Networks and Graph Convolutional Networks</strong> by Miltiadis Kofinas, supervised by Christos Diou and Anastasios Delopoulos.</p> <p>The original thesis is written in Greek</p> <blockquote> <p><strong>Νευρωνικά Δίκτυα Ανταλλαγής Μηνυμάτων και Συνελικτικά Δίκτυα Γράφων για Εξαγωγή Γράφου Σκηνής Εικόνων</strong><br> Μιλτιάδης Κοφινάς<br> <a href="https://ikee.lib.auth.gr/record/300900">https://ikee.lib.auth.gr/record/300900</a></p> </blockquote> <p>A summarized English version of the thesis can be accessed <a href="https://www.dropbox.com/s/m87ixw8c8ecrswm/mkofinas_thesis_english_scene_graph_generation.pdf?dl=0">here</a>.</p> <p>It contains region proposals for VGG-16 for all images, and metadata about the bounding box distribution and the predicate classes.</p>
Dataset for paper 'McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks'
<p>The .zip file includes four datasets for testing the performance of McAN (doi: https://doi.org/10.1093/bib/bbad174).</p>
Collaboration Spotting X - A Visual Network Exploration Tool
<p>Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.</p>
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
A visualization of co-authorship network of mathematicians with an Erdős number of at most 2
<p>Co-authorship network of mathematicians with an Erdős number of at most 2</p> <p>Based on the data available at: https://oakland.edu/enp/thedata/erdos1.</p> <p>As can be seen, Erdős has collaborated directly or through an intermediary with different groups of scientists.<br> Researchers in the field of graph and computer science (gold), specialists in number theory (pink), mathematicians in the field of set theory (blue), researchers in the field of computer engineering (green) and the isolated group of Peter Salamon and his colleagues are the main groups of these researchers.<br> The names of researchers with more than 190 colleagues in the network (the first 10% of researchers) are written in the figure.</p> <p>See https://sites.google.com/oakland.edu/grossman/home/the-erdoes-number-project/the-erdoes-number-project-data-files.</p> <p>This artifact is a part of https://math-sci.ui.ac.ir/article_25684.html.</p>
Supplementary datasets for the paper of "Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization"
<p>There are two eCLIP datasets (cell lines of K562 and HepG2). The eCLIP datasets were then divided into five categories for each cell line: low, medium 1, medium 2, high 1 and high 2 with peaks of >1,000 but <2,000, >2,000 but <4,000, >4,000 but <7,000, >7,000 but <10,000 and >10,000, respectively. </p>
Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database
<p><strong>Introduction</strong></p> <p><strong>T</strong>opological characterization of the <strong>R</strong>etinal microvascular n<strong>E</strong>twork visualized by portable fu<strong>ND</strong>us camera (<strong>TREND 2</strong>) is a database of digital color eye fundus images created as an addition to TREND database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg).</p> <p>TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.</p> <p> </p> <p><strong>Purpose</strong></p> <p>1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera</p> <p>2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature</p> <p>3) to aid the development of biomarkers of accelerated aging</p> <p>4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases.</p> <p>All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.</p> <p> </p> <p><strong>Data</strong></p> <p>The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network.</p> <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td><strong>Inclusion Criteria</strong></td> <td><strong>Inclusion Criteria</strong></td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> </tr> <tr> <td> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> <p>- controlled hypertension (blood pressure<140/90 mmHg), and/or</p> <p>- controlled type 2 diabetes mellitus, and/or</p> <p>- Alzheimer's dementia</p> </td> </tr> <tr> <td><strong>Exclusion Criteria</strong></td> <td><strong>Exclusion Criteria</strong></td> </tr> <tr> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> </tr> </tbody> </table> <p><strong>Files:</strong></p> <p>1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format)</p> <p>2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format)</p> <p>3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format)</p> <p>4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format)</p> <p>5_ASSOCIATED DATA (xslx format)</p> <p>6_RETINAL PATHOLOGY (docx format)</p> <p> </p> <p> </p>
Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"
<p><strong>Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the (C)PLOS-net metric-values</li> <li><strong>performance.csv</strong> contains the performance of the different algorithms on each instance</li> <li><strong>merged.csv</strong> contains the merged data from the 2 csv files above</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. <strong>Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness</strong>. In Genetic and Evolutionary Computation Conference (GECCO ’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 9 pages. <a href="https://doi.org/10.1145/3583131.3590474">https://doi.org/10.1145/3583131.3590474</a></p> <p><strong>Abstract</strong></p> <p>The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multi-objective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.</p>
Data from: Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks
Open the record for dataset details and reuse information.
Citation network nodelist, edgelist, and visualization (UNIGE MA Thesis)
<p>The data in this deposit were used in the <a href="https://archive-ouverte.unige.ch/unige:140928">author's Thesis</a> for the degree of Master of Arts in Philosophy with Specialization in the Philosophy of Science, under the supervision of Prof. Marcel Weber (Department of Philosophy, University of Geneva, Switzerland).</p> <p>This deposit contains three files: (i) a list of nodes; (ii) a list of edges; and (iii) a digital image of the network as it appears in the Appendix A of the Thesis. The lists of nodes and edges contained here were manually built by the author, and the visualization was obtained using the software Gephi.</p> <p>The nodes represent specific texts related to the historical development of Expected Utility Theory (as explained in section 3.1 of the Thesis), corresponding to the texts registered in the Citation Network Texts section of the References of the Thesis. The data and visualization in this deposit use the convention "authorsORIGINALYEAR". For example, Pareto (1909/1979) is represented as "pareto1909", and Safra et al. (1990a) as "safra.etal1990a". The color of each node depends on the subsection of the historical description (section 3.2) they appear in, corresponding to the categories of the "topic" column of the nodelist. And their sizes are proportional to how many times they were mentioned by others in the network.</p> <p>The edges represent citations between texts and their colors represent <em>mention types</em> (as defined in section 3.1), such that agreement are in green, disagreements are in light blue, and neutral mentions are in light gray. The reasons for the final categorization of potentially unclear mention types are noted in the "reasons" column.</p>
Data from: NetView P: a network visualization tool to unravel complex population structure using genome-wide SNPs
Network-based approaches are emerging as valuable tools for the analysis of complex genetic structure in both wild and captive populations. NetView P combines data quality control with the construction of population networks based on mutual k-nearest-neighbours thresholds applied to genome-wide SNPs. The program is cross-platform compatible, open-source and efficiently operates on data ranging from hundreds to hundreds of thousands of SNPs through multiprocessing in Python. We used the pipeline for the analysis of pedigree data from simulated (n = 750, SNPs = 1279) and captive Silver-lipped Pearl Oysters (n = 415, SNPs = 1107), wild populations of the European Hake from the Atlantic and Mediterranean (n = 834, SNPs = 380) and Gray Wolves from North America (n = 239, SNPs = 86,103). The population networks effectively visualize large- and fine-scale genetic structure within and between populations, including family-level structure and relationships. NetView P comprises a network-based addition to other population analysis tools and provides user-friendly access to a complex network analysis pipeline through implementation in Python.
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
<h1>Datasets and analysis code of the following publication:</h1> <p>Peng Liu, Ke Bo, Mingzhou Ding and Ruogu Fang (2024). Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. <em>PLOS Computational Biology. </em>DOI: 10.1371/journal.pcbi.1011943</p> <p>For any questions please contact the first author at mail pliu1 [at] ufl [dot] edu</p> <h2><strong>Contents:</strong></h2> <p><strong> Code_DataAnalysis</strong><br> - Extracted Selectivity</p> <p> -- IAPS and NAPS datasets</p> <p> -- Neurons In Alexnet and VGG networks</p> <p> --Networks are pre-trained on ImageNet and randomly initialized</p> <p> - Extracted Overlapped Selectivity across IAPS and NAPS.</p> <p> - Extracted tuning performance changes from two datasets and the VGG network</p> <p> -Code to replicate the key results including </p> <p> --Tuning quality </p> <p> -- Number of overlapped neurons</p> <p> -- Enhance neuron activity</p> <p> -- Lesion neurons</p> <p> <strong>TrainedNetworks</strong></p> <p> --Pre-trained VGG network on ImageNet </p> <p> --Pre-trained Alexnet network on ImageNet</p> <p>After pre-training these networks on ImageNet, we fixed their weights and trained them to classify pleasant, neutral, and unpleasant images into three emotion categories using both IAPS and NAPS datasets.<br> </p> <p><strong>Image datasets</strong></p> <p>Access image datasets by request from https://csea.phhp.ufl.edu/media/iapsmessage.html for IAPS and https://lobi.nencki.edu.pl/research/8/ for NAPS.</p>
Topographic deep neural networks predict the functional organization of the primate ventral visual pathway
<p>Recording of presentation at the Neuroscience 2021 annual meeting (held virtually). The abstract follows:</p> <p> </p> <p>The primate ventral visual pathway is organized into functional maps, including pinwheel-like arrangements of orientation-tuned neurons in primary visual cortex (V1) and patches of category-selective neurons in higher visual cortex. While deep convolutional neural networks (DCNNs) trained for object recognition accurately predict neural representations throughout the ventral pathway, they have no spatial layout for features at a given retinotopic location and are thus unable to predict the rich topographic organization of visual cortex. Here, we close this gap by first assigning each DCNN unit a position in a 2D cortical sheet, then training the network to minimize a cost function with two components: one encouraging accurate object recognition, and another favoring correlated responses among nearby units in each model layer (Figure 1A, 1B). </p> <p>We find that training with this composite spatial loss produces brain-like topographic maps in both early and later model layers (Figure 1B). Early layers contain smooth orientation preference maps with pinwheels, clusters of units preferring the same spatial frequency, and color-preference domains resembling V1 “blobs”. In a later layer of the same model, we observe clusters of category-selective units, e.g., face patches, whose spatial organization largely matches that found in primate higher visual cortex. Our model thus leverages local response correlations, which have been linked to theories of wire-length minimization, to accurately predict neuron responses and functional organization throughout the ventral visual pathway. In support of the wire-length minimization hypothesis, we find that our topographic DCNN would require shorter connections than a standard DCNN to support connections between similarly-tuned neurons within early (38% reduction) and later (31% reduction) model layers (Figure 1D). These results suggest that the functional organization of visual cortex can be explained by two constraints: the need to perform object recognition and pressure for local populations of neurons to have correlated responses.</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.