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7,370 results for “supplementation”
Supplementation of Heat-treated Lactiplantibacillus plantarum nF1 Changes the Production of Short-chain Fatty Acids in Healthy Infants
<p><strong>BACKGROUND:</strong> Imbalance of the gut microbiome and decrease in the number of short-chain fatty acid (SCFA)-producing bacteria often affect human health by altering intestinal and immune homeostasis. Probiotic use is an attractive method for modulating gut microbiota to prevent or treat intestinal dysbiosis. This study aimed to determine whether the oral consumption of heat-treated <em>Lactiplantibacillus plantarum</em> nF1 (HLp-nF1) induces changes in the gut environment in healthy infants by measuring changes in fecal SCFAs. <strong>METHODS:</strong> The study enrolled 43 infants aged <2 months, with 30 infants in the HLp-nF1 group receiving HLp-nF1 orally (2.5 × 10<sup>12</sup> cells/g/pack, daily dose of two packs) for 8 weeks. The fecal samples were collected and the questionnaires were administered at weeks 0 and 8. <strong>RESULTS:</strong> The concentrations of the total SCFAs, acetate, propionate, and butyrate significantly increased following HLp-nF1 supplementation (P < 0.0001, P < 0.0001, P < 0.0001, and P = 0.028, respectively). Additionally, gut microbiota composition was altered in the HLp-nF1 group. <strong>CONCLUSIONS:</strong> Using probiotics is a useful and easy method for manipulating SCFA formation by altering the human gut microbiota composition.</p>
Supplement A. Wolf et al: 'Deciphering local and regional hydroclimate resolves contradicting evidence on the Asian monsoon evolution'
<p>This repository contains all proxy data for TD3 and codes with instructions and examples presented and use in A. Wol et al, "Deciphering local and regional hydroclimate resolves contradicting evidence on the Asian monsoon evolution". The data can be used to replicate figures and analyses presented in the main text. </p>
Data supplement for "Molecular motors enhance microtubule lattice plasticity" Lecompte, William; John, Karin
<p>This dataset contains the data and source files for figures 2 (a-e), 3(a-d), 4(b,c,e) and Supplementary figures 5, 7(a-e), 8 and 9(a-c) in the following publication: </p> <p> </p> <p>W. Lecompte and K. John</p> <p> </p> <p>"Molecular motors enhance microtubule lattice plasticity"</p> <p> </p> <p>published in 2023 in PRX Life (ArXiv https://arxiv.org/abs/2209.09161)</p> <p> </p> <p>Please follow the instructions given in 'Readme.txt'.</p> <p> </p>
Supplemental data for characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks
<p>This is the supplemental data for the manuscript titled <em>Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks</em> submitted to <em>Nano Select</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1.</p> <p><strong>Dataset description:</strong></p> <p>Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder.</p> <ul> <li><em>particle_detection_training.ipynb:</em> This notebook can be used for network training.</li> <li><em>particle_detection_evaluation.ipynb:</em> This notebook is for evaluation of a trained network with simulated test images.</li> <li><em>particle_detection_evaluation_experiment.ipynb:</em> This notebook is for evaluation of a trained network with experimental test images.</li> <li><em>particle_detection_measurement_experiment.ipynb:</em> This notebook is for application of a trained network to experimental data.</li> </ul> <p>In addition, a script titled <em>particle_detection_functions.py</em> is provided which contains functions required by the notebooks. Details can be found within the comments.</p> <p>The zip archive <em>training_data.zip</em> contains the training data. The subfolder <em>HAADF</em> contains the images (sorted as training, validation and test images), the subfolder <em>json </em>contains the annotation (sorted as training, validation and test images). Each file within the <em>json</em> folder provides for each image the following information:</p> <ul> <li>aggregat_no: image id, the number of the corresponding image file</li> <li>particle_position_x: list of particle position x-coordinates in nm</li> <li>particle_position_y: list of particle position y-coordinates in nm</li> <li>particle_position_z: list of particle position z-coordinates in nm</li> <li>particle_radius: list of volume equivalent particle radii in nm</li> <li>particle_type: list of material types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></li> <li>particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</li> <li>rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</li> <li>deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</li> <li>cluster_index: list of cluster indices for each particle</li> <li>initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</li> <li>fractal_dimension: the intended fractal dimension of the aggregate</li> <li>fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</li> <li>fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</li> <li>fractal_prefactor: fractal prefactor</li> <li>mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</li> <li>mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</li> <li>particle_1_rho: density of TiO<sub>2</sub> used for the calculations</li> <li>particle_1_size_mean: mean TiO<sub>2</sub> radius</li> <li>particle_1_size_min: smallest TiO<sub>2</sub> radius</li> <li>particle_1_size_max: largest TiO<sub>2</sub> radius</li> <li>particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</li> <li>particle_1_clustersize: average TiO<sub>2</sub> cluster size</li> <li>particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</li> <li>particle_2_rho: density of WO<sub>3 </sub>used for the calculations</li> <li>particle_2_size_mean: mean WO<sub>3</sub> radius</li> <li>particle_2_size_min: smallest WO<sub>3</sub> radius</li> <li>particle_2_size_max: largest WO<sub>3</sub> radius</li> <li>particle_2_size_std: standard deviation of WO<sub>3</sub> radii</li> <li>particle_2_clustersize: average WO<sub>3</sub> cluster size</li> <li>particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</li> <li>number_of_primary_particles: number of particles within the aggregate</li> <li>gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</li> <li>gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</li> <li>mean_coordination: mean total coordination number (particle contacts)</li> <li>mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</li> <li>mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</li> <li>radius_equiv: list of area equivalent particle radii (in projection)</li> <li>k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</li> <li>polygons: list of polygons that surround the particle (COCO annotation)</li> <li>bboxes: list of particle bounding boxes</li> <li>aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</li> <li>n_pix: number of pixel per image in horizontal and vertical direction (squared images)</li> <li>pixel_size: pixel size in nm</li> <li>image_size: image size in nm</li> <li>add_poisson_noise: 1 if poisson noise was added, 0 otherwise</li> <li>frame_time: simulated frame time (required for poisson noise)</li> <li>dwell_time: dwell time per pixel (required for poisson noise)</li> <li>beam_current: beam current (required for poisson noise)</li> <li>electrons_per_pixel: number of electrons per pixel</li> <li>dose: electron dose in electrons per Å<sup>2</sup></li> <li>add_scan_noise: 1 if scan noise was added, 0 otherwise</li> <li>beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</li> <li>scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)</li> <li>add_focus_dependence: 1 if a focus effect is included, 0 otherwise</li> <li>data_format: data format of the images, e.g. uint8</li> </ul> <p>There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>The zip archive <em>experiment_test_data.zip</em> includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder <em>HAADF</em> includes thirteen images. The subfolder <em>json</em> includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in <em>json/COCO/annotation_COCO.json</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript.</p> <p>The zip archive <em>particle_detection.zip</em> includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file <em>particle_detection/logs/fit/20230622-222721/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>particle_detection/logs/fit/20230622-222721/config_file.py</em>.</p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Data and Code supplement to: Effects of intraspecific variation in a native species´ phenology on its coexistence with non-native plants
<p>Data and code to generate the results of the paper published at Oikos "Effects of intraspecific variation in a native species´ phenology on its coexistence with non-native plants". </p> <p>Both R files can be independently run, and datasets include the raw data to generate the interaction coefficients according Lasthenia phenology or simulations to investigate the effect of intraspecific trait variation of Lasthenia population abundances. </p>
Data and code for "Dietary carotenoid supplementation has long-term and community-wide effects on the amphibian skin microbiome"
<p>Data, R code and R markdown report for the manuscript "Dietary carotenoid supplementation has long-term and community-wide effects on the amphibian skin microbiome" by Risely, A., Phillip G. Byrne, Bethany J. Hoye, and Aimee J. Silla</p> <p><strong>Data</strong></p> <p>frog_final.RDS = Processed phyloseq dataset of frog skin microbiome</p> <p><strong>Code</strong></p> <p>Frog_diet_analysis_V1.Rmd = Rmarkdown code</p> <p>Frog_diet_analysis_V1.html = Rmarkdown report (html version - need to download to view)</p> <p>Frog_diet_analysis_V1.pdf = Rmarkdown report (PDF version)</p>
Supplemental material for the insight: High levels of mercury contamination in a necrophage dung-beetle of the Amazon rainforest (Scarabaeidae: Scarabaeinae)
<p>This study represents the first evidence of mercury contamination in the family Scarabaeidae, with a close focus to <em>Coprophanaeus lancifer</em>, the largest copro-necrophagous beetle in South America. This work shows the repartition of total mercury (THg) in the insect body and lays the groundwork for additional future studies.</p>
Data supplement for "Stick-slip Dynamics in the Forced Wetting of Polymer Brushes"
<p>This dataset contains supplementary data for the following publication:</p> <p>Greve, D., Hartmann, S. & Thiele, U.<br> Stick-slip Dynamics in the Forced Wetting of Polymer Brushes<br> Soft Matter, 2023, 19, 4041-4061</p> <p>We provide the source files and data for all figures as well as a video of the stick-slip motion of a liquid front on a polymer brush substrate corresponding to the data shown in Figs. 10 & 11 of the paper.</p>
Supplemental data for the publication: "The Role of Vitamin D in Emiliania huxleyi: A Microalgal Perspective on UV-B Exposure"
<p>The following table contains raw transcriptomic data of Emiliania huxleyi CCMP3266 grown under continuous UV radiation or control conditions for 7, 10, and 13 days, obtained using the MARS-seq protocol (Keren-Shaul H. et al. 2013)</p> <p>Column A - Gene ID (Sperfeld M. et al. 2023)<br> Column B - NCBI accession number<br> Columns C - T: raw read counts<br> Columns U - AL: normalized read counts<br> Columns AM - BD: rlog transform read counts<br> Columns BE - FB: Every 6 columns represent differential expression data between two treatments (time point or UV/nonUV)<br> Columns FC - FK: Functional annotations automatically generated, as previously described (Sperfeld M. et al. 2023)</p>
Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"
<p>Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"<br> ===========================================================</p> <p>This directory contains the data and scripts used to create the plots from our publication as well as the source<br> code modifications necessary to run this test case within the CESM and MPAS models.</p> <p>Generating Plots<br> ---------------</p> <p>The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour.</p> <p><em>In order to generate the plots from the paper, you need only install NCL and then run</em> run.bash. Instructions for installing NCL<br> can be found in the `run.bash` script.</p> <p>Source Code Modifications<br> ----------------</p> <p><strong>CESM</strong><br> The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow.</p> <p><strong>MPAS</strong></p> <p>The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.</p>
Supplemental; Materials - Current state of genomics in nursing: A scoping review of healthcare provider oriented (clinical and educational) outcomes (2012-2022)
<p>Supplemental Table S1 "Scoping Review Data Extraction Table"</p>
Reconciling chemical flame retardant exposure and fire risk in domestic furniture: supplemental materials
<p>Materials necessary for the replication and development of methods for modelling fire risk and chemical flame retardant exposure as described in the manuscript. [Manuscript in review, link to be added when available.]</p>
Interactive supplement on all pairwise comparisons carried out for constitutive activating PYL mutant lines
<p>Interactive HTML files on 10 comparisons that were carried out for differential gene expression analyses to understand the impact of constitutive PYL expression on the global gene expression of Arabidopsis thaliana.</p> <p> </p>
Data supplementing Lichtenberg et al. (2023) Differential effects of soil conservation practices on arthropods and crop yields. Journal of Applied Entomology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. Differential effects of soil conservation practices on arthropods and crop yields. J. Appl. Entomol. 147: 931-940. <a href="https://doi.org/10.1111/jen.13188">https://doi.org/10.1111/jen.13188</a></p> <p>Please cite the above article if you use any of the included data or code.</p> <p>Files are described in README.md.</p>
Supplemental data for the publication: "The Role of Vitamin D in Emiliania huxleyi: A Microalgal Perspective on UV-B Exposure"
<p>The following table contains raw transcriptomic data of Emiliania huxleyi CCMP3266 grown under continuous UV radiation or control conditions for 7, 10, and 13 days, obtained using the MARS-seq protocol (Keren-Shaul H. et al. 2013)</p> <p>Column A: E. huxleyi CCMP3266 gene locus IDs<br> Column B: E. huxleyi CCMP3266 representative gene transcripts<br> Columns C: Best matching transcripts in E. huxleyi CCMP1516<br> Columns D - U: Raw read counts<br> Columns V - AM: DESew2 normalized read counts<br> Columns AN - BE: rlog transformed read counts.<br> Columns BF - FC: Each block of 6 columns represents the results of the differential gene expression analysis between two treatments (time point or UV/nonUV).<br> Columns FD - FJ: Functional annotations </p>
Supplemental Information
<p><strong>Scatterplots of POPs for "Prenatal and early postnatal exposure to Persistent Organic Pollutants (POPs) – What is the correlation between Dioxins and long-chain Per- and Polyfluorinated Alkyl Substances (PFAS)?"</strong></p>
The effects of dissolved organic matter supplements on the metabolism of corals under heat stress
<p><span>Octocorals represent a</span><span> major alternative group in the benthic community of reefs that have diverged from hexacoral dominance. Despite their phototrophic symbionts, supplementing their diet with heterotrophic sources may promote their growth, particularly when compared to hexacorals in bleaching conditions. However, limited comprehensive data exists on octocorals' trophic ecology, especially regarding their ability to feed on dissolved organic matter (DOM), which comprises the largest pool of organic matter in reefs. This study aims to investigate the ability of two octocorals (<em>Sarcophyton glaucum </em>and <em>Lobophytum sp.</em>) to feed on DOM and compare this ability to that of hexacorals, such as <em>Stylophora pistillata</em> and <em>Turbinaria reniformis. </em>The study measured the net fluxes of DOM under varying DOM concentrations and under heat stress. The results demonstrate that all coral species were net producers of DOM at ambient concentrations, but became net consumers once seawater was supplemented with DOM. Furthermore, our study highlights a relationship between coral physiological responses to heat stress and DOM uptake. Corals that increased (<em>S. pistillata</em>) or maintained (<em>S. glaucum</em> and <em>Lobophytum sp.)</em> their DOM uptake rates at high temperatures were the most resilient to heat stress. In contrast, <em>T. reniformis</em> exhibited lower DOM uptake rates at high temperatures, which was associated with significant bleaching. </span><span>Understanding the ability of corals to feed on DOM may therefore provide insight on the resilience of species under ocean warming conditions.</span></p>
Genotype data for: Demographic and genetic consequences of a steelhead supplementation program
<p>Supplementation of naturally-spawning populations by the addition of hatchery-spawned individuals is commonly conducted for recovery of threatened and endangered populations and to support harvest opportunities. We present an analysis of steelhead, the anadromous form of Rainbow Trout (<em>Oncorhynchus</em> <em>mykiss</em>), returning to an integrated supplemented population in Southwest Washington over the course of 15 years. The goal of the supplementation program was to evaluate whether use of a juvenile captive broodstock and an integrated paradigm could be used to increase adult returns while avoiding negative genetic impacts to the population. Estimates of relative reproductive success (RRS) for fish spawned in the hatchery ranged from 2.4 for hatchery-origin females to 6.4 for natural-origin males, indicating that fish spawned in the hatchery produced more returning adult progeny than did fish allowed to spawn in the natural environment. We observed a slight reduction in reproductive success (RS) for hatchery-origin (relative to natural-origin) fish when spawning in the natural environment, but the difference was non-significant for males and marginally significant for females. In contrast to the relatively weak relationship between RS and origin (male P = 0.347, η<sup>2</sup> = 0.008; female P = 0.066, η<sup>2</sup> = 0.037), we observed a strong relationship between RS and return year (male P < 0.001, η<sup>2</sup> = 0.896; female P < 0.001, η<sup>2</sup> = 0.867) (i.e., hatchery- and natural-origin fish did well or poorly together each year). Hatchery-origin fish exhibited reduced genetic diversity, as well as evidence of increased temporal population structure among hatchery fish. We suspect the latter is an artifact of cultural practices that reduce diversity in age at smoltification. We conclude that the program was successful in achieving an increase in adult return, but not in avoiding negative genetic effects on the population, and that any lasting impacts of supplementation remain to be determined.</p>
Supplemental Data for "Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer"
<p>Supplemental data files for Bahnassy et al, "Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer"</p>
Supplemental data for 'Biophysical analysis reveals autophosphorylation as an important negative regulator of LRRK2 dimerization''
<p><strong>Supplemental mass photomerty raw data used in Figure 3</strong> of 'Guaitoli, G., Zhang, X., Saitta, F., Miglionico, P., Silbermann, L.M., Ho, F.Y., Zweydorf, F.v., Signorelli, M., Tych, K., Fessas, D., Raimondi, F., Kortholt, A., and Gloeckner, C.J. (2023). Biophysical analysis reveals autophosphorylation as an important negative regulator of LRRK2 dimerization. <em>bioRxiv</em>, 2023.2008.2011.549911 doi: 10.1101/2023.08.11.549911'</p> <p><strong>Raw data Figure 3A:</strong> Mass photometry raw data for LRRK2 wild-type in presence of different G-nucleotides at different LRRK2 concentrations. Three biological replicates have been considered for the statistical analysis.</p> <p><strong>Raw data Figure 3B: </strong>Mass photometry raw data for LRRK2 WT (left panel), LRRK2 WT + MLi-2 (middle panel) and kinase-dead LRRK2 (right panel) without and with ATP pre-incubation (-/+ ATP). Two biological and three technical replicates have been considered for the statistical analysis.</p> <p><strong>Raw data Figure 3C</strong><strong>:</strong> Mass photometry raw data for pathogenic LRRK2 variants in presence of different G-nucleotides. Two biological and two technical replicates have been considered for the statistical analysis<strong>.</strong></p> <p> </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.