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4,017 results for “Reduction”
Figure 2 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 2. Represents cytotoxicity analysis/percentage cell viability and standardized viability concentration (SVC) values of different treatment groups on NIH 3T3 Cells (A) Represents the percentage of NIH 3T3 cells viability treated with different concentrations of nanostructured lipid carriers (NLC), bone marrow-derived mesenchymal stromal cells lysate (BMSCs-L), and NLC loaded BMSCs lysate (NLC-BMSCs-L). N represents % age viability of normal cells that receive no treatment and no H 2O2 injury; (B) Cytotoxicity analysis of various concentrations (500µg/µL, 1mg/mL, 2mg/mL, and 3mg/mL) of BMSCs lysate (C) SVC of BMSCslysate on NIH 3T3 cells; (D) Cytotoxicity analysis of various concentrations (500µg/µL, 1mg/mL, 2mg/mL, and 3mg/mL) of NLC loaded BMSCs lysate (E) shows SVC of NLC loaded BMSCs lysate on cells. Where; ***P<0.0001, *shows significance between untreated and treated groups while α and ss sign shows significance between H 2O2 injury and other treatment groups, αss shows P<0.0001, and ns is non-significant.
Figure 1 in Inflammation reduction potential of nanostructured lipid carriers encapsulated with rat's bone marrow cells' lysate
Figure 1. (A) Scanning Electron Micrograph of NLC and (B) Scanning Electron Micrograph of NLC-BMSCs-L; (B) Characterization of nanostructured lipid carriers (NLC) loaded bone marrow-derived mesenchymal stromal cells (BMSCs) lysate via enzyme-linked immunosorbent assay (ELISA):vascular endothelial growth factor (VEGF) and interleukin-6 (IL-6) expression in NLC, BMSCs-L, and NLC loaded bone marrow-derived mesenchymal stromal cells lysate (NLC-BMSCs-L).Where; *P<0.05, **P<0.01, ***P<0.0001, ns is non-significant.
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
<p>Result Files for the Paper "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations" to be published at IEEE Vis 2024</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Reduction Results for an Artificial Fish Tail
<p>Computation Results for an extensive comparison of model reductions methods for structure preserving reduction of second order mechanical systems applied to an artificial fishtail model.</p>
Results from "Binary Reduction of Dependency Graphs"
<p>The raw data of the reduction of the 238 bugs as extracted from the runs of the four algorithms ddmin, verify, closure, binary. An extra column also exists that describes the run of Binary Reduction on the list of classes directly.</p> <p>The columns in `deliverable.csv` are predicate (the name of the decompiler), name (the name of the project we ran on), size (number of classes), scc (number of strongly connected components). Then for each tool we have size, scc (final size and scc), iters (iterations to last success), total-iters (iterations before finishing), time (time to last success (s)), total-time (time before finishing), timeout (did the predicate timeout, or succeed), check (did the bug still exist after reduction)</p> <p>The `benchmarks.csv` covers basic statistics about the programs used in the results. Lib is the number of library classes, LOC is lines of source code in the program, classes are the number of classes, edges are the edges in the dependency graph, degree is the average in and out degree in the graph, scc is the number of strongly connected components, out_degree and in_degree is the median in and out degree in the graph.</p>
Activity – or lack thereof – of RuO2 based electrodes in the electrocatalytic reduction of CO2
<p>Raw data for the article</p> <p><em>Activity – or lack thereof – of RuO<sub>2</sub> based electrodes in the electrocatalytic reduction of CO<sub>2</sub></em></p> <p>Stefano Mezzavilla, Yu Katayama, Reshma R Rao, Jonathan Hwang, Anna Regoutz, Yang Shao-Horn, Ib Chorkendorff, Ifan Erfyl Lester Stephens</p> <p>DOI: 10.1021/acs.jpcc.9b01431</p> <p> </p> <p>The dataset includes:</p> <p>1) Experimental Methods</p> <p>2) File with raw data of figures in the main text and supplementary Information file (Excel file with multiple tabs)</p>
Adequate vs. Inadequate Test Suite Reduction Approaches. Raw Data
<p>Context: Regression testing is an important activity that allows ensuring the correct behavior of a system after a change. As the system grows, the time and resources to perform regression testing increase. Test Suite Reduction (TSR) approaches aim to speed up regression testing by removing obsolete or redundant test cases. These approaches can be classified as adequate or inadequate. Adequate TSR approaches reduce test suites and completely preserve test requirements (e.g., covered statements) of the original test suites. Inadequate TSR approaches do not preserve test requirements. The percentage of satisfed test requirements indicates the inadequacy level.</p> <p>Objective: We compare some state-of-the art adequate and inadequate TSR approaches with respect to the size of the reduced test suites and their fault-detection capability. Specifcally, we aim to increase our body of knowledge on TSR approaches by performing the following comparisons: (i) well-known adequate TSR approaches; (ii) their inadequate variants; and (iii) several variants of a novel Clustering-Based (CB) approach for (adequate and inadequate) TSR.</p> <p>Method: We conducted an experiment to compare adequate and inadequate TSR approaches and this comparison is founded on a public dataset containing information on real faults.</p> <p>Results: The most important findings from our experiment can be summarized as follows: (i) there is not an inadequate TSR approach that performs better than others; (ii) some variants of the CB approach, and a few well-known inadequate approaches, outperform the adequate ones in terms of the reductions in test suite size with a negligible, or no, eect on fault-detection capability; and (iii) the CB approach is less sensitive than the other inadequate approaches, that is, variations in the inadequacy level have a small effect on the reduction in test suite size and on the loss in fault-detection capability.</p> <p>Conclusions: These findings imply that inadequate TSR approaches and especially the CB approach might be appealing because they lead to a greater reduction in test suite size (with respect to the adequate ones) at the expense of a small loss in fault-detection capability.</p>
Figure 1 in Strategies for false positive reduction and multimodal lesion characterization in computer-aided diagnosis of breast cancer
Figure 1. - Representative ultrasound images at four-month post copulation (8 Sep. 2010) before resorption, five-month post copulation (20 Oct. 2010) during resorption, and six-month post copulation (3 Nov. 2010) after resorption. A: Uterine horn; B: Fetus; C: Ovary; D: Follicle.
FIGURE 4 in Taxonomic loss and functional reduction over time in the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River, Southern Brazil
FIGURE 4 | Two-dimensional functional space (PCoA) of the functional β-diversity of the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River (the transitional zone between 2006 and 2020).
FIGURE 3 in Taxonomic loss and functional reduction over time in the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River, Southern Brazil
FIGURE 3 | Two-dimensional functional space (PCoA) of the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River (transitional zone between 2006 and 2020). A, B, C, D = 2006. E, F, G, H = 2020. FRic = Functional richness; FEve = Functional evenness; FDiv = Functional divergence; FDis = Functional dispersion.
FIGURE 2 in Taxonomic loss and functional reduction over time in the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River, Southern Brazil
FIGURE 2 | Non-metric multidimensional scaling (NMDS) of the ichthyofauna composition of the Taquaruçu Reservoir, lower Paranapanema River (the transitional zone between 2006 and 2020).
FIGURE 1 in Taxonomic loss and functional reduction over time in the ichthyofauna of the Taquaruçu Reservoir, lower Paranapanema River, Southern Brazil
FIGURE 1 | Location of the samplings points in the Taquaruçu Reservoir, lower Paranapanema River (transitional zone, 2020). Hydroelectric power plants: 1– Rosana; 2– Taquaruçu; 3– Capivara. MS = Mato Grosso do Sul State; PR = Paraná State; SP = São Paulo State.
Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols
<p><span>This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA</span><span>’</span><span>s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.</span></p> <p><span> </span></p> <p><span>The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:</span></p> <p><span> </span></p> <p><strong><span>Model code.zip:</span></strong><span> Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.</span></p> <p><span> </span></p> <p><strong><span>PD_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E20C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions and climate conditions; </span></p> <p><strong><span>20L20E20C</span></strong><span> represents simulations with present-day land use, emissions, and climate conditions.</span></p> <p><span>Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (<strong>DBF2CRO</strong>), evergreen broadleaf forest to cropland (<strong>EBF2CRO</strong>), evergreen needleleaf forest to cropland (<strong>ENF2CRO</strong>), grassland to cropland (<strong>GRA2CRO</strong>), shrubland to cropland (<strong>SHR2CRO</strong>), deciduous broadleaf forest to grassland (<strong>DBF2GRA</strong>), evergreen broadleaf forest to grassland (<strong>EBF2GRA</strong>), and evergreen needleleaf forest to grassland (<strong>ENF2GRA</strong>).</span></p> <p><span>Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>N, <strong>LLAT</strong>), mid-latitude (50</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>S and 20</span><span>°</span><span>N</span><span>–</span><span>50</span><span>°</span><span>N, <strong>MLAT</strong>), and high-latitude (south of 50</span><span>°</span><span>S and north of 50</span><span>°</span><span>N, <strong>HLAT</strong>) regions.</span></p> <p><span> </span></p> <p><strong><span>FU_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E21C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>18L21E21C</span></strong><span> represents simulations with pre-industrial land use, future emissions, and future climate conditions;</span></p> <p><strong><span>20L20E21C</span></strong><span> represents simulations with present-day land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>20L21E21C</span></strong><span> represents simulations with present-day land use, future emissions, and future climate conditions.</span></p>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Birdsong NOIZEUS: Bioacoustics noise reduction benchmark dataset
<p>------------------------------------------------------------------------<br>Birdsong noizeus dataset<br>------------------------------------------------------------------------</p> <p>Authors: Tim Sainburg & Asaf Zorea<br>Year: 2024</p> <p>------------------------------------------------------------------------<br>General information<br>------------------------------------------------------------------------<br>- There are 5 recordings from each of 14 individuals (European starlings) recorded in an acoustically isolated chamber. <br>- For each song, we apply noise at 5 SNR levels (0dB, 5dB, 10dB, 15dB). <br>- There are 8 noise types, each taken from a single noise clip from the "Soundscapes from around the world" dataset.<br>- They are "rain", "town", "wind", "waterfall", "insect", "swamp" "frogscape", "forest"<br>- Each soundscape contains multiple noise sources. <br>- Noise levels were estimated using the pyloudnorm software (Steinmetz et al., 2021)<br>- Audio is provided as waveforms at 44100 samplerate</p> <p>------------------------------------------------------------------------<br>Data format<br>------------------------------------------------------------------------</p> <p>- clean<br> - {bird_name}_{timestamp}.wav<br>- noisy<br> - {snr}dB<br> - {bird_name}_{timestamp}_{noise_category}_{snr}.wav<br>- noise_sample<br> - {snr}dB<br> - {bird_name}_{timestamp}_{noise_category}_{snr}.wav</p> <p>`clean` contains the original clean audio.<br>`noisy` contains the song+noise<br>`noise_sample` contains a 1-second sample of noise only. </p> <p>Timestamp is in the format YYYY-MM-DD_HH-MM-SS-MILLISECONDS and refers to the time that the song was recorded. </p> <p>------------------------------------------------------------------------<br>Data sources<br>------------------------------------------------------------------------</p> <p>Birdsong<br>---------<br>Birdsong are acoustically isolated songs from 14 European Starlings<br>https://zenodo.org/records/3237218</p> <p>Citation: <br>Arneodo, Z., Sainburg, T., Jeanne, J., & Gentner, T. (2019). An acoustically isolated European starling song library (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3237218</p> <p>This dataset is available under the following license:<br>- Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/legalcode)</p> <p><br>Noise<br>-----<br>Noise are taken from the Xeno-canto - "Soundscapes from around the world" dataset<br>https://www.gbif.org/dataset/ff571aeb-46bf-45c4-ad2c-af4d68315765</p> <p>Citation:<br>Vellinga W (2024). Xeno-canto - Soundscapes from around the world. Xeno-canto Foundation for Nature Sounds. Occurrence dataset https://doi.org/10.15468/9u3zaq accessed via GBIF.org on 2024-10-17.</p> <p>We sampled 8 soundscapes from this dataset:<br> 1. Rain https://www.gbif.org/occurrence/4523646364<br> - Virginia<br> - 457s<br> - rain, Recording of the feeders in the backyard with a light rain falling on the leaf litter, while a freight train passes by ~1/2 mile away.<br> 2. Town https://xeno-canto.org/696263<br> - 2:22<br> - the closer habitat are greater trees and coniferes ..smaller bushes ...some other Krautgärten ... traffic-noise is to hear..as airplanes,too.<br> - insects, birds, bells, traffic?, airplane<br> - Germany<br> 3. Wind https://xeno-canto.org/911773<br> - 5:29<br> - Very windy day, grassland to shrubland habitat<br> - Wisconsin<br> 4. Waterfall https://xeno-canto.org/406993<br> - 24:02<br> - sound scenes captured in a river forest with zarzas, hiedras and other matorrales at the edge of a small waterfall.<br> - Spain<br> 5. Insect https://xeno-canto.org/454914<br> - 5:45<br> - Australia<br> - cicadias, birds,<br> 6. Swanp https://xeno-canto.org/909875<br> - 2:24<br> - Swampy area along dirt road/trail. Species Include: American Bullfrog, Cricket Frogs, American Crow, Prothonotary Warbler, Red-winged Blackbird, Indigo Bunting, Northern Cardinal<br> 7. Frogscape https://xeno-canto.org/718213<br> - 4:42 <br> - European Tree Frog Hyla arborea Green Frog Pelophlyax sp.<br> - Austrial <br> 8. Forest https://xeno-canto.org/900744 <br> - 3:04<br> - Sweden<br> - A beautiful choir with frogs, toads, geese and ducks to enjoy in the darkness a foggy night.<br> <br> <br> <br>All noise recordings have one of the following licensesL<br>- Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)<br>- Creative Commons Attribution-ShareAlike 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)</p> <p><br>Additional information about the noise dataset</p> <p><br>------------------------------------------------------------------------<br>citations<br>------------------------------------------------------------------------</p> <p>Steinmetz, C. J., & Reiss, J. (2021, May). pyloudnorm: A simple yet flexible loudness meter in python. In Audio Engineering Society Convention 150. Audio Engineering Society.</p> <p>Arneodo, Z., Sainburg, T., Jeanne, J., & Gentner, T. (2019). An acoustically isolated European starling song library (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3237218</p> <p>Vellinga W (2024). Xeno-canto - Soundscapes from around the world. Xeno-canto Foundation for Nature Sounds. Occurrence dataset https://doi.org/10.15468/9u3zaq accessed via GBIF.org on 2024-10-17.</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.