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3,684 results for “extract”
A green extraction design for enhancing flavonoid compounds from the Ixora javanica flowers using a deep eutectic solvent
<p>In this study, an environmentally friendly extraction method for flavonoid compound from <em>Ixora javanica</em> as a new raw material candidate for herbal medicine and cosmetics, was developed. The objectives of the present work were to provide recommendations for the optimal extraction conditions and to investigate the effects of any extraction parameters on flavonoid yields from the <em>I. javanica</em> flower. The extraction process was performed using deep eutectic solvent (DES) (choline chloride and propylene glycol at molar ratio of 1:1) and ultrasound-assisted extraction (UAE) method. Both single-factor and response surface analyses using three-level and three-factor Box Behnken designs were conducted to obtain the optimum flavonoid concentrations. The results showed that the optimum extraction conditions for total flavonoids featured an extraction time of 40 min, 25% water content in DES, and a solid-to-liquid ratio of 1:25 g/mL. An extract obtained under optimum extraction conditions showed higher total flavonoid yields than an ethanolic extract which was used for comparison. Scanning electron microscope (SEM) images demonstrated that both of the solvents also showed different effects on the outer surface of the <em>I. javanica</em> flower during extraction process. In sum, our work succeeded in determining the optimum conditions for total flavonoids in the <em>I. javanica</em> flower using a green extraction method.</p>
iSDAsoil: soil extractable Aluminium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Aluminium (Al) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.al_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium mean value,</li> <li>sol_log.al_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.al_mehlich3 R-square: 0.881 Fitted values sd: 0.872 RMSE: 0.321 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.7042 -0.1036 0.0059 0.1189 3.3777 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.675492 2.771906 -0.244 0.807 regr.ranger 0.879567 0.005464 160.969 <2e-16 *** regr.xgboost 0.071537 0.005813 12.306 <2e-16 *** regr.cubist 0.150157 0.004553 32.979 <2e-16 *** regr.nnet 0.087603 0.431261 0.203 0.839 regr.cvglmnet -0.084440 0.003182 -26.534 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3208 on 63551 degrees of freedom Multiple R-squared: 0.8808, Adjusted R-squared: 0.8808 F-statistic: 9.391e+04 on 5 and 63551 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil extractable Potassium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Potassium (K) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.k_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium mean value,</li> <li>sol_log.k_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.k_mehlich3 R-square: 0.773 Fitted values sd: 0.938 RMSE: 0.509 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.3088 -0.2648 -0.0037 0.2639 6.8136 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.907726 6.134422 1.778 0.0754 . regr.ranger 1.004487 0.003878 259.026 <2e-16 *** regr.xgboost -0.004081 0.004739 -0.861 0.3892 regr.cubist 0.084556 0.004346 19.454 <2e-16 *** regr.nnet -2.205286 1.228586 -1.795 0.0727 . regr.cvglmnet -0.064510 0.003933 -16.401 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5092 on 139122 degrees of freedom Multiple R-squared: 0.7725, Adjusted R-squared: 0.7725 F-statistic: 9.451e+04 on 5 and 139122 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
iSDAsoil: soil extractable Zinc for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Zinc (Zn) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.zn_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc mean value,</li> <li>sol_log.zn_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.zn_mehlich3 R-square: 0.711 Fitted values sd: 0.588 RMSE: 0.375 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.1382 -0.2038 -0.0274 0.1632 3.6353 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.836394 1.555108 1.181 0.23766 regr.ranger 0.823144 0.013982 58.871 < 2e-16 *** regr.xgboost 0.037861 0.013644 2.775 0.00552 ** regr.cubist 0.169653 0.010091 16.813 < 2e-16 *** regr.nnet -1.511745 1.244933 -1.214 0.22463 regr.cvglmnet 0.009770 0.008046 1.214 0.22467 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3747 on 39344 degrees of freedom Multiple R-squared: 0.7109, Adjusted R-squared: 0.7109 F-statistic: 1.935e+04 on 5 and 39344 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Europe Road Network extracted from OpenStreetMap data
<p>The data extracts of Europe region downloaded on 04/07/2020 was used to create this dataset. From this the <strong>highways</strong> tagged as <strong>motorway, trunk, primary, secondary, tertiary, unclassified</strong> and <strong>residential </strong>are selected and the information was saved as line strings. CRS: WGS84 (EPSG:4326)<br> Files available are in parquet and csv format. Please feel free to convert the files in to desired file formats.</p>
A rare entity - percutaneous lead extraction in a very late onset pacemaker endocarditis - case report and review of literature
<p><strong>Video 1.</strong> Transthoracic two-dimensional echocardiography apical 4-chamber view: large hypoechogenic hyper-pedunculated mobile mass at the level of tricuspid valve; <strong>Video 2.</strong> Transesophageal two-dimensional echocardiography: low echogenicity pedunculated mass attached to the pacemaker lead, with multiple sites of binding, with no supplementary involvement of the tricuspid valve and myxomatous appearance of the posterior leaflet with hypermobility and rupture of chordae; <strong>Video 3. </strong>Fluoroscopy during the lead extraction procedure, after freeing the lead from adhesions: traction of the lead from the right ventricular apex, through the tricuspid valve, right atrium, superior vena cava and left subclavian vein, to complete lead removal; <strong>Video 4.</strong> Postprocedural transesophageal two-dimensional echocardiography: posterior leaflet chordae rupture, no residual vegetation, no pericardial effusion; <strong>Video 5.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, no additional cardiac masses, posterior leaflet chordae rupture of the tricuspid valve; <strong>Video 6.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, color Doppler: mild tricuspid regurgitation, with two small thin jets.</p>
Accuracy of the Group Velocity of Love Waves Extracted from Ambient Seismic Noise
<p>Love wave waveforms derived from the empirical Green's functions and Ground Truth earthquake in my manuscript submitted to Journal of Geophysical Research: Solid Earth.</p>
Extract of Local Notice to Mariners, 2018-2019, for Development of AIS Model of Texas Gulf Intracoastal Waterway Travel Times
<p>These files summarize mentions of restrictions or cautions for navigation on the Texas Gulf Intracoastal Waterway contained in Coast Guard Local Notice to Mariners files.</p>
Insecticidal activity of Lantana camara extract oil on controlling maize grain weevils
<p>Currently farmers are faced by serious post-harvest problems from weevils especially in storage of grains. When grains exposed to insects they become less marketable and loss quality of grain. In this study the insecticidal property of methanol, ethanol and ethyl acetate extracted oil from Lantana Camara leaf for controlling of maize weevils, Sitophilus Zeamais were studied. Gas Chromatography-Mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FT-IR) was used to identify the chemical composition and Functional group of solvent extracted oil respectively. Adult weevil's repellency and mortality were studied by the effect of extract oil concentration at 0% (w/w), 2% (w/w), 3% (w/w), 5% (w/w), 7% (w/w), and 10% (w/w) to weight of grain maize and exposed days. Repellency effect was also conducted at 6, 12 and 24 h at different concentration oil. The number of weevil's death increases significantly as exposed time increases. The extracted oil by the three-solvent fraction had direct repellent and toxic effect to the weevil. From all treatment applied, extracted by methanol fraction had showed highest percentage mortality (74%). The lowest mortality rate was observed in ethyl acetate fraction oil (26%), at 2% (w/w) concentration. The effect of lantana Camara leaf powder and extracted oil on repellency and mortality was showed directly proportional to concentration dosage.</p>
Wester Balgair Quarry - Milllstone Extraction
This model shows one of several curved indents quarried into the rock left from the extraction of millstones. This one has an arc scribed onto the quarried edge to delineate the outline of a millstone for quarrying. Source: Objaverse 1.0 / Sketchfab
GridKit extract of ENTSO-E interactive map
<p>This dataset was generated based on a map extract from May 11, 2016. This is an <em>unofficial</em> extract of the ENTSO-E interactive map of the European power system (including to a limited extent North Africa and the Middle East). The dataset has been processed by GridKit to form complete topological connections. This dataset is neither approved nor endorsed by ENTSO-E.</p> <p>This dataset may be inaccurate in several ways, notably:</p> <ul> <li>Geographical coordinates are transfered from the ENTSO-E map, which is known to choose topological clarity over geographical accuracy. Hence coordinates will not correspond exactly to reality.</li> <li>Voltage levels are typically provided as ranges by ENTSO-E, of which the lower bound has been reported in this dataset. Not all lines - especially DC lines - contain voltage information.</li> <li>Line structure conflicts are resolved by picking the first structure in the set</li> <li>Transformers are <em>not present</em> in the original ENTSO-E dataset, there presence has been derived from the different voltages from connected lines.</li> <li>The connection between generators and busses is derived as the geographically nearest station at the lowest voltage level. This information is again not present in the ENTSO-E dataset.</li> </ul> <p>All users are advised to exercise caution in the use of this dataset. No liability is taken for inaccuracies.</p>
Retinal status analysis method based on feature extraction and quantitative grading in OCT images
<p>The raw database includes 200 retinal OCT images judged as normal by ophthalmologists and 100 images with various abnormalities. The software includes the main steps for retinal status analysis. The software was carried out in Matlab.</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Facts Extracted from EuPMC OA publications 2016-05-01 to 2016-05-31
<p>Named entities were extracted from the fulltext Open Access articles in EuropePMC by loading the text into ElasticSearch and querying with the content of the ContentMine dictionaries (http://github.com/ContentMine/dictionaries)</p>
Application of high-throughput sequencing (HTS) metabarcoding to diatom biomonitoring: Do DNA extraction methods matter?
<p>The 8 benthic samples from Mainland France (stream Edian, stream Aire, lake Geneva), Sweden (stream Dåmman, Agricultural stream, lake Båtkåjaure) and Mayotte (stream Dapani, stream Majimbini) were collected by scraping material from the surface of stones, following the French standard (AFNOR 2007) used in routine biomonitoring programs.DNA was extracted from each sample (2 replicates) using five DNA extraction methods, followed by the amplification of a short rbcL DNA barcode (312bp) specific to diatoms. PCR products were then sequenced in one random direction using the Ion Torrent™ Personal Genome Machine® (PGM) System according to the manufacturer’s instructions. The data file contains one fastq file per library sequenced with the raw DNA reads, as provided by the sequencing platform (demultiplexing performed by the sequencing platform). An excel file is also provided to make the link between the fastq file number and the sample information (sample origin, DNA extraction method used, number of raw reads).</p>
Methods for Extracting and Characterizing RNA from Urine: for downstream PCR and RNAseq Analysis
<p>Readily accessible samples such as urine or blood are seemingly ideal for differentiating and stratifying patients, however, it has proven a daunting task to identify reliable biomarkers in such samples. Noncoding RNA holds great promise as a source of biomarkers distinguishing physiologic wellbeing or illness.</p>
Extracting positive descriptions and exploring landscape value using text analysis in the Cairngorms National Park
<p>The dataset was retrieved August 31, 2023, from <a href="http://data.geograph.org.uk/dumps/">http://data.geograph.org.uk/dumps/</a></p><p>Geograph project require all submitters to adopt a Creative Commons Attribution-ShareAlike licence on their photographic submissions.<br><a href="http://creativecommons.org/licenses/by-sa/2.0/">http://creativecommons.org/licenses/by-sa/2.0/</a></p>
Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from Clausena0 lansium (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis
<p>The raw data for MS "<strong>Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from </strong><i><strong>Clausena0 lansium</strong></i><strong> (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis".</strong></p>
Refined Bathymetric Prediction based on Feature Extraction of Gravity Field Signals: BATHY-FE
<p>BATHY-FE is a refined global seafloor model derived from extraction of learnt bathymetric signatures inherent in gravity field signals. It spans longitudes -180 ~ 180, and latitudes -80 ~ 80. It contains more short-wavelength seafloor features, and is superior to existing bathymetric models in almost all marine regions, especially at regions close to the poles. It is a GMT readable grid file (i.e., a matrix of seafloor model, and vectors of longitudes and latitudes) with a spatial resolution of 15 arc-seconds.</p><p>Included in this repository are a MATLAB livescript and sample data in which a demonstration of the algorithm over a region north of Alaska and Canada is presented.</p>
Data from Nicolle et al. LC-HRMS study of Streptomyces sp. AgN23 Culture Media Extract. Study of AgN23 exometabolome and analysis of Arabidopsis metabolomic responses to the bacteria
<p>This archive compiles several datasets related to studies of <i>Streptomyces</i> sp. AgN23 interaction with <i>Arabidopsis thaliana</i>. Ultra-high-performance liquid chromatography-high-resolution MS (UHPLC-HRMS) analyses were performed on a Q Exactive Plus quadrupole (Orbitrap) mass spectrometer, equipped with a heated electrospray probe (HESI II) coupled to a U-HPLC Ultimate 3000 RSLC system (Thermo Fisher Scientific, Hemel Hempstead, United Kigdom). For each biological sample, the RAW file obtained in ESI+ and ESI- mode were retrieved from the Xcalibur version 4.4 software and are deposited in separate sub-folders termed "RawPos" and "RawNeg". Each experimental cohort is grouped in a folder where the biological repeats can be retrieved, as well as QC (Quality Check, pool of all samples from the cohort), Blank samples and eventual alternative control such as Bennett, the mock control media of <i>Streptomyces</i> sp. AgN23. The details regarding samples preparation, analytic parameters and mass spectrometry, statistical treatment and visualization of the data will be made available in the publication relating to this archive. The folder " AgN23-WT_AgN23-pSC004" contains chromatograms related to metabolomic study of Wild-type and pSC004-1, pSC004-10, pSC004-16 and pSC004-22 mutants of <i>Streptomyces</i> sp. AgN23. The folder " Col-0_AgN23" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 responses to colonization by <i>Streptomyces</i> sp. AgN23-WT. The folder " Col-0_pad3-1_AgN23" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 and the <i>Arabidopsis</i> pad3-1 mutant responses to colonization by <i>Streptomyces</i> sp. AgN23-WT. The folder " Col-0_pSC004" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 responses to colonization by <i>Streptomyces</i> sp. AgN23-WT and the AgN23 mutants pSC004-10 and pSC004-22. It should be noted that in the publication associated with this archive, the pSC004-1, pSC004-10, pSC004-16 and pSC004-22 mutants are referred to as ΔgbnB-1, ΔgbnB-2, ΔgbnB-3 and ΔgbnB-4, respectively.</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.