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162 results for “Interpolation”
Tabulation and interpolation of NLO neutrino-antineutrino production and scattering rates at MeV temperatures
<p>This record contains data used in the paper "<em>Neutrino-antineutrino production, annihilation, and scattering at MeV temperatures and NLO accuracy</em>" <a href="https://arxiv.org/abs/2412.03958">2412.03958</a>. Please consult the main text for details on the tabulated coefficients and their proper implementations.</p> <p>We provide numerical data and an interpolation routine (c-code) for evaluating double-differential rates, intended to facilitate possible studies of the full kinetic equations for neutrino decoupling in the early universe. These data can also be used to obtain integrated quantities, such as the energy density transfer rates, or neutrino interaction rates (e.g. <a href="https://arxiv.org/abs/2312.07015">2312.07015</a>). The QED corrections to the spectral functions were computed using an adapted version of the public code: <a href="https://doi.org/10.5281/zenodo.3478143">https://doi.org/10.5281/zenodo.3478143</a> .</p> <p>The archive file contains:</p> <ul> <li><code>grid_ABCD.dat</code> : recorded coefficients A, B, C, D (on p+,p- grid in units of QED plasma temperature)</li> <li><code>interpolation.c</code> : code to interpolate and calculate the (integrated) energy density transfer rates</li> <li><code>aux/...</code> : additional files needed for multidimensional integration routine "<a href="https://github.com/stevengj/cubature/">cubature</a>"</li> </ul>
Interpolated data on bioavailable strontium in the southern Trans-Urals, 2020-2023, version 1.2. (current)
<p><strong>The Interpolated Strontium Values dataset Ver. 1.2 </strong>presents the interpolated data of strontium isotopes for the southern Trans-Urals, based on the data gathered in 2020-2023. The current dataset consists of five sets of files for two interpolations: based on grass, mollusks, soil, and water samples, as well as the average of three (excluding the mollusk dataset). Each of the five sets consists of a CSV file and a KML file where the interpolated values are presented to use with a GIS software (ordinary kriging, 5000 m x 5000 m grid). In addition, GeoTIFF and JPEG files are provided for each set for a visual reference. </p> <p>Version 1.2 fixes bugs in GeoTIFF files. They can now be accessed in Google Earth (choose "Scale" if prompted that an imported image is too large). </p> <p><strong>How to use it?</strong></p> <p>The data provided can be used to access interpolated background values of bioavailable strontium in the area of interest. Note that a single value is not a good enough predictor and should never be used as a proxy. Always calculate a mean of 4-6 (or more) nearby values to achieve the best guess possible. Never calculate averages from a single dataset, always rely on cross-validation by comparing data from all five datasets. Check the cross-validation rasters to make sure that the interpolation is reliable for the area of interest. </p> <p> </p> <p><strong>References</strong></p> <p>The interpolated datasets are based upon the actual measured values, partially (2020-2022) published as follows:</p> <p>Epimakhov, Andrey; Kisileva, Daria; Chechushkov, Igor; Ankushev, Maksim; Ankusheva, Polina (2022): Strontium isotope ratios (87Sr/86Sr) analysis from various sources the southern Trans-Urals. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.950380</p> <p>Kiseleva, D., Ankusheva, P., Maksim, A., Chechushkov, I., & Epimakhov, A. (2024). Strontium isotopes (87Sr/86Sr) data from southern Trans-Urals, 2023 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14257258</p> <p> </p> <p><strong>Description of the original dataset of measured strontium isotopic values</strong></p> <p>The present dataset contains measurements of bioavailable strontium isotopes (87Sr/86Sr) gathered in the southern Trans-Urals. There are two sample types, such as leached soil (n = 56) and water (n = 56), collected to measure bioavailable strontium isotopes. The analysis of Sr isotopic composition was carried out in the cleanrooms (6 and 7 ISO classes) of the Geoanalitik shared research facilities of the Institute of Geology and Geochemistry, the Ural Branch of the Russian Academy of Sciences (Ekaterinburg). Mollusk shell samples preliminarily cleaned with acetic acid, as well as vegetation samples rinsed with deionized water and ashed, were dissolved by open digestion in concentrated HNO 3 with the addition of H 2 O 2 on a hotplate at 150°C. Water samples were acidified with concentrated nitric acid and filtered. To obtain aqueous leachates, pre-ground soil samples weighing 1 g were taken into polypropylene containers, 10 ml of ultrapure water was added and shaken in for 1 hour, after which they were filtered through membrane cellulose acetate filters with a pore diameter of 0.2 μm. In all samples, the strontium content was determined by ICP-MS (NexION 300S). Then the sample volume corresponding to the Sr content of 600 ng was evaporated on a hotplate at 120°C, and the precipitate was dissolved in 7M HNO 3. Sample solutions were centrifuged at 6000 rpm, and strontium was chromatographically isolated using SR resin (Triskem). The strontium isotopic composition was measured on a Neptune Plus multicollector mass spectrometer with inductively coupled plasma (MC-ICP-MS). To correct mass bias, a combination of bracketing and internal normalization according to the exponential law 88 Sr/ 86 Sr = 8.375209 was used. The results were additionally bracketed using the NIST SRM 987 strontium carbonate reference material using an average deviation from the reference value of 0.710245 for every two samples bracketed between NIST SRM 987 measurements. The long-term reproducibility of the strontium isotopic analysis was evaluated using repeated measurements of NIST SRM 987 during 2020-2022 and yielded 87 Sr/ 86 Sr = 0.71025, 2SD = 0.00012 (104 measurements in two replicates). The within-laboratory standard uncertainty (2σ) obtained for SRM-987 was ± 0.003 %. </p>
Interpolated depth to water table (groundwater) maps for the continental United States
<p><strong>DATA:</strong></p> <p>This is a collection of depth to water table maps with uncertainty estimates for the continental United States for years 1989 and 2019. Data used to create these maps were obtained from the National Ground-Water Monitoring Network (NGWMN). Data included 14,351 sites and 17,632,047 observations for the years 1989-2019. To improve our inference a set of auxiliary variables proven to have a relation with depth to water table were included. We paired point estimates of depth to water table data with environmental data, as well as terrain variables derived out of a base digital elevation model (DEM) created by NASA at a 1x1km resolution. Climatic layers (temperature, precipitation, and snow melt equivalent) for 1989-2019 were obtained from Daymet (Version 4), which provides a continuous grid of historical monthly and annual weather data, with a 1x1km spatial resolution (Thornton et al., 2020). Out of the DEM, primary (slope, aspect) and secondary terrain attributes (curvatures, upslope contributing areas) were used to calculate a compound topographic index (CTI). </p> <p> </p> <p><strong>MODELING FRAMEWORK:</strong></p> <p>Water table depth analyses were conducted using a three-step interpolation approach: 1) we utilized gradient boosted regression trees (GBRT) to make predictions, 2) we used kriging interpolation on GBRT residuals to reduce bias from spatial autocorrelation, to incorporate a spatial correlation structure and to create uncertainty maps, and 3) we then combined the GBRT and kriging predictions for the final map. This method is equivalent to a Universal Kriging, where in our case, we evaluated the trend using GBRT. Model metrics were calculated for the training (80% of the data) and validation (20% of the data) datasets to evaluate overall performance.</p> <p>*** Uncertainty is greater surrounding the 1989 interpolations due to a lower number of observations.</p>
FastNLO interpolation tables for NLO pQCD predictions of Inclusive Jet Production in $pp$ collisions at $\sqrt{s}=200$ and $510$~GeV
<p>This is generated using the NLOJet++ interface using jet pT for renormalization and factorization scale.</p> <p><strong>run12pp200_R050.tab:</strong></p> <p>\sqrt{s} = 200 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 6.9 8.2 9.7 11.5 13.6 16.1 19.0 22.5 26.6 31.4 37.2 44.0 52.0</p> <p><strong>run12pp510.tab:</strong></p> <p>\sqrt{s} = 510 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 8.0 10.0 13.0 17.0 21.0 26.0 32.0 39.0 47.0 57.0 68.0 80.0</p> <p><em>This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute.</em></p> <p>If you use this, please cite: D. Britzger, T. Kluge, K. Rabbertz, F. Stober, M. Wobisch, arXiv:1109.1310</p>
Code and data for: Interpolant-based demosaicing routines for dual-mode visible/near-infrared imaging systems
<p><span>Dual-mode visible/near-infrared imaging systems, including a bioinspired six-channel design and more conventional four-channel implementations, have transitioned from a niche in surveillance to general use in machine vision. However, the demosaicing routines that transform the raw images from these sensors into processed images that can be consumed by humans or computers rely on assumptions that may not be appropriate when the two portions of the spectrum contribute different information about a scene. A solution can be found in a family of demosaicing routines that utilize interpolating polynomials and splines of different dimensionalities and orders to process images with minimal assumptions.</span></p>
OMS project for Kriging interpolation of precipitation and temperature in Isarco River Valley
<p>The OMS project contains the simulations, jar files of the components, the inputs and the ouputs used in the Chapter 6 of the thesis " A flexible approach to the estimation of water budgets and its connection to the travel time theory ", Bancheri (2017) and in the article "The design and implementatation of Kriging models in the Object Modelling System v.3.", Bancheri et al. 2018. The project allows the Kriging interpolation of the precipitation and temperature, using data from Isarco River Valley. </p>
Interpolated maps of bird density and flight vector over Europe
<p>This dataset contains the interpolated values of bird density and bird flight speed (N-S and E-W) resulting from the methodology presented in [<em>reference</em>].The methodology is explained in less detail at <a href="https://rafnuss-postdoc.github.io/BMM/">rafnuss-postdoc.github.io/BMM</a>. The resulting interpolation is a probability distribution (define the probability of each value to occurs). Only the median, quantile 10 and 90 are given in this file. </p> <p>The spatio-temporal grid has a resolution of 0.2° in latitude (43°-68°) and longitude (-5°-30°) and 15 minutes in time (19 September to 10 October 2016), resulting in 127x176x2017 nodes. Over this large data cube, the estimation are only computed at the nodes located (1) over land, (2) within 200km of the nearest radar and (3) during nighttime.</p> <p>The same dataset can be visualised on a dedicated web interface: <a href="https://bmm.raphaelnussbaumer.com/">www.bmm.raphaelnussbaumer.com</a> and data can be queried on a API (<a href="https://github.com/Rafnuss-PostDoc/BMM-web#how-to-use-the-api">documentation</a>).</p> <p>The csv file is structured as a table with the following columns:</p> <ul> <li>density_estimation: Median bird density [bird/km^2] (quantile 50)</li> <li>density_quantile10: Quantile 10 of bird density [bird/km^2]</li> <li>density_quantile90: Quantile 90 of bird density [bird/km^2]</li> <li>speedu_estimation: Mean bird speed east(+)/west(-) [m/s]</li> <li>speedu_std: Standard deviation of bird speed east(+)/west(-) [m/s]</li> <li>speedv_estimation: Mean bird speed north(+)/south(-) [m/s]</li> <li>speedv_std: Standard deviation of bird speed north(+)/south(-) [m/s]</li> <li>latitude</li> <li>longitude</li> <li>time</li> </ul> <p> </p>
Spatial interpolation of air pollutant and meteorological variables in Central Amazonia
<p>This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository. </p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites
Open the record for dataset details and reuse information.
Code and data for: Interpolant-based demosaicing routines for dual-mode visible/near-infrared imaging systems
Open the record for dataset details and reuse information.
Spatially interpolated non-smoke and smoke PM2.5 concentrations for the US from 2006-2023
Open the record for dataset details and reuse information.
Ensembles for neutron-star-matter equation-of-state interpolations
<p>Ensembles for neutron-star-matter equation-of-state interpolations to reproduce the figures in arXiv:2303.11356</p>
On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models
<p>In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.</p>
Interpolated Climatology -- CBIOMES-global (alpha version) -- surface variables
<pre>**CBIOMES-global (alpha version):** CBIOMES-global (alpha version) is a global ocean state estimate that covers the period from 1992 to 2011. It is based on Forget et al 2015 for ocean physics [MIT general circulation model](https://mitgcm.readthedocs.io/en/latest/index.html) and on Dutkiewicz et al 2015 for marine biogeochemistry and ecosystems [Darwin Project model](https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html). **References:** Forget, G., J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch, 2015: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscientific Model Development, 8, 3071-3104, <http://dx.doi.org/10.5194/gmd-8-3071-2015> or [this URL](http://www.geosci-model-dev.net/8/3071/2015/) Dutkiewicz, S., A.E. Hickman, O. Jahn, W.W. Gregg, C.B. Mouw, and M.J. Follows, 2015: Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model. Biogeoscience, 12, 4447-4481, <http://dx.doi.org/10.5194/bg-12-4447-2015> or [this URL](https://www.biogeosciences.net/12/4447/2015/)</pre>
Interpolation coefficients to go from LLC90 grid to 1/2 degree grid
<p>Interpolation coefficients to go from LLC90 grid to 1/2 degree grid. Created using MeshArrays.jl 's InterpolationFactors function</p>
1/8˚ resolution MOM6-COBALT physical and biogeochemical diagnostics interpolated to 1˚, monthly means between 1965-2017
<p>Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research. This dataset contains dissolved oxygen, nitrate, temperature and salinity data interpolated to monthly means with 1˚ latitude/longitude horizontal resolution, between 1965-2017.</p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p> <p> </p>
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Spherical variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of <strong>Spherical</strong> variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Northern Cameroon daily interpolated rainfall, 1948-2022, combined dataset obtained via ordinary kriging
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> This dataset was created by combining maps obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models and ordinary kriging with fitting of Spherical variogram models. The maps produced by the Spherical variogram models were taken as base of the combined dataset. Dates that could not be produced by the Spherical variogram models, but were produced by the Circular variogram models, were added to the combined dataset. Equally, dates produced by the Spherical variogram models containing negative values, but containing positive values when produced by the Circular variogram models, were replaced. The Mean Error calculated for both variogram models showed a similar bias and a paired t-test showed no significant difference. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Horizontal-plane HRTFs of KEMAR mannequin spatially interpolated with a resolution of 0.5°
<p>HRTFs of the KEMAR manikin (Gardner and Martin, 1995):</p> <ul> <li>"hrtf_M_normal pinna.sofa": the original HRTF dataset from the KEMAR manikin at a lateral resolution of 5° stored as a SOFA file (Majdak et al., 2013)</li> <li>"hrtf_M_normal pinna resolution 0.5 deg.sofa": A super-resolution HRTF set with a directional up-sampling of the original HRTF set to the lateral resolution of 0.5°. To this end, for each ear's HRTF set: <ul> <li>The broadband timing was removed by replacing the HRTF's phase spectrum by the minimum-phase spectrum (Oppenheim et al. 1999) corresponding to HRTF's amplitude spectrum.</li> <li>For the interpolation of the amplitude spectra, the complex spectra of the minimum-phase HRTFs for two adjacent available directions were averaged according to a weighting that corresponded to the interpolated target direction. </li> <li>For the interpolation of the timing, a continuous-direction model of the time-of-arrival (TOA) was applied (Ziegelwanger & Majdak, 2014). TOA is the broadband delay arising from the propagation paths from the sound source to the listener's ear. For a given direction of a sound, the interaural difference of TOAs corresponds to the ITD. The TOA model parameters describe listener's geometry (head and ears) and configure a continuous-direction function of broadband TOA. We used this function to calculate TOAs for directions in steps of 0.5°. To this end, for each ear, the model was fit to an HRTF set as described by Majdak & Ziegelwanger (2013) using the implementation from the Auditory Modeling Toolbox (Søndergaard & Majdak, 2013). Then, each minimum-phase HRTF was temporally up-sampled by a factor of 64, circularly shifted by the TOA obtained from the continuous-direction TOA model for the target direction, and then down-sampled to the sampling rate of 44.1 kHz. Note that the temporal oversampling was required to achieve an interaural resolution of 0.35 µs. </li> </ul> </li> </ul> <p> </p>
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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.