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2,113 results for “High resolution”
Figure 15 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 15. Selected Wuchiapingian representatives of ammonoids from Baghuk Mountain; all specimens stored in the collection of the Museum für Naturkunde, Berlin. (a) Prototoceras sp., specimen MB.C.30219 (Araxoceras beds). (b) Vedioceras sp., specimen MB.C.30220 (Vedioceras beds). (c) Eoaraxoceras sp., specimen MB.C.30221 (Araxoceras beds). (d) Urartoceras sp., specimen MB.C.30222 (Pseudotoceras beds). Scale bar units = 1 mm.
Figure 5 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 5. Stratigraphic subdivision of the Permian–Triassic boundary sections in the Julfa sections (from Ghaderi et al., 2014) and at Baghuk Mountain (from Farshid et al., 2016) with lithostratigraphic correlation. W – Wuchiapingian.
Figure 12 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 12. Columnar section of the basal part of the Elikah Formation at Baghuk Mountain with the position of microbial buildups, changes in bed thickness and frequency of bivalve shells.
Figure 1 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 1. Geographic position of Permian–Triassic boundary sections, including Baghuk Mountain (BM), in Central Iran.
Figure 11 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 11. Field photograph of in situ microbialite occurrence showing digitate upward growing branches of digitate stromatolite columns in the Baghuk Member; Baghuk Mountain K section. Scale bar units = 10 cm.
Figure 4 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 4. Correlation of the Permian–Triassic boundary beds in Central Iranian sections. Kuh-e-Hambast rock column after Kozur (2005). Position of the conodont-based Permian–Triassic boundary after Kozur (2005; 1, 3), Farshid et al. (2016; 2) and Richoz et al. (2010; 4). Asadabad section after unpublished data.
Figure 8. Section C in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 8. Section C with the top part of the Hambast Formation and the basal 40 m of the Elikah Formation including the Baghuk Member. View towards the west.
Figure 3 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 3. The Permian–Triassic boundary at Baghuk Mountain section C, Central Iran. View towards the north-west, in the background, summit composed of Triassic rocks.
Figure 9 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 9. Columnar sections of the Baghuk Member ("Boundary Clay") in some of the sections at Baghuk Mountain. Legend as in Figs. 4 and 6; EH – extinction horizon.
High-resolution projections of evapotranspiration and water availability for Europe under climate change
<p>Europe-wide high-resolution (1 km) gridded data of estimates of monthly and annual potential evapotranspiration (ET0), annual actual evapotranspiration (AET0) and water availability for a climate normal period largely preceding an anthropogenic warming signal (1961-1990) and for two CMIP5 multimodel future projections (2011-2040 and 2041-2070). In the ET0 calculation, the monthly and annual heat index <em>I</em> and annual <em>α</em> parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach.</p> <p>For citations and more details, please refer to "High-resolution projections of evapotranspiration and water availability for Europe under climate change" by Ştefan Dezsi, Marcel Mândrescu, Dănuţ Petrea, Praveen Kumar Rai, Andreas Hamann, Mărgărit-Mircea Nistor, published in <em>International Journal of Climatology</em> (<a href="https://doi.org/10.1002/joc.5537">https://doi.org/10.1002/joc.5537</a>)</p>
Images and supporting data for high-resolution μCT of a mouse embryo using a compact laser-driven x-ray betatron source
<p>A high resolution x-ray CT scan of an embryonic mouse sample was performed with the betatron x-ray source produced by a laser wakefield accelerator. This data deposition includes all of the raw images of the mouse sample, information regarding their indexing, featured slices of the tomogram and some further raw data regarding the x-ray source characterisation.</p>
High-resolution glomerular responses to a large variety of odorants in the mouse olfactory bulb
<p>Imaging of glomerular responses using intrinsic optical signal and synaptopHluorin. </p> <p>Find the software here: <a href="https://doi.org/10.5281/zenodo.3383874">https://doi.org/10.5281/zenodo.3383874</a></p> <p>The paper is here: </p> <p>Soelter, J., Schumacher, J., Spors, H., Schmuker, M.: Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map. <em>Sci Rep</em> 10, 77 (2020). <a href="https://doi.org/10.1038/s41598-019-56863-4">https://doi.org/10.1038/s41598-019-56863-4</a></p>
High Resolution Retinal Blood Vessels Datasets of Diabetic Retinopathy Patients
<p>This dataset contains 89 high resolution image files of blood vessels extracted from publicly available retina images available in DIARETDB1</p>
Ruthwell Cross 3D Model High Resolution (112M poly count)
<p>This is a very High-Resolution 3D model the Ruthwell Cross, developed as part of the ongoing Visionary Cross project. This model has a 112M poly count.</p> <p>This record contains:</p> <ul> <li>An xml record: <a href="http://zenodo.org/record/1490878/_Ruthwell_3DModel_112M_Metadata.xml">_Ruthwell_3DModel_112M_Metadata.xml</a></li> <li> A 3D Model of the Ruthwell cross: <a href="https://zenodo.org/record/1490878/cross_ColorMapped_112M.ply">cross_ColorMapped_112M.ply</a> </li> <li>A 2D thumbnail: <a href="https://zenodo.org/record/1490878/Ruthwell_Cross00.png">Ruthwell_Cross00.png</a></li> </ul> <pre>The DOI for this version of the record is <a href="https://zenodo.org/record/1490878">10.5281/zenodo.1490878</a>. The DOI <a href="https://10.5281/zenodo.1233638">10.5281/zenodo.1233638</a> always points to the latest version of this record.</pre> <p>This file in .ply format is best viewed using 3DHOP, but can also be viewed using 3D rendering software like MeshLab.</p> <p>For individual panels and other related material go to The Visionary Cross community at: https://zenodo.org/communities/the_visionary_cross/</p>
A High-resolution Mosaic of the Neutral Hydrogen in the M81 Triplet
<p>This dataset shows the distribution of neutral hydrogen in and around the M81 galaxy triplet (M81, M82, NGC 3077) and consists of a 3° × 3°, 105-pointing, high-resolution neutral hydrogen (H I) mosaic obtained with the Very Large Array C and D arrays. The data are described in the paper by <a href="http://adsabs.harvard.edu/abs/2018ApJ...865...26D">de Blok et al. (2018)</a>.</p> <p>Here we provide the following data products:</p> <p><strong>Cubes:</strong></p> <ul> <li>the natural-weighted cube of the VLA C+D mosaic: <em>m81.nat.cube.fits</em></li> <li>the robust-weighted cube of the VLA C+D mosaic: <em>m81.rob.cube.fits</em></li> <li>the natural-weighted cube using only D-array-like baselines: <em>m81_D.nat.cube.fits</em></li> <li>the natural-weighted and zero-spacing corrected data cube of the VLA C+D array and GBT single-dish data from <a href="http://adsabs.harvard.edu/abs/2011AJ....141....9C">Chynoweth et al. (2011)</a>: <em>m81.zero.cube.fits</em></li> </ul> <p><strong>Moment maps:</strong></p> <ul> <li>natural-weighted zeroth (column density), first (velocity field) and second (velocity dispersion) moment maps of the VLA C+D mosaic: <em>m81.nat.mom[0,1,2].fits</em></li> <li>robust-weighted zeroth (column density), first (velocity field) and second (velocity dispersion) moment maps of the VLA C+D mosaic: <em>m81.rob.mom[0,1,2].fits</em></li> <li>natural-weighted zeroth, first and second moment maps of the "D-array" mosaic: <em>m81_D.nat.mom[0,1,2].fits</em></li> <li>zero-spacing corrected natural-weighted integrated HI map (zeroth-moment) of VLA C+D and GBT data: <em>m81.zero.mom0.fits</em></li> </ul> <p><strong>Acknowledgements:</strong></p> <p>If you make use of these data please cite the original paper:</p> <p><a href="http://adsabs.harvard.edu/abs/2018ApJ...865...26D">de Blok et al. (2018) </a>- de Blok, W.J.G., Walter, F., Ferguson, A.M.N., et al. 2018, ApJ, 865, 26 (<a href="https://doi.org/10.3847/1538-4357/aad557">10.3847/1538-4357/aad557</a>)</p> <p> </p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry
<p>Supplementary data for <a href="https://doi.org/10.1016/j.petrol.2019.106563">10.1016/j.petrol.2019.106563</a></p> <p>Mass spectra were measured on a Q Exactive HF at 240k@200 m/z resolution in nanospray-ESI direct infusion using a Advion TriVersa NanoMate source. Broadband mass spectra were generated from SIM-segments using dimspy (https://github.com/computational-metabolomics/dimspy). Peaks were annotated using Formularity v.1.0.0 (10.1021/acs.analchem.7b03318) after internal calibration using a homologous CHN series (identified from preliminary KMD/KM plots). All plots were generated using python 3.6.6 and the plotly graphing library (https://plot.ly/python/).</p> <p> </p>
High resolution ICON simulation over the Tropical Atlantic
<p>This dataset contains binary info on liquid water content of an ICON simulation performed over the subtropics. Detailed information available in the readme file. This data is used in the publication titled: On the size dependence of cumulus cloud spacing.</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.