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1,003 results for “darkness”
Data: Measurement of Darkness
<p>This file contains the data and minimal working example code for the results underlying the manuscript:<br>"Direct measurement of darkness using a standard single-photon avalanche photodiode"<br>JOSA A 42:506/arXiv: 2410.06691<br>authored by T.H.A. van der Reep, D. Molenaar and W. Löffler</p> <p>See README in file for instructions. </p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Cosmological Initial Conditions (dark matter particles) for 85Mpc^3
<p>Files representing the initial conditions of Dark Matter Particles at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, simulated with 1024^3 cells and 1024^3 DM particles. The data are in hdf5 format and they were generated using the mpgrafic code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Supplementary Data: Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits
<p>The files in this record contain supplementary data for the study, "Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="http://github.com/patscott/pippi" target="_blank" rel="noopener">pippi</a>.</p>
Temperature response of dark respiration from the 1980-82 Eriophorum vaginatum reciprocal transplant experiment along Dalton Highway, Alaska.
These data were collected in July 2011 for tussocks transplanted in 1980-82 in a reciprocal transplant experiment and harvested in 2011. Important variables are garden name, source population, and dark respiration.
Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density
<p>Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "<em>Measuring the local Dark Matter density in the laboratory</em>" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>).</p> <p>Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood. </p>
Fig. 66. Begonia xanthina Hook. A. Dark leaf form. B. Male flowers. C in A revision and one new species of Begonia L. (Begoniaceae, Cucurbitales) in Northeast India
Fig. 66. Begonia xanthina Hook. A. Dark leaf form. B. Male flowers. C. Female flowers. Photographs courtesy of Darrin Norton of a plant in cultivation in a private collection.
Tibial apophysis of palp small and semi-translucent in lateral view (M1). Palpal tibia with a group of small dark papillae on a boss behind apophysis (M2) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Tibial apophysis of palp small and semi-translucent in lateral view (M1). Palpal tibia with a group of small dark papillae on a boss behind apophysis (M2)
Likelihoods for the CTA sensitivity to a dark matter signal from the Galactic centre (A. Acharyya et al., [arXiv:2007.16129])
<p>We present likelihoods to estimate upper limits on DM pair-annihilation in the Galactic centre, based on the<br> Cherenkov Telescope Array (CTA) consortium publication "Sensitivity of the Cherenkov Telescope Array to a<br> dark matter signal from the Galactic centre" [arXiv:2007.16129]. As explained in more detail in Sec. 5.2 of that<br> article, these likelihoods are suitable for models featuring cuspy dark matter profiles and generic gamma-ray<br> spectra produced from annihilating dark matter.</p> <p>The four files contain likelihoods that have been derived with respect to the full CTA South baseline array layout<br> and the initial construction phase of CTA South. For each of these two cases, as indicated by the filename, we<br> provide tables with and without the inclusion of systematic uncertainties (where the former refers to the benchmark<br> treatment of systematic uncertainties as described in the main publication). Further, all likelihoods are based on the<br> benchmark analysis settings with respect to masking known bright gamma-ray sources and the adopted<br> interstellar emission models.</p>
Stealth dark matter confinement transition and gravitational waves --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating the confinement transition of SU(4) stealth dark matter and the possibility that this early-universe transition may produce an observable stochastic background of gravitational waves. See the README for further information.</p> <p> </p>
UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception
<p>UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception</p> <p> </p> <p>This dataset contains:</p> <ul> <li>survey_data.xlsx: Read-only spreadsheet containing the answers of 541 participants of our online survey (48 participants opted to not make their answers publicly available);</li> <li>classification_data.xlsx: Read-only spreadsheet containing the overall and the individual categorization of 240 mobile apps with respect to the presence of dark patterns; and,</li> <li>Videos.zip: videos of 15 apps (10 minutes each) used to classify the apps. The complete set of videos is considerably large and can be provided upon request.</li> </ul>
Detection of ultra-weak laser pulses by free-running single-photon detectors: modeling dead time and dark counts effects
<p>In quantum communication systems, the precise estimation of the detector´s response to the incoming light is necessary to avoid security breaches. The typical working regime uses a free-running single-photon avalanche diode in combination with attenuated laser pulses at telecom wavelength for encoding information. We demonstrate the validity of an analytical model for this regime which considers the effects of dark counts and dead time on the measured count rate. For the purpose of gaining a better understanding of these effects, the photon detections were separated from the dark counts via a software-induced gating mechanism. The model was verified by experimental data for mean photon numbers covering three orders of magnitude as well as for laser repetition frequencies below and above the inverse dead time. Consequently, our model would be of interest for predicting the detector response not only in the field of quantum communications, but also in any other quantum physics experiment where high detection rates are needed.</p>
Supplementary Data: "Dark matter, fine-tuning and mu(g-2) in the pMSSM" (arxiv 2104.03245)
<p>The data consists of the output files of all the runs that were done. The files are organized in iterations, according to the iteration they were produced in. The map name within one iteration is labeled by the "Barbieri-Giudice fine-tuning measure"_"Electroweak fine-tuning measure"_"time stamp". </p> <p>The files that are contained include:</p> <ul> <li> <p>spheno_in.dat, spheno_out.dat, feynhiggs14.dat, slhafile.dat, slhafileGUT.dat, susyhit.dat: in and outputs of the spectrum generators that were used. The slhafile.dat is obtained from softsusy with inputs defined at the SUSY scale, fed to FeynHiggs (produces the feynhiggs14.dat file) and then fed to SUSYHIT to produce susyhit.dat. The slhafileGUT.dat is the same spectrum but the input parameters are run to the GUT scale (defined by the scale where the unification of the coupling constants happens).The susyhit.dat file is used as input for prospino, micromegas, SUSY-AI, the fine-tuning calculation, superiso, gm2calc and ddcalc. The spheno files are used to cross-check our spectra, but not used in the results of the paper;</p> </li> <li> <p>DDcalcv2.out: output of the DDcalc program;</p> </li> <li> <p>Micromegas5_2_1.out, micro.out: micromegas output (version 5.2.1 and version 5.0.8);</p> </li> <li> <p>Prospino.dat: output containing the cross sections for chi1pm chi1pm, chi1pm, neu2, and slepton slepton production;</p> </li> <li> <p>SUSY_AI.dat: output from SUSY-AI;</p> </li> <li> <p>ft_contri.dat: electro-weak fine-tuning calculation, contains the number and the source of the dominant contribution;</p> </li> <li> <p>gm2_out.dat: output of GM2Calc;</p> </li> <li> <p>output_superiso, output_superiso_true: command-line output (output_superiso_true) and the slha-file (output_superiso).</p> </li> </ul> <p> </p> <p>The data used in our plots is stored in a CSV file (datagm2_right_omegah2_only.csv). This contains all the files that have the right omegah^2. </p> <p>This contains:</p> <ul> <li>dir_name: name of saving directory;</li> <li>bsmumu_spheno,btaunu_spheno,bsgamma_spheno,gm2_spheno: calculations done by Spheno of Br(b_s -> mu+ mu-), Br(b->tau nu), Br(b->s \gamma), g-2_mu;</li> <li>flag_prospino.dat_valid,flag_gm2_out.dat_valid,flag_ft_contri.out_valid,flag_DDcalcv2.out_valid,flag_susyhit.dat_valid,flag_spheno_out.dat_valid,flag_micromegas5_2_1.out_valid,flag_output_superiso_true_valid,flag_SUSY_AI.dat_valid: flags to check whether the files were present in the output;</li> <li>flag_LSP: flag to check whether the LSP is the neutralino;</li> <li>sigmachan_5,sigmaSIn_5,sigmaSIp_5,sigmaSDn_5,sigmaSDp_5,sigmav_5,omegachan_5,sigmacontri_5,omegacontri_5,omegah_mic5,omega_chan_label,sigma_chan_label: micromegas 5.2.1 output of the dominant annihilation channel for sigmav, sigmaSIn, sigmaSIp, sigmaSDn, sigmaSDp, sigmav, dominant annihilation channel for omegah^2, contribution of the dominant annihilation channel for sigmav, contribution of the dominant annihilation channel for omegah^2, omegah^2, label for omegah dominant annihilation channel (used for plotting), label for sigmav dominant annihilation channel (used for plotting);</li> <li>pval_DarkSide,pval_XENON,pval_PICO,pval_CRESST: p-values for DarkSide, XENON, PICO, CRESST;</li> <li>gm2_mic5,dmunu_mic5,bsgnlo_mic5,dtaunu_mic5,bsmumu_mic5,btaunu_mic5: output of micromegas 5.2.1 for g-2, D->munu, b->s gamma, D->tau nu, B_s -> mu mu, B->tau nu;</li> <li>zdec_neutralinos_5: output of micromegas 5.2.1 for branching fraction of the invisible decay of the Z boson to neutralinos;</li> <li>dtaunu_iso,bsmumu_iso,btaunu_iso,bsmumuuntag_iso,bsgamma_iso,dmunu_iso,gm2_iso: the low-energy observables as calculated by SuperIso;</li> <li>susyaistopexcl,susyai8pval,susyaistoppval,susyaiewexcl,susyai13excl,susyaiewpval,susyai8excl,susyai13pval: p-values for the SUSY-AI exclusions;</li> <li>n12,n13,n11,n23,n14,n24,n31,n32,n21,n34,n22,n33,n41,n43,u11,u12,v22,v21,u21,v12,u22,n44,n42,v11: neutralino (n) and chargino (u,v) components of the mixing matrices;</li> <li>alpha_higgs: higgs mixing angle;</li> <li>mgluino,md2,mu1,md1,mu2,mc2,mc1,msneutrinotau,mH,mstop2,msbottom2,msbottom1,mstop1,mslepton1,mHpm,mDM,mn2,mn3,mn4,mstau1,mstau2,,msneutrinoe,mA0,mslepton2: masses of the pMSSM spectrum;</li> <li>MeL,M2,AB,M1,M3,MeR,Mq3L,MuR,MuL,SUSYSCALE,AT,MdR,ATAU,MbR,Ml3R,MA,MtR,tanb,mu,Ml3L: input parameters of the pMSSM spectrum;</li> <li>mhiggsFeyn: mass of the SM-like higgs boson as computed by FeynHiggs;</li> <li>FTEW: value for electro-weak fine-tuning;</li> <li>PandaX (2017),DarkSide 50,omegah_ddcalc,XENON1T (2018),DarkSide 20k,DARWIN,LZ,PICO-500,PICO-60 (2017): p-value outputs of DDcalc;</li> <li>gm2calc,gm2calc_unc: GM2Calc calculation for g-2 and the uncertainty;</li> <li>xsec_lRlR,xsec_c1pn2,xsec_lLlL,xsec_c1c1,xsec_c1mn2,xsec_stau1stau1: cross sections for slepton_R sleptonR, slepton_L, slepton_L, chi1pm chi1mp, chi1pm neu2, stau_1 stau_1;</li> <li>excl: number to determine whether the channel is excluded and for what reason (> 0.5 = excluded, < 0.5 = not excluded, 0.4 = sensitive to PICO-500, 0.26 = sensitive to LZ, 0.13 = sensitive to Darwin, 0.0 = not sensitive to any proposed DMDD future experiment). </li> </ul> <p> </p> <p> </p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Large-aperture Experiment to Detect the Dark Ages -- February 2016
<p>Initial data release of LEDA characterization data from February 2016 testing campaign. Please see manuscript, "Design and Characterization of a Radiometric Receiver for the Large-Aperture Experiment to Detect the Dark Ages (LEDA)", D. C. Price et. al.</p>
Seeing lightness in the dark
<p>These are data files for a manuscript about lightness perception under scotopic viewing conditions. The main finding was that observers do not perceive white during scotopic adaptation, implicating that the cones must be activated to produce the appearance of white. The publication will be published in Current Biology as a Correspondence under the title, "Seeing lightness in the dark," on June 19th, 2017. The data file contents are as follows:</p> <p>chipBlocks.csv - This CSV file contains the reflectances of our paper stimuli, relative to the reflectance of our PR650 white reference. Those numbers are in the first column. The remaining columns are for plotting purposes (third column = y-coordinate of squares along bottom of Fig. 1A in the associated paper, remaining columns = RGB values for coloring those squares).</p> <p>chipsVSA4.tsv - A TSV file containing the relevant ratings for Fig. 1B in the associated paper. The first column is the x-axis position for plotting, the second column is population mean white rating, and the third column is standard error of the mean for that rating. The first set of two rows are for peripheral viewing of the chips, the second set of two rows are for foveal viewing of the chips, and the third set of two rows are for ratings given to the larger A4 papers. The ratings are grouped in sets of two because each x-position is repeated twice: once for the rating of the darkest stimulus and a second time for the lightest stimulus. The gray values given to the data points in Fig. 1B were computed by taking the average white rating for that point and dividing it by 100, giving a value in the range of 0-1, which can be used as a normalized grayscale RGB value.</p> <p>obsDataChips.csv - This is a CSV file containing each individual white rating (from 0%-100% in steps of 10%) that observers gave to the chips, with two trials per chip across four different lightness levels and at two different positions of fixation. In the final report, fixation position was only considered for the scotopic condition. A header is in the file labelling the columns.</p> <p>fovealPopDataChips.csv - A CSV file containing white ratings of the chip stimuli when viewed with the fovea. First column is the reflectance of our paper stimuli, as in chipBlocks.csv. The remaining columns are the population mean and standard error of the mean (SEM) for the different paper stimuli under the different adaptation conditions, as follows: 2nd/3rd column = population mean and SEM for scotopic adaptation, 4th/5th column = population mean and SEM for mesopic adaptation, 6th/7th column = population mean and SEM for dim photopic adaptation, 8th/9th column = population mean and SEM for bright photopic adaptation.</p> <p>obsDataA4.csv - Same as obsDataChips.csv, but for the A4 sized papers. Observers were not given explicit instructions about different fixation positions during this experiment, so fixation was always listed as "center" and this column was ignored in later analysis.</p> <p>popDataA4.tsv - Same as fovealPopDataChips.csv, but for the A4 sized papers.</p> <p>whiteOLED.zip - A collection of MATLAB MAT files with data for each of the 17 subjects that performed the white patch adjustment task on an OLED, as described in the Supplementary Info of the associated paper. Each MAT file contains a 2x3 matrix variable named, "v", which holds the "luminance (v)alue" that each observer choose as their impression of white (see Supplementary Info for more details). The first row contains values for the larger patch and the second row contains the values for the smaller patch (each tested 3 times). The values are in normalized units of 0-1 for our monitor. To convert to luminance, use the following transformation: 2.04698e-5*((v*1023)^2.28231).</p>
Data archive for: Resting cells of Skeletonema marinoi assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions
<p>Data archive for: “Resting cells of <em>Skeletonema marinoi</em> assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions” <a href="https://doi.org/10.1111/1462-2920.16625">https://doi.org/10.1111/1462-2920.16625</a></p> <p> </p> <p>Dataset of single cell assimilation of organic/inorganic C/N by resting cells of the marine diatom <em>Skeletonema marinoi</em> captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The dataset also contains POC/PON changes over time during dormancy, DNRA (<sup>15</sup>N-NH<sub>4</sub><sup>+</sup> production), denitrification (<sup>15</sup>N-N<sub>2</sub> production) and a germination assay to determine survival rate, most probable number analysis (MPN). </p> <p>Two strains (GF04 and R05) were incubated in dark and anoxic conditions in two different incubation experiments.</p> <p>Incubation 1: Diatoms treated with antibiotics before entering dormancy compared to a control not treated with antibiotics then given <sup>15</sup>N-NO<sub>3</sub><sup>-</sup> in dark anoxic conditions.</p> <p>Incubation 2: Diatoms treated with antibiotics given, <sup>15</sup>N & <sup>13</sup>C urea, <sup>15</sup>N & <sup>13</sup>C urea + <sup>14</sup>N-NO<sub>3</sub><sup>-</sup>, <sup>13</sup>C-acetate, <sup>13</sup>C-acetate + <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>, or <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>.</p> <p>See the main manuscript for a extensive experimental setup.</p> <p> </p> <p><strong>Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.</strong></p> <p><strong>DNRA_and_denitrification.csv/xlsx:</strong> DRNA and denitrification depending on volume (Incubation 1)</p> <p><strong>DNRA_per_cell.csv/xlsx:</strong> DNRA per cell (Incubation 1 & 2)</p> <p><strong>MPN_data.csv/xlsx:</strong> Most probable number analysis (Incubation 1 & 2)</p> <p><strong>POC_PON.csv/xlsx:</strong> POC and PON per cell and volume (Incubation 1 & 2)</p> <p><strong>SIMS_data.csv/xlsx:</strong> SIMS data (Incubation 1 & 2)</p> <p> </p> <p> </p>
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm.
Fig. 2 in Two hundred years in the dark: A type for the moss Encalypta crispata
Fig. 2. – Original illustration of Encalypta crispata Hedw. from Hedwig's Species muscorum frondosorum of 1801, tab. 10. [Original figure legend: "fig. 1. planta naturali magnitudine, fig 2. eadem acuta lente o., fig. 3. folium augm. III., fig. 4. sporangium operculatum augm. I. fig. 5. sporangii pars augm. III. fig. 6. peristomii dens augm. IV. fig. 7. Flos masculus augm. III. fig. 8. folliculi augm. IV. fig 9. filum succulentum floris augm. IV."]
Fig. 3 in Two hundred years in the dark: A type for the moss Encalypta crispata
Fig. 3. – Ptychomitrium crispatum (Hedw.) A. Jaeger. A. Plants in dry (left) and wet (right) state; B. Leaves; C. Leaf cross-sections from leaf base, middle, upper part and apex (left to right); D. Basal cells; E. Cells from widest part of the leaf; F. Upper leaf cells; G. Leaf apex; H. Operculum; I. Calyptra; J. Peristome teeth (outer side). [A–D, F: Hedderson 15676 (BOL); E, G–J: Wilms s.n. (G)]
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.