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824 results for “Discrimination”
Figure 1 in Discrimination between six commercially relevant and ecologically diverse fish species across the Gulf of Tunis using fatty acid composition and otolith shape analyses
Figure 1. Study area and location of the sampling stations (■) from which individuals of the six species were collected from the Gulf of Tunis, Tunisia.
Exploring the potential of Near Infrared Hyperspectral Imaging and chemometrics to discriminate soil seed bank of two timber species central African : Erythrophleum suaveolens (Guill. & Perr.) Brenan, and Erythrophleum ivorense A. Chev.
<p>The data of this study are accessible by sending a request to the corresponding author at the email address: douhch382@gmail.com. <a href="https://doi.org/10.5281/zenodo.13908452" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13908452</a></p>
Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments
<p>Supplementary Dataset for the manuscript "Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments" under preparation for bioRxiv submission.<br>Data is packed into a zip archive according to ZENODO upload limitations.<br>in the top folder the actual analysis and respective files, as described in the publications supplementary materials can be found as <em>PLOP2 </em>folder (including raw data etc.).<br>The additional images explain the repo layout in a sketched form: <br>Additionally also for data readout the used <em>docker-compose.yml </em>and a github clone of the important <em>timsr </em>package is included.<br>PLEASE READ the README!.<br><br>Additionally the file `Calibration-2021-08-31_11-44-22.pdf` contains the calibration report, and `PLOP4_method.pdf` a hystar report of the used methodology.<br><br>For LSI Reporting Checklist see DOI: 10.5281/zenodo.13963972</p>
Linked collectors and determiners for: Multivariate discrimination and description of a new species of Tapinoma from the Western United States..
Natural history specimen data linked to collectors and determiners held within, "Multivariate discrimination and description of a new species of Tapinoma from the Western United States.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a>. Formatted as a Frictionless Data package.
Source discrimination - Mount St. Helens dataset
<p>Explosions and earthquakes are effectively discriminated by <em>P</em>/<em>S</em> amplitude ratios for moderate magnitude events (M ≥ 4) observed at regional to teleseismic distances (≥200 km). It is less clear if <em>P</em>/<em>S</em> ratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests of <em>P</em>/<em>S</em> discrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single-fired borehole explosions (M<sub>L</sub> 0.9–2.3) and 406 earthquakes (M<sub>L</sub> 1–3.3). The array provides up to 95 three-component broadband seismographs, and most source-receiver distances are <120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distance <em>P</em>/<em>S</em> ratios are investigated, including frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10–18 Hz performs better than lower or narrower bands because explosion-induced <em>S</em> wave amplitudes diminish relative to <em>P</em> for higher frequencies. Source depth and magnitude exhibited weak influences on <em>P</em>/<em>S</em> ratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustal <em>V</em><sub><em>p</em></sub> and <em>V</em><sub><em>s</em></sub> from traveltime tomography. Overall, the results indicate high potential for local distance <em>P</em>/<em>S</em> explosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.</p>
FIG. 7. — Discriminant score D in A taxonomic revision of the ant subgenus Coptoformica Mueller, 1923 (Hymenoptera, Formicidae)
FIG. 7. — Discriminant score D(5) calculated with the characters sqrtPDF, sqrtPDG, SL/CL, ML, and GLANZ to separate Formica foreli and pressilabris queens. The bars mark the position of the following type material: n, type series of F. naefi Kutter; g, type series of F. goesswaldi Kutter; t, paratype of F. tamarae Dlussky.
FIG. 6. — Discriminant score D in A taxonomic revision of the ant subgenus Coptoformica Mueller, 1923 (Hymenoptera, Formicidae)
FIG. 6. — Discriminant score D(4) calculated with the characters sqrtPDF, sqrtPDG, SL/CS, and TERG to separate Formica foreli and pressilabris worker nest samples; the bars mark the position of the following type material: n, type series of F. naefi Kutter, 1957; g, type series of F. goesswaldi Kutter, 1967; f, holotype of F. foreli Emery, 1909; t, topotypical series of F. tamarae Dlussky, 1964; p, type series of F. pressilabris Nylander, 1846.
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric).
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables).
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1).
Figure 2 in Inferring life history from ovipositor morphology in parasitoid wasps using phylogenetic regression and discriminant analysis
Figure 2. Distal part of ovipositor of the four parasitoid species whose life history is unknown, plus a selection of species whose life history is known. The complete ovipositor of the four species is also shown in profile, drawn relative to the width of the head of the species. Species with known life history are as follows (see Tables 1 and 2 for full names and classification). Endoparasitoids: host is exposed – a Aleiodes, b Zele, c Mesoleptus, d Megastylus, e Ophion, f Microgaster; host is leafminer – g Dacnusa, h Sathon; host is under fruit skin – i Pristomerus; host is in decaying fruit – j Asobara; wasp probes for deeply concealed host – k Orgilus, l Rhimphoctona, m Eubazus; host is stem-borer – n Collyria; host is gall-former – o Orthopelma. Ectoparasitoids: wasp probes for deeply concealed host – p Pseudorhyssa, q Stenobracon; host is leaf-miner – r Colastes; wasp bores for deeply concealed host – s Coeloides.
Figure 4 in Inferring life history from ovipositor morphology in parasitoid wasps using phylogenetic regression and discriminant analysis
Figure 4. Plots of biology vs. phylogenetic regression (PR) estimates of biology for the taxa where the biology is known; results from the three best PR models shown. State 0 = taxa are ectoparasitoids and state 1 = taxa are endoparasitoids. Philomacroploea and Mesoleptus, which are wrongly classified with PR, are indicated. The characters used in the models (U, P and H) are explained in Table 3. Estimates are derived as follows: PR (U) = (0.5281 + 0.8821) ¥ (U - 4730). PR (U + P) = 0.5144 + 1.001 ¥ (U - 0.4617) + 0.1531 ¥ (P - 0.1531). PR (U + P + H) = 0.4948 + 0.9818 ¥ (U - 0.4456) + 0.7428 ¥ (P - 0.1571) - 10.17 ¥ (H - 0.0054).
Top-down modulation of shape and roughness discrimination in active touch by covert attention
<p>Due to limitations in perceptual processing, information relevant to momentary task goals is selected from the vast amount of available sensory information by top-down mechanisms (e.g. attention) that can increase perceptual performance. We investigated how covert attention affects perception of 3D objects in active touch. In our experiment, participants simultaneously explored the shape and roughness of two objects in sequence, and were told afterwards to compare the two objects with regard to one of the two features. To direct the focus of covert attention to the different features we manipulated the expectation of a shape or roughness judgment by varying the frequency of trials for each task (20%, 50%, 80%), then we measured discrimination thresholds. We found higher discrimination thresholds for both shape and roughness perception when the task was unexpected, compared to the conditions in which the task was expected (or both tasks were expected equally). Our results suggest that active touch perception is modulated by expectations about the task. This implies that despite fundamental differences, active and passive touch are affected by feature selective covert attention in a similar way.</p> <p> </p> <p>There are zip files for the main experiment and the two pilot experiments, which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file with the participant's answers for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) for each session in separate folders.</p> <p>Variables of the main experiment are described in the file VARIABLE_CODES_MainExp.txt and the variables of the pilot experiments are described in the files VARIABLE_CODES_PilotRoughness.txt and VARIABLE_CODES_PilotShape.txt.</p>
Dataset Literature Review digital forensic AND discriminant analysis
<p>data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan discriminant analysis"</p>
Predator discrimination of prey promotes the predator-mediated coexistence of prey species
<p><span>The predator discrimination of prey can affect predation intensity and the prey density-dependence of predators, which has the potential to alter the coexistence of prey species. We used a predator–prey population dynamics model accounting for the predator's adaptive diet choice and predator discrimination of prey to investigate how the latter influences prey coexistence. The model revealed that: (1) prey species that are perceived as belonging to the same species by a predator are attacked in the same manner, and it is more difficult for them to coexist than those that are recognised as different prey species; and (2) prey species that are not discriminated by a predator—and therefore cannot coexist—may coexist in the presence of an alternative predator that does discriminate between them. These results suggest that prey diversity, which favours the predator discrimination of prey, and the different capabilities of predators to identify prey species both enhance prey coexistence.</span></p>
Data used in "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition"
<p>Datasets from the paper "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition", By D. T. Blewett, T. X. Choi, Y.-C. Zheng, and E. A. Cloutis, to be published in the journal <em>Earth and Space Science</em>.</p> <p>Reflectance spectra for <em>Apollo</em> lunar samples 14003, 15601, 70011, 14310, and 65015 were published by Wagner et al. (1987), <em>Icarus 69</em>, 14–28. The spectra were digitized by Amanda Hendrix and Faith Vilas (see Hendrix and Vilas (2006), <em>Astron. J. 132</em>, 1396–1404). I took the spectra that Hendrix and Vilas supplied to me and resampled them to RELAB wavelengths. The spectra are in a tab-delimited text file.</p> <p>The responsivities of the <em>Chang'E-3</em> PCAM RGB channels were published by X. Ren et al. (2014), <em>Res. Astron. Astrophys. 14</em>, 1557–1566. We digitized the RGB curves from Fig. 2 of the Ren paper. The curves are in tab-delimited text files.</p> <p> </p>
Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
<p><strong>Associated publication</strong></p> <p>[1] Samuel Ponting*, <strong>Takuma Morimoto</strong>*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a></p> <p>[2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244</p> <p> </p> <p>Datasets contain 2 folders and 1 mat file.</p> <p> </p> <p><strong>(Folder 1) Stimuli</strong></p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p> </p> <p>Details are described below.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 1) Stimuli</strong></p> <p> </p> <p><strong>Overview of datasets</strong></p> <p>This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.</p> <p> </p> <p>The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees).</p> <p>Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).</p> <p> </p> <p><strong>Each image file</strong></p> <p>Filename denotes a condition name and the camera angle as formatted in a following way.</p> <p> </p> <p>stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat</p> <p>e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”</p> <p> </p> <p>Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).</p> <p> </p> <p>Each image file contains two valuables : MacLeodBoynton, XYZ</p> <p> </p> <p>Each variable contains an image of 128*128*3 pixels (height*width*channel).</p> <p> </p> <p>MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M)</p> <p>XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)</p> <p> </p> <p>Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p>Filename denotes the condition and observers formatted in a following way.</p> <p> </p> <p>data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat</p> <p>e.g. data_en2_matte_session4_JH.mat or .csv</p> <p> </p> <p>Each file includes following variables:</p> <p> </p> <p>(Variable 1) threshold</p> <p>Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).</p> <p> </p> <p>MacLeod-Boynton chromaticity coordinates were calculated in a following way. </p> <p>These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).</p> <p> </p> <p>First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org).</p> <p>Each sensitivity curve was normalised to have 1.0 at the peak.</p> <p> </p> <p>Then, MB coordinates were calculated using equation (1-3).</p> <p> </p> <p>L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1)</p> <p>S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2)</p> <p>L+M = Lw*L+Mw*M - (3)</p> <p> </p> <p>where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.</p> <p> </p> <p>L, M and S denote L-cone, M-cone, S-cone excitations, respectively.</p> <p> </p> <p>Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.</p> <p> </p> <p>(Variable 2) staircase</p> <p> </p> <p>Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable.</p> <p>(staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)</p> <p> </p> <p>There are 5 fields:</p> <p>(i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle</p> <p> </p> <p>For each trial, the location of objects was defined in a following way.</p> <p>| 1 3 |</p> <p>| 2 4 |</p> <p> </p> <p>And each field stores following information for all trials in the staircase.</p> <p> </p> <p>(i) groundtruth</p> <p>Location of the target object</p> <p> </p> <p>(ii) response</p> <p>Location that the participant chose</p> <p> </p> <p>(iii) correct</p> <p>If the response was correct (1) or incorrect (0)</p> <p> </p> <p>(iv) magnitude</p> <p>Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).</p> <p> </p> <p>(v) Camera angle</p> <p>Camera angles assigned for four objects in each trial.</p> <p>This data and (i) groundtruth allow reconstruct of the exact image for each trial.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p>This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.</p> <p> </p> <p>1st channel: hue direction from 0 degree to 315 degree, 45 degree step</p> <p>2nd channel: magnitude from 1 to 100</p> <p>3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)</p> <p> </p>
Ultraviolet vision in anemonefish improves color discrimination
<p><span>In many animals, ultraviolet (UV) vision guides navigation, foraging, and communication, but few studies have addressed the contribution of UV vision to color discrimination, or behaviorally assessed UV discrimination thresholds. Here, we tested UV-color vision in an anemonefish (<em>Amphiprion</em> <em>ocellaris</em>) using a novel five-channel (RGB-V-UV) LED display designed to test UV perception. We first determined that the maximal sensitivity of the <em>A</em>. <em>ocellaris</em> UV cone was at ~386 nm using microspectrophotometry. Three additional cone spectral sensitivities had maxima at ~497, 515, and ~535 nm, which together informed the modelling of the fish's color vision. Anemonefish behavioral discrimination thresholds for nine sets of colors were determined from their ability to distinguish a colored target pixel from grey distractor pixels of varying intensity. We found that <em>A</em>. <em>ocellaris</em> used all four cones to process color information and is therefore tetrachromatic, and fish were better at discriminating colors (i.e., color discrimination thresholds were lower, or more acute) when targets had UV chromatic contrast elicited by greater stimulation of the UV cone relative to other cone types. These findings imply that a UV component of color signals and cues improves their detectability, which likely increases the salience of anemonefish body patterns used in communication and the silhouette of zooplankton prey.</span></p>
Interaural Time Difference discrimination threshold determined through three alternative 2I-2AFC procedures
<p>The present dataset has been collected through an experiment on fast ITD discrimination thresholds determination. Each participant was sitting in front of the laptop computer running the GUI during the test. At each trial, the task consisted of listening to two subsequent and randomly balanced stimuli, and then choosing which sound was the rightmost, by selecting it with the mouse on the computer screen. At the beginning of each session, five pilot trials were presented having ITD levels equal to 240, 200, 160, 120, and 80 μs. Correct guesses in all such trials were necessary for the measurements to start in correspondence with the sixth trial. The session lasted approximately 10 minutes. The protocol was designed for determining the PF 79.4% threshold, describing the subjective lateralization performance as a function of T defined as half of the actual ITD to be discriminated. The target (i.e., rightmost) stimulus was lateralized twice as much as a nominal ITD, and the reference source (i.e., leftmost) stimulus was instead lateralized with an opposite ITD. Hence, an ITD threshold equal to 2T means that a participant discriminated the target ITD by T μs from the reference ITD of −T μs. The protocol implemented three different procedures:<br> • adaptive 'three down, one up' two-interval 2AFC (2I-2AFC hereafter),<br> • Gaussian Process Classification (GPC) with Bayesian Active learning by disagreement (BALD) (BALD hereafter),<br> • GPC with random acquisition function (RANDOM hereafter).<br> Accordingly, every session included three series of trials respectively implementing such procedures in a randomly balanced order. When GPC was used, thus enabling active learning and random selection of ITDs, the number of trials was empirically set to 15.</p> <p>In the dataset you will find:</p> <p>- Participant: IDs of the participant<br> - Age: age of the participant<br> - Gender: gender of the participant<br> - ITD_2AFC: T values presented in the 2I-2AFC procedure<br> -LABEL_2AFC: binary labels for the right (1) or wrong (0) answers in the 2I-2AFC procedure<br> - PRED_2AFC: Weibull fitting predictions on 1-100 microseconds<br> - ITD_BALD: T values presented in the BALD procedure<br> - LABEL_BALD: binary labels for the right (1) or wrong (0) answers in the BALD procedure<br> - MEANS_BALD: means of the GPC Bernoulli likelihood computed on 1-100 microseconds at each iteration step in the BALD procedure<br> - VARS_BALD: variances of the GPC Bernoulli likelihood computed on 1-100 microseconds at each iteration step in the BALD procedure<br> - MEANS_LATENT_BALD: means of the latent GPC prior computed on 1-100 microseconds at each iteration step in the BALD procedure<br> - VARS_LATENT_BALD: variances of the latent GPC prior computed on 1-100 microseconds at each iteration step in the BALD procedure<br> - ITD_RAND: T values presented in the RANDOM procedure<br> - LABEL_RAND: binary labels for the right (1) or wrong (0) answers in the RANDOM procedure<br> - MEANS_RAND: means of the GPC Bernoulli likelihood computed on 1-100 microseconds at each iteration step in the RANDOM procedure<br> - VARS_RAND: variances of the GPC Bernoulli likelihood computed on 1-100 microseconds at each iteration step in the RANDOM procedure<br> - MEANS_LATENT_RAND: means of the latent GPC prior computed on 1-100 microseconds at each iteration step in the RANDOM procedure<br> - VARS_LATENT_RAND: variances of the latent GPC prior computed on 1-100 microseconds at each iteration step in the RANDOM procedure</p>
Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore (Main Study Data)
<p>Datasets from Main study of <strong>Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore</strong> (project linked here: https://doi.org/10.17605/OSF.IO/S6VDN).</p>
ScienceDex guides
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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.