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306 results for “impulsivity”
A multiple model high-resolution head-related impulse response database for aided and unaided ears (SOFA format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in SOFA format, with the data identical to the HDF5 files in <a href="https://zenodo.org/record/1226873">doi:10.5281/zenodo.1226873</a>.</p>
Far-field effects of impulsive noise on coastal bottlenose dolphins
<p>Increasing levels of anthropogenic underwater noise have caused concern over their potential impacts on marine life. Offshore renewable energy developments and seismic exploration can produce impulsive noise which is especially hazardous for marine mammals because it can induce auditory damage at shorter distances and behavioural disturbance at longer distances. However, far-field effects of impulsive noise remain poorly understood, causing a high level of uncertainty when predicting the impacts of offshore energy developments on marine mammal populations. Here we used a 10-year dataset on the occurrence of coastal bottlenose dolphins over the period 2009-2019 to investigate far-field effects of impulsive noise from offshore activities undertaken in three different years. Activities included a 2D seismic survey and the pile installation at two offshore wind farms, 20-75 km from coastal waters known to be frequented by dolphins. We collected passive acoustic data in key coastal areas and used a Before-After Control-Impact design to investigate variation in dolphin detections in areas exposed to different levels of impulsive noise from these offshore activities. We compared dolphin detections at two temporal scales, comparing years and days with and without impulsive noise. Passive acoustic data confirmed that dolphins continued to use the impact area throughout each offshore activity period, but also provided evidence of short-term behavioural responses in this area. Unexpectedly, and only at the smallest temporal scale, a consistent increase in dolphin detections was observed at the impact sites during activities generating impulsive noise. We suggest that this increase in dolphin detections could be explained by changes in vocalization behaviour. Marine mammal protection policies focus on the near-field effects of impulsive noise; however, our results emphasize the importance of investigating the far-field effects of anthropogenic disturbances to better understand the impacts of human activities on marine mammal populations.</p>
Binaural room impulse responses of a 5.0 surround setup for different listening positions
<p>Binaural Room Impulse Responses - KEMAR, room Calypso, TU Berlin, 5.0 Surround setup<br /> </p> <p>This dataset contains binaural room impulse responses (BRIRs) measured at nine<br /> different listening positions for a 5.0 surround setup in the listening room<br /> Calypso in the Telefunken building of Technische Universität Berlin, Berlin,<br /> Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s<br /> at a frequency of 1 kHz.</p> <p>"doc.zip" contains additional information for the measurement,<br /> "*.sofa" are the actual BRIRs, one file for every listening position, where the<br /> position is indicated by the X*-Y*-values.<br /> In order to work with those files you need a SOFA API for your programming<br /> language. For example, the one for Matlab can be found here:<br /> https://github.com/sofacoustics/API_MO/releases/latest<br /> If you want to create a single SOFA file containing all listening positions, you<br /> can execute the "combine_positions.m" script in Matlab after installing and<br /> starting the SOFA API.</p> <p>The BRIRs were with a head-orientation varying in the range of +-90° with 1°<br /> resolution. Room shape and size, listener and sound source positions are shown<br /> in "setup_calypso_surround_genelec8030A.pdf".</p> <p>Directory "./photos" contains photographs of the measurement setup.</p> <p>Copyright 2016 Hagen Wierstorf</p> <p>Licensed under Creative Commons (CC-BY-4.0)</p>
Head-related impulse responses of a loudspeaker array
<p>Head-related impulse responses measured with a KEMAR dummy head of a loudspeaker array consisting of 13 loudspeakers. The dummy head was placed at three different positions, which allows to combine the measurements to an loudspeaker array consisting of 35 loudspeakers.</p> <p>The measurement equipment was exactly the same as in http://dx.doi.org/10.5281/zenodo.55418</p>
Binaural room impulse responses of a 5.0 surround setup for different listening positions
<p>This dataset contains binaural room impulse responses (BRIRs) measured at nine<br> different listening positions for a 5.0 surround setup in the listening room<br> Calypso in the Telefunken building of Technische Universität Berlin, Berlin,<br> Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s<br> at a frequency of 1 kHz. The measurment was done with Genelec 8030A loudspeakers<br> and repeated for the central listening positon with the larger Genelec 8250A<br> loudspeakers.</p> <p>"doc.zip" contains additional information for the measurement,<br> "*.sofa" are the actual BRIRs, one file for every listening position, where the<br> position is indicated by the X*-Y*-values. The file<br> `KEMAR_Calypso_Surround.sofa` contains all listening positions in one file.<br> In order to work with those files you need a SOFA API for your programming<br> language. For example, the one for Matlab can be found here:<br> https://github.com/sofacoustics/API_MO/releases/latest</p> <p>The BRIRs were with a head-orientation varying in the range of +-90° with 1°<br> resolution. Room shape and size, listener and sound source positions are shown<br> in `setup_calypso_surround_genelec8030A.pdf` and <br> `setup_calypso_surround_genelec8250A.pdf`.</p> <p>Directory "./photos" contains photographs of the measurement setup.</p> <p>Copyright 2016 Hagen Wierstorf</p> <p>Licensed under Creative Commons (CC-BY-4.0)<br> </p>
Binaural room impulse responses: Same listener-source-setup at different positions in the room
<p>To study the perception of room acoustics in dependency of the position in the room, measurements with KEMAR head-and-torso-simulator were conducted. The dummy head was placed at 5 different positions in a small conference room (10.3mx5.8mx3.1m, RT=0.65s). The source, a loudspeaker Genelec 1030A, was always positioned in the same relation to the listening position. BRIRs were measured with an azimuth-resolution of 5° from 0°-360°. This data allows a psychoacoustical comparison of the room acoustical properties at different positions in the room.</p>
Binaural room impulse responses: Same listener-source-setup at different positions in the room
<p>To study the perception of room acoustics in dependency of the position in the room, measurements with KEMAR head-and-torso-simulator were conducted. The dummy head was placed at 5 different positions in a small conference room (10.3mx5.8mx3.1m, RT=0.65s). The source, a loudspeaker Genelec 1030A, was always positioned in the same relation to the listening position. BRIRs were measured with an azimuth-resolution of 5° from 0°-360°. This data allows a psychoacoustical comparison of the room acoustical properties at different positions in the room.</p>
Binaural room impulse responses recorded with KEMAR in a small meeting room
<p>The binaural room impulse responses (BRIRs) were measured in the small meeting room Spirit at the<br> Telefunken-building of TU Berlin. They were measured for three different loudspeaker positions placed around a table. The head of the dummy head was rotated with a resolution of 1° ranging from -90° to 90°. The measurement equipment was the same as described in Wierstorf et al. [1]</p> <p>[1] Wierstorf, H., Geier, M., Raake, A., Spors, S. (2011) “A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances,” 130th AES Convention, eBrief 6</p>
MeshRIR: Dataset of Room Impulse Responses on Meshed Grid Points
<p>MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods. </p> <p>See the link below for the details:</p> <p><a href="https://sh01k.github.io/MeshRIR/">https://sh01k.github.io/MeshRIR/</a></p>
The impulse response of optic flow sensitive descending neurons to roll m-sequences
<p>When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male <i>Eristalis tenax </i>hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.</p>
Sound examples of an Impulse Pattern Formulation model synchronizing to different click tracks
<p>In the publication, supplemented by these sound examples, the Impulse Pattern Formulation is used to model the synchronization of musicians to a collective tempo. Several click tracks are numerically created, representing eighth notes played by a musician or a metronome for different tempo changes.<br> By replacing every beat with a sound sample, audio files are created for a more musical evaluation of the results. The IPF is represented by a cowbell and the underlying click track with claves. Those sound files are in stereo, whereby the click track is at the left channel, and the IPF's signal is at the right channel. Thus, e.g., the balance potentiometer of a stereo system can be used to blend both sounds freely. The practical examples are:</p> <p><strong>Fig.2:</strong><br> IPF reacts to four different step changes in tempo.</p> <p><strong>Fig.5:</strong><br> The IPF reacting to the same changes in tempo as shown in Figure 2, when changing the tempo linear during 24 beats instead of step changes.</p> <p><strong>Fig.8:</strong><br> IPF adapting to a noisy click track: the upper line (a) and b)) shows white noise, and the lower line (c) and d)) Brownian noise. On the left (a) and c)), the fluctuation is <span class="math-tex">\(\pm1~\%\)</span>, and on the right (b) and d)) <span class="math-tex">\(\pm 5~\%\)</span>.</p> <p><strong>Fig.9:</strong><br> IPF adapting to a sinusoidally modulated click track: the upper line (a) and b)) shows a modulation period of 32 eighth notes, and the lower line (c) and d)) shows a modulation period of 8 eighth notes. On the left (a) and c)), the amplitude is 36 bpm, and on the right (b) and d)) 6 bpm, both centered around 113 bpm.</p> <p><strong>Fig.12:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF which considers phase differences: a) step change from 120 to 100 bpm, b) linear change from 120 to 130 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 120 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p><strong>Fig.13:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF optimized for polyrhythms: a) step change from 120 to 140 bpm, b) linear change from 100 to 120 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 90 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p>In all Figures, blue lines refer to the tempo of the click track, and red lines correspond to the tempo of the IPF. The single crosses represent single beats.</p> <p>A more in-depth description of how these sounds were synthesized can be found in the publication supplemented by these examples:</p> <p>Linke, S., Bader, R., & Mores, R. (2021). Modeling synchronization in human musical rhythms using Impulse Pattern Formulation (IPF). http://arxiv.org/pdf/2112.03218v1</p>
Synthetic recovery of impulse propagation in myocardial infarction via silicon carbide semiconductive nanowires
<p><strong> DataSet for the publication "Synthetic recovery of impulse propagation in myocardial infarction via silicon carbide semiconductive nanowires"</strong></p> <p>Pre-processed Confocal data for Figure 3 and Supplementary Figure 3. Acquired with Leica SP8 Laser-Scanning Confocal Microscope</p> <p>Pre-processed HPICM raw data for Figure 2, Supplementary Figure 2 Acquired with Ionscope Hopping Software</p> <p>Pre-processed double-patch clamp data for Figure 2b-c. Acquired with Clampfit 10.6</p> <p>Pre-processed EP raw data for Figure 6-7-8, supplementary Figure 4. Acquired with Clampfit 10.6</p> <p>Post-processed Kinematic trajectories for Supplementary Figure 5, Supplementary Figure 6 acquired with Video Spot Tracker V 8.00</p> <p> </p> <p> </p> <p> </p>
Multiphonic of clarinet synthesized using Impulse Pattern Formulation (IPF)
<p>In the publication, supplemented by these sound examples, the Impulse Pattern Formulation IPF is used to model multiphonics of a clarinet. Further, the sound of a multiphonic played on the clarinet was recorded and synthesized using the IPF. Different audio examples are provided:</p> <p><strong>Example 1:</strong><br> A recording of the multiphonic, described in Figure 3 of the supplemented publication.</p> <p><strong>Example 2:</strong><br> A synthesized version of this multiphonic, using the one period of a recorded clarinet playing a stable sound as an example of a somewhat realistic sound.</p> <p><strong>Example 3:</strong><br> A synthesized version of this multiphonic, using a Gaussian function as a very simple approximation.</p> <p><strong>Example 4:</strong><br> A synthesized version of this multiphonic, using one period of a bowed string as an example of a mismatched sound.</p> <p>A more in-depth description of how these sounds were synthesized can be found in the publication supplemented by these examples:</p> <p>Linke, S., Bader, R., & Mores, R. (2022, January 14). Multiphonic modeling using Impulse Pattern Formulation (IPF). https://arxiv.org/pdf/2201.05452</p>
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </p>
A dataset of measured spatial room impulse responses in different rooms including visualization
<p>An open-source dataset of captured spatial room impulse responses (SRIRs) is presented. The<br> data was collected in different enclosed spaces at the Technische Universität Ilmenau using an open self-build<br> microphone array design following the spatial decomposition method (SDM) guidelines. The included rooms<br> were selected based on their distinctive acoustical properties resulting from their general build and furnishing as<br> required by their utility. Three different classes of spaces can be distinguished, including seminar rooms, offices,<br> and classrooms. For each considered space different source-receiver positions were recorded, including 360°<br> images for each condition. The dataset can be utilized for various augmented or virtual reality applications, using<br> either a loudspeaker or headphone-based reproduction alongside the appropriate head-related transfer function sets.<br> In future, we plan to add more rooms and more source-receiver positions.</p> <p>Please cite our corresponding paper:</p> <p>Klein, F., Surdu, T., Aretz, A., Birth, K., Edelmann, N., Seitelman, F., Ziener, C., Werner, S., and Sporer, T., “A dataset of measured spatial room impulse responses in different rooms including visualization,” in 152nd AES Convention, 2022, https://www.aes.org/e%E2%80%90lib/browse.cfm?elib=21728</p> <p> </p>
Binaural Impulse Response Dataset: Square Plate in Anechoic Chamber
<p><span>The dataset at hand contains impulse responses that have been measured along two discretized trajectories in the vicinity of a 25 mm thick 1 m x 1 m medium density fiberboard plate. Such data can serve as reference for the modelling of acoustic edge diffraction. This dataset was used in the context of research on binaural perception of diffracted sound in a publication that is in press at the Journal of the Acoustical Society of America</span></p>
Multi-Purpose Room Impulse Response Dataset Measured on a 3D Spatial Grid
<h1>Introduction</h1> <p>The sound field inside a room depends on many factors, such as the room shape, the absorption characteristics of the materials that comprise the bounding surfaces, the furniture present in the room, and the source position and its acoustic characteristics. An increasing number of publicly available room impulse response (RIR) databases that aim to provide detailed descriptions of interior sound fields can be found in the literature. These databases can be utilized in research as well as in the development and verification of signal processing algorithms that use this information on the acoustic environment. The availability of many RIR databases covering diverse scenarios is beneficial to the community.</p> <p>We provide a database of RIRs, namely the <strong>M</strong>ulti-<strong>P</strong>urpose <strong>RIR</strong> (<strong>MP-RIR</strong>) dataset, which contains 68736 RIRs measured on a dense 3D grid inside a complex-shaped room. We used a measurement robot with a rotating arm that operates as a linear guide and is capable of moving a vertical, linear array of eight omni-directional microphones. Four different sources have been used and were placed at eight different positions inside the room. A detailed desciption of the measurement campaign and the dataset is presented in the paper (https://aes2.org/publications/elibrary-page/?id=22515). </p> <h1>Contents of the MP-RIR dataset</h1> <p>In the following, the contents and the structure of the provided dataset are described:</p> <ul> <li>Sk_Mrir.npy:<br>Matrix, which contains the RIRs for all measured grid points for the loudspeaker Sk, k = 1, 2, ..., 8.<br>The matrix has the shape [N_xy, N_z, N] = [1074 x 8 x 100096], where N_xy is the number of 2D grid positions to which the robot is moving the vertical microphone array of N_z microphones. The length of each RIR is described by N.</li> <li>Mxyz.npy:<br>Matrix, which contains the microphone coordinates of the measured RIRs and corresponds to the matrices Sk_Mrir.<br>The matrix has the shape [N_xy, N_z, N_d] = [1074 x 8 x 3]. The indexing for the first two dimensions is the same as for the matrices Sk_Mrir, so that the microphone coordinates can be immediately retrieved for the provided RIRs. The third dimension with the length N_d gives access to the x-, y- and z-coordinate values in meters.</li> <li>Setup.npz<br>Dictionary, which contains parameters related to the measurement setup, with the following keys:<br> <ul> <li>angles_speaker<br>Dictionary of azimuth angles in degrees of the loudspeakers, with the keys S1, S2, ..., S8.</li> <li>coord_speaker_center<br>Dictionary, which contains the x-, y- and z-coordinates of the loudspeaker positions at the center of the base of each loudspeaker. The coordinate arrays can be accessed with the keys S1, S2, ..., S8.</li> <li>coord_polygon<br>Array of shape [4,2], which contains the x- and y-coordinates in meters of the room corners C_q, q=0,1,2,3.<br>The first dimension of the array relates to the room corners and the second dimension relates to the coordinates. </li> <li>fs<br>Sampling rate in Hz.</li> <li>T_guard<br>Guard time in samples. The guard time provides additional samples at the beginning of the RIR to increase the quality of the RIR.</li> <li>T_system<br>Delay of the measurement system in samples.</li> </ul> </li> </ul> <h1>Further Information</h1> <p>The delay of the RIRs is composed of the guard time T_guard, the system delay T_system and the acoustic delay T_ac. The guard time and system delay can be retrieved from the file Setup.npz described above.</p> <p>A gain alignment procedure was applied to align the output SPL between the loudspeakers, as described in the paper. Additionally, all RIRs were scaled by the same value, the maximum absolute peak of all measured RIRs. As a result, the maximum absolute value in each individual RIR is less or equal to 1.</p>
Room Impulse Responses for Low-Frequency Sound Field Control
<h2>About</h2> <div> <div> <div> <p>A dataset of room impulse responses (RIRs) measured in the low frequency range, for different measurement signal lengths, in two rooms with different acoustic conditions. Acquired with the purpose of low-frequency sound zones rendering and evaluation, the dataset can be used in general for different sound field control methods.</p> <p>By design, the dataset is composed of two sets of RIRs: one intended for the design of the control strategies, and another one intended for evaluation [1]. The RIRs of the first set, obtained with measurement signals of different length, allow exploring the influence of the acquisition time of the RIRs in the control methods [2]. The RIRs of the second set allow evaluating the sound field generated at and around the position of the RIRs of the first set.</p> <p>The RIRs were acquired with the Synchronized Swept-Sine (SSS) method, proposed by Novak et al. [3], at a samplig frequency of 48 kHz with SSS signals varying from 15 Hz to 600 Hz. The obtained RIRs were re-sampled to 1.2 kHz.</p> <p>Two files with different contents have been added:</p> <ul> <li><strong>RIR_LF_SFC_Light:</strong> contains the documentation, MatLab codes, and the ready-to-use RIRs stored as 3D-arrays in .mat files.<br><br></li> <li><strong>RIR_LF_SFC_Full:</strong> in addition to files in <strong>RIR_LF_SFC_Light</strong>, it contains the original SSS signals and the signals recorded during the measurements. These were used to retrieve the RIRs and therefore, can be used for custom purposes. <br><br></li> </ul> </div> </div> </div> <div> <h2>Citation</h2> <p>Please cite the database with the following paper:</p> <p>@inproceedings{cadavid_ATvsSS_2024,<br>title={Spatial Sampling versus Acquisition Time of Room Impulse<br> Responses for Low-Frequency Sound Zones},<br> author={Cadavid, Jos{\'e} and M{\o}ller, Martin Bo and van Waterschoot, Toon and Bech, S{\o}ren and {\O}stergaard, Jan},<br> booktitle={Audio Engineering Society Convention 156},<br> year={2024},<br> organization={Audio Engineering Society} }</p> <h2>Acknowledgements</h2> <p>The authors would like to thank <a href="https://orcid.org/0000-0002-1452-2227" target="_blank" rel="noopener">Antonin Novak</a>, <a href="https://orcid.org/0000-0002-2175-6603" target="_blank" rel="noopener">Christian S. Pedersen</a>, and Claus Vestergaard for their help with the RIRs measurements.</p> <p>This project has received funding from the European Union’s (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions Grant No. 956369.</p> </div>
Room Impulse Response measurements of a rectangular room
<p>This archive contains the data for a Gitlab hosted project (https://github.com/epfl-lts2/joint_estimation_of_room_geometry_and_modes). This archive allows users to extract the Room impulse responses (RIRs) measurements for a real rectangular room. A total of 132 measurements are included. Furthermore, users have the option to perform several post processing steps on the RIRs such as filtering, downsampling and truncation in time domain. Please refer to the guidelines pdf for more information.</p>
Data archive for Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans'
<p>This archive contains the raw data from Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans', plus R code for the data analyses. The impulsivity measure (HMDT) used in studies 2 and 3 is also included here. </p> <p>The R script 'IndividualAnalysis.R' performs the analysis of each study individually. 'MetaAnalysis.R' performs the meta-analysis. The three .csv files are the raw data from the three studies respectively. </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.