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10,553 results for “measurements”
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2022-01-01 to 2022-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2021-01-01 to 2021-12-31 [RAW]
<div> <p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2021. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p> <p> </p> </div>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2020-01-01 to 2020-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2020. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 29.6m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2019-01-01 to 2019-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2019. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 30m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Crop and soil measurements of quinoa in Morocco and Belgium (SALAD project)
<p>Crop and soil measurements of quinoa used to calibrate the SWAP-WOFOST model for the SALAD project (https://www.saline-agriculture.com/en).</p> <p>The data were collected from two locations:</p> <p>1) Laayoune, Southern Morocco: ICBA-Q5 quinoa variety, grown in 2021 under irrigation with saline water at levels of 4, 12, and 20 dS/m (https://doi.org/10.3389/fpls.2023.1143170)</p> <p><br>2) Merelbeke, Belgium: Bastille quinoa variety, grown in 2018, 2019, 2022, and 2023 under rainfed and non-saline conditions (https://www.quinoalokaal.be/nl/, https://doi.org/10.3390/plants10122689, https://doi.org/10.3390/plants11030265)</p> <p> </p>
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states - raw count data set - RNAseq
<p>Raw count data of the RNAseq analysis of a project and manuscript under the title "Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states". The header of the table includes the subject identifiers except the first column "genes". The latter column includes all assessed gene identifiers.</p>
Survey data on people's forest use patterns and perceptions of border security measures in Białowieża Forest region, Poland
<p>A survey was conducted in June-July 2022 to obtain information about people's forest use patterns and opinions and feelings about border security measures (state of emergency, border zone closure, militarization) instituted in northeastern Poland starting in September 2021. Participants were informed that the survey was voluntary and anonymous. Participants were not obliged to respond to all questions and could stop the survey at any time. Survey completion and submission implied consent to participate. Participants had to be at least 18 years of age to take part in the survey. They had to be residents of the Białowieża Forest region. 100 persons participated in the survey. Of these 100 persons, 44 identified as local (born in the region). Data are coded and a key is provided. Some responses are aggregated and only responses to close-ended questions are shared, to prevent disclosure of potentially identifying information. </p>
PsPM-FER01: PSR, SCR, ECG and respiration measurements from a discriminant delay fear conditioning task with visual CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information and shock expectancy ratings at the end of the experiment for 30 healthy unmedicated participants (12 males and 18 females aged 23.9+/-4.4 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Fear acquisition consisted of 10 CS- and 32 CS+ trials (16 CSa+/16 CSb+). Half of the CS+ trials were paired with an electric shock. CS were colored triangles (yellow/red/blue). US consisted of a 500 ms train of 250 square pulses with individual pulse width of 0.2 ms. SOA between the CS onset and US was 3.5 seconds. CS and US co-terminated. The ITI was randomly determined as discrete values between 7-11 seconds (mean 9 seconds).</p> <p> </p>
Concentration of gaseous iodic acid measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>Measurements of iodic acid concentration in the gas phase obtained with a nitrate chemical ionization mass spectrometer (we used an APi-TOF mass spectrometer produced by Tofwerk AG coupled with a Chemical ionization inlet A70 produced by Airmodus). Iodic acid is detected in the mass spectrometer either as a deprotonated ion or as a cluster with the reagent ion (NO3-). The concentration is calculated as the area of these two peaks normalized to the concentration of the reagent ions (monomer, dimer and trimer) and multiplied by a calibration factor equal to 6.9E9 molecules cm<sup>-3</sup> that was experimentally derived at Paul Scherrer Institute in the summer 2017, after the campaign.</p> <p>Iodic acid can participate in both new particle formation and growth, affecting the Earth radiative balance and cloud properties. Iodic acid is produced from the iodine radical but the exact formation pathways is still unknown.</p> <p>Measurements were performed on the upper deck of the icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to March 19, 2017. There are no data for the first leg of the expedition because the instrument was not on the ship. The instrument was operated during leg 4 but data has not been processed yet. Data were collected with one-second time resolution but integrated to five minutes to increase the signal to noise ratio. Concentrations are reported as molecules per cubic centimeter in five minutes averages. The lower limit of detection was estimated to be lower than 6E3 molecules cm<sup>-3</sup>. Data below the detection limit were replaced by the detection limit divided by the square root of 2.</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al. 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 2 polluted data). No direct influence of pollution on the iodic acid concentration was found.</p> <p>***** Dataset contents *****</p> <p>- 01_gas_iodic_acid_concentration_data.csv, data file, comma-separated values</p> <p>- 02_IodicAcid_file_header.txt, metadata, text format</p> <p>- 03_README.txt, metadata, text format</p> <p> </p>
Size distribution of neutral and charged particles smaller than 42 nm measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>The size distribution of neutral and charged particles was measured using a neutral cluster and air ion spectrometer (NAIS) instrument. The concentration was corrected for diffusional losses in the inlet.</p> <p>The concentration and temporal dynamics of small particles is fundamental to characterize the first step of new particle formation (NPF) and growth. Moreover, naturally charged particles and ions can provide information about the role of ion induced nucleation. Newly formed particles can grow to larger sizes where they act as cloud condensation nuclei, directly affecting the Earth radiative budget and cloud properties.</p> <p>Measurements were performed on the upper deck of icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to April 11, 2017. The concentration is reported as dN/dlog(Dp) per cubic centimetre, where Dp indicates the corresponding diameter size bin. Data were collected with one-second time resolution and averaged automatically by the acquisition software to 120 seconds before January 31 2017 and to 90 seconds after that date. The instrument was calibrated before the campaign by the manufacturer and periodically cleaned during the campaign (one time per leg).</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al., 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 0 polluted data).</p> <p> </p> <p>***** Dataset contents *****</p> <p>- 01_neutral_particles_size_distribution.csv, data file, comma-separated values</p> <p>- 02_negative_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 03_positive_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 04_neutral_particles_size_distribution_header.txt, metadata, text</p> <p>- 05_negative_ions_size_distribution_header.txt, metadata, text</p> <p>- 06_positive_ions_size_distribution_header.txt, metadata, text</p> <p>- README.txt, metadata, text</p> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p>
Single aerosol measurements from a wideband integrated bioaerosol sensor, collected during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set contains the time series of single particle data, measured by the wideband integrated bioaerosol sensor (WIBS-4, University of Hertfordshire, Hatfield, UK), during the Antarctic Circumnavigation Expedition (ACE), which was conducted between 20th of December 2016 and 19th of March 2017. WIBS provides aerosol optical diameter (5 μm - 14 μm), asymmetry factor and fluorescent signals on three different channels. WIBS measures single aerosol particles at a sampling rate of 125 Hz. More technical details about WIBS could be found in Kaye et al. (2005).</p> <p><strong>Dataset contents</strong></p> <ul> <li>part_1_Cape_Town_Kerguelen.csv, data file, comma-separated values</li> <li>part_2_Kerguelen_Hobart.csv, data file, comma-separated values</li> <li>part_3_Hobart_Mertz.csv, data file, comma-separated values</li> <li>part_4_Mertz_Punta Arenas.csv, data file, comma-separated values</li> <li>part_5_Punta_Arenas_Cape_Town.csv, data file, comma-separated value</li> <li>part_6_Cape_Town_Bremerhaven.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This aerosol measurement dataset collected using a WIBS during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles measured during the Antarctic Circumnavigation Expedition
<p><strong>Dataset abstract</strong></p> <p>This dataset consists of a 5-minute time series of number concentrations of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition in the austral summer of 2016/2017. Furthermore, the dataset includes the fraction of fluorescent classes of aerosol particles, according to the ABC classification of Perring et al. (2015). The dataset provides information on fluorescent and hyper-fluorescent aerosols which were obtained by considering low and high fluorescence thresholds. For this dataset, aerosol particles that have optical diameter greater than 1 μm are considered. Since the ship’s exhaust could considerably affect the fluorescence properties of aerosol particles, in this dataset we removed the periods where it is likely that the samples were contaminated by the ship's exhaust.</p> <p><strong>Dataset contents</strong></p> <ul> <li>N_fluorescent.csv, data file, comma-separated values</li> <li>N_hyper_fluorescent.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This number concentration and fluorescent class fraction of fluorescent and hyper-fluorescent aerosol particles dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland
<p>Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".</p> <p>Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.</p> <p>The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.</p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
Supplementary Materials for 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction'
<p><strong>Supplementary materials for the article 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction' in <em>Linguistic Typology </em>(Vol. and No. TBD).</strong></p> <p>Abstract:</p> <p>We provide a discussion of some of the challenges in using statistical methods to investigate the morphology-syntax distinction cross-linguistically. The paper is structured around three problems related to the morphology-syntax distinction; (i) the boundary strength problem; (ii) the composition problem; (iii) the architectural problem.<br> The boundary strength problem refers to the possibility that languages vary in terms of how distinct morphology and syntax are or the degree to which morphology is autonomous. The composition problem refers to the possibility that languages vary in terms of how they distinguish morphology and syntax: what types of properties distinguish the two systems. The architecture problem refers to the possibility that languages vary in terms of whether a global distinction between morphology and syntax is motivated at all and the possibility that languages might partition phenomena in different ways.<br> This paper is concerned with providing an overarching review of the methodological problems involved in addressing these three issues. We illustrate the problems using three statistical methods: correlation matrices, random forests with different choices for the dependent variable, and hierarchical clustering with validation techniques.</p> <p> </p> <p>Overview of materials:</p> <ul> <li>SM1: csv with the data</li> <li>SM2: code and pdf for generating the correlation matrices</li> <li>SM3: code and pdf for the random forest analyses</li> <li>SM4: code and pdf for the clustering and cluster validation analyses</li> </ul>
Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation
<p>The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at</p> <p>(1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting<br> (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting.</p> <p>A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series.</p> <p>The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey.</p> <p>.m files are included for both .mat data files, with details on how to read the data.</p> <p>An article describing the data has been submitted by the authors to Acta Acustica, 2022.</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</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.