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1,118 results for “Time series”
Vegetation greenesss data for the Aït Benhaddou Catchment, Morocco. Includes: 1984-2019 NDVI time series, breakpoint analysis results, and resillience indicator results, among others.
<p>This dataset is comprised of two main parts, both originating from different but related works. </p> <p>The NDVI timeseries and breakpoint analysis were originally developed by Vermeer (2021) for the MSc thesis: Vermeer, A. L. (2021). <em>Ecological stability in the face of climatic disturbances: a case study of a dryland ecosystem in the Moroccan High Atlas Mountains</em>. These data include a harmonized timeseries of Normalized Difference Vegetation Index from different Landsat missions at 30x30 meter resolution for the Aït Benhaddou catchment in Morocco. It also includes the output of a breakpoint analysis that was conducted using this dataset, which showcases different statistical breakpoints in NDVI after a severe drought that occured between 1998 and 2002. Shapefiles, a DEM and masks of irrigiated areas for the catchment are also included. For more information about these data, consult Vermeer (2021).</p> <p>The secondary part of this dataset was produced by Grootoonk (2024) for the MSc thesis: Grootoonk, W. (2024). <em>Relations between temporal resilience indicators and trend breakpoints in a dryland high-mountain catchment, </em>drawing upon the original dataset from Vermeer (2021). These data include Kendall's tau values for the resillience indicators variance and lag-one autocorrelation, computed using a rolling window for each pixel. Results for differerent window sizes (WS) for both indicators are included. </p> <p>Beyond these main results, a number of additional data sources are provided. These are Kendall's tau for precipitation variance in the area, produced using CHIRPS data (https://www.chc.ucsb.edu/data/chirps) and a NSI soil salinity map produced from Landsat imagery. See Grootoonk (2024) for more information. </p>
2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development
<p>2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development</p> <p>------------------------------------------------------------------------------------</p> <p>This dataset comes from Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described and used in Figure 3 in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>Files for the series in Figure 3 as they appear in that paper to demonstrate the Two-Stage Paired Thickness Test:</p> <p>Figure 3a:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRcevp01a_2.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3b:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3c:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p> </p> <p>Where the name "evp" or "eap" in the file name corresponds to the EVP and EAP simulations as described in the manuscript. These are selected timeseries from 1996 to 2000 from the RASM simasimulations described in section 2 of the above manuscript. CICE namelist settings are identical to the description for the dataset:</p> <p>Roberts, A. (2018). RASM simulations: Quality Control for Community Based Sea Ice Model Development [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1308236</p> <p> </p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads
<p>This map contains the data used in the study 'Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads'. In this study a new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. Recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands. Results are compared with recharge estimates based on the saturated zone chloride mass balance.</p> <p>The data are ascii text files saved in the following compressed maps:<br> - Groundwater_chloride_concentration.zip<br> - Groundwater_head_time_series.zip<br> - Makkink_reference_evaporation_time_series.zip<br> - Measured_precipitation_time_series.zip<br> - Precipitation_chloride_concentrations.zip<br> - Pumping_time_series.zip</p> <p>For further details, please refer to the readme files contained in each submap.</p>
Table with the results of the time series gene expression analysis for fins.
<p>Differences between transcriptomes of two Cottus fish lineages were assessed in the natural environment. Sampling was performed in a time series covering whole year. Two tissues were investigated: fins and livers. Present table shows results of a time series differential gene expression analysis performed on fin tissues.</p>
Time series of gas, particle, and environmental variables measured at the Mäkelänkatu urban street canyon site in May 2017
<p>Time series data of gas, particle, and environmental variables measured at the Mäkelänkatu urban street canyon site, in Helsinki, Finland, in May 2017.</p>
Data package for paper "Transformer models for astrophysical time series and the GRB prompt-afterglow relation"
<p>This is a data package accompanying the paper "Transformer models for astrophysical time series and the GRB<br>prompt-afterglow relation". The code used to acquire the data is in the "data" folder. The code used to analyse the data is in the "analysis" folder.</p> <p>DOI paper: <a href="https://doi.org/10.1093/rasti/rzae026">10.1093/rasti/rzae026</a></p>
New Zealand coastal station sea temperature time series
<p>The following two tables are the daily observations and model output of coastal water temperature analyzed in Souza et al., 2022 (displayed in Figure 13). The first column in both tables are the time in MATLAB datenum format. The subsequent 10 columns are daily temperature values from the following stations with lat/lon information:</p> <p> {'Ahipara' -35.1667 173.1000}<br> {'Leigh' -36.2693 174.7984}<br> {'Moturiki' -37.6330 176.1830}<br> {'Tauranga' -37.6980 176.6160}<br> {'Newplymouth' -39.0500 174.0330}<br> {'Napier' -39.4830 176.9167}<br> {'Evans' -41.3000 174.8000 }<br> {'Lytleton' -43.6330 172.9000 }<br> {'Portobello' -45.8160 170.6500}<br> {'Bluff' -46.6000 168.3000 }</p> <p>Missing data are marked with the value -999. Further observation and station details and quality control procedures are described in the technical data report, Chiswell, S. M. and Grant, B.: New Zealand Coastal Sea Surface Temperature, Tech. rep., National Institute of Water & Atmospheric Research, 2019 </p> <p> </p> <p>We acknowledge the contribution of professional, technical and academic staff from NIWA, University of Otago and University of Auckland who have maintained the coastal SST records and made these available for model validation purposes</p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
Displacement time series from Foamquake and Gelquake in single- and double asperity configurations: Supplementary material to "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory"
<p><span>This dataset includes displacement data from 4 experiments performed with Foamquake and Gelquake (Mastella et al. 2022, Corbi et al., 2013), two scaled seismotectonic models reproducing the megathrust seismic cycle running at the Laboratory of Experimental Tectonics LET (Univ. Roma Tre). These models enable the generation of hundreds of quasi-periodic cycles of stress accumulation and sudden release through the spontaneous nucleation of frictional instabilities within one or many analog seismic asperities. Models are monitored by the means of a high-resolution top-view monitoring camera acquiring images at 7.5 and 50 frames per second for Gelquake and Foamquake, respectively. This dataset has been created with particle image velocimetry (PIV, using MatPIV (Sveen 2004)) through the cross-correlation between consecutive images. The PIV provides us with velocity field time series. These are integrated to obtain displacement time series. From the whole model surface, in each experiment we selected data from a cross-section striking parallel to the trench and located at the downdip center of the asperities. Cross sections are discretized in 28 and 29 target points in Gelquake and Foamquake, respectively. </span></p> <p><span>Displacement time series have been normalized to zero mean and unit variance to ensure the same level of magnitude for comparison between different experiments. Linear and second order polynomial trends have been removed to make the stick-slip confined in a given range and avoid non-stationary behavior. Time series data are not passed through filters (e.g., smoothing or moving average).</span></p> <p><span>Filename informs about the nature of the analog upper plate (i.e., foam and gel) and geometrical configuration of asperities (i.e., mono and twin). Together with individual files for each experiment, this dataset includes a Matlab script (i.e., all_timeseries.m) that allows visualization of displacement time series from individual target points. </span></p> <p><span>This dataset is supplementary to the paper in SEISMICA "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory” by Corbi et al. (2024), where detailed descriptions of models and experimental results can be found.</span></p>
Pressure time series for waves on shelf
<div> <div>Time: in UTC</div> <div> <div>Position 29.4412 -92.0613</div> </div> <div> <div>Platform CSI 3</div> <div>Station ioos:station:WAVCIS:CSI03</div> <div>Description Marsh Island, LA</div> <div>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)</div> </div> <div>Instrument: RBRvirtuoso pressure sensor</div> <div>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</div> <div>PI: Chunyan Li</div> <div>Funded by NSF (1736713)</div> <div> </div> </div> <p> </p>
GPS position time series data for the Tatun Volcano Group (TVG) region
<p>GPS Time Series Data Used in "Transient Deformation in the Tatun Volcano Group, Taiwan: A Spatiotemporal GPS Analysis" by Chang et al.:</p> <p>1. Time series data of six GPS stations in TVO (YM03, YM05, YM06, YM07, YMN4, and YMSM, see Figure 1 of the main text) are included in the zip file "tvo.final_igb14.pos.tar.gz".</p> <p>2. The files are in plain text with the stardard PBO data format (https://www.unavco.org/data/gps-gnss/derived-products/docs/knowledgetree-docs-old/gps_timeseries_format.pdf), which is also listed and explained at the top of each file.</p>
FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks
<p>Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defense to preserve the network's quality of service. This novel dataset, FAN-GHETS24, is designed for early time series classification of various grey hole attack scenarios. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and universal deployment across UAVs; and secondly, the application of feature engineering techniques to format the data for machine learning model integration.</p> <div> <div>The dataset is split across several zip files, combine and extract them by issuing these command:</div> </div> <div> <div>$ zip -FF fan-ghets24.zip --out fan-ghets24-combined.zip</div> <div>$ unzip fan-ghets24-combined.zip</div> </div>
Time series transcriptomes resolve metabolic pathways underlying crocin's anti-cancer activity | Dataset: sequencing reads (2,6,12,24 hr crocin treatments)
<p><span>Natural products like saffron show promise in treating hepatocellular carcinoma (HCC), but their mechanisms remain unclear. Here, we used time-series transcriptomics to elucidate crocin's anti-cancer mechanisms in HCC cells. We treated HepG2 cells with 1 and 2 mM crocin for 2, 6, 12, and 24 hours and analyzed transcriptomic profiles at each timepoint. The strongest transcriptional response occurred at 2 hours with 1 mM crocin, with diminishing effects at later timepoints. We observed upregulation of metabolic-, adhesion-, and endocytosis-related genes across all timepoints. Pathway analysis revealed activation of DNA damage checkpoints and senescence while proliferation pathways were suppressed. Notably, 52 genes involved in non-alcoholic fatty liver disease were downregulated at 24 hours (FDR p = 8 × 10⁻⁸), suggesting reversal of carcinogenic pathways. Strikingly, crocin consistently downregulated spliceosomal machinery genes across all timepoints while upregulating senescence and autophagy pathways. This spliceosome targeting represents a clinically relevant mechanism, as aberrant splicing drives oncogenesis in more than 90% of cancers. The transcription factor PAX5 was significantly upregulated while oncogenic ELK1 targets were downregulated. Our findings show that crocin treatment is accompanied by HCC cell senescence induction through coordinated spliceosome disruption and metabolic reprogramming, providing novel therapeutic targets for hepatocellular carcinoma.</span></p> <p><strong><span>Keywords: </span></strong><span>Hepatocellular carcinoma (HCC), crocin, transcriptomics, spliceosome, senescence, natural anti-cancer compounds</span></p>
A comprehensive time-series dataset linked to cyanobacterial blooms in Lake Taihu
<p>Lake Taihu has a history of recurrent harmful cyanobacterial blooms under the pressures of climate change and human activities. Despite efforts to limit nutrient loading, there is a need to better understand the water environment of Lake Taihu in order to improve methods for controlling the cyanobacterial blooms. The spatial and temporal characteristics of the water quality, bio-optical parameters, climate, and anthropogenic data of Lake Taihu (THQBCA) could provide comprehensive information regarding cyanobacterial blooms. The THQBCA dataset contains 26 variables organized into four categories: water quality, bio-optics, climate, and anthropogenic data. The dataset spans more than 15 years (8 of which cover approximately 35 years, 4 of which cover 20 years), and the spatial resolutions of the satellite-derived data range from 30 m to 500 m. The THQBCA dataset is expected to advance research on forecasting and early warning of cyanobacterial blooms, and to support science-based management decisions for sustainable ecological development.</p>
Data from: Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data
Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the mechanism underlying the B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore highlights the utility of SSMs to enhance inference for species interactions from time series data. In particular, our findings provide evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.
PASTIS - Panoptic Segmentation of Satellite image TIme Series
<p>Public dataset for Panoptic segmentation of agricultural parcels from satellite image time series.</p> <p>See companion <a href="https://github.com/VSainteuf/pastis-benchmark">github repository </a> for more information.</p>
Large ensemble climate modelling time series for the Rhine catchment, including drought2018 storylines
<p>Dataset associated with <strong>Van der Wiel, Lenderink, De Vries (2021): Physical storylines of future European drought events like 2018 based on ensemble climate modelling, <em>Weather and Climate Extremes, </em>DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>.</strong></p> <p>Large ensemble climate modelling time series for the Rhine catchment. The dataset contains three ensembles (present-day, pre-industrial + 2C-warming, pre-industrial + 3C-warming) of 2000 years each, various variables related to drought are included. All data is derived from the EC-Earth global climate model (v2, Hazeleger et al. 2012, DOI <a href="https://doi.org/10.1007/s00382-011-1228-5">10.1007/s00382-011-1228-5</a>). Descriptions of large ensemble experimental setup can be found in Van der Wiel et al. (2019, DOI <a href="http://doi.org/10.1029/2019GL081967">10.1029/2019GL081967</a>). Files: *_d_ECEarth_??_Rhine.tar.gz</p> <p>Additionally, three sets of storylines of droughts similar to the western European drought of 2018 are included. These are the simulated events selected from the large ensembles, using metrics 1, 2 and 3 of Van der Wiel et al. (2021, DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>). Files: drought18_m[123]_Rhine.tar.gz</p>
A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry (Data)
<p>This is the underlying data for the publication "A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry" by Daniel D Conley and Erin N. R. Hollander published in 2021.</p>
Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003 to 2020 InSAR Time Series
<p>This repository contains the data used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022258">Mirzadeh et al., 2021</a>. It includes two InSAR time-series datasets from Envisat and Sentinel-1 satellite in both ascending and descending orbits, acquired over Yazd-Ardakan Plain, Iran, as well as, the population density information and weather data for this study area.</p> <p>Dataset 1: Envisat ascending track 99 and descending track 20</p> <ul> <li>Date: 06 Sep 2004 - 12 Jul 2010 (17 ascending acquisitions) + 26 Mar 2003 - 23 Oct 2010 (23 descending acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LODcor_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 64</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 10 Oct 2014 - 24 Mar 2020 (119 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products, and the MintPy commands/functions.</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.