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100 results for “Vegetation Indices”
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites</p> <p>The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. </p> <p>Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. </p> <p>All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. </p> <p>Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). </p> <p>More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p> <p> </p>
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>
Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application
<p>Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. 'Penn G2') putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems.</p>
Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application
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Datasets and relevant code in the Manuscript "Estimation of fire counts and fire radiative power using satellite optical and microwave vegetation indices with random forest method"
<p>1. multiyears_season_fire_ndvi_fwi_edvi_0.25.mat<br>Multiyear averages of ln (FC), ln (FRP), DMC, ISI, EDVI10-18, EDVI18-36, and NDVI over East Asia in 2003–2010</p> <p>2. RF_edvi_data.mat<br>Estimated FC and FRP based on RF model with EDVIs and NDVI </p> <p>3.RF_fwis_data.mat<br>Estimated FC and FRP based on RF model without EDVIs and NDVI </p> <p>4. temporal_variations.mat<br>East Asia Regional Time Series Dataset</p> <p>5.rf_train_cv_forest_review.py<br>Random forest model python code</p>
FIGURE. Taphonomic process corresponding to the abundance of different kinds of plant remains in the three layers of "vegetational Pompeii" tuff bed. Single, double and triple repeated icons in different layers indicate rare, moderate and frequent occurrence respectively. Note that the thickness of the tuff bed is scaled but that of the two coal beds is neglected. in Discovery of coprolites in an Early Permian fern mesophyll
FIGURE. Taphonomic process corresponding to the abundance of different kinds of plant remains in the three layers of "vegetational Pompeii" tuff bed. Single, double and triple repeated icons in different layers indicate rare, moderate and frequent occurrence respectively. Note that the thickness of the tuff bed is scaled but that of the two coal beds is neglected.
Molecular gut content analysis indicates the inter- and intra-guild predation patterns of spiders in conventionally managed vegetable fields
<p>Inter- and intra-guild interactions are important in the coexistence of predators and their prey, especially in highly disturbed vegetable cropping systems with sporadic food resources. Assessing the dietary range of a predator taxon characterized by diverse foraging behavior using conventional approaches, such as visual observation and conventional molecular approaches for prey detection, has serious logistical problems.<i> </i>In this study, we investigated the trophic interactions of a functionally diverge group of predators -spiders- to accomplish the ultimate goal that is the predation of spiders on major crop pests. We used high-throughput sequencing (HTS) and biotic interaction networks to precisely annotate the predation spectrum and highlight the predator–predator and predator-prey interactions in Brassica fields. The prey taxa in the gut of spiders were mainly enriched with insects (including dipterans, coleopterans, orthopterans, hemipterans and lepidopterans) and arachnids (such as Araneae) along with a wide range of other prey factions. Despite the generalist foraging behavior of spiders, the community structure analysis and interaction networks highlighted the overrepresentation of particular prey taxa in the gut of each spider family, as well as showed the intra-family predation between different spiders. Identifying the diverse trophic niche proportions underpins the importance of spiders as predators of pests in highly disturbed agroecosystems. More specifically, combining HTS with advanced ecological community analysis reveals the preferences and biological control potential of particular spider taxa, so provides a valuable evidence base for targeted conservation biological control efforts in complex trophic networks.</p>
Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"
<p>This repository includes the data and code to reproduce the results for the study "<em>Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space</em>". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.</p>
Land surface phenology derived from 3 sets of vegetation indices
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Use of synthetic aperture radar data for the determination of vegetation indices
<p>Database for the article "Use of synthetic aperture radar data for the determination of vegetation indices"</p>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
Molecular gut content analysis indicates the inter- and intra-guild predation patterns of spiders in conventionally managed vegetable fields
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Data from: Plant functional indicators of vegetation response to climate change, past present and future: I. Trends, emerging hypotheses and plant functional modality
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An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
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Long-term vegetation changes in Nardus grasslands indicate eutrophication, recovery from acidification, and management change as the main drivers
<p>Abstract</p> <p>Questions</p> <p>Which trends and patterns of community change occurred in <em>Nardus</em> grasslands over recent decades in parts of the Continental biogeographic region of Germany? Are patterns and trends consistent across two study regions? Do impacts of environmental changes on <em>Nardus</em> grasslands in Central Europe correspond to those identified in the European Atlantic biogeographic region?</p> <p>Location</p> <p>East Hesse Highlands, Germany</p> <p>Methods</p> <p>In 2012-2015, we re-surveyed quasi-permanent plots that had been initially surveyed between 1971 and 1987, and re-measured soil parameters. We tested for differences in species frequency and cover, mean Ellenberg indicator values, species richness, and soil variables. Nitrogen- and sulphur-deposition data were analysed to evaluate possible effects of atmospheric pollutants. We used regression- and redundancy analyses to identify environmental drivers responsible for changes in species composition.</p> <p>Results</p> <p>Across regions, we found significant increases in soil pH, Ellenberg R and N indicator values, plant-nutrient indicators, forbs, species of agricultural grasslands and of fallows. By contrast, the C:N ratio<em>, Nardus</em> grassland specialists, low-nutrient indicators, and graminoids declined. Changes in species composition were related to changes in pH and management. There was a strong decrease in sulphur and a moderate increase in nitrogen deposition, whose local scale pattern did not correlate with changes in soil parameters. However, there was an effect of local NH<sub>y</sub> changes on species composition.</p> <p>Conclusion</p> <p>The findings indicate significant overall eutrophication, a trend towards less acidic conditions and insufficient management, which are widely consistent across our study regions and correspond to recent reports of vegetation changes and recovery from acidification in the Atlantic biogeographic region. We assume the reduced sulphur deposition during recent decades to be a major driver of these changes, combined with increased nitrogen deposition and reduced management intensity. This suggests a large-scale validity of processes that influenced changes in <em>Nardus</em> grasslands of Western and Central Europe.</p>
Data repository from Boudewijn van Lieshout' thesis A comparison between Normalized Difference Vegetation Indices calculated from Sentinel 2A satellite data and high-resolution UAV imagery in Tanzania
<p>Monitoring vegetation is imperative for policy design and efficiency measurements. Frequent data collection, easy and inexpensive accessibility of images and the possibility of large area analysis, makes it still valuable to use satellite imagery. This study aims to examine how a Normalized Difference Vegetation Index (NDVI) measured with Sentinel-2A satellite data relates to an NDVI from Unmanned Aerial Vehicles (UAV henceforth) in study areas Chamwino Mlimwa and Chemba Waida, Tanzania.</p>
Vegetation Index and Phenology (VIP) Vegetation Indices Monthly Global 0.05Deg CMG V004
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS.The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year.Known Issues* The Relative Azimuth Angle (RAA) for the input MODIS data is computed based on absolute values of the finer resolution pixels resulting in positive values and has minor usefulness.* The RAA for the input AVHRR data contain values in the -360° to 360° range. The routine to restrict the values in the -180° to 180° range was accidentally missed and can be corrected using the following routine described in Section 4.2.1 of the User Guide and Algorithm Theoretical Basis Document: * SinRelativeAz=sin(RAA) * CosRelativeAz=cos(RAA) * Correct-RAA = atan2(SinRelativeAz,CosRelativeAz)
MODIS/Aqua Vegetation Indices 16-Day L3 Global 500m SIN Grid V006
The MYD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A1 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A1.061) data product.The MYD13A1 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues* The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of "NoData" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned.* For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces.* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).Improvements/Changes from Previous Versions* The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MYD09A1](https://doi.org/10.5067/MODIS/MYD09A1.006)) in the 16-day period.* This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product.* The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. * A frequently updated long-term global Climate Modeling Grid (CMG) Average Vegetation Index product database is used to fill the gaps in the CMG product suite.
MODIS/Aqua Vegetation Indices Monthly L3 Global 1km SIN Grid V006
The MYD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MYD13A3 Version 6.1](https://doi.org/10.5067/MODIS/MYD13A3.061) data product.The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the [MYD13A2](https://doi.org/10.5067/MODIS/MYD13A2.006) products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion.Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers.Known Issues* The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have [impacted](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=174) MYD13 Vegetation Index data products particularly over arid bright surfaces.* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=6).Improvements/Changes from Previous Versions* The 16-day composite VI is generated using the two 8-day composite surface reflectance granules ([MYD09A1](https://doi.org/10.5067/MODIS/MYD09A1.006)) in the 16-day period.* This surface reflectance input is based on the minimum blue compositing approach used to generate the 8-day surface reflectance product.* The product format is consistent with the Version 5 product generated using the Level 2 gridded daily surface reflectance product. * A frequently updated long-term global Climate Modeling Grid (CMG) Average Vegetation Index product database is used to fill the gaps in the CMG product suite.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.