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711 results for “surround”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term composited land surface temperature for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of land surface temperature (LST) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). We derived LST values based on the thermal band from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations: - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022
This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Ecological Survey of Central Arizona: a survey of key ecological indicators in the greater Phoenix metropolitan area and surrounding Sonoran desert, ongoing since 1999
The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey, formerly known as the survey 200 and renamed to ESCA in 2015, assesses conditions at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Beginning in 2000, the study is conducted every five years except the 2020 survey, which was conducted in 2023 owing to delays to due Covid. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. Measurements include an inventory of all plants (identified to the lowest possible taxonomic unit, typically species), plant biovolume, soil coring for physicochemical properties, arthropod sweep-net sampling, photo documentation, and a visual survey of site and area characteristics. The objectives of the survey are to (1) characterize patches in terms of key biotic, physical, and chemical variables, and (2) examine relationships among land use, general plant diversity, native plant diversity, plant biovolume, soil nutrient status, and social-economic indices along an indirect urban gradient. A pilot survey was conducted in 1999, and the first full ESCA was conducted in 2000. The maiden survey in 2000 featured a suite of measurements that were not assessed in later surveys, including data from a portable weather station set up during the field survey at each location, organic matter decomposition, pollen, and mycorrhizae. In 2010, the survey was expanded to include an assessment of one of the residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the p
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Composited land surface temperature of the greater Phoenix, Arizona, USA metropolitan area and surrounding Sonoran desert derived from cloud-free, summer (June, July, and August) Landsat imagery: 1985-2020
This project calculates land surface temperature (LST) from remotely sensed imagery. The intent is to extend the previous version of the LST data for the CAP LTER study area in central Arizona, USA to include 2020 and update the products so that they are based on a composite of images from each year (all available cloud-free acquisitions from June, July, and August) in the analysis to reduce the potential for outlier images or pixels to impact analyses. The aim is to make updated LST data accessible to stakeholders and researchers studying the greater Phoenix, Arizona, USA metropolitan area. LST is calculated from cloud-free Landsat 5 and 8 imagery (30m resolution) from summer months (June, July, and August) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. All images are cropped to the CAP LTER study area boundary.
Urban Ecological Infrastructure (UEI) in the greater Phoenix, Arizona metropolitan area and surrounding Sonoran desert region (2010-2017)
Urban ecological infrastructure (UEI) encompasses all infrastructure in a city that supports ecological structure and function, and by extension, provides ecosystem services to urban residents and is a broad, all-encompassing concept for "nature in cities". This idea includes commonly recognized forms of infrastructure, such as parks, residential yards, community gardens, lakes and rivers, and street trees. But UEI also includes less recognized forms, such as vacant lots, agricultural fields, canals, and water retention basins. Despite being widely recognized as important to urban landscapes, the wide variety, and various forms of urban ecological infrastructure are rarely documented in a single source. To address this, we consolidated various aquatic, terrestrial, and wetland UEI throughout the Phoenix Metropolitan area so researchers can incorporate this UEI into project designs and models. Since people’s perceptions of UEI differ not only by the three broad classifications but also by the individual characteristics of UEI, each feature is classified not only as aquatic, terrestrial, or wetlands but also given on of fifteen unique classifications. Incorporation of UEI into both planning and research design can promote practices that increase both biodiversity and human well-being while also possibly limiting negative landscape perceptions.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
Chronological Distribution of the Documents from Yahudu and Its Surroundings
<p>This file presents the chronological distribution of the documents from the village of Yahudu and its surroundings in the Babylonian countryside. It relates to Chapter 4 in Tero Alstola, <em>Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE</em>. Culture and History of the Ancient Near East. Leiden: Brill. For further information, see the readme file.</p>
Ecological Survey of Central Arizona: a survey of key ecological indicators in the greater Phoenix metropolitan area and surrounding Sonoran desert, ongoing since 1999 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/652/3. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey, formerly known as the survey 200 and renamed to ESCA in 2015, is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. Measurements include an inventory of all plants (identified to the lowest possible taxonomic unit, typically species), plant biovolume, soil coring for physicochemical properties, arthropod sweep-net sampling, photo documentation, and a visual survey of site and area characteristics. The objectives of the survey are to (1) characterize patches in terms of key biotic, physical, and chemical variables, and (2) examine relationships among land use, general plant diversity, native plant diversity, plant biovolume, soil nutrient status, and social-economic indices along an indirect urban gradient. A pilot survey was conducted in 1999, and the first full ESCA was conducted in 2000. The maiden survey in 2000 featured a suite of measurements that were not assessed in later surveys, including data from a portable weather station set up during the field survey at each location, organic matter decomposition, pollen,
Long-term monitoring of peatlands located near oil sands mining activities surrounding Fort McMurray, Alberta, Canada (2009-Present)
Oil sands mining activities in the Fort McMurray region of Alberta, Canada, have led to increased atmospherically deposited nitrogen (N) and sulfur (S), with N steadily increasing over time and S peaking in 2009, then decreasing with the installation of scrubbers on upgrader stacks. Ecosystems (such as ombrotrophic bogs) near these mining activities see an increase to their depositional load. These peatlands are isolated from groundwater and receive inputs only from precipitation, making them uniquely susceptible to changing depositional scenarios. To evaluate the effect of oil sands development on bogs in this area, since 2009, we have collected and analyzed porewater (pH, conductivity, NH 4 + -N, NO 3 - -N, SO 4 2- -S, and total dissolved N), N and S as represented in extractions of ion exchange resin precipitation collectors (NH 4 + -N, NO 3 - -N, SO 4 2- -S), samples of new growth from the most dominant plant species (C, N, and S, with Ca, Mg, K, and P analyzed in later years), and have recorded annual growth of vegetation. For a majority of the years, we have sampled at least 3 times (June, July, and August). Some sites have burned and have been replaced by others, however, collections are on-going and data from these collections are uploaded as they are published.
MCR LTER: Coral Reefs: Coral recruitment to 25 m2 plots on the forereef surrounding Moorea, French Polynesia, 2011-2015
These data report the number of Pocilloporid, Acroporid and Poritiid corals recruiting annually to permanent 5 m X 5 m plots established at a depth of approximately 10 m on the forereef of Moorea, French Polynesia. Plots were established following an outbreak (2007-2010) of the corallivorous crown-of-thorns seastars (Acanthaster planci) and the close passage of Cyclone Oli to Moorea in February, 2010. These two perturbations resulted in a significant loss of live coral coral from the forereef on an island-wide scale (crown-of-thorns) and the loss of habitat structural complexity from forereef habitats on Moorea's north shore (Cyclone Oli). See Adam, T.C. et al. 2011 "Herbivory, connectivity and ecosystem resilience: response of a coral reef to a large-scale perturbation" PLoS One e23717 for a more complete description of the system, the nature and magnitudes of the perturbations and the short-term response by the coral community to the perturbations. Data were obtained by counting all Pocilloporid, Acroporid and Poritiid coral recruits < 3 cm in colony diameter observed within 25 5 m x 5 m plots established at four sites in a depth of approximately 10 m on the forereef of Moorea, French Polynesia. Two sites, Resilience 1 (R1) containing five 5 m x 5 m plots and Resilience 2 (R2) containing ten 5 m x 5 m plots were established along the north shore of the island. Two additional sites, Resilience 4 (R4) and Resilience 5 (R5), each containing five 5 m x 5 m plots were established on the southeast and southwest shores of the island, respectively. The locations of the plots are permanently marked using stainless steel eye-bolts cemented into the reef matrix so that counts of coral recruits can be made repeatedly within the same plots on an annual basis. This dataset, knb-lter-mcr.5023, is a subset of a larger dataset, knb-lter-mcr.7008, and has been produced in support of a manuscript submission. The parent dataset, knb-lter-mcr.7008, contains additional data on the numb
Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
<p>These datasets are supplementary to the paper "<strong>Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites</strong>" by Chu et al. </p> <ul> <li>Dataset S1. Summary of site-specific footprint metrics <ul> <li>filename: All_site_fpt_summary.csv</li> <li>readme: All_site_fpt_summary-README.csv</li> </ul> </li> <li>Dataset S2. All monthly footprint climatology weight maps <ul> <li>filename: monthly_footprint_climatology_weight_map.zip <ul> <li>the zip folder contains individual files of all monthly footprint weight maps</li> <li>filename: <Site-ID>_<Year>_<Month>_<DAY/NIGHT>_fpt_weight.tif</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. <ul> <li>filename: site-year_footprint_climatology_realcolor_map.zip <ul> <li>the zip folder contains individual files of footprint climatologies from all site-years</li> <li>filename: <Site-ID>_<Year>_<Spatial_Extent>_shrink_footprint_climatology.png</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. <ul> <li>filename: All_site_land_cover_dominant_summary2.csv</li> <li>readme:All_site_land_cover_dominant_summary2-README.csv</li> </ul> </li> <li>Dataset S5. Site-specific results and representativeness index based on the EVI analysis. <ul> <li>filename: All_site_Landsat_EVI_fpt_comparison2.csv</li> <li>readme: All_site_Landsat_EVI_fpt_comparison2-README.csv</li> </ul> </li> <li>Dataset S6. All available site-month EVI and time-explicit representativeness. <ul> <li>filename: All_site_Landsat_EVI_all_cutout2.csv</li> <li>readme: All_site_Landsat_EVI_all_cutout2-README.csv</li> </ul> </li> </ul>
Cross Platform Dataset with Posts Surrounding U.S capitol attack
<p>This is a cross-platform dataset containing the posts around specific hashtags related to U.S. Capitol protests on January 6th 2021. </p>
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Recordings for loudness analysis of the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Mat files of live recordings of the music mixes for the comparison of wave field synthesis, surround, and stereo listening test. The recordings were performed at different levels and were analyzed with a loudness model afterwards for adjusting the levels. For details, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment.
<p>The R code scripts and Supplementary data files from the paper: "Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment," accepted in Communications Biology. All R code and data necessary to reproduce the published results are available. </p>
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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.