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4,243 results for “seasonality”
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
Spot and continuous seasonal pCO2 sensor measurements from lagoon and river sites along the Alaska Beaufort Sea coast, 2019-ongoing
pCO2 (partial pressure of carbon dioxide) was measured in multiple seasons and depths in coastal ecosystems as part of the Beaufort Lagoons Ecosystems LTER core sampling program. Sites include several Beaufort Lagoons from across the North Slope of Alaska and several river sites near the lagoons. Data includes averages of sensor data for each site and depth deployment of pCO2 (uatm), CO2 (ppm), water temperature (°C), and absolute pressure (kPa). Standard deviation is included for pCO2 (uatm). Surface measurements were taken either 30 cm below the water surface, or bottom of the ice in ice-cover season, and bottom measurements were taken 30 cm from the seafloor of the lagoons. For lagoon sites, data were logged every one or five minutes; for river sites data were recorded every five minutes. Each site and depth was recorded for at least 30 minutes and up to four hours and taken as spot measurement.
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
Seasonal Distribution of Ammonia-Oxidizing Archaea and Ammonia-Oxidation Rates in the South Atlantic Bight from April to November 2014
Previous work in nearshore waters of the Georgia USA coast has demonstrated mid-summer peaks in the abundance of Thaumarchaeota (blooms with 100 to 1,000-fold increases) accompanied by spikes in nitrite concentration. These studies were performed at one location, so the areal extent of the bloom is unknown, nor has it been demonstrated conclusively that it develops in inshore waters. We collected data on rates of ammonia oxidation and the distribution of Thaumarchaeota, ammonia-oxidizing Betaproteobacteria (AOB), nitrite-oxidizing Nitrospina and environmental variables during 6 cruises aboard the UNOLS vessel R/V Savannah from April to November 2014 on transects of the South Atlantic Bight to evaluate the areal extent and timing of the bloom. This data set includes measurements of Chlorophyll-a concentration, PAR attenuation coefficient, oxygen concenrations, temperature, salinity and nitogenous nutrient concentrations (nitrite, nitrite + nitrate, ammonium, urea), and estimates of Archaea, bacteria and diatom gene concentration based on quantitative PCR.
Plant species composition in black sand extended growing season experiment, 2018 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes measurements of plant species composition.
ANPP, NDVI and canopy height in black sand extended growing season experiment, 2019 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. This dataset includes measurements of aboveground net primary productivity, plant canopy height and NDVI.
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
Seasonal non-destructive vegetation measurements at 15 net primary production (NPP) study sites at Jornada Basin LTER, 1989-ongoing
This data package contains non-destructive quadrat measurements collected for the long-term Net Primary Production (NPP) study at the Jornada Basin LTER. Data here include measurements of horizontal cover and vertical height of plants observed at permanent NPP quadrats at 15 study sites. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. All sites are excluded from domestic grazing. Eleven sites are in non-grazed pastures, and at the other four sites 1 hectare areas around the observational plots were fenced in 1988. At all sites a grid of 49 (48 at one playa location) 1m x 1m replicate quadrats was laid out when sampling began in 1989. Grids consist of 49 quadrats arranged in a square 7 x 7 pattern, with quadrats 10 m apart (P-COLL has 48 quadrats in a 3 x 16 pattern). Standing vegetation in quadrats is sampled three times a year: in winter (February - March), before shrubs begin spring growth; in spring (May), when shrubs and spring annuals have reached peak biomass; in fall (late summer; October), when summer annuals have reached peak biomass but before killing frosts. Additional observations of plant count and phenological stage are also made. This dataset is subsequently used to determine quadrat biomass and net primary production. Details and linked data packages are described in the methods element. This is an ongoing dataset with new quadrat measurements collected in the spring, fall and winter of each year. Attention: 1) For most species, these data are not appropriate for estimates of percentage cover because of the way the data are collected. See Note 1 in the methods element for further details.
WSC - Water surface elevation (WSE) and water table depth (WTD) from 14 points at the Wibu field site, 2012-2013 growing seasons
Observation wells were installed for the purpose of continuously monitoring the water table level during the 2012 and 2013 growing seasons at the Wibu field site. These data were then used to study the yield response of corn to water table depth, soil texture, and growing season weather conditions (Zipper et al., in prep). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site. The 2012 growing season was characterized by severe drought, and the water table fell below the bottom of most wells in late June/early July.
Soil moisture, temperature, and electrical conductivity data from the black sand extended growing season length experiment, 2018 - 2024, hourly.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how changes in growing season length may affect biotic and abiotic components, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These blocks include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control (black sand was added to these plots after snow had naturally melted). This dataset includes measurements of soil temperature, moisture, and electrical conductivity.
Time-lapse camera (phenocam) imagery of black sand extended growing season length experiment, 2022 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and potentially earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes phenocam images from 2022-2023.
Characterization of photosynthetic epilithic biomass on the river bed of the Upper Clark Fork River (Montana, USA) during the algal growing season of 2019
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for data in this product was to assess the response of the river algal community to the floodplain restoration. Data characterize epilithic biomass on the river bed including measurements of benthic standing stocks as organic matter and abundance of pigments associated with primary producers. Metrics of characterization include areal density of chlorophyll a, areal density of phaeophytin, the ratio of carotenoid to chlorophyll absorbance, areal density of organic matter, percent organic matter, and category of biomass composition (filamentous algae vs. other epilithon). Samples were obtained from collecting and scrubbing five rocks at any given site. Estimates of organic matter in biomass were obtained from area-corrected ash-free dry mass. Pigment concentrations were obtained through the use of extraction in acetone followed by spectroscopy. Data are from the 2019 algal growing season. Data were collected
Characterization of pigments in photosynthetic benthic biomass on the river bed of the Upper Clark Fork River (Montana, USA) during the algal growing season of 2020
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for data in this product was to assess the response of the river algal community to the floodplain restoration. Data are measurements of benthic biomass organic matter standing stocks and pigments associated with primary producers. Benthic biomass data were collected on the UCFR (USGS HUC 17010201) at seven monitoring sites distributed from Warm Springs (near Anaconda, MT) to Bonita (east of Missoula, MT). Data from the Deer Lodge and Garrison sites were obtained from the River Algal Succession Study, a project funded by the Montana Consortium for Research on Environmental Water Systems. Data from Bonita were obtained from the Nitrogen Fixation Algal Study, a Research Experience for Undergraduates project. Benthic samples were obtained using a cylindrical benthic sampler isolating a known area of the river bed. Five samples were obtained at each site. Estimates of organic matter in biomass were obtained fr
Marcell Experimental Forest seasonal soil moisture, 1966 - ongoing
This data publication contains available soil water measured three times a year (1966 - ongoing) at the Marcell Experimental Forest (MEF) in Balsam Township, Itasca County, Minnesota. The data came from six peatland / upland forest watersheds instrumented for long-term hydrological and biogeochemical research. The Marcell Experimental Forest in Itasca County, Minnesota is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Fish Food on Floodplain Farm Fields, California Central Valley, Seasons 2019 and 2021
2019 Water Year (October 1, 2018 through September 30, 2019) In the winter and spring of 2018-2019, 5,000 acres of agricultural land in Yolo County, California was intentionally flooded. These “dry-side” rice fields, although on the former floodplain of the Sacramento River, are separated from the fish-bearing Sacramento River (the “wet-side”) by high flood levees. Today, levees cut off 95% of the Central Valley’s floodplains from river channels so that Central Valley aquatic ecosystems no longer recruit floodplain the food web resources needed to support robust aquatic food webs, create fish biomass and sustain abundant fish populations. In this experiment we asked whether floodplain food web resources “grown” in intentionally inundated “dry-side” agricultural fields could be exported back to the river via flood drainage infrastructure. If so, we were interested to know whether those resources could improve juvenile salmon foraging success and increase growth rates. In order to test these questions, we caged fish in the floodplain drainage canal, at the location where the floodplain drainage water entered the river and at locations both up- and downstream. We hypothesized that zooplankton abundance and fish growth rates would be elevated at the managed floodplain outfall location, relative to the upstream location. We measured water quality parameters, zooplankton species assemblage and abundance, and juvenile Chinook salmon growth rates with PIT tagged, hatchery-origin fish confined to enclosures at the study locations. The 5,000 acres of managed floodplain was drained over the coarse of 5 weeks in February and March, 2019 at a maximum rate of 1,000 cfs. The Sacramento River flow during the experiment ranged from 20,000-30,000 cfs. Fish growth rates at the floodplain outfall location were up to five times greater than growth rates upstream of the outfall and enclosure fish experienced growth rate benefits at least up to a mile downstream from the managed floodplai
Leaf Area Index every 15 cm of 1m x 1m chamber flux and point frame plots and sites where dataloggers monitored photosynthetically active radiation (PAR) above, within and below Salix pulchra and Betula nana canopies during the growing season at the Toolik Field Station in AK, Summer 2012.
Leaf area index (LAI) measurements were taken with the Delta-T SunScan wand every 15 cm from the ground to above the canopy under both direct and diffuse light. conditions The data includes all outputs from the SunScan wand: time of measurement, transmitted light, spread of photosynthetically active radiation (PAR) sensors, beam fraction, and zenith angle. These measurements were taken for 1m x 1m chamber flux and point frame plots sampled in tall Salix pulchra and Betula nana shrub canopies as well as sites monitored remotely by PAR sensors situated above, within, and below tall shrub canopies at the Toolik Field Station in the summer of 2012.
Late season thaw depth measured in the Arctic Long Term Ecological Research (ARC LTER) moist acidic tussock experimental plots at Toolik Field station, Alaska Arctic 1993 to 2025
Late season thaw depth was measured in the Arctic Long Term Ecological Research (ARC LTER) experimental plots (1981 Moist Acidic Tussock, 1989 Moist Acidic Tussock, 2006 Low Fertilization Moist Acidic Tussock, 1989 Moist Non-acidic Tussock, 1989 Moist Non-acidic Non-tussock and 1989 Wet Sedge tundra) at Toolik Lake, Alaska using a steel thaw probe. Note: for 2017-2018 only 1989 Moist Non-Acidic Tussock Tundra and 2006 Low fertilization Moist Acidic Tussock Tundra were measured. For other sites it has become difficult to distinguish rocks from frozen soil with a steel thaw probe. Starting in 2023 weekly thaw depth measurements were made throughout the growing season in the 2006 Low fertilization Moist Acidic Tussock Tundra experiment.
Meteorological data collected on Toolik Lake during the ice free season for 2014-2020, Arctic LTER, Toolik Research Station, Alaska
File describing the meteorological conditions on Toolik Lake (named the Toolik Lake Climate station), adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This is a floating climate station and should not be confused with the Toolik Field Station Climate site (TFS Climate Station or Met Station) which is a terrestrial station (located on land). Note that this land station has been called the "Toolik Main Climate Station", and the station on the lake is located where the main lake sampling site is located so it has also been called the Toolik Lake Main Climate Station. Measurements include air temperature, relative humidity, wind speed and direction, and radiation. Note: There are no discharge data for 2013 because of equipment malfunction.
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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.