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Dataset results
52 results for “sample points”
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). 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.
Summary of three different Leaf Area Index (LAI) methodologies of 19 1m x 1m point frame plots sampled near the LTER Shrub plots at Toolik Field Station in AK the summer of 2012.
Summary of three methods used to estimate the Leaf Area Index (LAI) of 19 1m x 1m plots sampled with a point frame near the LTER Shrub plots at the Toolik Field Station in AK the summer of 2012. The methods used were: (1) exponential relationship between LAI and Normalized Leaf Index (NDVI) as measured above the canopy with a Unispec spectroradiometer; (2) Delta-T SunScan canopy analyzer held at 5 cm above the ground under both direct and diffuse light conditions; (3) pin-drop point frame technique. Where values have been averaged (such as for the NDVI and SunScan measurements), the standard deviation is given. Raw data are available upon request for the Unispec data; the raw SunScan data is available under the file "PF_SunScan_LAI".
Baltimore forest patch point-sample overstory and understory vegetation, 2018-2020
Urban forest patches, including woodland interiors and bounding edge habitat, result from secondary succession and fragmentation of more extensive forested landscapes in the eastern USA. Management regimes, surrounding land use, and successional processes lead to distinct environments and contribute to local and regional heterogeneity. However, many woodlands are degraded due to frequent disturbance, aggressive exotic species, and heavy browsing, which stress canopies, reduce regeneration, and may reduce ecosystem services. Effective management requires rapid, repeatable assessment of forest composition, structure, and condition at the scale of local decision-making. We present and apply a protocol for characterizing urban woodlands that generates new insight into the status of urban woodlands and baseline data for change detection over time. Samples of overstory composition, ground cover, surface soil measurements, and the Schumacher Vine Encroachment Index were collected at 845 points across each of 47 patches across Baltimore, Maryland. Simple citywide summaries allowed characterization of Baltimore’s urban overstories as overwhelmingly native, though dominated by a range of successional conditions. By contrast, we found that ground layers were predominantly exotic, with abundant invasives or ruderal native species benefitting from disturbed conditions. Seven overstory types were distinguished, the majority under threat from aggressive vines. Most soils showed little evidence of compaction, but variable organic content. Distributed data allowed crosspatch comparison as well as within-patch analyses along edge-to-interior gradients. Species diversity, nativity, and overstory basal area all increased towards woodland interiors, whereas soil compaction and vine encroachment decreased. Structural and compositional shifts in both overstory and ground layer species revealed indicators of edge (15.2-18.7 m) and interior (> 41.5 m) conditions, as well as evidence of tran
Vegetation Data Collected with Point Frame for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains point frame data for vegetation less than 1.3 m, including vascular plants, bryophytes, lichens, leaf litter and bare ground, as well as species codes used, as described in Jean et al. 2017 Canadian Journal of Forest Research. Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Plot descriptions and location data from datalogger, 1m x 1m chamber flux and point frame plots sampled near Toolik Field Station in Alaska the summer of 2012.
"2012_GS_PFandCH_GPS" contains GPS locations of all datalogger, 1m x 1m chamber flux and point frame plots sampled IVO Toolik Field Station in Alaska during the summer of 2012. The sorting variables (YEAR, DATE, SITE, GROUP, PLOT, TREAT, PLOT SIZE) are identical to those in other files with data collected that season. The main purpose of this file is for reference and as an aid in interpretation of data analyses and among-site comparisons.
Raw pin-hit data from 19 1m x 1m point frame plots sampled near the LTER Shrub plots at Toolik Field Station in AK the summer of 2012.
This dataset includes every pin-hit recorded from 19 1m x 1m point frame plots of tall Betula nana and Salix pulchra canopies sampled at the Toolik Field Station, AK the summer of 2012. Twenty-five evenly spaced holes within the plot were sampled for each point frame for which the height and species was recorded for each leaf, stem, or plant that intersected the pin when lowered perpendicular to the ground. Non-woody species were grouped into functional groups (e.g. forb, graminoid, moss) and not identified to species. Stem diameter and the length of gramminoid blades were also recorded.
Percent species cover from 14 flux canopy and 19 point frame 1m x 1m plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
Total and individual subsample species percent cover data for all plots where flux or point frame measurements were made in 2012 IVO the LTER Shrub vegetation plots at Toolik Field Station. All plots sampled were dominated either by B. nana or S. pulchra canopies. Cover estimates were made for the five most dominate functional groups using a 1m x 1m grid with 20cm2 blocks with each square representing four percent of the total area. Percentages represent absolute cover so do not sum to 100%.
Maximum canopy height from 14 flux canopy and 19 point frame plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
Maximum canopy height measurements for deciduous shrub canopies sampled for both 1m x 1mc hamber flux polots (n=14) and point frame plots (n=19) in the summer of 2012 near LTER shrub plots at Toolik Lake, AK. The canopies were dominated either by Salix pulchra or Betula nana species, and plot locations were preferentially selected for tall canopies (height > 75 cm). The methods for the chamber flux and point frames are outlined here briefly, though the data from these measurements are contained in separate files.
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E) in Ciliates of fresh waters and soils of the Greater Caucasus (within Azerbaijan)
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E)
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan) in Free-living protozoa of freshwater and soils of the North-East Azerbaijan
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan)
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin in Molting feathers and eggshells in monitoring rare bird species populations: Evidence from special protected natural territories
Рис. 1. Схема точек отбора проб на ООПТ в бассейне Амура Fig. 1. Map of sampling points at the protected areas in the Amur River Basin
Text-fig. 5. Reyispermum parvum gen. et sp. nov. seeds from the Early Cretaceous Vale de Água locality, Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Holotype (S174178; Vale de Agua sample 141) in lateral view showing shape and cell pattern; remains of mounting media (¤). b) Cut volume rendering of seed (cut at yz0553) showing the slightly raised tissue immediately adjacent to the lower edge of the hilum (arrow head) and palisade-shaped cells of exotesta. c) Apical view of seed showing hilar depression (hi), position of micropylar slit (mi) and the slightly raised raphal ridge (ra). d) Seed in lateral view showing raised tissue immediately adjacent to the lower edge of the hilum (arrow head) (S174495, Vale de Água sample 300). e) Cut volume rendering (cut at yz0500) of the seed in (5d) showing the raised tissue (arrow head) immediately adjacent to the lower edge of the hilum and sclerenchyma cells of exotesta. f) Detail of seed in (5d) showing micropylar slit (mi), hilum (hi) and raised tissue (arrow head) immediately adjacent to the lower edge of the hilum. g, h) Seed in lateral view (g) and view towards raphe (h) showing seed shape, the raised tissue below hilum (arrow head) and the raphal ridge (ra); note pointed micropylar area (S174179, Vale de Água sample 141). i) Seed surface of seed in (5d) showing the raised outlines of the undulate anticlinal walls of the exotestal cells. Scale bars = 250 µm (a–e, g, h); 125 µm (f, i). in Extinct Taxa Of Exotestal Seeds Close To Austrobaileyales And Nymphaeales From The Early Cretaceous Of Portugal
Text-fig. 5. Reyispermum parvum gen. et sp. nov. seeds from the Early Cretaceous Vale de Água locality, Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Holotype (S174178; Vale de Agua sample 141) in lateral view showing shape and cell pattern; remains of mounting media (¤). b) Cut volume rendering of seed (cut at yz0553) showing the slightly raised tissue immediately adjacent to the lower edge of the hilum (arrow head) and palisade-shaped cells of exotesta. c) Apical view of seed showing hilar depression (hi), position of micropylar slit (mi) and the slightly raised raphal ridge (ra). d) Seed in lateral view showing raised tissue immediately adjacent to the lower edge of the hilum (arrow head) (S174495, Vale de Água sample 300). e) Cut volume rendering (cut at yz0500) of the seed in (5d) showing the raised tissue (arrow head) immediately adjacent to the lower edge of the hilum and sclerenchyma cells of exotesta. f) Detail of seed in (5d) showing micropylar slit (mi), hilum (hi) and raised tissue (arrow head) immediately adjacent to the lower edge of the hilum. g, h) Seed in lateral view (g) and view towards raphe (h) showing seed shape, the raised tissue below hilum (arrow head) and the raphal ridge (ra); note pointed micropylar area (S174179, Vale de Água sample 141). i) Seed surface of seed in (5d) showing the raised outlines of the undulate anticlinal walls of the exotestal cells. Scale bars = 250 µm (a–e, g, h); 125 µm (f, i).
Fig. 1. Sample places location. Points number 1, 3, 7, 12, 14, 18 and 20 in Reproductive biology of Rhaphiodon vulpinus (Ostariophysi: Cynodontidae) in the Tocantins River Basin, Brazil
Fig. 1. Sample places location. Points number 1, 3, 7, 12, 14, 18 and 20 = Tocantins river; 2 = Sono river; 4 = Lajeadinho river; 5 = Santa Luzia river; 6 = Mangues river; 8 = Areias river; 9 = Crixás river; 10 = Capivara Lagoon; 11 = Manuel Alves river; 13 = São Valério river; 15 = Santa Tereza river; 16 = Dionízio Lagoon; 17 = Água Branca Lagoon; 19 = Paranã river and 21 = Bonita Lagoon.
Point sample database with spatial holdbacks for global forest edge model training
<p>Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0</p> <p>Field schema:</p> <p>accessibility_to_cities_2015_30sec_compressed (Integer64)<br> altitude_10sec_compressed (Integer64)<br> baccini_carbon_data_2014_compressed (Integer64)<br> bio_01_30sec_compressed (Real)<br> bio_02_30sec_compressed (Real)<br> bio_03_30sec_compressed (Real)<br> bio_04_30sec_compressed (Real)<br> bio_05_30sec_compressed (Real)<br> bio_06_30sec_compressed (Real)<br> bio_07_30sec_compressed (Real)<br> bio_08_30sec_compressed (Real)<br> bio_09_30sec_compressed (Real)<br> bio_10_30sec_compressed (Real)<br> bio_11_30sec_compressed (Real)<br> bio_12_30sec_compressed (Real)<br> bio_13_30sec_compressed (Real)<br> bio_14_30sec_compressed (Real)<br> bio_15_30sec_compressed (Real)<br> bio_16_30sec_compressed (Real)<br> bio_17_30sec_compressed (Real)<br> bio_18_30sec_compressed (Real)<br> bio_19_30sec_compressed (Real)<br> cec_0-5cm_mean_compressed (Integer64)<br> cec_5-15cm_mean_compressed (Integer64)<br> cfvo_0-5cm_mean_compressed (Integer64)<br> cfvo_5-15cm_mean_compressed (Integer64)<br> clay_0-5cm_mean_compressed (Integer64)<br> clay_5-15cm_mean_compressed (Integer64)<br> fc_stack_hansen_forest_cover2014_compressed (Integer64)<br> gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real)<br> gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> hillshade_10sec_compressed (Integer64)<br> masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64)<br> night_lights_10sec_compressed (Real)<br> night_lights_5min_compressed (Real)<br> nitrogen_0-5cm_mean_compressed (Integer64)<br> nitrogen_10sec_compressed (Integer64)<br> nitrogen_5-15cm_mean_compressed (Integer64)<br> phh2o_0-5cm_mean_compressed (Integer64)<br> phh2o_5-15cm_mean_compressed (Integer64)<br> sand_0-5cm_mean_compressed (Integer64)<br> silt_0-5cm_mean_compressed (Integer64)<br> silt_5-15cm_mean_compressed (Integer64)<br> slope_10sec_compressed (Real)<br> soc_0-5cm_mean_compressed (Integer64)<br> soc_5-15cm_mean_compressed (Integer64)<br> tri_10sec_compressed (Real)<br> wind_speed_10sec_compressed (Real)</p>
Stability (similarity) values of output maps generated using different background point sampling strategies
<p>These tables represent the model settings and similarity values of Species Distribution Models generated using different background pointssampling strategies. The similarity values are our own newly designed way to investigate model stability and were determined by comparing an output map of an SDM fitted using real data (original map) with an output map of an SDM fitted using virtual occurrences generated from the original map.</p>
Sceilig Mhichíl - Sampled Point Cloud (2.5D)
Sceilig Mhichíl is an island west of the Iveragh Peninsula in County Kerry, Ireland. The island is a UNESCO World Heritage Site and is best known for its well-preserved, medieval Gaelic monastery. The island is also known as the [filming site](https://youtu.be/g_0lk8BY0Vk) of Star Wars Episode VII "The Force Awakens". The topographical dataset of Sceilig Mhichíl is produced by the Geological Survey Ireland (GSI) and is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The original raster data (2.5D, 0.1m resolution) can be downloaded from the [GSI Open Topo Viewer](https://dcenr.maps.arcgis.com/apps/webappviewer/index.html?id=b7c4b0e763964070ad69bf8c1572c9f5). Great information about Sceilig Mhichíl can be found in [this report](https://www.chg.gov.ie/app/uploads/2015/10/skellig-michael-world-heritage-site.pdf) from the Irish Dept. of Culture, Heritage and the Gaeltach. Source: Objaverse 1.0 / Sketchfab
Development and Implementation of a Tool for Data Capture @ Point of Sampling
<p><span>Poster presented at EFSA Symposium “Data Readiness for Artificial Intelligence”, held on 23-24 October 2024. </span></p> <p><span> </span><span>It describes the project “Development and Implementation of a Tool for Data Capture @ Point of Sampling” coordinated by the Portuguese Economic and Food Safety Authority (ASAE) with technical support from LIACC (Artificial Intelligence and Computer Science Laboratory, University of Porto), and involving two Danish organisations (Danish Veterinary and Food Administration and Technical University of Denmark) as end users of an application designed to enhance the harmonization and robustness of data collection processes. </span></p> <p><span> </span><span>The project is focused on two key areas: the further development of the mobile tool for data capture at the point of sampling in EFSA's SSD2 format, including SSD2 catalogue integration and translation into national languages, and the installation of this tool within partner entities across participating countries. </span></p> <p><span>The new system will incorporate validation features through the integration of the SSD2 data format and the FoodEx2 classification at the point of data creation.</span></p>
Fractal sampling point nesting
<b>Description: </b><p>This dataset contains a table showing the nesting of sampling points within the fractal sampling design at the SAFE project and an R code file used to create the nesting from a GIS file of the core sampling locations.<br><br>There are five fractal levels across 17 sites: six experimental blocks (A-F), two edge transects (LFE, VJR) and three sets of triplets of sites to sample specific habitats (Old growth: OG1 - 3, Oil palm: OP1 - 3, Twice logged forest: LF1 - 3). Within each site, there are sampling points at five fractal scales with fractal order 1 as the smallest scale and fractal order 5 being the largest scale. This file identifies the child - parent relationships of points across the fractal sampling levels. For more details, see <a>https://www.safeproject.net/dokuwiki/safe_gis/safe_fractal</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=179">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Fractal_point_nesting.xlsx, Nested_fractal_table_maker.R</p><p><b>Fractal_point_nesting.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Fractal sampling point nesting</b> (described in worksheet Fractal_point_nesting)</p><p>Description: This table contains one row for each fractal order 1 sampling point (finest scale) and then identifies the parent sampling points at the higher fractal scales (2-5). Note that not all sites have a central fractal order 5 point.</p><p>Number of fields: 9</p><p>Number of data rows: 579</p><p>Fields: </p><ul><li><b>Site</b>: Site ID prefix (Field type: ID)</li><li><b>Habitat</b>: Focal habitat of sampling site (Field type: Categorical)</li><li><b>Logging</b>: Logging history of sampling site (Field type: Categorical)</li><li><b>Frag_Area</b>: Estimated area of fragment (Field type: Numeric)</li><li><b>FirstOrder</b>: First order fractal point (Field type: Location)</li><li><b>SecondOrder</b>: Second order fractal parent (Field type: Location)</li><li><b>ThirdOrder</b>: Third order fractal parent (Field type: Location)</li><li><b>FourthOrder</b>: Fourth order fractal parent (Field type: Location)</li><li><b>FifthOrder</b>: Fifth order fractal parent (not applicable for all sites) (Field type: Location)</li></ul></li></ol><p><b>Nested_fractal_table_maker.R</b></p><p>Description: This R code creates the table stored in Fractal_point_nesting from the SAFE Core sampling stations GIS layer. </p><p><b>Date range: </b>2010-01-01 to 2020-01-01</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9474 to 117.7031</p>
BES bird survey habitat features. This is a collection of the habitat features for the BES bird sampling points. Features include number of houses, proximity to trees, shrubs, grass, annuals, and other vegetation and physical features.
This dataset is associated with BES Bird Monitoring Bird Monitoring Project: ================= The BES Bird Monitoring Project is a breeding bird survey designed to find out what birds are found in the breeding season in Baltimore and where. Our monitoring efforts will show associations among block group socioeconomic variables, land cover, land use, and habitat features with breeding bird abundance, to provide information for land managers on possible consequences of land use changes on bird communities. A distinguishing feature of the bird monitoring at BES LTER, relative to other urban bird work, is the capacity for long-term monitoring of features at multiple scales through links to other parts of the project. Different processes influence habitat for birds at different scales, e.g. ongoing household level human decision-making at lot scale vs. block or neighborhood scale abandonment/re-development. Our project seeks to understand how these processes impact bird occurrence, abundance, and composition differ at the lot, block and neighborhood scale. The data consists of four major elements, Sites, Surveys, Taxalist, and Birds. Sites records the sites and their characteristics. Surveys describe the actual outings or sampling sessions. They describe the weather, the temperature, the sites visited. Taxalist provides the integration of speciaies abbreviations and common names, and Birds describes the actual sightings, linking to the other three tables. Attribute information: Here are the fields Surveys: site_id FK->Sites[site_id] survey_id survey_date time_start time_end observer wind_speed wind_dir air_temp temp_units cloud_cover notes Sites: site_id park_code park_district park_name point_code point_location park_acreage Taxalist: species_id common_name Birds: survey_id FK->surveys[survey_id] site_id FK->surveys[site_id] species_id FK->taxalist[species_id] distance bird_count notes seen heard direction time_class
Germline and malignant melanoma sampled sequences containing point mutations
<p>Data files generated as part of a study into the influence of neighbouring bases on point mutation. The data are sampled from the Ensembl (http://www.ensembl.org) MySQL databases or COSMIC (http://cancer.sanger.ac.uk/cosmic) and processed using custom scripts that will be uploaded separately and associated with this submission via gthe related identifier.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.