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709 results for “soil carbon”
Soil nitrogen availability and acidity: effects on aboveground production and belowground carbon allocation in mid- and late-successional mixed temperate forests (2009-2021)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mid- and late-successional mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains forest productivity (wood, litterfall, and aboveground net primary production), total belowground carbon flux (TBCF), and leaf litterfall and fine root chemistry (C and N concentration) data collected from all experimental plots. It also includes plot-level, species-weighted estimates of measured and modeled photosynthesis (Anet) for the late-successional stands. Wood production, litterfall production, and litterfall chemistry data were collected between 2009 and 2019. Aboveground net primary production data are reported for a pre-treatment interval (2009-2011) and the interval including years 6-9 of experimental treatment (2016-2019). All other properties were measured between years 9 and 11 of the experiment (2019-2021).
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
Model estimates of runoff, dissolved organic carbon, soil temperature and moisture for Elson Lagoon watershed, Alaska, 1981-2020
This dataset contains model estimates of dissolved organic carbon (DOC) yield (mg C/m^2) and runoff (mm), for surface and subsurface flows, soil temperature (degree C), and soil moisture (% of soil volume) for grid cells spanning the Elson Lagoon watershed in northwest Alaska. Daily air temperature, precipitation, and wind speed data from Utqiagvik airport were used for meteorological forcings for the daily simulation by the Permafrost Water Balance Model (PWBM) from 1981 to 2020. The DOC and runoff data files are organized by grid cell and month. The soil temperature and soil moisture files are organized by grid cell and day of year (DOY), and contain values for the first eight model soil layers, with centers of the layers at 1, 3, 8, 13, 23, 33, 45, 55 cm depth. The estimates are most useful for analyses of the dynamics of the watershed’s surface and subsurface runoff and DOC yield. Leachate DOC concentrations can be obtained using the gridded runoff and yield values. A manuscript describing the data and associated analysis has been accepted for publication in Environmental Research Letters (Rawlins et al., 2021).
Percentage of Carbon and Nitrogen of Soil Sediments from the Shark River Slough, Taylor Slough and Florida Bay within Everglades National Park (FCE LTER), Florida, USA, August 2008 - ongoing
These data represent the results of CHN Analysis from annual soil sampling from all 17 FCE LTER transect locations from Year 2008 thru ongoing. In 2017, the samples were collected before Hurricane Irma (2017a) and post Hurricane Irma (2017b). Surface soils from 0-10 cm been homogenized and analyzed from Sawgrass and mangrove sites and Florida Bay sites. Soils and sediments were analyzed for percentage by weight of total Carbon and Nitrogen with a PerkinElmer CHNS /O 2400 Series II Elemental Analyzer. These analyses are completed to document the differences in soil composition among transect sites, and to provide a baseline dataset against which long-term changes in the properties of the soils can be detected.
Water, Soil, Floc, Plant Total Phosphorus, Total Carbon, and Bulk Density data (FCE) from Everglades Protection Area (EPA) from 2004 to 2016
These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.
Carbon exchange responses of rehydrated and incubated biological soil crust samples from White Sands National Park in 2020-2022
This dataset contains photosynthetic light response data from biological soil crusts collected from a gypsum sand sheet at White Sands National Park, NM, USA in three different seasons. This study aims to 1) assess the carbon fixation capacity of biocrust types; 2) assess biocrust carbon fixation response under varying incubation times; 3) and understand variability in carbon fixation response in different seasons. Sample collection occurred in July 2020 (summer), September 2021 (fall), and March 2022 (winter). The biocrust types of interest were light cyanobacterial, dark cyanobacterial, Peltula lichen, Clavascidium lichen, and moss crusts. Samples were collected with the intention of taking carbon fixation measurements after different incubation periods (30 min, 2 hr, 6 hr, 12hr, or 24 hr in 2020, and 30 min, 2 hr, 6 hr, 12hr, 24 hr, or 36 hr in 2021 and 2022). For each condition (biocrust type and incubation time) there were five replicates in 2020 (total n=125) and ten replicates in 2021 and 2022 (total n=300). After collection, the intact samples were re-wetted and subjected to their respective incubation period and measured for photosynthetic response. The resulting light response curves and photosynthetic information was be used for comparing biocrust type, incubation time response differences, and seasonal variation to understand variability of biocrust carbon flux response at a single site. This data set includes the light response curve values and photosynthetic data calculated from these curves and raw LICOR output files compiled into 3 spreadsheet files. The included 2020 data is also associated with the White Sands National Park data from Jornada Study 549. This dataset accompanies the in-press article by Hoellrich et al. (2023) cited below, and the study is now complete. Hoellrich, Mikaela R., Darren K. James, David Bustos, Anthony Darrouzet-Nardi, Louis S. Santiago, and Nicole Pietrasiak. "Biocrust carbon exchange varies with crust type and time on Ch
Soil nitrous oxide and carbon dioxide concentration data for Niwot Ridge and Loch Vale watershed, 1994.
Concentrations of carbon dioxide and nitrous oxide from snow-covered alpine soil surfaces were measured at Niwot Ridge. Six sites characterized by relatively shallow snowpacks were sampled in 1993. A total of 27 sites were sampled in 1994. Nine of the 1994 sites were located in the naturally shallow snowfield sampled in 1993, 9 sites were located in a formerly shallow snowpack site where snow depth was augmented by the construction of a 2.8-m high, 60-m long snowfence, and the 9 remaining sites were located in a naturally deep snowpack. Concentrations of N2O and CO2 at the soil surface were measured monthly from January until March, biweekly through April, and weekly until snowmelt was complete. Elevated levels of CO2 under the snowpack, suggesting microbial activity, were first observed under the shallow snowpacks in early March of 1993. N2O production under snow was first observed in April 1993, when soil temperatures had warmed above -3 degrees C. In 1994 shallow snowpack sites exhibited diminished and sporadic production of both CO2 and N2O, apparently due to the inconsistent snow cover compared to 1993. The snowfence sites exhibited elevated CO2 and N2O levels beginning in February 1994. Both CO2 and N2O fluxes from the snowfence site were similar to those measured under the naturally deep snowpack. These data suggest that the timing and depth of snow cover during the alpine winter control microbial activity by insulating soils from extreme air temperatures. To obtain a regional perspective on subnivean trace gas fluxes, both CO2 and N2O samples were determined at sites below treeline on Niwot Ridge and at Loch Vale in Rocky Mountain National Park.
Soil nitrous oxide and carbon dioxide flux data for Niwot Ridge and Loch Vale watershed, 1994.
Fluxes of carbon dioxide and nitrous oxide from snow-covered alpine soils were measured at Niwot Ridge. Six sites characterized by relatively shallow snowpacks were sampled in 1993. A total of 27 sites were sampled in 1994. Nine of the 1994 sites were located in the naturally shallow snowfield sampled in 1993, 9 sites were located in a formerly shallow snowpack site where snow depth was augmented by the construction of a 2.8-m high, 60-m long snowfence, and the 9 remaining sites were located in a naturally deep snowpack. Concentrations of N2O and CO2 at the soil surface were measured monthly from January until March, biweekly through April, and weekly until snowmelt was complete. Elevated levels of CO2 under the snowpack, suggesting microbial activity, were first observed under the shallow snowpacks in early March of 1993. N2O production under snow was first observed in April 1993, when soil temperatures had warmed above -3 degrees C. In 1994 shallow snowpack sites exhibited diminished and sporadic production of both CO2 and N2O, apparently due to the inconsistent snow cover compared to 1993. The snowfence sites exhibited increased CO2 and N2O fluxes beginning in February 1994. Both CO2 and N2O fluxes from the snowfence site were similar to those measured under the naturally deep snowpack. These data suggest that the timing and depth of snow cover during the alpine winter control microbial activity by insulating soils from extreme air temperatures. To obtain a regional perspective on subnivean trace gas fluxes, both CO2 and N2O samples were determined at sites below treeline on Niwot Ridge and at Loch Vale in Rocky Mountain National Park.
Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Laboratory Dataset on Self-ignition of Carbon-Rich Soil
<p>The file attached contains a complete set of experimental data from carbon-rich soil self-heating ignition cubic basket experiments for a range of soil inorganic content (IC) ranging from 3% to 86%. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and soil temperatures. The data reported includes the dates of experiments, volume of soil baskets being tested, oven ambient temperature, inorganic content present in the sample, bulk density of the soil and if the sample ignited or not. This data is in support of the journal paper:</p> <p>F. Restuccia, X. Huang, G. Rein, <strong>Self-ignition of Natural Fuels: Can Wildfires of Carbon-Rich Soil Start by Self-heating?</strong>, <em>Fire Safety Journal </em>2017, http://doi.org/10.1016/j.firesaf.2017.03.052.</p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.</p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.<br> </p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.</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)
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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.