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54 results for “nitrogen source”
MCR LTER: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality
Data are from an 18-month field experiment on the fore reef of Moorea, testing how different forms of nitrogen (nitrate vs. urea) impact coral bleaching and mortality during two mild thermal stress events in the Austral summers of 2016 and 2017. These data are associated with a manuscript currently in review at Ecosystems. Tentative mansucript title and author list are: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality Deron E. Burkepile, Andrew A. Shantz, Thomas C. Adam, Katrina S. Munsterman, Kelly E. Speare, Mark C. Ladd, Mallory M. Rice, Shelby McIlroy, Andrew J. Brooks, Russell J. Schmitt, and Sally J. Holbrook These data are part of the NSF project: RAPID: How does nutrient availability alter coral bleaching, mortality, and recovery on Moorea coral reefs? (funded wholly or part by NSF Awards OCE-1619697).
Growth and metabolome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources
<p><em>Raw data: Metabolome of S. uvarum (Su) and S. cerevisiae (Sc) in wine fermentations in 13 different nitrogen conditions. Concentrations of compounds expressed in mg/L at 60 g/L CO2 sampling point and at the end of fermentation. The volatile compounds are grouped according to the chemical functional group (ethyl esters, acetate esters, higher alcohols, medium chain fatty acids (MCFA) and branched-chain fatty acids (BCFA)). The central carbon metabolites (CCM) and sugars conform the last group. Each value is the mean of three biological replicates. The raw data is reported in a processed form in the manuscript entitled “The growth and metabolome of Saccharomyces uvarum in wine fermentations is strongly influenced by the route of nitrogen assimilation” </em></p>
Transcriptome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources Created Jun 9, 2022 12:06:22 PM, modified Jun 9, 2022 12:07:57 PM
<p>Transcriptome analysis of S. uvarum grown on synthetic wine must with different nitrogen sources: Ammonium, Phenylalanine, Asparagine, or Methionine. This is a dataset for the thesis of Angela Coral (Autumn 2022) , which can be accessed at www.ucc.ie. The work will also be submitted for publication and this will be a supplementary data file.</p>
Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed
<p>The attached dataset and code accompanies a Data in Brief article, "Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed."</p> <p>Abstract: Quantifying point source nitrogen loads to the Long Island Sound watershed is important because they contribute to the annual occurrence of hypoxia in Long Island Sound. However, the data are not easily accessible and are not available at a central location, making it difficult to characterize the magnitude of loading, seasonal patterns and long-term trends. For the period between April 1989 and September 2021, we gathered all available monthly nitrogen data for wastewater treatment plants within the Long Island Sound watershed from the U.S. Environmental Protection Agency Integrated Compliance Information System-National Pollutant Discharge Elimination System (ICIS-NPDES) and Permit Compliance System, and Connecticut Department of Energy and Environmental Protection databases. Data were checked for quality assurance and unreasonable outliers were imputed with means of other observations within the same year and season. Estimates were also imputed for missing observations. We publish both raw data with missing observations and a curated dataset with imputed estimates for missing observations. Monthly point source nitrogen load data can be used for water quality modeling and to infer, by subtraction, the relative contribution of nonpoint sources to gauged watershed loads. Additionally, the scripts used to reformat data from the ICIS could be repurposed to collect similar data in other regions.</p>
Assessing pre‐plant nitrogen sources and waterlogging on corn growth and yield
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Nitrogen economy of alpine plants on the north Tibetan Plateau: nitrogen conservation by resorption rather than open sources through biological symbiotic fixation
<p>Nitrogen (N) is one of the most important factors limiting plant productivity, and N fixation by legume species is an important source of N input into ecosystems. Meanwhile, N resorption from senescent plant tissues conserves nutrients taken up in the current season, which may alleviate ecosystem N limitation. N fixation was assessed by the <sup>15</sup>N dilution technique in four types of alpine grasslands along the precipitation and soil nutrient gradients. The N resorption efficiency (NRE) was also measured in these alpine grasslands. The aboveground biomass in the alpine meadow was 4–6 times higher than in the alpine meadow-steppe, alpine steppe, and alpine desert-steppe. However, the proportion of legume species to community biomass in the alpine steppe and the alpine desert steppe was significantly higher than the proportion in the alpine meadow. N fixation by the legume plants in the alpine meadow was 0.236 g N m<sup>-2</sup>, which was significantly higher than N fixation in other alpine grasslands (0.041 to 0.089 g N m<sup>-2</sup>). The NRE in the alpine meadows was lower than in the other three alpine grasslands. Both the aboveground biomass and N fixation of the legume plants showed decreasing trends with the decline of precipitation and soil N gradients from east to west, while the NRE of alpine plants showed increasing trends along the gradients, which indicates that alpine plants enhance the NRE to adapt to the increasing droughts and nutrient-poor environments. The opposite trends of N fixation and NRE along the precipitation and soil nutrient gradients indicate that alpine plants adapt to precipitation and soil nutrient limitation by promoting NRE (conservative nutrient use by alpine plants) rather than biological N fixation (open sources by legume plants) on the north Tibetan Plateau.</p>
Food sources of benthic communities at the Caiwei Guyot and Yap Trench, northwestern Pacific Ocean: inferences from carbon and nitrogen isotopes
<p>To investigate nutritional resources for benthic communities at two sites in the northwestern Pacific Ocean (the Caiwei Guyot and the Yap Trench), stable isotopes of carbon and nitrogen (δ<sup>13</sup>C and δ<sup>15</sup>N) were measured in the tissues of megabenthic consumers (Porifera, Asteroidea, Crinoidea, Holothuroidea, Ophiuroidea, Gammaridea, and Actiniaria) as well as four potential food sources (suspended particles, sinking particles, zooplankton, and sedimentary organic matter, SOM). Fast-sinking particles are generally thought to be the primary food source for benthic consumers, but that paradigm does not seem to apply at these abyssal sites. Here, the δ<sup>13</sup>C and δ<sup>15</sup>N signatures of fast-sinking particles (as collected by sediment traps; δ<sup>13</sup>C = −24.1 to −22.6‰, δ<sup>15</sup>N = 1.4 to 5.4‰) were significantly lower than those of the megabenthos (δ<sup>13</sup>C = −20.1 to −16.1‰, δ<sup>15</sup>N = 10.2 to 17.9‰), indicating that these particles are not likely a direct food source for the animals. Buoyant particles (and slow-sinking particles), on the other hand, seem to be a significant direct food source for the megabenthos. Sedimentary organic matter and zooplankton are also important direct food sources. Trophic level analysis similarly indicates a diversity of food sources and suggests that for at least some animals, microbes (e.g., bacteria) may be a food source as well.</p>
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Sarrazin, F. J., Attinger, A., Kumar, R., Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)" is freely available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO) data and German Cosmetic, Toiletry, Perfumery and Detergent Association (IKW) data that underlie the dataset.</p> <h4><strong>Data description</strong></h4> <p>The dataset includes estimates of nitrogen (N) and phosphorus (P) emissions from wastewater in Germany (1950-2019) at (1) grid level, and at different levels of aggregation, namely (2) at Nomenclature of Territorial units for statistics level 1 (NUTS-1), that correspond to the 16 German federal states (for this, we used the 2020 NUTS classification; BKG, 2020) and (3) at river basin level for 3778 river basins of the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013). It also includes the input and calibration data (at NUTS-1 and grid level) that were used to estimate the N and P emissions.</p> <ol> <li> <p>Input and calibration data at NUTS-1 level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019 (input data), 1987-2019 (calibration data)<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: input data, calibration data, parameter sample<br><strong><em>file format</em></strong>: CSV<br><em><strong>number of files</strong></em>: 3<br><br></p> </li> <li> <p>Input data at grid level:<br><strong><em>spatial extent</em></strong>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><strong><em>time period</em></strong>: 1950-2019 (population data), 2020 (NUTS-1 map)<br><strong><em>frequency</em></strong>: annual<br><strong><em>variables</em></strong>: urban and rural population counts, NUTS-1 map<br><em><strong>file format</strong></em>: netCDF<br><strong><em>number of files</em></strong>: 2<br><br></p> </li> <li>Emission data at grid level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P wastewater treatment plants (WWTPs) outgoing emissions (treated point sources)<br>- N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: netCDF<br><em><strong>number of files</strong></em>: 100<br> </li> <li>Emission data aggregated at NUTS-1 (federal state) level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P gross emissions<br>- N and P total point sources (sum of treated and untreated components)<br>- N and P treated point sources (WWTPs outgoing load)<br>- N and P untreated point sources (emissions collected in the public sewer system that are not treated in WWTPs)<br>- N and P emissions that are removed during treatment in WWTPs<br>- N and P emissions lost during wastewater collection and transport<br>- N and P emissions applied to agricultural soils in sewage farms<br>- N and P emissions that are not collected in the sewer system nor treated in WWTPs<br>- N and P incoming WWTPs load<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 100 realisations corresponding to 100 different parameter sets<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 18<br> </li> <li>Emission data aggregated at river basin level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: river basins from the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013).<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: N and P WWTP outgoing emissions (treated point sources), N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 600</li> </ol> <h4><strong>Acknowledgements and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A). We thank Olaf Büttner for providing the WWTPs data that were collected from the authorities of the German federal states (<a href="https://www.hydroshare.org/resource/2f2c2fa04e6e417ba0eb7b0fb14b1090/">Büttner et al., 2020</a>). The dataset produced in this work builds on the NUTS map of the German Federal Agency for Cartography and Geodesy © GeoBasis-DE/BKG that is under a <a href="https://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0 license</a>; the History Database of the Global Environment (HYDE) dataset available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>; protein data provided by the Food and Agriculture Organization of the United Nations © FAO provided under a <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/igo/">CC BY-NC-SA 3.0 IGO license</a>; detergent data from the German Cosmetic, Toiletry, Perfumery and Detergent Association © IKW (<a href="https://www.ikw.org/impressum">license here</a>); data from the statistical offices of Germany and the federal states and the German and federal state authorities (details on data sources in the publication reported above: Sarrazin et al., submitted to Earth System Science Data); WWTP data available in the Waterbase dataset from the European Environment Agency © EEA under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>. The river basins come from © HydroSHEDS (<a href="https://www.hydrosheds.org/terms-of-use">license here</a>).</p> <h4><strong>Contact</strong></h4> <p>Fanny Sarrazin (<a href="mailto:fanny.sarrazin@ufz.de">fanny.sarrazin@inrae.fr</a>)<br>Rohini Kumar (<a href="mailto:rohini.kumar@ufz.de">rohini.kumar@ufz.de</a>)</p> <h4><strong>References</strong></h4> <p>BKG (Bundesamt für Kartographie und Geodäsie) (2020), NUTS regions 1 : 250 000, 31.12.2020, GeoBasis-DE [data set], Leipzig, Germany, https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html (last access: 1 November 2022).</p> <p>Büttner, O. (2020), DE-WWTP - data collection of wastewater treatment plants of Germany (status 2015, metadata), HydroShare [data set],<br>https://doi.org/10.4211/hs.712c1df62aca4ef29688242eeab7940c.</p> <p>HydroSHEDS (2014), HydroBASINS v1.c, https://www.hydrosheds.org/products/hydrobasins (last access: 23 October 2023).<br><br>Lehner, B. and Grill, G. (2013), Global river hydrography and network routing: baseline data and new approaches to study the world's large river<br>systems, Hydrological Processes, 27, 2171–2186, https://doi.org/10.1002/hyp.9740.</p> <p><strong>Changes compared to v1.0 dataset version</strong></p> <p>Compared to the version 1.0 of the dataset, this version v1.1 contains the input and calibration data at NUTS-1 level and the input data at grid level used to calculate the emissions.</p>
Data from: Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes
<p>Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source-sink relationships of a large data set to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The data set came from 13 years' semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis aboveground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype-dependent. Our analysis provides an overall picture of post-anthesis source-sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaption to climate change should account for complex multi-factor interactions.</p>
On the Contributions of Nitrogen from Different Sources to Exported Organic Nitrogen from the East China Sea
<p>Processed model data for "On the Contributions of Nitrogen from Different Sources to Exported Organic Nitrogen from the East China Sea" submitted to Geophysical Research Letters. Detailed information is described in readme.txt.</p>
A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge. Part B: Mantle source heterogeneities
<p>All data appearing in the manuscript and any supplementary documents, figures, or tables of the paper "A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge.<strong> </strong>Part B: Mantle source heterogeneities"<strong> </strong>at Geochimica et Cosmochimica Acta<strong> </strong>by<strong> </strong>Bekaert et al., are available through this open access data repository.</p>
Data and Python codes used in "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"
<p>These are the dataset and python codes used in a manuscript titled "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"</p>
Data from: Upwelling as the major source of nitrogen for shallow and deep reef-building corals across an oceanic atoll system
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Data from: Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes
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Food sources of benthic communities at the Caiwei Guyot and Yap Trench, northwestern Pacific Ocean: inferences from carbon and nitrogen isotopes
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Nitrogen economy of alpine plants on the north Tibetan Plateau: nitrogen conservation by resorption rather than open sources through biological symbiotic fixation
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Data from: Urine is an important nitrogen source for plants irrespective of vegetation composition in an Arctic tundra: insights from a 15N-enriched urea tracer experiment
1. Mammalian herbivores can strongly influence nitrogen (N) cycling and herbivore urine could be a central component of the N cycle in grazed ecosystems. Despite its potential role for ecosystem productivity and functioning, the fate of N derived from urine has rarely been investigated in grazed ecosystems. 2. This study explored the fate of <sup>15</sup>N-enriched urea in tundra sites that have been either lightly or intensively grazed by reindeer for more than 50 years. We followed the fate of the <sup>15</sup>N applied to the plant canopy, at 2 weeks and 1 year after tracer addition, in the different ecosystem N pools. 3. <sup>15</sup>N-urea was rapidly incorporated in cryptogams and in aboveground parts of vascular plants, while the soil microbial pool and plant roots sequestered only a marginal proportion. Further, the litter layer constituted a large sink for the <sup>15</sup>N-urea, at least in the short term, indicating a high biological activity in the litter layer and high immobilization in the first phases of organic matter decomposition. 4. Mosses and lichens still constituted the largest sink for the <sup>15</sup>N-urea 1 year after tracer addition at both levels of grazing intensity demonstrating their large ability to capture and retain N from urine. Despite large fundamental differences in their traits, deciduous and evergreen shrubs were just as efficient as graminoids in taking up the <sup>15</sup>N-urea. The total recovery of <sup>15</sup>N-urea was lower in the intensively grazed sites, suggesting that reindeer reduce ecosystem N retention. 5. <i>Synthesis</i> The rapid incorporation of the applied <sup>15</sup>N-urea indicates that arctic plants can take advantage of a pulse of incoming N from urine. In addition, δ <sup>15</sup>N values of all taxa in the heavily grazed sites converged towards the δ <sup>15</sup>N values for urine, bringing further evidence that urine is an important N source for plants in grazed tundra ecosystems.
Data from: Fog as a source of nitrogen for redwood trees: evidence from fluxes and stable isotopes
A defining feature of the redwood forest in coastal California is the presence of fog in the summer months, a time when there is typically little rainfall. Our goal was to determine the role of summer fog in canopy transformation of nitrogen, nitrogen uptake by trees and photosynthesis within a coastal redwood forest ecosystem. We measured horizontal and vertical inputs of nitrogen, the isotopic composition of nitrogen in a variety of atmospheric sources (summer fog, winter rain and throughfall throughout the year), nitrogen pools (soil solution) and plant tissue (roots and foliage), as well as rates of photosynthesis and nitrogen uptake by trees. Throughfall nitrogen fluxes were greater at the forest edge compared to the interior both within the canopy (sampled 10 m above-ground) and onto the forest floor (sampled 1 m above-ground; P < 0.05). Similarly, soil solution inline image and total inorganic nitrogen were greater at the forest edge compared to the interior (P = 0.0014 and 0.009, respectively). Whereas natural abundance δ15NO3 values were not significantly different between winter rain (measured as bulk precipitation) and summer fog water (average δ15N = −1.2 ± 0.680/00), δ15NH4 values were significantly greater in fog water (11.4 ± 2.70/00) compared to rain (1.2 ± 0.90/00). We found no difference in δ15N in roots from forest edge trees compared to interior trees. In contrast, nitrogen concentrations and δ15N in foliage from forest edge trees were significantly greater compared to interior trees (P < 0.0001), suggesting that the leaves of forest edge trees may be obtaining a greater proportion of their nitrogen from fog compared to those of the interior trees. Natural abundance 13C of leaf sugars and rates of photosynthesis were significantly higher at the forest edge compared to the interior during the fog season (P < 0.05), but not different between locations in the rain season (P > 0.05). Nitrification in the forest floor, rather than the canopy, is the primary source of inline image in these soils throughout the year. Synthesis. Summer fog provides nitrogen directly and indirectly to redwood trees, especially those at the forest edge, and affects the physiologic function of redwood trees.
Dataset for paper "Aquacultural source of nitrous oxide revealed by nitrogen isotopes"
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Data from: Accumulation rates and sources of external nitrogen in decaying wood in a Norway spruce dominated forest
Microbial respiration in dead wood contributes substantially to the long-lived forest carbon (C) pool and has a significant role in the forest nitrogen (N) cycle. Wood N content has been found to increase during the decay process; however, temporal dynamics and the sources of this external N remain unclear. To examine N dynamics at various stages of decomposition, we combined high variety of analytical methods on Norway spruce logs, including wood δ15N, N%, 14C-dating, fungal composition and N2 fixation rate. For N2 fixation rate, we also determined its dependency on ambient temperature and decay class, when estimating annual N2 fixation rates for our study site. N2 fixation was observed to have a major role in increasing wood N content during decay. For the most decayed wood, it accounted for 60% of the total N accumulation. Compared to other reports, where the annual temperature was similar to our site, the calculated annual fixation rate of 85 g N ha−1 year−1 is a low estimate. However, previous studies have not taken appropriately into account the dependency of N2 fixation rate on ambient temperature and decay class. Our δ15N model describing the sources of external N, statistical analysis and the fungal DNA composition of decayed wood suggest that other sources of external N accumulating in wood were soil-foraging wood-decay fungi and mycorrhizal fungi. Our study improves knowledge of the temporal dynamics of N accumulation in wood with advancing wood decay, the potential sources of external N and their relative significance. All of these factors are important for nitrogen as well as carbon models dealing with ecosystem responses to climate change.
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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.