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25 results for “nitrous oxide (N2O)”
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.
TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: </p> <p><span>The </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>N2O Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated N2O profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these N2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>N2O bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved N2O profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean N2O profiles:</span></p> <ul> <li> <p><code><span>zmn2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmn2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023.</span></p>
Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.
<p>Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N<sub>2</sub>O emissions from salt marsh soils from cold and warm climates</a>.</p> <p>The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (<em>Sporobolus alterniflorus</em> formerly known as <em>Spartina alterniflora </em>and<em> Sporobolus pumilus</em> formerly known as<em> Spartina patens</em>). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21<sup>st</sup> July 2021 from Louisiana and the 9-10<sup>th</sup> August 2021 from Quebec. Environmental data was collected including <em>in-situ</em> soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d<sup>13</sup>C and soil d<sup>15</sup>N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1).</p> <p>Stable <sup>15</sup>N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving <sup>15</sup>N-NO<sub>3</sub><sup>‑ </sup>for denitrification rates and one core receiving <sup>15</sup>N-NH<sub>4</sub><sup>+</sup> for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO<sub>3</sub><sup>-</sup>, 2.0 NH<sub>4</sub><sup>+</sup>; 2.9 NO<sub>3</sub><sup>-</sup>, 2.5 NH<sub>4</sub><sup>+</sup>; 0.5 NO<sub>3</sub><sup>-</sup>, 7.3 NH<sub>4</sub><sup>+ </sup>and 5.7 NO<sub>3</sub><sup>-</sup>, 2.8 NH<sub>4</sub><sup>+</sup> mg g wet soil<sup>-1</sup> for Quebec <em>S. alterniflorus</em>, Quebec <em>S. pumilus</em>, Louisiana <em>S. alterniflorus</em> and Louisiana <em>S. pumilus</em>, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub>) and <sup>15</sup>N in denitrification-derived N<sub>2</sub>O, denitrification-derived N<sub>2</sub> and nitrification-derived N­<sub>2</sub>O.</p> <p>Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup>, one subsample was extracted with Olsen-P solution (0.5 M NaHCO<sub>3</sub>, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N.</p> <p>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub> concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH<sub>4</sub> analysis and a micro electron capture detector (mECD) for N<sub>2</sub>O analysis. CO<sub>2</sub> was methanised to CH<sub>4</sub> before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N<sub>2</sub>O, 72 ppb CH<sub>4 </sub>and 31 ppm CO<sub>2</sub>. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The <sup>15</sup>N content of the N<sub>2</sub> and N<sub>2</sub>O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d<sup>15</sup>N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l<sup>-1</sup> and 10 mg l<sup>-1</sup> standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l<sup>-1</sup>, and 0.5 and ±0.3 mg N l<sup>-1</sup>, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO­<sub>3</sub>, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l<sup>-1</sup> and accuracy of ±6 %. Soil total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d<sup>13</sup>C and d<sup>15</sup>N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
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Machine learning reveals dynamic controls of soil nitrous oxide (N2O) emissions from diverse long-term cropping systems
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Soil nitrous oxide (N2O) and carbon dioxide (CO2) flux from a Central Iowa crop field and accompanying soil edaphic and climatic variables.
To quantify the magnitude of soil nitrous oxide flux and the drivers of nitrous oxide emissions in a representative central Iowa corn-soybean agricultural system, we measured greenhouse gas emissions (N2O and CO2) from 2017 to 2019 (primarily using custom automated chambers) along with soil chemical and physical parameters across a topographic gradient in a typically managed agricultural field near Ames, Iowa, USA. More details can be found in the associated manuscript, Lawrence et al. (2021).
Data from: Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems
Nitrous oxide (N2O) is a major greenhouse gas and cultivated soils are the most important anthropogenic source. N2O production and consumption are known to occur at depths below the A or Ap horizon but their magnitude in situ is largely unknown. At a site in SW Michigan USA we measured N2O concentrations at different soil depths and used diffusivity models to examine the importance of depth-specific production and consumption. We also tested the influence of crop and management practices on subsurface N2O production in 1) till vs. no-till, 2) a nitrogen fertilizer gradient, and 3) perennial crops including successional vegetation. N2O concentrations below 20 cm exceeded atmospheric concentrations by up to 900 times, and profile concentrations increased markedly with depth except immediately after fertilization when production was intense in the surface horizon, and in winter, when surface emissions were blocked by ice. Diffusivity analysis showed that N2O production at depth was especially important in annual crops, accounting for over 50% of total N2O production when crops were fertilized at recommended rates. At nitrogen fertilizer rates exceeding crop need, subsurface N2O production contributed 25-35% of total surface emissions. Dry conditions deepened the maximum depth of N2O production. Tillage did not. In systems with perennial vegetation, subsurface N2O production contributed <20% to total surface emissions. Results suggest that the fraction of total N2O produced in subsurface horizons can be substantial in annual crops, is low under perennial vegetation, appears to be largely controlled by subsurface nitrogen and moisture, and is insensitive to tillage.
Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity
<p>The long-term contribution of nitrification to nitrous oxide (N<sub>2</sub>O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of <i>in situ</i> N<sub>2</sub>O flux measurements with site-specific Michaelis-Menten kinetics of nitrification-derived N<sub>2</sub>O, we test the relative importance of nitrification-derived N<sub>2</sub>O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to <i>in situ</i> N<sub>2</sub>O fluxes was 13-17% in a conventionally fertilized annual cropping system, 27-42% in a low-input cover-cropped annual cropping system, and 52-63% in perennial systems including a late successional deciduous forest. Actual values are likely to be less than 10% of these values because of low N<sub>2</sub>O yields in cultured nitrifiers (typically 0.04 to 8% of NH<sub>3</sub> oxidized) and competing sinks for available NH<sub>4</sub><sup>+</sup> <i>in situ</i>. Most nitrification-derived N<sub>2</sub>O was produced by ammonia oxidizing bacteria (AOB) rather than archaea (AOA), who appeared responsible for no more than 30% of nitrification-derived N<sub>2</sub>O production in all but one ecosystem. Although the proportion of nitrification-derived N<sub>2</sub>O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification-derived N<sub>2</sub>O (higher V<sub>max</sub>) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N<sub>2</sub>O in all ecosystems examined.</p>
Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity
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Data from: Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems
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Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
<p><span>The potent greenhouse gas nitrous oxide (N</span><sub><span>2</span></sub><span>O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N</span><sub><span>2</span></sub><span>O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited </span><span><span>geographic coverage;</span></span><span> process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured </span><span><span>soil</span></span><span> parameters. We used sub-daily N</span><sub><span>2</span></sub><span>O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. </span><span><span>This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements.</span></span><span> This coupled approach </span><span><span>offers promise</span></span><span> for better predictions of agricultural N</span><sub><span>2</span></sub><span>O emissions and thus more precise global models and more effective </span><span><span>agricultural mitigation interventions.</span></span></p>
Pericervical Analgesia Versus Analesia With Nitrous Oxide (N2O) in Outpatien Operative Hysteroscopy With Miniresector
ClinicalTrials.gov study NCT06092541. IPD Sharing: YES. Countries: 1. Publications: 15.
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
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A Comparison of Intranasal Midazolam and Nitrous Oxide (N2O) Minimal Sedation for Minor Procedures in a Pediatric Emergency Department
ClinicalTrials.gov study NCT03085563. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Nitrous Oxide (N2O) on Intraocular Pressure in Healthy Volunteers
ClinicalTrials.gov study NCT00967694. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MLS/Aura Level 2 Nitrous Oxide (N2O) Mixing Ratio V005 (ML2N2O) at GES DISC
ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.
MLS/Aura Level 3 Monthly Binned Nitrous Oxide (N2O) Mixing Ratio on Assorted Grids V004 (ML3MBN2O) at GES DISC
ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information.The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.
HIRDLS/Aura Level 3 Nitrous Oxide (N2O) 1deg Lat Zonal Fourier Coefficients V007 (H3ZFCN2O) at GES DISC
The "HIRDLS/Aura Level 3 Nitrous Oxide (N2O) Zonal Fourier Coefficients" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data.The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.
MLS/Aura Level 3 Daily Binned Nitrous Oxide (N2O) Mixing Ratio on Zonal and Similar Grids V005 (ML3DZN2O) at GES DISC
ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.
MLS/Aura Level 3 Daily Binned Nitrous Oxide (N2O) Mixing Ratio on Assorted Grids V005 (ML3DBN2O) at GES DISC
ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information.The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.
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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.