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140 results for “N2O”

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edi56/100

Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)

This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.

openCC (other)Jul 2025View details →

zenodo52/100

ABG-IMRHT and Ames-2000K IR line lists for N2O as reported in "Accurate N2O IR Line Lists with Consistent Empirical Line Positions: ABG-IMRHT and Ames-2000K"

[2025-06-17, v2.0, updated from v1.1 (<a href="https://doi.org/10.5281/zenodo.14834513">10.5281/zenodo.14834513</a>)] (1). This upgraded version is recommended for analysis involving B1b or ABG(-IMRHT) related line lists, or E' > 10,000 cm-1.    (2). Energy level lists computed on the B1b PES and related 296K ABG-IMRHT line intensity and line lists, and B1b-based 1000-3000K line list files have been updated, along with n2olist.f90.v1.4.  (3). The full 296 K IR line lists of all 12 stable isotopologues are provided in "n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz", in which the line intensities assume 100% abundance for every isotopologue. (4). Please note that the B1b PES-based energy levels in "empirical.corrections.for.ABG-IMRHT.zip" are still valid, but the B1b PES based energy levels are not updated in the files inside that .zip.  [Link to <a href="https://www.sciencedirect.com/science/article/pii/S0022407325001645">the N2O paper</a> published at JQSRT (2025) 343, 109502, part of VSI:HITRAN2024, doi:<a href="https://doi.org/10.1016/j.jqsrt.2025.109502">10.1016/j.jqsrt.2025.109502</a>] [Updated from v1.0 (<a href="https://doi.org/10.5281/zenodo.14174307">10.5281/zenodo.14174307</a>). The J=151-210 transitions of 14N216O were missing from v1.0  files ] 1. Second generation of Ames-296K IR line list for "natural" Nitrous Oxide (N2O), denoted ABG-IMRHT, which was computed from Ames-B1b PES refinement (using Benjamin Schröder's ab initio PES Comp I, doi:10.1515/zpch-2015-0622) and 2023 dmsG-10K accurately fitted from CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles. In this major upgrade to Ames-296K N2O line list (10.1080/00268976.2023.2232892 and 10.5281/zenodo.7888194), rovibrational energy levels computed from NOSL-296 EH model were adopted to match and replace ~100,000 14N216O levels.  More consistent empirical corrections are determined for multiple isotopologues from comparison with RITZ (IAO), MARVEL (ExoMol), HITRAN, and JPL datasets.  The ABG-IMRHT line list provides the most reliable and consistent IR intensity predictions and accurate line positions in the range of 0 - 10,000 cm-1. All 12 stable isotopologues are included.  2. Ames-2000K IR line list provides complete, reliable and consistent IR predictions in the 0 - 15,000 cm-1 range. It includes transitions of 12 isotopologues. Their intensities are scaled by corresponding terrestrial "natural" abundances. Ames-2000K is a composite list, including 4 component lists, to achieve better accuracy and reliability needs at both shorter and longer wavelengths:  <ul> <li>#1. a hot line list of 14N216O, computed on Ames-1 PES and 2023 dmsG-wgt2d, J'<210, E'<25,000 cm-1, T=1000 / 1500 / 2000 / 3000 K.  It occupies 24 GB in compressed .xz format.  </li> <li>#2. 1000 K line lists of #2-#12 minor isotopologues, computed on Ames-1 PES and DMS, J'<150, E'<0.125 au - zpe (iso 2-6) or 0.08 -0.10 au - zpe (iso 7-12), S1000K > 10-34 cm/molecule, size-reduction with 99.9% intensity conservation in cm-1 bins. </li> <li>#3. a hot line list of 14N216O, computed on Ames-B1b PES and 2023 dmsG-10Kcm-1, J'<150, E'<16,000 cm-1, T=1000 / 1500 / 2000 / 3000 K.</li> <li>#4. ABG-IMRHT IR line list at 296 K, J'<150, E'<16,000 cm-1, with best empirical line positions and highly consistent intensity predictions up to 10,000 cm-1.  Coverage beyond 10,000 cm-1 is limited to strong lines.</li> </ul> 3. List of files:  (decompress .xz files first, "xz -dkf -T0 file.xz") <ul> <li>IAO_N2O_levels.tar.xz:  rovibrational energy levels of 6 N2O isotopologues, as computed using global Effective Hamiltonian (EH) models developed by Dr. Sergey Tashkun from IAO (Institute of Atmospheric Optics, Tomsk, Russia, <a href="https://www.iao.ru/">https://www.iao.ru/</a>).  </li> <li>N2O.Ames-B1b.PES.and.Ames-2023.DMS.zip:   Ames-B1b PES subroutine & coefficient file, see the note in n2opes2.f90; Ames 2023 dmsG subroutine and coefficients for fits up to 10K/12K/15K/17K/20K/25K cm-1, along with fitting residuals and compared to Ames-1 style (dmsC) coeffs and residuals.  The geometry set has ~80 points in each 100 cm-1.  </li> <li>n2olist.f90.v1.4:  the main Fortran program for Ames-2000K generation, customizable, see the note at its beginning.  [ default Ames-2000K = Ames-1 (12 iso) + [B1b (446) + ABG-IMRHT (12 iso)] if (E'<15,000 cm-1 & J<=150) ] </li> <li>empirical.corrections.for.ABG-IMRHT.zip: subroutine and paired/corrected energy level lists used in the empirical correction of energy levels computed on B1b PES [this file is not updated from v1.1 to v2.0] </li> <li>n2o.partition.1-4000K.iso.1-12.scaled: partition sum for 12 isotopologues, input file required by n2olist.f90, excluding g_n n2o.partition.1-4000K.iso.1-12.scaled.with.degeneracy.txt:  same as above, g_n included, see the note inside </li> <li>list.of.n2o.xz.files :  list of .xz files to read/regenerate from, under subdir "xz", input file required by n2olist.f90 ames.n2o.xx000-yy000.cm-1.xz:  compressed data files for Ames-2000K (line list component #1+#2) </li> <li>n2o.iso1-12.levels.Ames-1.dat.xz:  energy levels of 12 N2O isotopologues on Ames-1 PES, input file required by n2olist.f90 </li> <li>n2o.446.B1b-PES.levels.xz:  energy levels of 14N216O computed on Ames-B1b PES, input file to n2olist.f90 </li> <li>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.compressed.xz: data file for 14N216O hot list on Ames-B1b and dms 2023-G10K (component #3), input file required by n2olist.f90 n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.dat.xz:  independent line list (component #3) </li> <li>n2o.iso1-12.levels.ABG-IMRHT.dat.xz:  energy levels in ABG-IMRHT list, with empirical corrections included, input file required by n2olist.f90</li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5.corrected.xz:  Latest Ames-296K line list with empirical energy level corrections (component #4), input file for n2olist.f90 n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5.corrected.xz:  same as above, in HITRAN format, including line-broadening parameters </li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5corrected.100pct-abundance.xz: Latest Ames-296K line list with empirical energy level corrections, including the full set of IR transitions for 12 stable isotopologues, assuming 100% abundannce for every isotopologue, and 1E-31 cm/molecule intensity cut-off at 296 K.</li> <li>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz:  same as above, in HITRAN format, including line-broadening parameters </li> <li>ames.n2o.intensity.xz:  line count and intensity sum (original, selected, iso #1 and iso #2-12)  in each 0.01 cm-1 bins at 296 K, 1000 K, 1500 K, 2000 K, and 3000 K, for reference and statistics, optional input file to n2olist.f90 </li> <li>ames.n2o.sint.reductions.xz: A check-point file during Ames-2000K file generation, for reference only. including # of lines (total & selected), intensity sum (total, iso #1, iso #2-12), and intensity retention ratio in each 0.01 cm-1 bins for total, iso #1, and iso #2-12. </li> </ul> 4. List of 12 isotopologues, abundances adopted, and number of lines in ABG-IMRHT and Ames-2000K line list,  [updated 2025-06-17 v2.0: the #lines in ABG-IMRHT are updated to S(296K) cut-off at 1E-31 cm/molecule, instead of 5E-31] <table> <tbody> <tr> <td>#</td> <td>ISO</td> <td>abundance</td> <td># lines in ABG-IMRHT</td> <td># lines in Ames-2000K</td> </tr> <tr> <td>1</td> <td>446</td> <td>0.990333</td> <td>1388017</td> <td>3014643103</td> </tr> <tr> <td>2</td> <td>456</td> <td>3.64093E-3</td> <td>375541</td> <td>97932924</td> </tr> <tr> <td>3</td> <td>546</td> <td>3.64093E-3</td> <td>411353</td> <td>118432059</td> </tr> <tr> <td>4</td> <td>448</td> <td>1.98582E-3</td> <td>377552</td> <td>88169721</td> </tr> <tr> <td>5</td> <td>447</td> <td>3.69280E-4</td> <td>238921</td> <td>40212389</td> </tr> <tr> <td>6</td> <td>556</td> <td>1.33858E-5</td> <td>93933</td> <td>7534736</td> </tr> <tr> <td>7</td> <td>548</td> <td>7.30080E-6</td> <td>93674</td> <td>5583963</td> </tr> <tr> <td>8</td> <td>458</td> <td>7.30080E-6</td> <td>86446</td> <td>4995485</td> </tr> <tr> <td>9</td> <td>547</td> <td>1.35765E-6</td> <td>55360</td> <td>2588347</td> </tr> <tr> <td>10</td> <td>457</td> <td>1.35765E-6</td> <td>50754</td> <td>2275332</td> </tr> <tr> <td>11</td> <td>558</td> <td>2.68412E-8</td> <td>15814</td> <td>500190</td> </tr> <tr> <td>12</td> <td>557</td> <td>4.99134E-9</td> <td>8537</td> <td>274623</td> </tr> <tr> <td> </td> <td>Total</td> <td>1.00000069</td> <td>3195902</td> <td>3383142872</td> </tr> </tbody> </table> 5. This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358.  Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

opencc-by-4.0Dec 2024View details →

edi52/100

A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023

The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.

openCC (other)Feb 2026View details →

edi52/100

Interpolated N2O fluxes on the GLBRC Scaleup Sites at the Kellogg Biological Station, Hickory Corners, MI (2010 to 2014)

Dataset AbstractTo create a complete time series of N2O fluxes from the GLBRC Scaleup fields the fluxes were interpolated from 2010 to 2014.original data source http://lter.kbs.msu.edu/datasets/168

openCC (other)Oct 2022View details →

zenodo48/100

A global dataset of specialty crop biomass and N2O emissions

<div> We reviewed global field studies of vineyard, orchard, and vegetable cropping systems, which were also included in a meta-analysis (<a href="https://doi.org/10.1111/gcb.17233">https://doi.org/10.1111/gcb.17233</a>). We narrowed down the studies to those with field measurements of adequate variables (biomass C, N, and N2O) covering at least one growing season. As a result, cumulative N₂O emission measurements (per growing rotation, season, or year), along with biomass data of different plant organs from the same regions, were compiled for grape (Vitis vinifera), almond [Prunus dulcis (Mill.) D.A. Webb], peach (Prunus persica L.), walnut (Juglans regia), lettuce (Lactuca sativa), broccoli (Brassica oleracea var. italica P.), cauliflower (Brassica oleracea var. botrytis L.), and tomato (Lycopersicon esculentum L.) planting system. These observations were collected from fields spanning seven Koppen-Geiger climate types and five countries (the United States, Germany, Spain, France, and Australia). When only dry mass was measured, biomass C content for aboveground vegetable crops and berry fruit was assumed at 43%; nut fruit and woody organs of orchard tree at 48%. Area-weighted averages of N2O emissions were used (tree/vine row and interrow).   Corresponding author: Mu Hong (mu.hong@colostate.edu) </div>

opencc-by-4.0Sep 2024View details →

zenodo48/100

data-base of CO2, CH4, N2O and ancillary data in the Congo River

data-base of CO2, CH4, N2O and ancillary data in the Congo River relative to paper "Variations of dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity" by Borges et al. (https://doi.org/10.5194/bg-2019-68)

opencc-by-4.0Sep 2019View details →

zenodo48/100

4.5 years of peatland forest N2O flux data data measured using automatic chambers

The data contains daily mean N2O fluxes and supporting environmental data from June 2015 to September 2019. The measurement site is Lettosuo peatland forest (ICOS associate site, FI-Let) located in Tammela, Finland. The site is nutrient-rich and drained for forestry in 1969. Light selection harvest was done at the automatic chamber location in March 2016. Six automatic chambers operated year-round and each chamber measured N2O flux once in an hour. Environmental variables were measured close to the automatic chambers or in the nearest automatic weather station. For more information about the site and automatic chamber system, see Koskinen et al., (2014) and Korkiakoski et al. (2017, 2020).

opencc-by-4.0Aug 2023View details →

edi48/100

Global N2O Database version 1.0

Agriculture is the primary source of the powerful greenhouse gas (GHG) nitrous oxide (N2O) and an important source of GHG emissions. Due to sampling limitations, N2O measurements have traditionally been sparse; with research studies that often have less than 50 sampled days within a year. Nitrous oxide emissions are highly variable and short-lived peak emission periods may contribute more than 50% to annual emissions. Gap filling around these peaks, if measured at all, can result in poor estimations under the standard practice using linear interpolation. Improved gap filling methods that reflect covariate data will likely reduce uncertainty and improve annual N2O estimates. The Global N2O Database was created to serve as a repository for these datasets as well as become a resource for publicly available data and analytical advances. These datasets have been joined in data sheets that use the same formatting, allowing for easy access and comparison of data sets. We hope that this data availability will lead to improvements in N2O understanding and mitigation.

openCC (other)Nov 2021View details →

zenodo44/100

Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update

As part of the EYE-CLIMA project, GAINS emission data for CH4, N2O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF6) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH4 emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR. The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation. Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity). The updated dataset covers files as follows (internally, all files now carry version number V05): ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype. This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project. Compared to version 1.0, GAINS benefitted from a number of new developments such as the following: *) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990). *) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data. *) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025. *) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</a>). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts. *) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.

opencc-by-4.0Mar 2024View details →

zenodo44/100

Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project

Soil greenhouse gas emissions (CO2 and N2O) data and metadata of an almond crop diversified with Thymus hyemalis (diversification 1) and with Capparis spinosa (diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu

opencc-by-4.0Jul 2022View details →

zenodo44/100

N2O raw data from static greenhouse gas chamber measurements

This dataset contains N2O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils. The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate). The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)

opencc-by-nd-4.0Feb 2017View details →

zenodo44/100

Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "

This is code and data for manuscript:  "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems:  A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments" Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis,  Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu "code" foler includes code for data processing, model training, and results plotting. "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study; "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D.,  Turner, P. A., Deventer, M. J., Chen, Z., Yu,  Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review

opencc-by-4.0Sep 2021View details →

zenodo44/100

Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology

This dataset contains direct measurements of biomass burning emission factors for CO2, CO, CH4 and N2O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file.

opencc-by-4.0Feb 2023View details →

edi44/100

Dissolved N2O measurements from the Connecticut River Watershed

This dataset supports the paper accepted to Limnology and Oceanography: An intense precipitation event causes a temperate forested drainage network to shift from N2O source to sink Authored by Kelly S. Aho, Jennifer H. Fair, Jacob D. Hosen, Ethan D. Kyzivat, Laura A. Logozzo, Lisa C. Weber, Bryan Yoon, Jay P. Zarnetske, and Peter A. Raymond

openCC (other)Dec 2021View details →

edi44/100

Denitrification and N2O potential of streams, lakes and uplands in boreal Alaska

Includes: denitrification enzymatic activity (denitrate), N2O production potential, relative production of N2O (fN2O), AFDM, extractable, stream water and pore water NO3, NH4, DOC, thaw depth of upland sites, bulk density. A warming climate causes permafrost to thaw, especially in the region of discontinuous permafrost, where soil temperatures may only be a few degrees below 0 degC. Permafrost thaw may be exacerbated by more frequent and severe fires that remove insulating organic layers above permafrost. Soil thaw releases carbon and nitrogen (N) into the actively cycling pools, and whereas carbon emissions following permafrost thaw are well documented, the fates of N remain unclear. Denitrification could release thawed N as nitrous oxide (N2O) or nitrogen gas (N2), but the contributions of these processes to the high-latitude N cycle remain uncertain. We quantified microbial capacity for denitrification and N2O production in boreal soils, lakes, and streams, and assessed correlates of denitrifying enzyme activity (DEA) in Interior Alaska. Across all landscape positions, DEA under anoxia and nitrate and organic carbon amendment was 4.15 microgram N2O-N /kg dry soil*h (range -6.39 to 479.94). Riparian soils and stream sediments supported the highest potential rates of denitrification, upland soils were intermediate, and lakes supported lower rates, whereas deep permafrost soils supported little denitrification. Time-since-fire had no effect on denitrification potential in upland soils. Across all landscape positions, DEA was negatively correlated with ammonium pools. Within each landscape position, potential rate of denitrification increased with soil or sediment organic matter content. Widespread N loss to denitrification in the boreal forest could constrain the capacity for N-limited primary producers to maintain carbon stocks in soils following permafrost thaw.

openOpenAug 2021View details →

edi44/100

PIE LTER time series of methane, CO2 and N2O ebullition measurements at four headwater streams in Massachusetts and New Hampshire.

Methane ebullition was monitored at four headwater streams during 2018 and 2019. Stationary bubble traps were deployed from approximately May through October. CC and SB were monitored in 2018 and 2019, while DB and CB were only monitored in 2019. 12 traps were deployed at CC, SB, and DB, and 9 traps were deployed at CB. The concentration measured in the emitted gas was multiplied by the volume measured in a trap to calculated the total methane flux via ebullition. The traps were visited at least once weekly. The mean, median, minimum, and maximum rate of ebullition across all traps at a site over a two week period are listed here. Relevant publications: Robison, A.L. (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. Robison, A.L., W.M. Wollheim, B. Turek, C. Bova, C Snay, & R.K. Varner (in review). Spatial and temporal heterogeneity of methane ebullition in lowland headwater streams. Limnology and Oceanography.

openCC (other)Jul 2021View details →

dryad40/100

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.

opencc-zeroDec 2015View details →

zenodo40/100

TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning

Methodology:   The TOMCAT simulation 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 ERA-5 reanalysis data. <h3>N2O Profile Processing and Bias Correction</h3> Collocated N2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these N2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate N2O bias corrections 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. Height-resolved N2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean N2O profiles: <ul> <li> <code>zmn2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmn2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: 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, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Nov 2022View details →

dryad40/100

Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system

Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (5 m2 area and 1.5 m soil depth) in an ecotron facility, continuously measuring ecosystem-level CO2, N2O, and H2O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO2 emissions. Endogeic earthworms reduced N2O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.

opencc-zeroNov 2023View details →

zenodo40/100

Compiled soil N2O emission data from Eastern China forests

We searched the Thomson Reuters Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) theses database for literature published before January 1, 2020, using the terms “forest” AND “greenhouse gas” OR “N2O” OR “nitrous oxide” in the titles, abstracts, and keywords; this returned 5,948 records and 590 records, respectively. Considering the different preferences for terminology (e.g., nutrient addition, simulated N deposition), we refined our search manually. The criteria for our refinement were as follows: (a) N addition experiment was conducted in eastern China forests with detailed records of N addition levels; (b) N2O flux was observed using the static chamber method so that data from different sites were comparable. A total of 58 papers from 38 research sites met our criteria.  We also collected the data of soil N2O emission rates under natural conditions. The data were collected from literature that met the following criteria: (a) N2O fluxes were observed in eastern China forests using the static chamber method; (b) no manipulated experiments were conducted, and no nitrogen was added except for natural N deposition. There were 42 papers and theses that met these criteria, covering 30 different sites.  From the abovementioned papers and theses, the soil N2O emission rates and the auxiliary information (coordinates, ecoregion type, natural N deposition rate, multi-year mean annual temperature, multi-year mean annual precipitation, and soil texture) were compiled (ds01). To obtain the soil N2O emission rates of Eastern China forests on grid level (10km × 10km), another dataset (ds02) on the environmental factors (ecoregion type, natural N deposition rate, multi-year mean annual temperature, multi-year mean annual precipitation, and soil texture) of each grid was compiled from the spatial datasets, including the Chinese vegetation ecoregion map from the Resources, Environmental Sciences, and Data Center at the Chinese Academy of Sciences (<a href="https://www.resdc.cn/">https://www.resdc.cn/data.aspx?DATAID=133</a>), as well as the soil texture raster data (<a href="https://www.resdc.cn/data.aspx?DATAID=260">https://www.resdc.cn/data.aspx?DATAID=260</a>), mean annual temperature raster data (1995–2015; <a href="https://www.resdc.cn/data.aspx?DATAID=228">https://www.resdc.cn/data.aspx?DATAID=228</a>), and mean annual precipitation raster data (1995–2015; <a href="https://www.resdc.cn/data.aspx?DATAID=229">https://www.resdc.cn/data.aspx?DATAID=229</a>). Land use and land cover raster data (2000–2015) were obtained from the Project on Big Earth Data Science Engineering (<a href="https://data.casearth.cn/sdo/detail/5c19a5650600cf2a3c557aae">https://data.casearth.cn/sdo/detail/5c19a5650600cf2a3c557aae</a>). Finally, annual N deposition rate and multi-year mean N deposition (MAN) data were derived from a N deposition dataset from mainland China previously constructed by our group.

opencc-by-4.0Jul 2022View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record