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2,582 results for “nitrogen”
Dissolved Organic Carbon (DOC) and Dissolved Total Nitrogen (DTN) from Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2022
Dissolved organic carbon and dissolved total nitrogen are measured from discrete bottle samples collected during CTD rosette casts on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) transect cruises (ongoing since 2022). Sampling frequency is approximately seasonal. Sample collection is paired with particulate organic carbon at surface, subsurface chlorophyll max, and sometimes a third depth. Samples are filtered directly from the CTD rosette and acidified in the field, then analyzed using a Shimadzu TOC-LCPH total organic carbon analyzer coupled to a TNM-L analyzer for total nitrogen. Values are reported in micromoles per liter.
Particulate organic carbon (POC) and nitrogen (PON) from Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2017
Particulate organic carbon and nitrogen are measured from discrete bottle samples collected during CTD-rosette casts on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises (ongoing since 2017). Sampling frequency is approximately seasonal. Samples were filtered and collected on combusted glass fiber filters, pelletized using ultra clean tin disks, and combusted using a Flash EA1112 CHN analyzer to calculate concentrations of particulate organic carbon and particulate organic nitrogen in micromoles per liter. Values are also reported as concentrations in micrograms per liter and carbon to nitrogen molar ratio.
Vertical fluxes of particulate carbon, nitrogen and phosphorus from a sediment trap deployed west of Palmer Station, Antarctica at a depth of 170 meters, 1992-2019.
Particulate organic matter is exported from the upper ocean euphotic zone in the form of large sinking particles and as dissolved material. Particle fluxes to depth link the surface and mesopelagic realm and supply food to the benthos. Sedimentation flux is typically measured with sediment traps of various designs. Palmer LTER has deployed a time-series trap near 64.5degrees S, 66.0degrees W since late 1992. The trap is moored in 300 m depth and collects sinking particles at 150 m. Deployments and analyses were performed by David Karl, University of Hawaii until 2002 when Hugh Ducklow took over the sediment trap operations.Sedimentation at the PAL site of the West Antarctic Peninsula demonstrates extreme seasonality, with a well-defined pulse in the Austral summer following sea ice retreat. Daily sedimentation rates during the summer flux event are among the highest recorded globally. During the Austral winter when the ocean is covered by sea ice and shrouded in darkness, fluxes are among the lowest observed anywhere. Sedimentation rates at PAL typically vary by 4 orders of magnitude. There is also order of magnitude variability in the total annual flux (area under the curve).
Estimates of nitrogen and phosphorus excretion rates in individual marine and estuarine animals
This dataset contains nitrogen and phosphorus excretion rate, as well as dry biomass, estimates for individual vertebrate and invertebrate animals in marine and estuarine environments. This dataset is a product of an LTER Synthesis Working Group aimed at evaluating the spatiotemporal variability in consumer nutrient dynamics in the wake of global change across eight long-term ecological research projects. These projects include seven long-term ecological research programs (LTER) funded by the National Science Foundation: (1) California Current Ecosystem, (2) Florida Coastal Everglades, (3) Moorea Coral Reef, (4) Northern Gulf of Alaska, (5) Plum Island Ecosystems, (6) Santa Barbara Coastal, and (7) Virginia Coast Reserve LTER projects. Additionally, the dataset includes data from (8) The Partnership for Interdisciplinary Science of Coastal Oceans (PISCO) research program. The temporal coverage of each time series data varies among projects, with the earliest record in 1997 and the most recent in 2023. This data package also includes two folders of R scripts used for data harmonization, identical to those in the LTER Synthesis Working Group: Consumer-Mediated Nutrient Dynamics Project, v2.0.0. You can find the release in GitHub here: https://github.com/lter/lterwg-marine-cnd/releases/tag/v2.0.0
Nitrogen Fertilization Experiment (NFert): Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico
This dataset is part of a long-term study at the Sevilleta LTER, begun in spring 2004, which examines how fertilization affects above-ground biomass production (ANPP) in a mixed desert-grassland. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and foliage, over time and incorporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV186, "Nitrogen Fertilization Experiment (NFert): Seasonal Biomass and Seasonal and Annual NPP Data."
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Niño frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental change on grassland community composition and the growth of introduced creosote seeds and seedlings. The focus is on the response of three dominant species, all of which are near their range margins and thus may be particularly susceptible to environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Furthermore, it is thought that the growth and survival of introduced creosote (Larrea tridentata) seeds and seedlings will be promoted by heightened winter precipitation, N addition, and warmer nighttime temperatures. Treatment effects on limiting resources (soil moisture, nitrogen mineralization), species growth (photosynthetic rates, creosote shoot elongation), species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics. To measure above-ground NPP (i.e., the change in plant biomass, represented by stems, flowers, fruit and foliage, over time), the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these pl
Nitrogen Fertilization Experiment (NFert): Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
Begun in spring 2004, this long-term study at the Sevilleta LTER examines how fertilization affects above-ground biomass production (ANPP) in a mixed desert-grassland. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots (SEV155, "Nitrogen Fertilization Experiment (NFert): Net Primary Production Quadrat Data") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data." This site was burned by a prescribed fire in 2003.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Seasonal Biomass and Seasonal and Annual NPP at the Sevilleta National Wildlife Refuge, New Mexico
Begun in winter 2006, this long-term study at the Sevilleta LTER examines how heightened winter precipitation, N addition, and warmer nighttime temperatures affect above-ground biomass production (ANPP) in a mixed desert-grassland. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots (SEV176, "Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Nitrogen budget in a Chihuahuan desert grassland long-term nitrogen fertilization experiment at the Sevilleta National Wildlife Refuge
Although the negative consequences of increased nitrogen (N) supply on plant communities and soil chemistry are well known, most studies have focused on mesic grasslands, and the fate of added N in arid and semi-arid ecosystems remains unclear. To study the impacts of long-term increased N deposition on ecosystem N-pools, we sampled a 26-year-long fertilization (10 g N m-2 yr-1) experiment in the northern Chihuahuan Desert at the Sevilleta National Wildlife Refuge (SNWR) in New Mexico. To determine the fate of the added N, we measured multiple soil, microbial, and plant N pools in shallow soils at three time points across the 2020 growing season.
Nitrogen addition alters plant competition directly more than indirectly through soil microbes.
Eutrophication, the excessive addition of nutrients to ecosystems, is a pervasive component of global environmental change that can alter community dynamics. Although nitrogen addition experiments have widely documented important declines in plant diversity and shifts in plant species composition, the underlying causes of these outcomes are widely debated. Nitrogen inputs may directly affect plant competition for light or soil water or may influence plant species indirectly by altering the composition of soil microbes. In a 28-year field nitrogen addition experiment, we tested whether nitrogen-induced changes to soil microbes could indirectly alter the outcome of competition between codominant foundation plant species. In the field, long-term addition of inorganic nitrogen slowed the competitive take-over of blue grama grass (Bouteloua gracilis) by black grama grass (B. eriopoda) and thereby stabilized the ecotone between two grassland ecosystems in central New Mexico, USA.
A Lagrangian study of the contribution of the Canary coastal upwelling to the nitrogen budget of the open North Atlantic
<p>The attached datasets constitute the particle trajectory data produced in the experiment for Hailegeorgis et al..</p> <p>The "traj_upwell_1d_70m_1d-variables.nc" contains variables that describe different aspects of each upwelled particle (mostly regarding a particle's release or its initial or final conditions).</p> <p>The rest of the files with the format "traj_upwell_1d_70m_XXX-traj.nc" describe an attribute XXX (location or nutrient concentration) along the trajectory of upwelled particles tracked as part of the experiment.</p> <p>With ARIANE, particles are released and tracked in a ROMS simulation of the Canary coastal upwelling region. Out of the ~10M particles, the trajectories of the ~353K (~3.6%) that upwell are included. The variable "index_in_full_exp" in file "traj_upwell_1d_70m_1d-variables.nc" shows the index of each of these upwelling particles in the larger pool of released particles. For each upwelled particle, out of the 720-day trajectories starting from its release into the coast, the values from its upwelling step to its exit from the experiment are included, with the values outside this range being filled with a generic value (1.e20). An upwelled particle exits the experiment when it leaves the regional ROMS simulation altogether or when it leaves the coast and returns to the coast to re-upwell (more details in the paper).</p> <p>Be mindful of the different values of time. In "traj_upwell_1d_70m_1d-variables.nc", the variable "release_time" tells each particle's release time, in days since onset of the ROMS simulation, while variable "coast_exit_time" tells each particle's day of exiting coast, in days since its release. In each particle's trajectory (in traj_lon, traj_lat, etc), the first and last steps with valid values are the same as the days of its upwelling and its exit, respectively, since its release.</p> <p>The files contain the name and description of each variable. Along with the details in the publication, the descriptions here should be enough to fully interpret the information and replicate our analysis.</p>
Belowground nitrogen cycling in a montane grassland exposed to elevated CO2, warming and drought
<p>#### Data description<br> Data from a multi-factorial global change experiment (elevated CO<sub>2</sub>, warming and drought) in a montane grassland experiment in Austria (ClimGrass). Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper will be linked following manuscript publication.</p> <p>#### Metadata<br> climgrass_soil_N_cycling.csv data description</p> <p>Year: 2017<br> Harvest: three harvests (May 30, July 25, October 3)<br> Season: numerical column for harvest number<br> Plot: location of plot within the ClimGrass experiment<br> Treatment: eight treatment levels<br> c0t0 (ambient CO<sub>2</sub>, ambient temperature)<br> c0t1 (ambient CO<sub>2</sub>, + 1.5°C)<br> c0t2 (ambient CO<sub>2</sub>, + 3°C)<br> c1t1 (+150 ppm, +1.5°C)<br> c2t0 (+300 ppm, ambient temperature)<br> c2t2 (+300 ppm, +3°C)<br> c0t0-d (ambient CO<sub>2</sub>, ambient temperature, extended drought)<br> c2t2-d (+300 ppm, +3°C, extended drought)<br> CO2_ppm: three values of carbon dioxide enrichment treatment (+0, +150, or +300 ppm)<br> Temp_C: three values of elevated temperature treatment (+0, +1.5, +3°C)<br> Drought: two levels (control, drought)<br> Prot_depoly: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> FAA_uptake: gross free amino acid uptake rates (µg N g-1 d-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA: free amino acids (µg N g-1)<br> Mineralization: gross mineralization rates (µg N g-1 d-1)<br> Nitrification: gross nitrification rates (µg N g-1 d-1)</p> <p>#### References<br> Additional information on the experimental design can be found in the following paper:<br> Piepho, H.-P., Herndl, M., Pötsch, E.M., Bahn, M., 2017. Designing an experiment with quantitative treatment factors to study the effects of climate change. Journal of Agronomy and Crop Science 203, 584–592. doi:https://doi.org/10.1111/jac.12225</p> <p>More information on the isotope pool dilution method used can be found in the following paper:<br> Wanek, W., Mooshammer, M., Blöchl, A., Hanreich, A., Richter, A., 2010. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15 N isotope pool dilution technique. Soil Biology and Biochemistry 42, 1293–1302. doi:10.1016/j.soilbio.2010.04.001</p>
Rising CO2 and warming reduce global canopy demand for nitrogen
<ul> <li>Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO<sub>2</sub>and climate change. By extension, it has been suggested that declining carboxylation capacity (<em>V</em><sub>cmax</sub>) and leaf N content in enhanced-CO<sub>2</sub>­ experiments and satellite records signify increasing N limitation of primary production.</li> <li>We predicted <em>V</em><sub>cmax </sub>using the coordination hypothesis, and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level <em>V</em><sub>cmax</sub> at 25˚C. Whole-canopy photosynthetic N waas derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for <em>V</em><sub>cmax</sub>, and converted to annual N demand using estimated leaf turnover times.</li> <li>The predicted spatial pattern of <em>V</em><sub>cmax </sub>shares key features with an independent reconstruction from remotely-sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.28 %/year, while observed leaf (total) N declined by 0.2–0.25 %/year. Predicted global canopy N (and N demand) declined from 1997 onwards, despite increasing LAI.</li> <li>Leaf-level responses to rising CO<sub>2</sub>, and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.</li> </ul>
Supplementary dataset for "Rising CO2 and warming reduce global canopy demand for nitrogen"
<p>This repository contains the dataset used for “<strong>Rising CO<sub>2</sub> and warming reduce global canopy demand for nitrogen” </strong></p> <p>The deposition consists of:</p> <ol> <li>An satellite-derived leaf chlorophyll vcmax25 database (Luo<em> et al.</em>, 2019)</li> <li>Simulated <em>V<sub>cmax</sub></em> with all the factors based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax </sub></em>with CO<sub>2</sub> fixed at 340 ppm based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax</sub> </em>with fixed climate based on the coordination hypothesis</li> <li>Simulated turnover time.</li> <li>Simulated leaf-level <em>N</em><sub>rubisco</sub> (g m<sup>–2</sup> leaf area), canopy-level <em>N<sub>rubisco</sub></em> (g m<sup>–2</sup> ground area), annual leaf-level <em>N<sub>rubisco</sub></em>demand (g m<sup>–2</sup> leaf area year<sup>–1</sup>), and annual canopy-level of <em>N<sub>rubisco</sub></em> demand (g m<sup>–2</sup> ground area year<sup>–1</sup>) in figure 4.</li> <li>Lifespan of evergreen</li> </ol> <p>Note. </p> <ol> <li>LAI products used in the paper , such as TCDR LAI during 1982­–2016; GLASS LAI during 1982–2014; and GLOBMAP LAI during 1982–2011 are public available, the details information see Jiang <em>et al </em>(2017).</li> <li>Evergreen, deciduous and herbaceous vegetation fractions data derived from ESA CCI land cover products is publicly available, the details information see Li <em>et al </em>(2018).</li> <li>The climate force for <em>V<sub>cmax </sub></em>simulation was used CRU TS4.3 (Harris <em>et al,</em> 2020) for 1982–2016 at 0.5° resolution, which is publicly available at </li> </ol> <p><a href="https://crudata.uea.ac.uk/cru/data/hrg/">https://crudata.uea.ac.uk/cru/data/hrg/</a>.</p> <p>The data files are all in netcdf format at 0.5 resolution </p> <p>Reference:</p> <ol> <li><strong>Luo X, Croft H, Chen JM, He L, Keenan TF. 2019.</strong> Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. <em>Global Change Biology</em> <strong>25</strong>(7): 2499-2514.</li> <li><strong>Jiang C, Ryu Y, Fang H, Myneni R, Claverie M, Zhu Z. 2017.</strong> Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. <em>Global Change Biology</em> <strong>23</strong>(10): 4133-4146.</li> <li><strong>Li W, MacBean N, Ciais P, Defourny P, Lamarche C, Bontemps S, Houghton RA, Peng S. 2018.</strong> Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015). <em>Earth Syst. Sci. Data</em> <strong>10</strong>(1): 219-234.</li> <li><strong>Harris I, Osborn TJ, Jones P, Lister D. 2020.</strong> Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. <em>Scientific Data</em> <strong>7</strong>(1): 109.</li> </ol>
SIA-BRA: The carbon and nitrogen stable isotope ratios of animals of Brazilian biomes and coastal marine areas
<p>SIA-BRA is a compilation of C and N stable isotope ratios of terrestrial and aquatic animals sampled in Brazilian biomes and coastal-marine areas.</p> <p>Version 1.0 contains isotopic data of c. 21,804 non-captive wildlife specimens, excluding livestock production or laboratory<br> experiments. They were 13,881 vertebrates and 7,923 invertebrates. There are 11 phyla, with a clear dominance of Chordata (64%) and Arthropoda (29%), 36 classes, 154 orders, 473 families, 894 genera and 1,157 species.</p> <p>They were divided into the following habitats: terrestrial (30% of the total), freshwater (27%), oceanic (40%)<br> and estuarine (4%) (see <a href="https://doi.org/10.1111/geb.13449">https://doi.org/10.1111/geb.13449</a>)</p> <p>Software format: Data are supplied as delimited text files (.csv).</p>
Characterization of the angular-dependent emission of nitrogen-vacancy centers in nanodiamond
<p>We report on the characterization of the angular-dependent emission of single-photon emitters based on single nitrogen-vacancy (NV-) centers in nanodiamond at room temperature. A theoretical model for the calculation of the angular emission patterns of such an NV-center at a dielectric interface will be presented. For the first time, the orientation of the NV-centers in nanodiamond was determined from back focal plane images of NV-centers and by comparison of the theoretical and experimental angular emission pattern. Furthermore, the orientation of the NV-centers was also obtained from measurements of the fluorescence intensity in dependence on the polarization angle of the linearly polarized excitation laser. The results of these measurements are in good agreement. Moreover, the collection efficiency in this setup was calculated to be higher than 80% using the model of the angular emission of the NV-centers.</p>
Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to <em>AGU Geophysical Research Letters</em>.</p> <p> </p> <p><strong>file01</strong> contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022.</p> <p><strong>file02</strong> contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file03</strong> contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file04</strong> contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file05</strong> contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file06</strong> contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file07</strong> contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file08</strong> contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO.</p> <p><strong>file09</strong> contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file10</strong> contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file11</strong> contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file12</strong> contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file13</strong> contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file14</strong> contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file15</strong> contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file16</strong> contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file17</strong> contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022.</p> <p><strong>file18</strong> contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file19</strong> contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file20</strong> contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022.</p> <p><strong>file21</strong> contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.</p> <p> </p> <p><strong>file22</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file23</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file24</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file25</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file26</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file27</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file28</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file29</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file30</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file31</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file32</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file33</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file34</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file35</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file36</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file37</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file38</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file39</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p> </p> <p><strong>file40</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file41</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file42</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file43</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file44</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file45</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file46</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file47</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file48</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file49</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file50</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file51</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file52</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file53</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file54</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file55</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file56</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file57</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p>
Dataset of nitrogen in rivers and streams in sub-Saharan Africa
<p>This is a dataset on concentrations and export of all nitrogen compounds in rivers and streams in sub-Saharan Africa reported in scientific literature (<em>n</em>=254) until July 2024. Data are aggregated by site and, where possible, data are reported for the dry and wet season separately. In addition to concentrations and export of nitrogen compounds, data on ancillary parameters, such as pH, electrical conductivity and dissolved oxygen area also included. Each site for which (approximate) coordinates could be extracted from the original study was assigned to a land cover class based on open source data on tree cover and the extent of cropland, settlement and wetlands across Africa (see second tab in the file 'Dataset.xlsx' for land cover classes and corresponding classification conditions as well as data sources). The third tab in the file 'Dataset.xlsx' contains links to the individual studies and full references are provided in the file 'Reference list.pdf'.</p>
Water quality data (River sediment, Nitrogen and Phosphorus loads) for Africa
<p>Output data on African water quality and scripts for preprint - "One third of African rivers fail to meet the 'good ambient water quality' nutrient targets" at <a href="https://dx.doi.org/10.2139/ssrn.4829742">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4829742</a> . Please check the readme file for data description. The data includes river flow, sediment load, nitrogen and Phosphorus loads for Africa at daily and yearly time scale. This work is currently under review in Ecological Indicators journal. </p>
Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae
<p>Data and code supporting the manuscript "Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae" by Tesnière et al. (2019) PLoS ONE 14(4): e0215870. https://doi.org/10.1371/journal.pone.0215870</p> <p>README.pdf or README.md files contain information about the files in this archive.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.