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3,963 results for “oxides”
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>
Laboratory simulations of benzene oxidation and formation of highly oxygenated organic molecules (HOM)
<p>This dataset supplements the following manuscript:<br> Garmash, O., Rissanen, M. P., Pullinen, I., Schmitt, S., Kausiala, O., Tillmann, R., Percival, C., Bannan, T. J., Priestley, M., Hallquist, Å. M., Kleist, E., Kiendler-Scharr, A., Hallquist, M., Berndt, T., McFiggans, G., Wildt, J., Mentel, T., and Ehn, M.: Multi-generation OH oxidation as a source for highly oxygenated organic molecules from aromatics, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-582, in review, 2019.<br> It presents data from Table 1, Tables S1-S4 and Figures 5, A1 and A2, including model input data.</p>
Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide"
<p>Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide" (DOI: <a href="https://doi.org/10.3762/bjnano.15.12" target="_blank" rel="noopener">https://doi.org/10.3762/bjnano.15.12</a>)</p>
The role of etching anisotropy in the fabrication of freestanding oxide microstructures on SrTiO3(001), SrTiO3 (110), and SrTiO3 (111) substrates
<p>Datafiles for the Figures and Supplementary Material of the article "The role of etching anisotropy in the fabrication of freestanding oxide microstructures on SrTiO3(001), SrTiO3 (110), and SrTiO3 (111) substrates" published in Applied Physics Letters by Alejandro E. Plaza et al. (2021)</p>
Dataset for: Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus 'Methylomirabilis' in stratified lake basins
<p>Dataset containing treated 16S rRNA amplicon sequence data, accompanying the manuscript "Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus ‘Methylomirabilis’ in stratified lake basins" </p> <p>Files: </p> <p>- Mapping file</p> <p>- ASV table</p> <p>- Refseq file</p> <p>-Tree file</p> <p>- Relative abundances of dominant methanotrophs in the water column of Lake Lugano North Basin (used to create Fig. 6c)</p> <p>- Multi annual dataset of water chemistry data of the Lake Lugano North Basin</p> <p> </p> <p> </p> <p> </p>
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </p> <p> </p>
Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar
<p>This dataset corresponds to the open access article "Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar" published in Agriculture, Ecosystems & Environment (<a href="https://doi.org/10.1016/j.agee.2023.108642">https://doi.org/10.1016/j.agee.2023.108642</a>) funded by the Swiss Federal Offices for the Environment (BAFU), Agriculture (BLW) and Energy (BFE).</p> <p> </p> <p> </p>
Missouri reservoir profile data including depth, temperature, oxygen, photopigments, conductivity, pH, turbidity, and oxidative-reductive potential starting in 2023
This dataset of annual limnological profiles starts in 2023 and is from reservoirs in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Physical parameters derived from sensors include temperature and oxygen with depth. Sondes used were Yellow Springs Instruments (YSI) EXO3s; profiles include a range of physical, chemical, and biological parameters including depth, conductivity, pH, oxidative-reductive potential (ORP), chlorophyll a, phycocyanin (PC), and turbidity. After May 2024, the turbidity sensor was replaced with a phycoerythrin (PE) sensor. Most of the profiles were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of profiles were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. The profiles are divided into single files for each year. This data has been quality controlled for basic errors and any data outside of normal factory issued sensor ranges.
Baltimore Ecosystem Study: Soil atmosphere fluxes of carbon dioxide, nitrous oxide and methane, 1998 - ongoing
The Baltimore Ecosystem Study (BES) established a network of long-term permanent biogeochemical study plots in 1998. These plots provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The network of study plots includes forest plots (upland and riparian), chosen to represent the range of forest conditions in the area and grass plots (to represent home lawns). Plots are instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature, and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998 and 2015. Data from these plots has been published in Groffman et al. (2006, 2009), Groffman and Pouyat (2009), Savva et al. (2010), Costa and Groffman (2013), Duncan et al. (2013), Waters et al. (2014), Ni and Groffman (2018), Templeton et al. (2019). Literature Cited Costa, K.H. and P.M. Groffman. 2013. Factors regulating net methane flux in urban forests and grasslands. Soil Science Society of America Journal 77:850 - 855. Duncan, J. M., L. E. Band, and P. M. Groffman. 2013. Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification. Journal of Geophysical Research Biogeosciences 118:1-5; DOI: 10.1002/jgrg.20090 Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C.
Hubbard Brook Experimental Forest: Soil-atmosphere fluxes of carbon dioxide, nitrous oxide and methane on Watershed 1 and Bear Brook, 2002-2024
Soil atmosphere fluxes of the trace gases; carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF) including 1) the "freeze" study reference plots that provide contrast between stands dominated (80%) by sugar maple versus yellow birch and low and high elevation areas, 2) the Bear Brook Watershed where trace gas sampling is coordinated with long-term monitoring of microbial biomass and activity and 3) watershed 1 where trace gas sampling locations were co-located with long-term microbial biomass and activity monitoring sites that are located near a subset of the lysimeter sites established for the calcium addition study on this watershed. This dataset contains the Watershed 1 and Bear Brook data. Freeze plot trace gas can be found in: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=251. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Sediment Properties Drive Spatial Variability of Potential Methane Production and Oxidation in Small Streams
<ul> <li>This dataset contains 20 data tables (Fig.2.csv, Fig.3.csv, data_PLS_stream-main-stem.csv, Fig.4_a.csv, data_PLS_subcatch.-stream-sect.csv, Fig.4_b.csv, Fig.5.csv, Fig.S1.csv, Fig.S2.csv, Fig.S3.csv, Fig.S4.csv, Fig.S5_a.csv, Fig.S5_b.csv, Fig.S6_a.csv, Fig.S6_b.csv, Fig.S6_c.csv, Fig.S6_d.csv, TableS1_data-adjustment.csv, TableS1_lit-data.csv, PMO_surface-water.csv), we separated our data tables in the respective figures/analyses presented in our paper</li> <li>We added the units to each column title of each respective data table</li> <li>Please see "Metadata.pdf" and our paper (same title as the dataset) for more information</li> </ul> <p> </p>
The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating
<p>This dataset contains the raw data used for the publication:</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------<br> "The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating"<br> by C. Reiner-Rozman, R. Hasler, J. Andersson, T. Rodrigues, A. Bozdogan and P. Aspermair<br> --------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><br> It consists of the SEM images (in .tif format) and the determined surface coverages (in .dat format) as well as the measured electrical data (in .dat format) of the prepared graphene field-effect transistor chips. Headers/information in the data files are in English. When using this data in any form please refer to the above-mentioned publication.</p> <p>The data is structured according to the figures of the paper. Each folder contains the data relevant to validate the results presented in the respective figure of the publication. The files are labeled according to the following description:</p> <p>"measurement-type"_"chip-number"_"GO-concentration"_"spin-coating speed"</p> <p>"measurement-type": SEM, IDVG, baseline<br> "chip-number": an increasing number of fabricated device (only used when needed)<br> "GO-concentration": 143/214/285 µg/mL of graphene oxide (GO) in solution<br> "spin-coating speed": in rpm</p>
Effect of superparamagnetic iron oxide nanoparticles on glucose homeostasis on type 2 diabetes experimental model
<p>The data correspond to figures in the paper by Ali, L.M.A. et al. Life Sciences 245 (2020) 117361. doi:10.1016/j.lfs.2020.117361.</p>
RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)
<p>RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY mesosphere--lower thermosphere limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY mesosphere--lower thermosphere (MLT, 50--150 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The Mesosphere--Lower Thermosphere (MLT) measurement mode was carried out from 07/2008 until the end of the mission for one day every 15 days. This data set comprises 84 days of SCIAMACHY MLT NO measurements, each<br> containing about 15 orbits.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY NO data were compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
Spectrophotometric Assay for the Detection of 2,5-Diformylfuran and Its Validation through Laccase-Mediated Oxidation of 5-Hydroxymethylfurfural
<p>Modern biocatalysis requires fast, sensitive, and efficient high-throughput screening methods to screen enzyme libraries in order to seek out novel biocatalysts or enhanced variants for the production of chemicals. For instance, the synthesis of bio-based furan compounds like 2,5-diformylfuran (DFF) from 5-hydroxymethylfurfural (HMF) via aerobic oxidation is a crucial process in industrial chemistry. Laccases, known for their mild operating conditions, independence from cofactors, and versatility with various substrates, thanks to the use of chemical mediators, are appealing candidates for catalyzing HMF oxidation. Herein, Schiff-based polymers based on the coupling of DFF and 1,4-phenylenediamine (PPD) have been used in the set-up of a novel colorimetric assay for detecting the presence of DFF in different reaction mixtures. This method may be employed for the fast screening of enzymes (Z' values ranging from 0.68 to 0.72). The sensitivity of the method has been proved, and detection (8.4 μM) and quantification (25.5 μM) limits have been calculated. Notably, the assay displayed selectivity for DFF and enabled the measurement of kinetics in DFF production from HMF using three distinct laccase–mediator systems.</p>
The potential of graphene oxide surfaces to reduce biofilms formed by uropathogens
<p>Polydimethylsiloxane (PDMS), 5 wt.% GNP/PDMS (GNP/PDMS), 1 wt.% graphene oxide (GO)/PDMS (GO1/PDMS), 3 wt.% GO/PDMS (GO3/PDMS), 5 wt.% GO/PDMS (GO5/PDMS)) were analyzed regarding hydrophobicity and roughness. Staphylococcus aureus and Pseudomonas aeruginosa biofilms were developed on these different surfaces under static conditions for 24h at 37°C. The number of biofilm total and culturable cells was quantified by flow cytometry and plate counts, respectively, whereas the biofilm amount was determined by crystal violet staining. Moreover, the mechanisms of action of GO were characterized using the flow cytometer.</p>
Siloxide tripodal ligands as a scaffold for stabilizing lanthanides in the +IV oxidation state
<p>This upload contains raw data (NMR, X-Ray, EPR, Cyclic Voltammetry, UV, IR, Magnetism) files for the article</p>
Origin of the Low Overpotential for Isopropanol Oxidation on PtRu Electrocatalysts
<p>Article and supported data of Cyclic Voltammetry (CV), Synchrotron radiation photoelectron spectroscopy (SRPES), Scanning Tunnelling Microscopy (STM), Electrochemical Infrared Reflection Absorption Spectroscopy (EC-IRRAS).</p>
Coupling charge and topological reconstructions at polar oxide interfaces
<p>Dataset corresponding to the publication 'Coupling charge and topological reconstructions at polar oxide interfaces' (<a href="https://arxiv.org/abs/2107.03359">arXiv:2107.03359</a>) (Phys. Rev. Lett. <strong>127</strong>, 127202) </p>
ScienceDex guides
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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.