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296 results for “OH”
Fig. 1 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 1. Proximal (A), distal (B), lateral (C) and dorsal (D) views of the OH 8 navicular showing the measured lengths, and proximal (E), distal (F), lateral (G) and dorsal (H) views of a left gorilla navicular showing the measured angles; a = talar facet major axis (dorsoplantar) diameter, b = talar facet minor axis (mediolateral) diameter, c = ectocuneiform facet dorsoplantar diameter, d = ectocuneiform facet mediolateral diameter, e = mesocuneiform facet dorsoplantar diameter, f = mesocuneiform facet mediolateral diameter, g = entocuneiform facet mediolateral diameter, h = entocuneiform facet dorsoplantar diameter, i = navicular maximum length, j = cuboid facet dorsoplantar diameter, k = cuboid facet mediolateral diameter, l = depth of talar facet along major axis, m = depth of talar facet along minor axis; 1 = frontal talocuboid angle, 90° 2 = navicular torsion, 3 = frontal mesoectocuneiform angle, 4 = entoectocuneiform angle 5 = sagittal taloectocuneifrom angle, 6 = transverse mesoectocuneiform angle, 7 = transverse cuboectocuneiform angle. In all cases the lines chosen for angular measurements bisect facets into approximately equal halves. For comparative purposes the major bisecting axes of the talar head and cuneiform facets are referred to in the text as the dorsoplantar axes. In neither great apes nor humans do all these axes have a dorsoplantar orientation, but are held in varying inclination to a dorsoplantar axis according to talar head and navicular torison and the frontal mesoectocuneiform angle. The crosssectional area of the talar, ectocuneiform, mesocuneiform entocuneiform and cuboid facets are given by the products of a and b, c and d, e and f, g and h, and j and k, respectively. Relative crosssectional area for each facet is compared as a percentage of the sum of all of the navicular facets. The subtended angle of curvature and the radius of curvature of the talar facet along the dosoplantar (major) and mediolateral (minor) axes are given by 4 arctan(2l/a) and (l2 + a2/4)/ 2l, and 4 arctan(2m/b) and (m2 + b2/4)/2m, respectively.
Fig. 2 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 2. Talar facet crosssectional area (mm2) vs. total navicular facet crosssectional area (mm2) in humans, great apes, and fossil hominoids. Arrows point to fossils.
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
Analysis of particles size distributions in Mg(OH)2 precipitation from highly concentrated MgCl2 solutions
<p>Magnesium is a raw material of great importance, which attracted increasing interest in the last years. A promising<br> route is to recover magnesium in the form of Magnesium Hydroxide via precipitation from highly concentrated<br> Mg2+ resources, e.g. industrial or natural brines and bitterns. Several production methods and<br> characterization procedures have been presented in the literature reporting a broad variety of Mg(OH)2<br> particle sizes. In the present work, a detailed experimental investigation is aiming to shed light on the<br> characteristics of produced Mg(OH)2 particles and their dependence upon the reacting conditions. To this<br> purpose, two T-shaped mixers were employed to tune and control the degree of homogenization of reactants.<br> Particles were analysed by laser static light scattering with and without an anti-agglomerant treatment based<br> on ultrasounds and addition of a dispersant. Zeta potential measurements were also carried out to further assess<br> Mg(OH)2 suspension stability.</p>
TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017
<p>Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017.</p> <p>Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006).</p> <p>Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021). </p> <p>Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP. </p>
Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening
<p>This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset.</p> <p>The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain) calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format.</p> <p>The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF).</p> <p>A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts:</p> <p>1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx).</p> <p>2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).</p> <p> </p> <p>The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.</p> <p> </p> <p>If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.</p>
Evaluation data used in "An innovative STEM outreach model (OH-Kids) to foster the next generation of geoscientists, engineers, and technologists"
<p>This repository contains all data of the evaluation questionnaire used to assess modifications in pupils’ perceptions of same water resources concepts and science and scientist resulting from the application of OH-Kids outreach model in six Mexican primary schools (n=344 pupils).</p>
OH-IIUNAM network precipitation data
<p>NetCDF files for all 51 working stations of the OH-IIUNAM (Observatorio Hidrológico del Instituto de Ingeniería de la Universidad Nacional Autónoma de México), a network of 39 laser disdrometers and 12 weighting rain gauges deployed across Mexico City.</p> <p>The OH-IIUNAM network relies on the OTT Parsivel<sup>2</sup> disdrometer and Pluvio<sup>2</sup> L weighing gauge<sup> </sup>instruments, which use a laser diode to produce a horizontal sheet of light to detect the number and size of hydrometeors and, a weight-based sensor to measure liquid and solid precipitation, respectively.</p>
WHU-OHS: A benchmark dataset for large-scale Hyperspectral Image classification
<p>The WHU-OHS dataset is made up of 42 OHS satellite images acquired from more than 40 different locations in China. The imagery has a spatial resolution of 10 m (nadir) and a swath width of 60 km (nadir). There are 32 spectral channels ranging from the visible to near-infrared range, with an average spectral resolution of 15 nm. We cropped each image into 512 × 512 pixels with a stride of 32. There are 4822, 513, and 2460 sub-images in the training, validation, and test sets, respectively.</p> <p>For transferability test, we choose eight pairs of OHS images, and each pair contains one source image (S) and one target image (T):</p> <p>S1: Changchun</p> <p>T1: Jilin</p> <p>S2: Wuxi</p> <p>T2: Shanghai</p> <p>S3: Guangzhou</p> <p>T3: Zhongshan</p> <p>S4: Xining</p> <p>T4: Lanzhou</p> <p>S5: Hetian</p> <p>T5: Kelamayi</p> <p>S6: Anyi</p> <p>T6: Nanchang</p> <p>S7: Changde</p> <p>T7: Changsha</p> <p>S8: Tianjin</p> <p>T8: Tangshan</p> <p>The 26 OHS images except for the eight pairs:</p> <p>O1: Baoding</p> <p>O2: Chongqing</p> <p>O3: Fujin</p> <p>O4: Huainan</p> <p>O5: Huhehaote</p> <p>O6: Jinzhong</p> <p>O7: Luliang</p> <p>O8: Manasi_1</p> <p>O9: Manasi_2</p> <p>O10: Nanmulin</p> <p>O11: Neimenggu</p> <p>O12: Qingdao</p> <p>O13: Qinghuangdao</p> <p>O14: Shawan</p> <p>O15: Shenyang</p> <p>O16: Shuozhou</p> <p>O17: Songpan</p> <p>O18: Taian</p> <p>O19: Tongjiang_1</p> <p>O20: Tongjiang_2</p> <p>O21: Wuzhong</p> <p>O22: Xundian</p> <p>O23: Xuzhou</p> <p>O24: Yidu</p> <p>O25: Zangzu</p> <p>O26: Zhongshan</p> <p>The image patches have been normalized and scaled by 10000 to reduce storage cost. Divide the pixel values by 10000 and then the image patches can be used directly.</p>
Data for the paper "Effective Emission Heights of Various OH Lines From X-shooter and SABER Observations of a Passing Quasi-2-Day Wave"
<p>The provided ASCII tables are related to the paper "Effective Emission Heights of Various OH Lines From X-shooter and SABER Observations of a Passing Quasi-2-Day Wave", which has been published in Journal of Geophysical Research: Atmospheres, Vol. 127, 2022 (DOI: 10.1029/2022JD036610).</p>
Data for the paper "Climatologies of Various OH Lines From About 90,000 X-shooter spectra"
<p>The provided ASCII tables are related to the paper "Climatologies of Various OH Lines From About 90,000 X-shooter spectra", which has been published in Journal of Geophysical Research: Atmospheres, Vol. 128, 2023 (DOI: 10.1029/2022JD038275).</p> <p>v1.0.0: initial submission</p> <p>v1.1.0: ohclp_linepar.dat with additional column for line wavelengths</p>
Data, scripts and simulations for ProxyOH-[OH] analysis using ATom data and F0AM and AM3 simulations
<p>This dataset provides the simulations and analysis code used in Baublitz et al., An observation-based, reduced-form model for oxidation in the remote marine troposphere, <em>Proceedings of the National Academy of Sciences</em>, <strong>120</strong>.</p> <p><strong>Code, package versions</strong></p> <p>The code is generally written in Python and saved to a Jupyter Notebook (.ipynb) format, except for the component developing the Bayesian regressions, which is written in R. For improved accessibility, the code has also been printed to PDF format so that it may be readable without requiring access to Jupyter. The code used to create the main text figures is specified in the file names. When the primary focus of a script is to create supplemental figures, the figure names have also been specified in the script file name. The code for creating other supplemental figures is also available in the script corresponding to the section where that figure is referenced. </p> <p>The following packages and package versions were used to develop this analysis:</p> <p><em><strong>Python </strong></em>(v3.10.0)</p> <ul> <li>anaconda 4.13.0 <ul> <li>collections (native to anaconda installation)</li> <li>datetime</li> <li>os<span> </span></li> <li>random</li> </ul> </li> <li>jupyter 1.0.0, jupyter-core 4.9.1</li> <li>matplotlib (visualization) 3.5.1</li> <li>notebook 6.4.6</li> <li>numpy 1.21.4</li> <li>pandas 1.3.4</li> <li>scipy 1.7.3</li> <li>seaborn (figure formatting) 0.11.2</li> </ul> <p>The full environment is specified in the YAML file "atom_env.yml." Anaconda users (not tested, potentially restricted to Windows) may load this environment with this file and the following command:</p> <p><em>$ conda env create -f atom_env.yml</em></p> <p><em><strong>R </strong></em> (v4.1.2, includes package parallel)</p> <ul> <li>tidyverse 1.3.1</li> <li>rjags 4-12</li> <li>runjags 2.2.0-3</li> <li>lattice 0.20-45</li> <li>lme4 1.1-31</li> <li>loo 2.4.1</li> <li>ggpubr 0.4.0</li> <li>matrixStats 0.61.0</li> </ul> <p><strong>Zipped directory contents</strong></p> <p>The full set of global, hourly AM3 model simulations developed for this project are included in this repository (AM3_hourly_simulations_global_ATom1-4.zip) <em>for reference and potential future application, though they are not used in the code</em>. They are described here (vs listed) and span the dates for each campaign leg and are broken into four variable categories, concentrations and met fields ('stp_conc_v2'), individual reaction rates ('ind_rate'), integrated reaction rates ('all_rate') and deposition velocities or photolysis rates ('dep_jval'). Some of these files include all days in the range, while others include only the days that the campaign took measurements.</p> <p>In addition, a subset of the AM3 simulations that specifically include variables used in the manuscript analysis that have been sampled along the ATom flight is included, along with the 10 s ATom merge data (AM3_model_simulations_sampled.zip). <em>This is the file that should be downloaded for reproducing the manuscript in the analysis.</em></p> <ul> <li>AM3_model_simulations_sampled.zip <ul> <li>atom1_10s_ss_030122.csv</li> <li>atom2_10s_ss_030122.csv</li> <li>atom3_10s_ss_030122.csv</li> <li>atom4_10s_ss_030122.csv</li> </ul> </li> <li>bayes_data.zip <ul> <li>bayes_atom_10s_model_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_allPOH_030723.csv</li> <li>base/ <ul> <li>.Rhistory</li> <li>atom_jags_010723.R</li> <li>atom_lmer_model_122122.R</li> <li>atom_sens_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>gelman_list_base.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>model_b0.csv</li> <li>model_b1.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altCH4_CO_HCHO_MHP_031423.R</li> <li>atom_jags_altCH4_CO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO/ <ul> <li>.Rhistory</li> <li>atom_altCO_HCHO_031423.R</li> <li>atom_jags_altCO_HCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_co_hcho.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO_MHP/ <ul> <li>atom_altCO_HCHO_MHP_031423.R</li> <li>atom_jags_altCO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_CO_HCHO_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>H2O2_O3_CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altH2O2_O2_CH4_CO_HCHO_MHP_122122.R</li> <li>atom_jags_altH2O2_O3_CH4_CO_HCHO_MHP_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>HCHO/ <ul> <li>atom_altHCHO_031423.R</li> <li>atom_jags_altHCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_HCHO.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>MHP/ <ul> <li>atom_altMHP_122122.R</li> <li>atom_jags_altMHP_122122.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> </ul> </li> <li>F0AMv3.2.zip <ul> <li>mean_ratio_OH_loss_bins_oce.npy</li> <li>mean_ratio_OH_prod_bins_oce.npy</li> <li>mean_ratio_OH_prod_loss_bins_oce.npy</li> <li>Data/ <ul> <li>atom1/ <ul> <li>atom1_output_alt.cs</li> <li>atom1_output_CO.csv</li> <li>atom1_output_H2O.csv</li> <li>atom1_output_lat.csv</li> <li>atom1_output_lon.csv</li> <li>atom1_output_lossOH_ppt_lump15.csv</li> <li>atom1_output_M.csv</li> <li>atom1_output_NO.csv</li> <li>atom1_output_OH.csv</li> <li>atom1_output_prodOH_ppt_lump15.csv</li> <li>atom1_output_startTime.csv</li> <li>atom1_output_sza.csv</li> </ul> </li> <li>atom2/ <ul> <li>atom2_output_alt.csv</li> <li>atom2_output_CO.csv</li> <li>atom2_output_H2O.csv</li> <li>atom2_output_lat.csv</li> <li>atom2_output_lon.csv</li> <li>atom2_output_lossOH_ppt_lump15.csv</li> <li>atom2_output_M.csv</li> <li>atom2_output_NO.csv</li> <li>atom2_output_OH.csv</li> <li>atom2_output_prodOH_ppt_lump15.csv</li> <li>atom2_output_startTime.csv</li> <li>atom2_output_sza.csv</li> </ul> </li> <li>atom3/ <ul> <li>atom3_output_alt.csv</li> <li>atom3_output_CO.csv</li> <li>atom3_output_H2O.csv</li> <li>atom3_output_lat.csv</li> <li>atom3_output_lon.csv</li> <li>atom3_output_lossOH_ppt_lump15.csv</li> <li>atom3_output_M.csv</li> <li>atom3_output_NO.csv</li> <li>atom3_output_OH.csv</li> <li>atom3_output_prodOH_ppt_lump15.csv</li> <li>atom3_output_startTime.csv</li> <li>atom3_output_sza.csv</li> </ul> </li> <li>atom4/ <ul> <li>atom4_output_alt.cs</li> <li>atom4_output_CO.csv</li> <li>atom4_output_H2O.csv</li> <li>atom4_output_lat.cs</li> <li>atom4_output_lon.cs</li> <li>atom4_output_lossOH_ppt_lump15.csv</li> <li>atom4_output_M.csv</li> <li>atom4_output_NO.csv</li> <li>atom4_output_OH.csv</li> <li>atom4_output_prodOH_ppt_lump15.csv</li> <li>atom4_output_startTime.csv</li> <li>atom4_output_sza.csv</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>For any further questions on the model simulations or code included here, please contact the corresponding author (Colleen Baublitz, cbb2158@columbia.edu). </p>
The Merc | Dayton OH abandoned steam plant
its a 100 year old building that was remodeled in 2017. I just wanted to upload what i had, stll a work in progress imo. Source: Objaverse 1.0 / Sketchfab
Fig. 8 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 8. Total cuneiform facet crosssectional area (mm2) vs. total navicular facet crosssectional
Data: The genesis of OH-stretching vibrational circular dichroism in chiral molecular crystals
<p>This data supplements our article "The genesis of OH-stretching vibrational circular dichroism in chiral molecular crystals"</p>
Four organic sea surface temperature proxies (UK'37, TEXH86, RI-OH' and LDI) in the westernmost Mediterranean for the last 35 kyr
<p>We present a high-resolution paleotemperature reconstruction from a marine sediment core (GP04PC) recovered in the westernmost Mediterranean, the Alboran Sea basin, over the last 35 kyr using for the first time in pararell four independent organic sea surface temperature (SST) proxies (U<sup>K'</sup><sub>37</sub>,<sup> </sup>TEX<sup>H</sup><sub>86</sub>,RI-OH' and LDI). We also present the δ<sup>18</sup>O of planktonic foraminifera <em>G. bulloides</em> record together with records of bulk parameters (total organic carbon content, δ<sup>13</sup>C<sub>org</sub>) and the accumulation rates of different biomarkers, providing insights in terrestrial input and primary productivity variations. </p> <p>We have also examined the Bayesian calibrations BAYSPLINE for U<sup>K'</sup><sub>37 </sub>and BAYSPAR for TEX<sub>86</sub>, although the non-Bayesian calibrations are used in this study.</p> <p> </p> <p> </p>
Evolutionary Map of the Universe (EMU): discovering 18-cm OH maser sources in ASKAP continuum images of the SCORPIO field
<p>Data cubes and spectra from the ATCA C3414 project.</p> <p>Data cubes are in fits format.</p> <p>Spectra were extracted from the data cubes following what described in the text. The format is the standard output of the CASA profile tools. Each spectrum contains an header with the column description.</p>
Data for the study for total OH reactivity measurements in a suburban site of Shanghai
<p>The experimental results of interference corrections in the CRM system in the campaign of the measured total OH reactvity in a suburban of Shanghai. In addition, the time series of NMVOCs, calculated and measured total OH reactvity in a suburban of Shanghai. </p>
Particle-Phase Uptake and Chemistry of Highly Oxygenated Organic Molecules (HOMs) from α-Pinene OH Oxidation
<p>Secondary organic aerosol (SOA) forms a major part of the tropospheric submicron particle mass. Still, the exact formation mechanisms of SOA have remained elusive. It is now admitted that highly oxygenated organic molecules (HOMs) can contribute to a large fraction of SOA formation. In this study, we performed a set of chamber experiments to investigate the SOA formation, and the HOMs uptake and processing directly formed by OH-radical initiated oxidation of α-pinene under two different aerosol seed conditions. Numerous HOM compounds were identified using advanced online and offline analytical techniques and grouped into four classes according to their different uptake behaviors. For the first time, individual HOMs uptake coefficients ranging from 1.1×10<sup>-2</sup> to 1.5×10<sup>-1</sup> were experimentally determined and analyzed using a resistance model which considers uptake limitations by individual gas- and/or particle-phase processes. This study demonstrates that the uptake coefficients of HOMs strongly depend on their molar mass and their respective O/C ratio. Results show that aerosol seed composition and phase state affect the initial uptake of HOMs. Furthermore, the study demonstrates that the acidity and/or different seed phase-state can significantly enhance the subsequent uptake through occurring acidity-driven reactions reflected in a reactive behavior, particularly under (NH<sub>4</sub>)HSO<sub>4</sub> seed conditions, promoting up to 3 times a higher SOA mass formation including the formation of highly-oxidized organosulfates (HOOS). Overall, the present study implies that HOMs and their subsequent chemical processing can play an important role in both the early growth of newly formed particles and SOA formation when particle acidity is high.</p>
Data from: CoZr nanocomposites in a ceramic-metal AlOx(OH)y/Al matrix with different Co/Zr ratio and its potential for syngas processing
<p>Data from article in Dalton Transaction</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.