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568 results for “condensate”
Cloud Condensation Nuclei number concentrations over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Cloud Condensation Nuclei (CCN) are a subclass of atmospheric aerosol particles, which can be activated to cloud droplets at a certain supersaturation, with respect to water. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as CCN. It was found that CCN are relevant for the Earth’s radiation budget, by affecting cloud albedo and lifetime. When giving a number concentration of CCN, also the supersaturation at which it was measured has to be given.</p> <p>With additional information on particle number size distribution, the hypothetical diameter of particle activation (critical diameter) was derived. Further, the particle hygroscopicity parameter (kappa) was calculated using the critical diameter. Values of kappa can be a proxy for bulk chemical composition of the sampled CCN population.</p> <p>Our dataset gives CCN number concentrations measured by a CCN counter (type CCN-100 by DMT, Boulder, US) operated at five different levels of supersaturation (0.15%, 0.2%, 0.3%, 0.5%, 1%) during the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project. Temporal coverage is from December 20, 2016 to March 19, 2017. We give 5-minute averaged and quality controlled CCN number concentrations, critical diameter and kappa values.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_number_concentration_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_critical_diameter_SS100.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS015.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS020.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS030.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS050.csv, data file, comma-separated values</li> <li>ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS100.csv, data file, comma-separated values</li> <li>data_file_header_number_concentration.txt, metadata, text</li> <li>data_file_header_critical_diameter.txt, metadata, text</li> <li>data_file_header_hygroscopicity_parameter.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p>The files listed above contain Cloud Condensation Nuclei (CCN) number concentration (N_CCN), critical diameter (D_crit) and particle hygroscopicity parameter (KAPPA) values for the Antarctic Circumnavigation Expedition from in-situ measurements. Each file contains only N_CCN, D_crit or KAPPA values for one of the five measured levels of supersaturation (SS), e.g., N_CCN at SS=0.15% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv or N_CCN at SS=0.2% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv etc. In addition, for each N_CCN value the respective temperature of the CCNCs measurement column (T_col) is given. Values are from 1 Hz measurements and averaged to represent 5-minute intervals.</p> <p>For every given value of CCN number concentration, the respective supersaturation level is given, although files only contain values for one level only. Additionally, longitude and latitude for the ship’s position at the start time of the averaging period are given.</p> <p>For latitude and longitude nan values are given, in cases where positioning data was not available for the given time period. There are no nan values for CCN number concentration included, in a way that only quality assured data is given.</p> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to specify ACE cruise</li> <li>change time resolution to 5 minutes</li> <li>addition of critical diameter data</li> <li>addition of hygroscopicity parameter data</li> <li>create separate data_file_headers</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
Data for the publication: Recombinant silk protein condensates show widely different properties depending on the sample background
<p>This entry includes raw data for the publication "Recombinant silk protein condensates show widely different properties depending on the sample background". The original publication was published in: Journal of Materials Chemistry B, DOI: 10.1039/d4tb01422g</p> <p>The folder "Videos_Micropipette_Aspiration_Zenodo.zip" contains 9 TIF files, labeled Number1 - Number9. The numbering corresponds to the numbering of IMAC condensates studied with micropipette aspiration in the publication. Each TIF file is an image stack from a time series.</p> <p>The folders "Videos_IMAC_silk_with_BG_lysate_coalescence.zip", "Videos_HT_silk_coalescence.zip", and "Videos_IMAC_silk_coalescence.zip" all contain subfolders labeled with the purification method, the framerate of the videos and then consecutive numbering. Each of these folders contains the frames of the video as single TIF files.</p> <p>Please find more information in the read_me file uploaded.</p>
Test dataset for "Steam condensation scaled experiment in the presence of non-condensable gases for small modular reactor containment passive safety"
<p>This study presents scaled experiments using steam condensation with non-condensable gas (NCG)—helium (He), simulating hydrogen, and nitrogen (N<sub>2</sub>)—as these experiments are pivotal for water-cooled reactor passive containment cooling system (PCCS) design and analysis. Research into PCCSs for small modular reactors (SMRs) is especially important in light of SMR system design; however, studies in the literature reflect limitations due to test geometry and operational condition variations, without considering SMR prototypic design. To address these challenges, a scaled test facility was developed to accurately replicate SMR PCCSs. This facility includes vertical down-flow condensing test sections with 1-, 2-, and 4-in.-diameter condensing tubes, accompanied by annular water cooling. Experiments were conducted using both superheated and saturated steam, with steam mass flow rates in the presence of NCG varying from: (a) 55 to 66 kg/hr. of steam, and 1.8 to 22 kg/hr. of He (as the NCG); (b) 58 to 63 kg/hr. of steam, and 4.4 to 13.3 kg/hr. of N<sub>2</sub> (as the NCG). Test data were collected on (a) the axial temperatures of the annular cooling water; (b) the outer wall temperature of the condensers; and (c) the mass flow rate, temperature, and pressure at the test section inlets and outlets. These primary test data were used in conjunction with a standard data reduction methodology to estimate essential thermal parameters such as heat fluxes, heat transfer coefficients, and condensation rates. The effects of NCGs on steam condensation within the geometry of the scaled test sections were then presented in regard to various testing conditions.</p>
The generic nature of the condensed state of proteins | Talk - I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
PhasAGE Training School 2 - Condensation through liquid-liquid separation-LECTURE
<p>PhasAGE Training School 2 “Biomolecular condensates in cell function, aging and disease” is the<strong> second</strong> edition of a series of PhasAGE training activities.</p> <p>The main goal of this training school is to raise awareness and provide expertise on fundamental aspects of phase separation and formation of <strong>biomolecular condensates</strong>, specifically covering the importance of this process to cellular biology and its contribution to the aging process and age-related diseases.</p>
Vortices and vortex stripes in a dipolar Bose-Einstein condensate
<p>Quantized vortices are a prototypical feature of superfluidity that have been observed in multiple quantum gas experiments. But the occurrence of vortices in dipolar quantum gases — a class of ultracold gases characterized by long-range anisotropic interactions — has not been reported yet. Here, we exploit the anisotropic nature of the dipole-dipole interaction of a dysprosium Bose-Einstein condensate to induce angular symmetry breaking in an otherwise cylindrically symmetric pancake-shaped trap. Tilting the magnetic field towards the radial plane deforms the cloud into an ellipsoid, which is then set into rotation. At stirring frequencies approaching the radial trap frequency, we observe the generation of dynamically unstable surface excitations, which cause angular momentum to be pumped into the system through vortices. Under continuous rotation, the vortices arrange into a stripe configuration along the field, in close agreement with numerical simulations.</p>
Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign
<p>This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.</p>
BrainIAK Tutorials: Condensed Datasets
<p>This is a collection of datasets used by BrainIAK <a href="https://brainiak.org/tutorials">tutorials</a>. These datasets are pre-processed and ready to use. They have been condensed, by reducing the number of subjects from the original studies, to keep the file size small. Each tutorial is paired with a dataset as listed below. The file brainiak_datasets.zip contains the data for all the tutorials, and in unzipped form uses 18GB of space. If you wish to download data for specific tutorials, use the list below to find the correct dataset to download and use.</p> <p>Tutorial 2: VDC (Kim et al., 2017) and 02-data-handling (this is a simulated dataset)</p> <p>Tutorials 3-5: VDC (Kim et al., 2017)</p> <p>Tutorial 6: Ninety Six (Kriegeskorte et al., 2008)</p> <p>Tutorials 7: Face-scene (Turk-Browne et al., 2012). The script for within subject searchlight uses the VDC dataset.</p> <p>Tutorial 9: Face-scene (Turk-Browne et al., 2012)</p> <p>Tutorial 8: Latatt (Hutchinson et al., 2016)</p> <p>Tutorial 10: Pieman2 (Simony et al., 2016)</p> <p>Tutorial 11: Raider (Haxby et al., 2011) and Pieman2 (Simony et al., 2016)</p> <p>Tutorial 12: Sherlock_processed (Chen et al., 2017)</p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Simulation Data for the article 'The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure'
<p>This dataset contains the NetCDF output files from simulations using PlanetCARMA in support of the work published in the manuscript, "The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure." A summary of the included NetCDF data is found in the README file that is part of the data object. The submission version of this dataset contains only those simulations that provided data that were discussed in the accepted final manuscript. However, additional simulations were carried out in the course of the work, and are described in the manuscript. Upon request, the authors will revise this data repository by adding such data products from among that list as may be requested by others.</p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Bose-Einstein condensation of non-ground state caesium atoms
<p>We report the Bose-Einstein condensation of caesium atoms in the Zeeman-excited<br> mf = 2 state. As the magnetic field is varied, we identify two regions in which the<br> dipolar relaxation rate is sufficiently suppressed so that condensation becomes possible.<br> We characterize the phase transition and quantify the loss processes, finding unusually<br> high three-body losses in one of the two regions. Our experimental results otherwise<br> agree well with the theoretical expectation. In particular, we confirm the presence of<br> a narrow Feshbach resonance near a zero crossing in the background scattering length.<br> Our results open up new possibilities for the mixing of quantum-degenerate gases and<br> the study of impurity transport in strongly correlated one-dimensional quantum wires.</p>
NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR A LOW PRESSURE.
<p>As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three</p>
MicroED datasets from two proteinase K lamellae using a 20 micrometre C2 condenser aperture
<p>These four diffraction data sets were used to determine the proteinase K structure from two crystalline lamellae by electron diffraction, as deposited in the PDB with id <a href="http://doi.org/10.2210/pdb6ZEV/pdb">6ZEV</a> and published in <a href="https://doi.org/10.3389/fmolb.2020.00179">https://doi.org/10.3389/fmolb.2020.00179</a></p> <p>A script is provided that allows indexing of the diffraction spots using DIALS 3.1. This is adapted from the commands used to process the data for the publication, which used DIALS 1.10.</p>
Quantum Fluctuations in Quasi-One-Dimensional Dipolar Bose-Einstein Condensates
<p>Raw data for the depicted graphs in the given paper/letter</p>
Dataset from 'Topological interfaces crossed by defects and textures of continuous and discrete point group symmetries in spin-2 Bose-Einstein condensates'
<p>Dataset associated with the publication 'Topological interfaces crossed by defects and textures of continuous and discrete<br>point group symmetries in spin-2 Bose-Einstein condensates' in Physical Review Research. <br>Source data for Figures 3-8 in the manuscript.</p>
Dataset from 'Composite cores of monopoles and Alice rings in spin-2 Bose-Einstein condensates'
<p>Dataset associated with the publication 'Composite cores of monopoles and Alice rings in spin-2 Bose-Einstein condensates ' in Physical Review Research. <br>Source data for Figures 2-5 in the manuscript.</p>
Dataset: Field-induced Bose-Einstein condensation and supersolid in the two-dimensional Kondo necklace
<p>The open Dataset for "Field-induced Bose-Einstein condensation and supersolid in the two-dimensional Kondo necklace". </p>
Dataset for Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing
<p>This dataset includes one year long measurements of particle number size distributions, chemical composition of PM2.5, gaseous precursors, and meteorological parameters in urban Beijing, China, from March 1, 2018, to March 1, 2019. It is the supplementary data for "Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing", which is published by Environmental Science & Technology Letter. Please cite: Wei Du, Jing Cai, Feixue Zheng, Chao Yan, Ying Zhou, Yishuo Guo, Biwu Chu, Lei Yao, Liine M. Heikkinen, Xiaolong Fan, Yonghong Wang, Runlong Cai, Simo Hakala, Tommy Chan, Jenni Kontkanen, Santeri Tuovinen, Tuukka Petäjä, Juha Kangasluoma, Federico Bianchi, Pauli Paasonen, Yele Sun, Veli-Matti Kerminen, Yongchun Liu, Kaspar R. Daellenbach, Lubna Dada, and Markku Kulmala Environmental Science & Technology Letters Article ASAP DOI: 10.1021/acs.estlett.2c00159</p>
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