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32 results for “condensate formation”
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>
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>
Measurement of solubility product reveals the interplay of oligomerization and self-association for defining condensate formation
<p>This data set includes the raw confocal microscopy images, DLS, and SEC-MALS data used for Chattaraj, Baltaci, et al. <em>Mol. Biol. Cell.</em> 2024. </p>
Dataset for "On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles"
<p>Data for Kulmala et al. (2024 )"On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles" (https://doi.org/10.5194/ar-2024-14).</p> <p>Included in the file are number concentrations of sub-2 nm ions and 2-2.3 nm ions measured with Cluster Ion Counter (CIC) and Neutral cluster and Air Ion Spectrometer (NAIS) at SMEAR II station in Hyytiälä, Finland. Concentrations of 1-2 nm ions measured with the NAIS are also included. Sub-2 nm (2-2.3 nm) ion concentrations measured with CIC are refered as Channel 1 (Channel 2-Channel 3) in the .csv file.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Data for Taylor Dispersion-Induced Phase Separation for the Efficient Characterisation of Protein Condensate Formation
<p>This archive contains data files the Python 3 code needed to reproduce the analysis done for the publication "Taylor Dispersion-Induced Phase Separation for the Efficient Characterisation of Protein Condensate Formation". Most of the data files are recorded on the Fida 1 instrument (Fidabio, Denmark) and consists of fluorescence recordings at the end of a 1 m long microfluidic channel (Ø 75 µm). Additional data file types include raw microscopy images (Leica SP8 confocal microscope, Germany), spectroscopy and light scattering files from Probedrum/Labbot (Labbot, Sweden), and simulation files generated by Comsol 6 (COMSOL AB, Sweden). Comsol project files are also supplied.</p> <p>The Python code is supplied in the form of Jupyter Notebooks. A python file "TDIPS.py" contains general routines used in the data analysis notebooks, and is for example capable of calculating the viscosity of various salt solution mixtures using table values. This is used for normalisation of the Fida 1 instrument data, when several measurements are done at different salt concentrations.</p> <p>The scripts are running Python 3.11.7 and packages Numpy (1.26.4), Matplotlib (3.8.0), Pandas (2.1.4), Scipy (1.11.4), and LMfit (1.2.2).</p> <p>All figures are included as Scalable Vector Graphics (<em>SVG</em>) files and can be opened using Inkscape.</p> <p>© Technical University of Denmark</p>
ATG9A regulates dissociation of recycling endosomes from microtubules leading to formation of influenza A virus liquid condensates - MAIN FIGURES
<p>Metadata for the manuscript entitled "ATG9A regulates dissociation of recycling endosomes from microtubules leading to formation of influenza A virus liquid inclusions" - MAIN FIGURES</p>
ATG9A regulates dissociation of recycling endosomes from microtubules leading to formation of influenza A virus liquid condensates - SUPPLEMENTARY FIGURES
<p>Metadata for the manuscript "ATG9A regulates dissociation of recycling endosomes from microtubules leading to formation of influenza A virus liquid condensates" - Supplementary Figures</p>
Dermal Condensate Niche Fate Specification Occurs Prior to Formation and Is Placode Progenitor Dependent
GEO Series GSE122026. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing.
DEAD-Box Helicase 3 Modulates the Non-Coding RNA Pool in Ribonucleoprotein Condensates During Stress Granule Formation
GEO Series GSE303136. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions (RNA-Seq)
GEO Series GSE272874. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
N6-adenosine Methylation of Enhancer RNAs and YTHDC1 Facilitate Transcriptional Condensate Formation and 3D Chromatin Organization
GEO Series GSE143441. Homo sapiens. 62 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Pcf11/Spt5 condensates stall RNA polymerase II to facilitate termination and piRNA-guided heterochromatin formation [RNA-Seq]
GEO Series GSE261847. Drosophila melanogaster. 22 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome-wide mRNA condensation precedes stress granule formation and excludes stress-induced transcripts
GEO Series GSE265963. Saccharomyces cerevisiae. 279 samples. Type: Other; Expression profiling by high throughput sequencing.
IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions (RIP-Seq)
GEO Series GSE272873. Homo sapiens. 25 samples. Type: Other.
Dataset for "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean"
<p>Atmospheric and oceanic observations were conducted over the North Pacific Ocean, Bering Sea, and Arctic Ocean during a cruise (MR19-03C) in early autumn of 2019. The dataset includes trace gases, chemical composition, fluorescent particles in the ambient and bioindicators in the surface seawater, observed along the ship track (time stamp and coordinates are included). This dataset is for Kawana et al., "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean", Atmospheric Chemistry and Physics, 2024 (in press).</p>
JMJD1C facilitates long distance genomic interactions by promoting condensates formation of key leukemic transcription factors in multiple AML cells [RNA-seq]
GEO Series GSE251728. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions
GEO Series GSE272875. Homo sapiens. 51 samples. Type: Expression profiling by high throughput sequencing; Other.
Molecular features driving condensate formation and gene expression by the BRD4-NUT fusion oncoprotein are overlapping but distinct [RNA-seq]
GEO Series GSE233302. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Molecular features driving condensate formation and gene expression by the BRD4-NUT fusion oncoprotein are overlapping but distinct [ChIP-seq]
GEO Series GSE233301. Homo sapiens. 14 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Pcf11/Spt5 condensates stall RNA polymerase II to facilitate termination and piRNA-guided heterochromatin formation.
GEO Series GSE291110. Mus musculus; Drosophila melanogaster. 76 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other; Expression profiling by high throughput sequencing.
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.