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1,385 results for “liquid”
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Data for "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids"
<p>Raw data (microscopy videos and image sequences) and scripts used for the analysis for the publication "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids", Science Advances 7 (38), eabh1642</p>
Values, distributions and approximations of the empirical liquidity cost function for various futures contracts.
<p>The figures presents the values, distributions and approximations of the empirical liquidity cost function for various futures contracts. The raw data was obtained from the LOB snapshots for the cash-settled futures contracts on the RTS index (RI), on Brent oil (BR) and FX-rate of US dollar versus Russian ruble (Si). The data corresponds to the period from 05 May 2020 to 26 Feb 2021. The tables summarize the results.</p>
Analytical expressions for thermophysical properties of solid and liquid aluminum relevant for fusion applications
<p>Aluminum is being actively employed by the fusion community as a non-toxic chemical proxy to beryllium, since both materials form covalent hydrides, high-melting oxides as well as alloys with tungsten [1]. Characteristic examples include studies of in situ cleaning of diagnostic first mirrors [2,3], investigations of hydrogen retention or deposited layer formation [4,5] and experiments dedicated to sputtered material transport in diagnostic ducts [6]. Aluminum has also served as a surrogate for beryllium in high heat flux tests, given its low melting point and low mass density. Characteristic examples concern experiments on the interaction of adhered Al dust with transient and stationary plasmas carried out in Magnum-PSI [7] and the controlled melting of Al blocks exposed in the DIII-D divertor under steady L-mode discharge conditions using the DiMES manipulator [8]. In order to reliably model the macroscopic metallic melt motion realized in the sloped geometry Al L-mode exposures in the DIII-D divertor, the material library of the MEMENTO melt dynamics code, that previously concerned tungsten [9], beryllium [10], niobium [11,12] and iridium [11,12], has to be extended to aluminum.</p> <p>Reliable experimental data have been analyzed for the specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, latent heat of fusion, enthalpy of vaporization, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of aluminum as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid aluminum measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] L. Marot, C. Linsmeier, B. Eren, L. Moser, R. Steiner and E. Meyer, "Can aluminium or magnesium be a surrogate for beryllium: A critical investigation of their chemistry", Fus. Eng. Des. 88 (2013) 1718.<br> [2] A. Maffini, L. Moser, L. Marot, R. Steiner, D. Dellasega, A. Uccello, E. Meyer and M. Passoni, "In situ cleaning of diagnostic first mirrors: an experimental comparison between plasma and laser cleaning in ITER-relevant conditions", Nucl. Fusion 57 (2017) 046014.<br> [3] A. Litnovsky, V. S. Voitsenya, R. Reichle et al., "Diagnostic mirrors for ITER: research in the frame of International Tokamak Physics Activity", Nucl. Fusion 59 (2019) 066029.<br> [4] A. Kreter, T. Dittmar, D. Nishijima, R. P. Doerner, M. J. Baldwin and K. Schmid, "Erosion, formation of deposited layers and fuel retention for beryllium under the influence of plasma impurities" Phys. Scr. T159 (2014) 014039.<br> [5] C. Quirós, J. Mougenot, G. Lombardi, M. Redolfi, O. Brinza, Y. Charles, A. Michau and K. Hassouni, "Blister formation and hydrogen retention in aluminium and beryllium: A modeling and experimental approach", Nucl. Mater. Energy 12 (2017) 1178.<br> [6] N. A. Babinov, A. G. Razdobarin, I. M. Bukreev et al, "Three-dimensional modeling of sputtered materials transport in diagnostic ducts of fusion devices", Nucl. Fusion 62 (2022) 126004.<br> [7] S. Ratynskaia, P. Tolias, M. De Angeli, D. Ripamonti, G. Riva, D. Aussems and T. W. Morgan, "Interaction of adhered beryllium proxy dust with transient and stationary plasmas", Nucl. Mater. Energy 17 (2018) 222.<br> [8] D. L. Rudakov, T. Abrams, I. Bykov et al., "Controlled low-Z metal melting in the DIII-D divertor", Abstract submitted for the 19th International Conference on Plasma-Facing Materials and Components for Fusion Applications, 22-26 May 2023, Bonn, Germany.<br> [9] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [10] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.<br> [11] P. Tolias, S. Ratynskaia and K. Paschalidis, "Thermophysical properties for the published article - Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Zenodo. https://doi.org/10.5281/zenodo.6778824.<br> [12] S. Ratynskaia, K. Paschalidis, P. Tolias et al., "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> </p>
Material Property Database of Organic Liquids, Ices, and Hazes on Titan
<p>Titan has a diverse range of materials in its atmosphere and on its surface: the simple organics that reside in various phases (gas, liquid, ice) and the solid complex refractory organics that form Titan's haze layers. These materials all actively participate in various physical processes on Titan, and many material properties are found to be important in shaping these processes. Future in-situ exploration on Titan would likely encounter a range of materials, and a comprehensive database to archive the material properties of all possible material candidates will be needed.</p> <p>Here we archive several important material properties of the organic liquids, ices, and the refractory hazes on Titan that are available in the literature and/or that we have computed. These properties include thermodynamic properties (phase change points, sublimation and vaporization saturation vapor pressure, and latent heat), physical property (density), and surface properties (liquid surface tensions and solid surface energies).</p> <p>We have archived all the data involved in our first paper (https://arxiv.org/abs/2210.01394 for the Arxiv version and https://doi.org/10.3847/1538-4365/acc6cf for the publisher version) here to make them available to the science community. These data can be used as inputs for various theoretical models to interpret current and future remote sensing and in-situ atmospheric and surface measurements on Titan. The material properties of the simple organics may also be applicable to giant planets and icy bodies in the outer solar system, interstellar medium, and protoplanetary disks.</p> <p>The "Summary of Data Tables and Jupyter Notebook Files" summarizes the names of all the data files (.csv) and Jupyter Notebook files (.ipynb) and their corresponding Tables in the paper.</p> <p><strong>Please cite our paper in your use of the data: Yu et al. (2023), https://doi.org/10.3847/1538-4365/acc6cf</strong></p> <p><strong>Yu, X., Yu, Y., Garver, J., Li, J., Hawthorn, A., Sciamma-O’Brien, E., ... & Barth, E. (2023). Material Properties of Organic Liquids, Ices, and Hazes on Titan. The Astrophysical Journal Supplement Series, 266(2), 30.</strong></p>
LIF-based quantification of the species transport during droplet impact onto thin liquid films (Dataset)
<p>This database includes Supplementary Data and videos for <em>Experiments in Fluids </em>manuscript: LIF-based quantification of the species transport during droplet impact onto thin liquid films.</p> <p>Number of figure in the file name is changed:</p> <ul> <li>fig.2 to fig. 3</li> <li>fig.3 to fig. 4</li> <li>fig.4 to fig. 5</li> <li>fig.5 to fig. 6</li> <li>fig.8 to fig. 11</li> <li>fig.9 to fig. 12</li> <li>fig.10 to fig. 13</li> <li>fig.11 to fig. 14</li> <li>fig.12 to fig. 15</li> <li>fig.13 to fig. 16</li> <li>fig.14 to fig. 17</li> </ul> <p> </p>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator
<p>Drift velocity and longitudinal diffusion for the manuscript:</p> <p>Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator</p> <p>https://arxiv.org/abs/2303.13963</p> <p> </p>
Distinct States in an Active Liquid Crystal of Flagella and Kinesin-Powered Microtubules under Varying Kinesin Concentrations or Active Stress
<p>At a constant nematic elasticity (flagella concentration) and confinement (H=100μm), higher KSA (120nM) or motor concentrations induce robust active stress that disrupts nematic alignment, resulting in chaotic behavior. Conversely, lower KSA (<60nM) concentrations fail to overcome the nematic elastic background's alignment, leading to a unique state where active microtubule bundles phase-separate into lanes within a uniform flagella background. The confocal micrograph reveals this distinct phenomenon, showing uniform flagella distribution alongside microtubule segregation into lane-like structures within the channel's limited confinement.</p>
Distinct States in an Active Liquid Crystal of Flagella and Kinesin-Powered Microtubules under Varying Confinement
<p>At significant confinement levels, the potent active stress disrupts the alignment of the flagella, leading to a turbulent state in the composite liquid crystal. Conversely, minimal confinement enables the dominant nematic alignment to counteract the active stress, inducing the phase separation of microtubules within a uniform flagella background. The confocal micrograph unveils uniform flagella distribution across the medium, while the microtubules segregate into a distinct lane-like structure within the channel's narrowest confinement.</p>
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Vertical Compositional Variations of liquid hydrocarbons in Titan's Alkanofers
<p>This data set is composed of six archives (.TAR) which collect input and output files from GROMACS (2018 version) simulations on binary and ternary mixtures representative of liquids in Titan's alkanofers:<br> 3000CH4+1000C2H6+1000N2_90K.tar<br> 3000CH4+1000C2H6+1000N2_95K.tar<br> 4000CH4+1000C2H6_90K.tar<br> 4000CH4+1000C2H6_95K.tar<br> 4000CH4+1000N2_90K.tar<br> 4000CH4+1000N2_95K.tar</p> <p>The system under study is always composed of 5000 molecules. Simulations at 90 K correspond to a pressure of 1.5 bar while those at 95K correspond to a pressure of 120 bar.</p> <p>Each archive contains 5 directories:</p> <p>1)<strong> INPUTS:</strong> It contains all the GROMACS input files (.GRO, .ITP, .TOP) needed to build the simulation box as well as the files required to prepare the molecular dynamics (MD) simulations (.MDP and .TPR). More details about the content of these files is available in the GROMACS manual (https://www.gromacs.org/).<br> 2 README files are added to inform the reader on useful GROMACS commands used here:<br> - README_box.txt: commands to build the simulation box.<br> - README_NVT+NPT-runs.dat: commands to run MD simulations and treat some data.</p> <p>2) <strong>NVTOUT_EQ</strong>: It contains all the files related to the 1-ns NVT equilibration phase, namely, the script for job submission (.SH) together with the related standard ouput files (.OUT and .ERR), and typical GROMACS output files (.GRO, .LOG, .EDR, .TRR, .CPT).<br> Post-treatment data are collected in 2 files:<br> - stats_nvt-eq.dat: Average potential energy, kinetic energy, total energy, temperature and pressure.<br> - energy_nvt-eq.xvg: Potential energy (col. 2), kinetic energy (col. 3), total energy (col. 4), temperature (col. 5), and pressure (col. 6) as a function of time (col.1).</p> <p>3) <strong>NPTOUT_EQ</strong>: It contains the same kind of files as NVTOUT_EQ but for the first 9 ns of the 19-ns NPT equilibration phase.<br> Post-treatment data are collected in 2 files:<br> - stats_npt-eq.dat: Average potential energy, kinetic energy, total energy, temperature, pressure, volume, density, and enthalpy.<br> - energy_npt-eq.xvg: Potential energy (col. 2), kinetic energy (col. 3), total energy (col. 4), temperature (col. 5), and pressure (col. 6), volume (col.7), density (col.8), and enthalpy (col. 9) as a function of time (col. 1).</p> <p>4) <strong>NPTOUT_EQ-RERUN1</strong>: It contains the same kind of files as NPTOUT_EQ but for the last 10 ns of the 19-ns NPT equilibration phase.</p> <p>5) <strong>NPTOUT_ACC</strong>: It contains the same kind of files as NPTOUT_EQ but for the 10-ns NPT accumulation phase. Additional data files also provide diffusion coefficients and shear viscosities depending on the mixture under consideration (see below).</p> <p><em>For binary mixtures</em>, the mean squared displacements (MSD) of species and the corresponding diffusion coefficients are estimated with the "gmx msd" GROMACS command.<br> Data are collected in two files:<br> - msd-CH4.xvg / msd-C2H6.xvg / msd-N2.xvg: MSD (col. 2) as a function of time (col. 1). The value of the corresponding diffusion coefficient (in cm<sup>2 </sup>s<sup>-1</sup>) is indicated as a comment in the preamble of these files.<br> - DCH4.dat / DC2H6.dat / DN2.dat: Estimated diffusion coefficient (in cm<sup>2 </sup>s<sup>-1</sup>) for the three moelcules under study.</p> <p><em>For ternary mixtures</em>, transverse current autocorrelation functions (TCAF) are computed with the "gmx tcaf" GROMACS command to get values of the shear viscosity:<br> - tcaf-slurm.sh, tcaf.out tcaf.xvg. tcaf_all.xvg, tcaf_cub.xvg, tcaf_fit.xvg: script (.SH) and several output files with transverse current autocorrelation functions (.XVG).<br> - visc_k.xvg: shear viscosity (col.2) as a function of the wave number (col.1). The last four viscosities can be fitted to get the shear viscosity at infinite wavelength (see GROMACS manual).</p>
Supplementary Material: Computational Study of Quasi-2D Liquid State in Free Standing Platinum, Silver, Gold, and Copper Monolayers
<p>Supplementary files for <em>Condensed Matter</em> <strong>2016</strong>, <em>1</em>(1), 1; doi:10.3390/condmat1010001; http://www.mdpi.com/ 2410-3896/1/1/1.</p> <p>Captions:</p> <p><strong>Video S1.</strong> (Pt 2400 K 5 ps) 5 ps Molecular Dynamics Movie of Pt Freestanding Monolayer at 2400 K. </p> <p><strong>Video S2.</strong> (Ag 1050K 6 ps) 6 ps Molecular Dynamics Movie of Ag Freestanding Monolayer at 1050 K.<br /> <br /> <strong>Video S3.</strong> (Au 1600K 4ps) 4 ps Molecular Dynamics Movie of Au Freestanding Monolayer at 1600 K.<br /> <br /> <strong>Video S4.</strong> (Cu 1400K 3ps) 3 ps Molecular Dynamics Movie of Cu Freestanding Monolayer at 1400 K. </p>
Optimized geometries for selected ions of ionic liquids and small molecules
<p>The geometries of these selected chemical entities were optimized at the ab initio or semiempirical levels of theory. They can be conveniently used to create more complicated systems through combining species like the free PACKMOL software offers. </p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
An energy harvester based on UV-polymerized short-alkyl-chain-modified [DBU][TFSI] ionic liquid electrets
<p>This dataset contains the measurement data for figures published in the journal article: </p> <p>An energy harvester based on UV-polymerized short-alkyl-chain-modified [DBU][TFSI] ionic liquid electrets (https://doi.org/10.1039/D3TA05448A)</p> <p>by Topias Järvinen, Nemanja Vucetic, Petra Palvölgyi, Olli Pitkänen, Tuomo Siponkoski, Helene Cabaud, Robert Vajtai, Jyri-Pekka Mikkola and Krisztian Kordas</p>
Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid (data evaluation)
<p>This record contains a docker image for data evaluation of AFM-IR data for our publication 'Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid'. The evaluations can be accessed by running the container and accessing the contained Jupyter Lab via a browser. The calculations are contained in 'Bottom_illuminated_PTIR.ipynb'.</p> <p>To run the container (requires docker):</p> <p>1.download 'container.tar.gz'</p> <p>2. in the command line, execute 'docker load -i container.tar.gz'. This will return something like 'Loaded image: <image_name>'</p> <p>3. then 'docker run -p 8888:8888 <image_name>' replacing the brackets with the actual name of the image, such as paper_bottom_illuminated:submission</p> <p>4.In your command line a link starting in 'http://127.0.0.1:8888/lab?token=...' will appear. Open this link in your browser to access the evaluation.</p>
Short-range correlation of stress chains near solid-to-liquid transition in active monolayers
<p>Using a three-dimensional model of cell monolayers, we study the spatial organization of active stress chains as the monolayer transitions from a solid to a liquid state. The critical exponents that characterize this transition map the isotropic stress percolation onto the two-dimensional random percolation universality class suggesting short-range stress correlations near this transition. This mapping is achieved via two distinct, independent pathways: (i) cell-cell adhesion and (ii) active traction forces. We unify our findings by linking the nature of this transition to high-stress fluctuations, distinctly linked to each pathway. The results elevate the importance of the transmission of mechanical information in dense active matter and provide a new context for understanding the non-equilibrium statistical physics of phase transition in active systems.</p>
Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles. Small 2024, 2406599
<p>Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles</p> <p>N. V. Vassilyeva, A. Forment-Aliaga, E. Coronado, <em>Small</em> <strong>2024</strong>, e2406599.</p> <p>doi: 10.1002/smll.202406599</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.