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3,206 results for “property (T)”
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter : Weighted Monte Carlo samples for neutron star observables
<p>This data release contains weighted Monte Carlo samples associated with</p> <p>Legred, Chatziioannou, Essick, Han, and Landry, 2021</p> <p>"Impact of PSR J0740+6620 radius constraint on the properties of high-density matter"</p> <p>Phys. Rev. D 104, 063003;</p> <p>doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset: Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes
<p>This set of data complements the published article "Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes" published on Nanomaterials <a href="https://doi.org/10.3390/nano11092179">https://doi.org/10.3390/nano11092179</a></p> <p>Ferrero, R.; Barrera, G.; Celegato, F.; Vicentini, M.; Sözeri, H.; Yıldız, N.; Atila Dinçer, C.; Coïsson, M.; Manzin, A.; Tiberto, P. Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes. Nanomaterials 2021, 11, 2179. https://doi.org/10.3390/nano11092179</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables
<p>This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes.</p> <p>The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a></p> <p>Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337</p> <p>Details about some of the Supplementary tables:</p> <p>TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used).</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4)</p> <p>Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE.<br> TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion<br> TableS6b_max_PFE_GRN.xt - max PFE<br> TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments. </p> <p>Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1).</p> <p>TableS8a_max_AUFE_SBM.csv<br> TableS8b_max_PFE_SBM.csv<br> TableS8c_middle_AUFE_PFE_SBM.csv</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters.</p> <p>TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426)</p> <p>TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts</p> <p>TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).</p> <p>TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts</p> <p>All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated </p> <p>with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details. </p> <p><strong>femliu1: </strong></p> <p><em>fem-3(q20)ts.; dhs-3p::dhs-3::gfp</em></p> <p><strong>femsod3:</strong></p> <p><em>fem-3(q20)ts.; sod-3p::gfp</em></p> <p><strong>glp1lgg1:</strong></p> <p><em>glp-1(e2144); lgg-1p:lgg-1:gfp</em></p>
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution
<p><strong>BigBrain-MR</strong> is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available <a href="https://bigbrainproject.org/">BigBrain histological dataset</a> and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain.</p> <p>The <strong>dataset</strong> includes:</p> <ul> <li>BigBrain original contrast and a new atlas with 20 ROIs;</li> <li>T<sub>1</sub>-weighted image and T<sub>1</sub> map;</li> <li>T<sub>2</sub>*-weighted images and R<sub>2</sub>* map;</li> <li>Magnetic susceptibility map (QSM);</li> <li>Background magnetic field map;</li> <li>Complex coil sensitivity maps (32ch-receive RF array);</li> <li>Bias field map.</li> </ul> <p>Information about each image/map (including data type and amplitude scaling) is provided in <em>data_info.txt</em>.</p> <p>Additionally, we have included a script with <strong>usage examples</strong> in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes.</p> <p>BigBrain-MR is presented, described and tested in the following <strong>peer-reviewed article</strong>:</p> <p>C. Sainz Martinez, M. Bach Cuadra, J. Jorge. <em>BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development</em>. NeuroImage 2023. <strong>DOI:</strong> <a href="https://doi.org/10.1016/j.neuroimage.2023.120074">10.1016/j.neuroimage.2023.120074</a></p> <p> </p>
Microwave Single Scattering Properties Database (Horizontally Aligned Aggregates of Dendrites)
<p>The database contains physical and microwave single scattering properties of horizontally aligned frozen hydrometeors as large as 11 cm in diameter. </p> <p>A description of the aggregation model used for particle generation can be found in:<br> Leinonen, J., and Szyrmer, W. (2015), Radar signatures of snowflake riming: A modeling study, <em>Earth and Space Science</em>, 2, 346– 358, doi:<a href="https://doi.org/10.1002/2015EA000102">10.1002/2015EA000102</a>.<br> The code used for particle generation is freely available at: <a href="https://github.com/jleinonen/aggregation">https://github.com/jleinonen/aggregation</a></p> <p>The scattering properties of particles were computed using discrete dipole approximation using ADDA software package (<a href="https://github.com/adda-team/adda">https://github.com/adda-team/adda</a>)</p> <p>Terminal velocity of snowflakes was computed using 4 hydrodynamical models that were implemented as a part of snowScat library (<a href="https://github.com/OPTIMICe-team/snowScatt">https://github.com/OPTIMICe-team/snowScatt</a>)</p> <p>Approximately one half of the snowflake structure files and one quarter of scattering properties (for X, Ku, Ka and W band) were generated for the publication of Leinonen and Szyrmer (2015). The remaining part of the dataset was generated using the ALICE High Performance Computing Facility at the University of Leicester.</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>
Single Scattering properties at W-band of ice populations
<p>The file contains coefficients of the polynomials that approximate radar observables at the W-band for a population of ice particles. </p> <p> </p> <p> </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>
Thermodynamic properties of ammonia-water (NH3H2O mixture). In Esperanto
<p>Thermodynamic data for the ammonia-water mixture are adapted from: Ibrahim, O. M. (1993). Thermodynamic properties of ammonia-water mixtures. In ASHRAE Transactions: Symposia (Vol. 93, p. 1495). <br> <br> </p>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Dataset for the optical properties of tilted surfaces in material jetting
<p>Dataset for the optical properties of tilted surfaces in material jetting:</p> <p>Including dataset for gloss, haze, scattering, specular BRDF, reflectance, transmittance, and statistical analysis</p>
Metrics As Scores Dataset: Price, Weight, and Other Properties of Over 1,200 Ideal-Cut and Best-Clarity Diamonds
<p>This dataset is a subset of the original diamonds dataset with more than 54,000 diamonds. It was reduced to only contain diamonds of the best cut (ideal) and clarity (IF). The group is now given by the colors from J (worst) to D (best). This dataset comes from the R-package ggplot2 (Wickham 2016). For each color, we can examine the following attributes (<strong>features</strong>) of each diamond:</p> <ul> <li><em>Carat</em>: Weight of the diamond</li> <li><em>Depth</em>: Total depth percentage</li> <li><em>Price</em>: Price in US dollars [discrete]</li> <li><em>Table</em>: Width of top of diamond relative to widest point</li> <li><em>X</em>: Length in mm</li> <li><em>Y</em>: Width in mm</li> <li><em>Z</em>: Depth in mm</li> </ul> <p>It has a total of 7 Colors (<strong>groups</strong>): <em>D</em>, <em>E</em>, <em>F</em>, <em>G</em>, <em>H</em>, <em>I</em>, and <em>J</em>. The best color is <em>D</em> and the worst color is <em>J</em>. This dataset was created to analyze whether there are differences between the colors.</p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
Does size matter? Quality assessment of the size property in research data repositories
<p>Code and data for master's thesis on quality assessment of the size property in research data repositories. Research questions:</p> <ul> <li> <p> For what semantic concepts is the size property of repositories being used?</p> </li> <li> <p>What kind of quality factors can be detected when assessing the size property in a registry for research data repositories?</p> </li> <li> <p>Which automated and intellectual measures can improve the quality of the size property?</p> </li> </ul> <p>Method 1: Data analysis of size and related properties over all re3data records</p> <ul> <li> <p>Property selection</p> </li> <li> <p>Data extraction from API</p> </li> <li> <p>Data normalization</p> </li> <li> <p>Typing of patterns: mainly units of size</p> </li> <li> <p>Analysis: ~quantitative, mainly univariate, but also some multivariate / time</p> </li> </ul> <p>[Included in the publication:</p> <p>Method 2: Case Study of size in individual repositories</p> <ul> <li> <p>Repository selection: purposive sampling</p> </li> <li> <p>Data capture from GUI / API</p> </li> <li> <p>Analysis: ~qualitative]</p> </li> </ul>
General soil properties of wheat fields along 9 Pedoclimatic regions in Europe
<p>This data set contains general soil characteristics from wheat fields sampled (0-25 cm) in conventional and organic farms from 9 European pedoclimatic regions (Mediterranean South, Mediterranean North, Lusitanean, Atlantic Central, Atlantic North, Continental, Pannonian, Nemoral and Boreal).</p> <p>This data set is part of the work performed in WP3 SoildiverAgro project, funded by the European Commission Horizon 2020 programme [grant agreement 817819].</p>
Bathymetry, sediment thickness, and geotechnical-geophysical properties of sediments of Lake Seracchi in Rutor proglacial area
<p>This dataset contains the data of a geophysical-geotechnical investigation of Lake Seracchi (L4) of the Rutor basin, Aosta Valley, Italy, The fieldwork was mainly carried out in 10-11 July 2021.</p> <p>The data are:</p> <p>.tif ready-to-use maps of the bathymetry and the sediment thickness.</p> <p>Time Domain Reflectometry (TDR) data of electrical permittivity and conductivity of the lake sediments</p> <p>Geotechnical analyses, such as Grain Size Distribution and Atterberg's Limits, performed on the lake sediments.</p> <p>The details are reported in the README .txt file.</p> <p> </p>
HyUSPRe Report & Data on 'New experimental data on reactions between H2 and well cement and effects on fluid flow and mechanical properties of well cement
<p>In this study, new experimental data is presented of the effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties of oil well (class G) cement, relevant for underground hydrogen storage operations. Changes in mechanical properties (Young’s modulus, Poisson’s ratio and ultimate strength) have been analyzed using unconfined compressive strength (UCS) tests and confined cyclic loading tests on class G cement samples that were unreacted (cured for 3 days at 80°C) and exposed to lime-saturated brine and N<sub>2</sub> or H<sub>2</sub> for 1 and 2 months. Changes in cement mineralogy were analyzed by XRD analysis of the unreacted and exposed samples. The mechanical properties of elastic modulus and Poisson’s ratio are within the expected range of an oil well cement. Differences in Young’s modulus, Poisson’s ratio and ultimate strength are limited between unreacted, N<sub>2</sub>-exposed and H<sub>2</sub>-exposed samples, when comparing UCS tests or confined cyclic loading tests. Repeated UCS tests seem to indicate that the variation in Young’s modulus and ultimate strength increases after N<sub>2</sub> and H<sub>2</sub> exposure, but this observation needs to be confirmed in additional tests. During cyclic axial loading of confined cement samples, irreversible (plastic) deformation (compaction) occurs that affect static Young’s modulus. Also, effects of exceeding yield and failure strength on Young’s modulus are observed. Dynamic Young’s moduli and Poisson’s ratios derived from acoustic velocity measurements during confined cyclic tests show limited variation, in particular if static and dynamic Young’s modulus are compared. The mineralogical changes as identified using XRD analysis suggest minor changes between unexposed and H<sub>2</sub>- and N<sub>2</sub>-exposed samples, although XRD patterns indicate some minerals that could not be identified. The main conclusion is that effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties and mineralogical changes of class G cement is limited compared to unreacted or N<sub>2</sub> exposed samples for the investigated conditions. There is no indication that changes in mechanical properties of cement are such that cement integrity of wells used for underground hydrogen storage will be significantly affected. It should be emphasized that this conclusion is based on experiments on one type of cement (class G) and a limited set of conditions. In particular, additional tests to assess the reproducibility of current results and tests on samples that were exposed longer to H<sub>2</sub> and N<sub>2</sub> are of interest. Detailed effects of changing properties for the durability and integrity of wells can be derived by performing a parameter sensitivity analysis with well integrity modelling for the range in mechanical properties measured in this study.</p>
Temperature-Dependent THz Properties and Emission of Organic Crystal BNA
<p>This dataset is accompanying the paper "Temperature-Dependent THz Properties and Emission of Organic Crystal BNA"</p> <p><strong>General data acquisition:</strong></p> <p>The data was acquired with a modified Menlo Tera K-15 THz-TDS, consisting of a photoconductive emitter/receiver and four off-axis-parabolic mirrors (OAP). The second and third OAP, focusing and collecting the THz, are with a longer focus length to have enough space for the cryostat (Janis ST-100), which is equipped with 3 mm z-cut quartz windows for entry and exit of the THz beam. The delay line offers delays up to 1600 ps but the range was restricted to cut out the reflections from the z-cut quartz windows. Instead of averaging with Menlo’s own software ScanControl, each single trace is read out. 10 000 traces are saved for each unique measurement condition (crystal orientation, temperature) and saved in a single HDF-5 file. HDF-5 is an efficient (binary), cross-platform data format and can be read easily by i.e. Python or Matlab.</p> <p> </p> <p><strong>The structure is as follows:</strong></p> <p><strong>raw_data </strong></p> <p>The folder raw_data contains four folders. The folder “dark” contains a single file since this is independent of crystal orientation and temperature of the cryostat. For this measurement, the THz beam was blocked but all electronics, selected delay range etc. kept the same, to measure the noise-floor of the system.</p> <p>The folder reference was captured with the cryostat incl. windows, vacuum and crystal holder in place. Even though there should be no change in the transfer function by changing the temperature (due to the large aperture of the crystal holder), we still recorded reference traces for each temperature.</p> <p>The folder “BNA_orientation_001” contains the data with the organic crystal BNA in vertical orientation (<001>).</p> <p>The folder “BNA_orientation_100” contains the data with the organic crystal BNA in horizontal orientation (<100>).</p> <p><strong>averaged_corrected_data</strong></p> <p>The folder “averaged_corrected_data” reduces the large amount of raw data due to averaging. The program “Correct@TDS” (developed in the group of Dr. Romain Peretti, Terahertz Photonics Group @ IEMN - CNRS (UMR 8520), publication in preparation), is used to fit specific correction parameters for the delay, dilatation, amplitude noise and periodic sampling. The mean data is saved for each temperature in a text file called “mean.txt”. The other output of “Correct@TDS” is diagnostic information about the correction parameters and about the standard deviation in frequency- and time-domain.</p> <p><strong>extracted_n_alpha</strong></p> <p>The folder “extracted_n_alpha” contains the refractive index, absorption coefficient and more in a single HDF-5 file, extracted by the program phoeniks (<a href="https://github.com/TimVog/phoeniks">https://github.com/TimVog/phoeniks</a>), which is developed in our group. All results for the paper are saved in the internal folder structure of the HDF-5 file (for crystal orientation and temperature).</p> <p>The folder “nelly” shows the extraction of n and alpha done with Nelly [1] (<a href="https://github.com/YaleTHz/nelly">https://github.com/YaleTHz/nelly</a>) for the vertical orientation, which was used for the supplementary document.</p> <p> </p> <p>[1] Nelly: A User-Friendly and Open-Source Implementation of Tree-Based Complex Refractive Index Analysis for Terahertz Spectroscopy</p> <p>Uriel Tayvah, Jacob A. Spies, Jens Neu, and Charles A. Schmuttenmaer</p> <p>Analytical Chemistry 2021 93 (32), 11243-11250</p> <p>DOI: 10.1021/acs.analchem.1c02132</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.