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3,206 results for “property (T)”
Properties of different biomass materials and their higher heating values (% Dry basis)
<p>Properties of biomass materials and their higher heating values (% Dry basis) from literature. Aggregated for BSE 619: Mathematical Modeling for Engineers course for Fall 2021 semester at the University of Tennessee, Knoxville.</p>
Correlations between bulk and surface properties of meibomian lipids with alteration of wax- to-sterol esters content
<p>These are the surface pressure/area isotherms and the stress relaxations of the transient dilatational elasticity modulus used in the study "Correlations between bulk and surface properties of meibomian lipids with alteration of wax- to-sterol esters content" in Chemistry and Physics of Lipids https://www.sciencedirect.com/science/article/pii/S000930842100116X</p> <p>The measurements are performed with by Langmuir surface balance µ Trough XS, area 135 cm<sup>2</sup>, volume 100 mL (Kibron, Helsinki, Finland), via the Wilhelmy wire probe method (instrumental accuracy 0.01 mN/m). Physiological saline solution buffer (PBS, pH 7.4) was utilized as trough subphase.</p> <p> </p> <p> </p>
Dataset for quantifying avian inertial properties using calibrated computed tomography
<p>Estimating centre of mass and mass moments of inertia is an important aspect of many studies in biomechanics. Characterising these parameters accurately in three dimensions is challenging with traditional methods requiring dissection or suspension of cadavers. Here, we present a method to quantify the three-dimensional centre of mass and inertia tensor of birds of prey using calibrated computed-tomography (CT) scans. The technique was validated using several independent methods, providing body segment mass estimates within approximately 1% of physical dissection measurements and moment of inertia measurements with a 0.993 R<sup>2</sup> correlation with conventional trifilar pendulum measurements. Calibrated CT offers a relatively straightforward, non-destructive approach that yields highly detailed mass distribution data that can be used for three-dimensional dynamics modelling in biomechanics. Although demonstrated here with birds, this approach should work equally well with any animal or appendage capable of being CT scanned.</p>
Jemma Properties Methods - TLOC
<p>Jemma Properties Methods - TLOC</p> <p>> file: Jemma_Properties_Methods_TLOC.csv<br> > size: 335.5 MB<br> > num_rows: 8,544,618<br> > columns: method_id, total_lines_of_code</p>
Jemma Properties Methods - SLOC
<p>Jemma Properties Methods - SLOC</p> <p>> file: Jemma_Properties_Methods_SLOC.csv<br> > size: 335.0 MB<br> > num_rows: 8,544,618<br> > columns: method_id, source_lines_of_code</p>
Jemma Properties Methods - CMPX
<p>Jemma Properties Methods - CMPX</p> <p>> file: Jemma_Properties_Methods_CMPX.csv<br> > size: 267.1 MB<br> > num_rows: 6,845,749<br> > columns: method_id, cyclomatic_complexity</p>
Jemma Properties Methods - MXIN
<p>Jemma Properties Methods - MXIN</p> <p>> file: Jemma_Properties_Methods_MXIN.csv<br> > size: 267.0 MB<br> > num_rows: 6,845,749<br> > columns: method_id, max_indent</p>
Jemma Properties Methods - NMOP
<p>Jemma Properties Methods - NMOP</p> <p>> file: Jemma_Properties_Methods_NMOP.csv<br> > size: 334.5 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_operators</p>
Jemma Properties Methods - NMLT
<p>Jemma Properties Methods - NMLT</p> <p>> file: Jemma_Properties_Methods_NMLT.csv<br> > size: 333.4 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_literals</p>
Jemma Properties Methods - NMPR
<p>Jemma Properties Methods - NMPR</p> <p>> file: Jemma_Properties_Methods_NMPR.csv<br> > size: 333.3 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_parameters</p>
Jemma Properties Methods - NUID
<p>Jemma Properties Methods - NUID</p> <p>> file: Jemma_Properties_Methods_NUID.csv<br> > size: 335.6 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_unique_identifiers</p>
Jemma Properties Methods - NTID
<p>Jemma Properties Methods - NTID</p> <p>> file: Jemma_Properties_Methods_NTID.csv<br> > size: 336.7 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_identifiers</p>
Jemma Properties Methods - NMTK
<p>Jemma Properties Methods - NMTK</p> <p>> file: Jemma_Properties_Methods_NMTK.csv<br> > size: 342.5 MB<br> > num_rows: 8,544,618<br> > columns: method_id, num_tokens</p>
Jemma Properties Methods - NAME
<p>Jemma Properties Methods - NAME</p> <p>> file: Jemma_Properties_Methods_NAME.csv<br> > size: 432.0 MB<br> > num_rows: 8,544,618<br> > columns: method_id, method_name</p>
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
<ul> <li> Description</li> </ul> <p>The 1520 water clusters were extracted from Ref. 1. The respective energies and atomic forces were recomputed at the revPBE-D3/def2-TZVP [2-6], B3LYP-D3/def2-TZVP [4-6, 7, 8], and BLYP-D3/def2-TZVP [4-6, 7, 9] level.</p> <ul> <li> Format</li> </ul> <p>The data is stored in python compressed array format (.npz) with the atomization energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five np.ndarray</p> <pre><code>import numpy as np data = np.load('revpbe.npz') data['R'] # Cartesian coordinates of nuclei in Ang. data['E'] # Total energy in kcal/mol data['F'] # Atomic forces in kcal/mol/Ang. data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges</code></pre> <p>References</p> <ul> </ul> <p>[1] Molpeceres G., Zaverkin V., and Kästner J., “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc. 499, 1373 (2020).</p> <p>[2] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994).</p> <p>[3] Y. Zhang and W. Yang, “Comment on “generalized gradient approximation made simple”,” Phys. Rev. Lett. 80, 890 (1998).</p> <p>[4] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, “A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu,” J. Chem. Phys. 132, 154104 (2010).</p> <p>[5] F. Weigend and R. Ahlrichs, “Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy,” Phys. Chem. Chem. Phys. 7, 3297 (2005).</p> <p>[6] F. Weigend, “Accurate Coulomb-fitting basis sets for H to Rn,” Phys. Chem. Chem. Phys. 8, 1057 (2006).</p> <p>[7] A. D. Becke, “Density-functional thermochemistry. iii. the role of exact exchange,” J. Chem. Phys. 98, 5648 (1993).</p> <p>[8] P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch, “Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields,” J. Phys. Chem. 98, 11623 (1994).</p> <p>[9] C. Lee, W. Yang, and R. G. Parr, “Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density,” Phys. Rev. B 37, 785 (1988).</p>
Data associated to the article "Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study"
<p>Contains input file and data used to generate the figures of the article:</p> <p>Effects of fluoride salt addition to the physico-chemical properties of the MgCl<sub>2</sub>-NaCl-KCl heat transfer fluid : a molecular dynamics study</p> <p>Weiguang Zhou, Yanping Zhang, Mathieu Salanne</p> <p>https://chemrxiv.org/engage/chemrxiv/article-details/618e903a2bf8a950c7d98e5d</p> <p>The files <em>data.inpt</em> and <em>runtime.inpt </em>are used to simulate the system using the software MetalWalls</p> <p>The files <em>MgNaKCl.txt, MgNaKClF01.txt, MgNaKClF05.txt, MgNaKClF10.txt, MgNaKClF20.txt</em> contain the computed densities, viscosities and thermal conductivities at various temperatures for several compositions (provided in the header of the files)</p>
Dataset for "Strong dispersion property for the quantum walk on the hypercube"
<p>Dataset for <em>Figure 1</em> and <em>Figure 2</em> presented in "<a href="https://doi.org/10.1088/1751-8121/aca6b9">Strong dispersion property for the quantum walk on the hypercube</a>" (preprint available at <a href="http://arxiv.org/abs/2201.11735">arxiv.org/abs/2201.11735</a>).</p> <p>The rows of <em>data.csv</em> file contain the calculated quantities related to the quantum walk on the hypercube:</p> <ul> <li>the first row is the maximum probability of a vertex during a walk on the 50-dimensional hypercube;</li> <li>the second row contains the number of steps to minimize the aforementioned probability for various <em>n</em>;</li> <li>the third row is the maximum probability of a vertex after approximately 0.849<em>n</em> steps, for various <em>n</em>;</li> <li>the fourth row is the probability of the walker to be at the 0<em><sup>n</sup></em> vertex (<em>n</em>=50) during a walk on the 50-dimensional hypercube.</li> </ul> <p> </p> <p>The rows of <em>aux.csv</em> contain auxiliary data needed to plot the figures:</p> <ul> <li>the first row contains the integers 0 to 199 and corresponds to the variable '<em>t</em>' in Figure 1;</li> <li>the second row contains the integers from 1 to 200 and corresponds to the variable '<em>n</em>' in Figure 2;</li> <li>the third row is the value of the linear function -0.754 + 0.849*<em>n</em>, depicted in the upper panel of Figure 2;</li> <li>the fourth row is the value of the function 5*1.93^(-<em>n</em>), depicted in the lower panel of Figure 2.</li> </ul> <p> </p> <p>To generate the figures, the following Matlab commands may be used (after loading the CSV files into variables <em>aux</em> and <em>data</em>):</p> <pre><code>figure; scatter(aux(1,:),data(1,:),15); set(gca,'YScale','log') % F1: upper figure; scatter(aux(1,1:2:end),data(1,1:2:end),15); hold on; scatter(aux(1,:),data(4,:),15,'s'); hold off; set(gca,'YScale','log') % F1: lower figure; scatter(aux(2,:),data(2,:),15); hold on; plot(aux(2,:),aux(3,:)); xlim([0,100]);hold off; %F2: upper figure; scatter(aux(2,:),data(3,:),15);set(gca,'YScale','log'); hold on; semilogy(aux(2,:), aux(4,:));hold off; xlim([0,100]); %F2: lower</code></pre> <p> </p>
Dataset related to article "Functional characterization and immunomodulatory properties of Lactobacillus helveticus strains isolated from Italian hard cheeses "
<p>This record contains raw datarelated to article "Functional characterization and immunomodulatory properties of Lactobacillus helveticus strains isolated from Italian hard cheeses "</p> <p>Lactobacillus helveticus carries many properties such as the ability to survive gastrointestinal transit, modulate the host immune response, accumulate biopeptides in milk, and adhere to the epithelial cells that could contribute to improving host health. In this study, the applicability as functional cultures of four L. helveticus strains isolated from Italian hard cheeses was investigated. A preliminary strain characterization showed that the ability to produce folate was generally low while antioxidant, proteolytic, peptidase, and β-galactosidase activities resulted high, although very variable, between strains. When stimulated moDCs were incubated in the presence of live cells, a dose-dependent release of both the pro-inflammatory cytokine IL-12p70 and the anti-inflammatory cytokine IL-10, was shown for all the four strains. In the presence of cell-free culture supernatants (postbiotics), a dose-dependent, decrease of IL-12p70 and an increase of IL-10 was generally observed. The immunomodulatory effect took place also in Caciotta-like cheese made with strains SIM12 and SIS16 as bifunctional (i.e., immunomodulant and acidifying) starter cultures, thus confirming tests in culture media. Given that the growth of bacteria in the cheese was not necessary (they were killed by pasteurization), the results indicated that some constituents of non-viable bacteria had immunomodulatory properties. This study adds additional evidence for the positive role of L. helveticus on human health and suggests cheese as a suitable food for delivering candidate strains and modulating their anti-inflammatory properties.</p> <p> </p>
Effect of experimental flour preparation and thermal treatment on the volatile properties of aqueous chickpea flour suspensions
<p>Final data used for figures in the paper: Noordraven, L. E., Buvé, C., Grauwet, T., & Van Loey, A. M. (2022). Effect of experimental flour preparation and thermal treatment on the volatile properties of aqueous chickpea flour suspensions. <em>LWT</em>, 113171.</p>
Nanotubes from the Misfit Layered Compound (SmS)1.19TaS2: Atomic Structure, Charge Transfer, and Electrical Properties_experimental dataset
<p>This dataset contains the raw experimental data for the Sreedhara et al., Nanotubes from the Misfit Layered Compound (SmS)1.19TaS2: Atomic Structure, Charge Transfer, and Electrical Properties, <em>Chem. Mater.</em> 2022, 34, 4, 1838–1853</p>
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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.