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800 results for “mixtures”
Thermophysical properties of hydrogen mixtures relevant for the development of the hydrogen economy: Review of available experimental data and thermodynamic models
<p>File: 1-s2.0-S096014812201271X-mmc1.docx</p> <p>This file (DOCX) contains additional figures associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc2.xlsx</p> <p>This file (XLSX) contains tables with the coordinates of the VLE associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc3.xlsx</p> <p>This file (XLSX) contains tables with the density data associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc4.xlsx</p> <p>This file (XLSX) contains tables with the calorific data associated with the hydrogen-containing systems.</p> <p> </p> <p>File: 2022_Renewable Energy_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in Renewable Energy (2022, 198, 1398-1429). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.renene.2022.08.096</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Northern shoveler data for "Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit"
<p>Repeated count data for Northern shoveler analyzed in Knape et al. Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit", Methods in Ecology and Evolution.</p> <p>count.csv contains Northern shoveler counts repeated 10 times at 50 sites in a 50 x 10 matrix. Each row corresponds to a specific site and columns correspond to visits.</p> <p>date.csv is a 50 x 10 matrix containing the julian date of each count, using the same ordering of visits (columns) and sites (rows) as in count.csv.</p> <p>site.csv contains covariates for each of the 50 sites. Sites (rows) are ordered in the same way as in count.csv and date.csv. The first column represents the area of water (ha) covered by the wetlands where the counts were conducted, the second columns is the percentage of the wetland area covered by reeds, and the third column is the latitude of the wetland in RT90 coordinates.</p> <p> </p> <p> </p>
Supporting files for "Estimating the number of contributors to DNA mixtures provides a novel tool for ecology"
<p>Supporting files for Sethi et al. Methods in Ecology and Evolution, in press. Contents include an alternative likelihood formulation for a DNA mixture estimator, associated R script to implement the likelihood, pcr multiplex conditions for yellow perch, and a supplemental figure.</p>
Initial abundances generator; allows choices of chemical mixture and network
<p>MESA work directory associated with an initial abundances generator; allows choices of chemical mixture and network</p> <p> </p>
Heteroplasmy Benchmark Dataset - mitochondrial DNA mixture model - HiSeq - M1-M4 - BAM
<p>Illumina HiSeq data of mixtures M1 (50%), M2 (10%), M3 (2%) and M4 (1%) of haplotypes H1c6 and U5a2e (decreasing).</p> <p>See <a href="https://doi.org/10.1371/journal.pone.0135643">https://doi.org/10.1371/journal.pone.0135643</a> for technical/lab-related informations</p>
Development of a Self-Healing and Rejuvenating Mechanisms for Asphalt Mixtures Containing Recycled Asphalt Shingle
<p>Corresponding data set for Tran-SET Project No. 17BLSU06. Abstract of the final report is stated below for reference:</p> <p>"The objective of this study was to test the hypothesis that hollow-fibers encapsulating a rejuvenator product could improve both self-healing, rejuvenation, and mechanical properties of asphalt mixtures. Hollow-fibers containing a rejuvenating product were synthesized via a wet spinning procedure with sodium-alginate polymer as the encapsulating material. An optimization of the production parameters for the synthesis of fibers was performed to develop fibers suitable for high-temperature and shear stress environment typical of asphalt mixture production. A self-healing experiment was conducted to evaluate the healing/rejuvenation capabilities of sodium-alginate fibers in asphalt mixtures with varying types of binders and recycled materials. Based on the self-healing experiment, a 5% fiber content was determined to be the optimum fiber content to enhance the self-healing ability of asphalt mixtures. In addition, the effect of different fiber contents on binder blends and asphalt mixtures was evaluated by performing the Multiple Stress Creep Recovery (MSCR) and Semi-Circular Bending (SCB) tests. Results of the self-healing experiment showed that the enhancement in the healing recovery depends on the breakage of the fibers. When the fibers break, the rejuvenator is released resulting in softening of the binder. In contrast, when the fibers do not break, they act as a reinforcement for the mix. Loaded Wheel Tester (LWT) test results showed a performance improvement against permanent deformation for asphalt mixtures containing recycled materials with sodium-alginate fibers compared to conventional asphalt mixtures. Furthermore, SCB test results showed that the addition of sodium-alginate fibers enhanced the fracture properties of asphalt mixtures with Recycled Asphalt Shingle (RAS) at intermediate temperatures. Moreover, the addition of fibers in mixtures with recycled materials resulted in an improved performance against low-temperature cracking as the mixtures resisted higher stresses before failure."</p>
Soil-Recycled Aggregate-Geopolymer Road Base/Subbase Mixtures: Steps Towards Sustainability
<p>Corresponding data set for Tran-SET Project No. 18GTLSU10. Abstract of the final report is stated below for reference:</p> <p>"This study deals with the development of Soil-Geopolymer mixtures using flyash, alkali activator and recycled aggregates (RAG) including recycled concrete (RCA) and reclaimed asphalt (RAP) as an alternative to soil-cement for pavement base and subbase layers. Several mix constituents were varied such as flyash type and content, RCA and RAP content and ratio of sodium silicate and sodium hydroxide. Experiment design was established and mechanical and durability characteristics of Soil-RAG-Geopolymer mixtures were evaluated and then compared to the conventional soil-cement mixtures. The results of the testing showed that for the selected Soil-RAG-Geopolymer mixtures the strength, stiffness, permanent deformation, and durability characteristics were either comparable or better than the soil-cement mixtures. However, such mixtures required more curing time at room temperature to achieve needed strength. In order to further optimize the practical applications of this technology in the field, other variables such as molarity of alkali activator, curing conditions, early strength development at room and ambient temperatures, gradation of RAG and shrinkage characteristics need be investigated."</p>
Modena test datasets: human 18S rRNA, curlcakes, oligos, oligos mixtures
<p>The human 18S rRNA, curlcakes, oligos and oligos mixtures test datasets accompanying the article "<em>Nanopore based computational method for detecting a wide range of epigenetic/epitranscriptomic modifications".</em></p> <ol> <li>Human 18S rRNA dataset contains a control sample and a native sample. Each sample comprises six subsamples with coverage depths ranging from 10 to 500.</li> <li> <p>Curlcake-M1 dataset contains a single sample. This sample comprises eight subsamples with coverage depths ranging from 10 to 1000. For each given coverage depth, a subsample contains four modified curlcakes, where each adenine (A) was replaced with m6A. Curlcake-UNM1 dataset contains a single sample. This sample comprises eight subsamples with coverage depths ranging from 10 to 1000. For each given coverage depth, a subsample contains four unmodified curlcakes. Curlcake-M2 dataset contains a single subsample with a coverage depth of 2000. This subsample contains four modified curlcakes, where each adenine (A) was replaced with m6A. Curlcake-UNM2 dataset contains a single subsample with a coverage depth of 2000. This subsample contains four unmodified curlcakes.</p> </li> <li> <p>Oligo-1, Oligo-2 and Oligo-3 contain three modified samples of the three Oligos variants, while Oligo-C contains three unmodified samples. Each sample comprises eight subsamples with coverage depths ranging from 10 to 2000.</p> </li> <li> <div>Oligo-mixtures dataset contains 10 samples. Each sample comprises subsamples with coverage depths ranging from 10 to 2000. Additionally, for each sample and coverage depth, there are three different mixtures. Each of these three mixtures has a ratio of modified to unmodified reads of either 25:75, 50:50, or 75:25.</div> </li> </ol> <p>If you have any questions about the content of this dataset, feel free to contact Siniša Biđin at sinisa@heuristika.hr.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
SAXS Data of Phospholipid Mixtures forming the Inverted Hexagonal Phase
<h1>Lipids and Experimental Info</h1>
Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures
<p>This dataset is associated with "<a href="https://doi.org/10.1063/5.0221221">Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures</a>" by L. Galliano, R. Rende and D. Coslovich. It includes data and workflow to reproduce the figures of the manuscript.</p> <p><strong>Requirements</strong></p> <p>A working installation of Julia (tested with version 1.10.4) is the only software required for this workflow. You can install Julia by following the instructions on the <a href="https://julialang.org/downloads/">Julia website</a>.</p> <p><strong>Dataset</strong></p> <p>Some of the simulations for this project ran for more than a week on a cluster. The Trajectories folder contains results from these simulations, which are necessary to reproduce the figures in the paper. Each folder includes a summary file detailing the simulation parameters and other files containing measured quantities such as potential energy, average acceptance, and parameter values. These folders also contain results from postprocessing the original simulation trajectories carried out with <a href="https://framagit.org/atooms/postprocessing">atooms-postprocessing</a>.</p> <p><strong>Instructions</strong></p> <p>To fully reproduce the analysis from postprocessed data, use the convenience script make at the root of the data set:</p> <p><code>./make all</code></p> <p>This comprises two main steps:</p> <ul> <li><code>./make setup</code> will set up the software environment by checking the Julia installation and installing the required packages in a virtual environment.</li> <li><code>./make workflow</code> will execute the scripts to perform the workflow from the preprocessed trajectory files in the Trajectories folder.</li> </ul> <p>There is also a <code>./make clean</code> option, which uninstalls the virtual environment and removes the results of the workflow.</p> <p><strong>Notes</strong></p> <ul> <li><em>Figure 3</em>: This figure is the result of a two-dimensional simulation that requires the complete Policy-guided Monte Carlo code. Consequently, it is not reproduced by this workflow.</li> <li><em>Figure formats</em>: The Julia scripts generate figures in SVG format. After producing the figures, you can use the <code>./make pdf</code> option to convert them to PDF files. This option requires a working installation of Inkscape, which can be downloaded from <a href="https://inkscape.org">Inkscape website</a></li> </ul> <p><strong>Changelog</strong></p> <ul> <li>1.0.0: initial submission</li> </ul>
Interactive Outputs for "Automated Mixture Analysis via Structural Evaluation"
<p>HTML files with interactive outputs related to the mixture assignments discussed in the paper "Automated Mixture Analysis via Structural Evaluation."</p>
Reference Spectra, Laboratory Mixture Spectra, list of NIMS cubes used in this study and the manual corrections to these NIMS cubes for better alignment.
<p>Reflectance spectra for the endmember library, including both the public data and data made in this study. <br><br>Reflectance spectra for the 100% SAO, 10% SAO, 25% SAO, 80% SAO, and 100% Water ice mixtures.<br><br>Text File of the PDS IDs of NIMS data cubes analyzed in this study.</p> <p>(X,Y) offsets applied to each data cube for better alignment between NIMS data and the Global Mosaic.</p>
silo discharge of mixtures of soft and hard grains in a 3D silo
<p>This dataset contains experimental data as well as simulation data for the study of granular mixtures of hard frictional beads and soft low-friction beads flowing in a cylindrical silo. The data processing is described in readme.txt respectively. The corresponding scripts are included in the folders.</p> <p>The discharge of granular mixtures of hard frictional beads and soft low-friction beads was studied in a cylindrical silo through experiments and simulations. Flow rate depends on fill height for 100% soft grains but remains constant for 100% hard grains. Mixing the two types of grains causes an abrupt transition: adding just 20% hard grains to soft grains changes the flow behavior significantly, it reduces the slope of the flow rate curve by 50-70%. Simulations show that this is due to the stress sensitivity near the orifice. Additional tests reveal increased dissipation with more hard grains, though not enough to explain the sharp flow rate change.</p> <p> </p>
The effect of conditions of thermal treatment of Ti+Al+C mixture on the formation of MAX phases
<p>Datasets for article The effect of conditions of thermal treatment of Ti+Al+C mixture on the formation of MAX phases. </p>
Utilization of high-performance concrete mixtures for advanced manufacturing technologies
<p><span>The presented experimental program focuses on the design of high-performance dry concrete mixtures, which could find application in advanced manufacturing technologies, for example additive solutions. The combination of high-performance concrete (HPC) with advanced or additive technologies provides new possibilities for constructing architecturally attractive buildings with high material requirements. The purpose of this study was to develop a dry mixture made from high-performance concrete that could be distributed directly in a advanced or additive technologies of solutions in the pre-prepared condition with all input materials (except for water) in order to reduce both financial and labor costs. This research specifically aimed to improve the basic strength characteristics, including mechanical (assessed using compressive strength, tensile splitting strength, and flexural strength tests) and durability properties (assessed using tests of resistance to frost, water, and defrosting chemicals), of hardened mixtures, with partial insight into the rheology of fresh mixtures (consistency as assessed using the slump-flow test). Additionally, the load-bearing capacity of the selected mixtures in the form of specimens with concrete reinforcement were tested using a three-point bending test. A reference mixture with two liquid plasticizers—the first based on polycarboxylate and polyphosphonate and the second based on polyether carboxylate—was modified using a powdered plasticizer, based on the polymerization product Glycol, to create a dry mixture; the reference mixture was compared with the developed mixtures with respect to the above-mentioned properties. In general, the results show that the replacement of the aforementioned liquid plasticizers by a powdered plasticizer based on the polymerization product Glycol in the given mixtures is effective up to 5 % (of the cement content) with regard to the mechanical and durability properties. The presented work provides an over-view of the compared characteristics, which will serve as a basis for future research into the development of additive manufacturing technologies in the conditions of the Czech Republic while respecting the principles of sustainable construction.</span></p>
IR spectra of pure and apolar ice mixtures
<p>This data set corresponds to the publication "JWST ice band profiles reveal mixed ice compositions in the HH 48 NE disk".</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240908117B/abstract">NASA ADS link</a></p> <p>Files contain the laboratory-measured IR spectra (wavenumber vs. absorbance) for pure ices and apolar ice mixtures in temperature intervals of 10 K. See Table 3 in the paper for details. No baseline subtraction has been performed.</p>
Data & Code from: Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs
<p>Data & Code for the study "Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs"</p> <p>Data:<br> "Rice_traits.csv": this file contains trait and productivity data measured at the individual plant level. It has one row per plant and one column per trait.</p> <p>Column headers:<br> "IDplant": unique plant identifier (1 to 200)<br> "IDpot": pot identifier with two plants per pot (1 to 100)<br> "Bloc": bloc identifier, with 20 pots per bloc (A, B, C, D, E)<br> "Treatment": P0 vs P+ = no P supply vs P supply<br> "Asso": pot type, either monoculture (M) or mixture (P)<br> "IDcouple": concatenation of the identifiers of the two genotypes in a pot (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDgeno": focal genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDnei": neighbour genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "BIOM_above": aboveground biomass (g)<br> "Tillers": number of tillers<br> "PH": Plant height (cm)<br> "Biovolume": biovolume (m3)<br> "SLA": Specific Leaf Area (m2/kg)<br> "RB_top": Root biomass between 0 and 20 cm below the soil surface(g)<br> "RB_deep: Root biomass between 20 and 60 cm below the soil surface(g) (!!! Only measured at the pot-level)<br> "D_ad"/"D_bas": Mean root diameter (mm) of adventitious/basal roots, respectively<br> "SRL_ad"/"SRL_bas": Specific Root Length (m/g) of adventitious/basal roots, respectively<br> "RTD_ad"/"RTD_bas": Root Tissue Density (mg/cm3) of adventitious/basal roots, respectively<br> "RBI_ad"/"RBI_bas": Root Branching Intensity (nb tips/cm) of adventitious/basal roots, respectively<br> "PfR_ad"/"PfR_bas": Proportion of fine roots (diameter < 0.1 mm) (%) in adventitious/basal roots, respectively</p> <p>Code:<br> "Rice_mixtures_analysis.R": this file contains the main statisticl analysis presented in the study. It uses "Rice_traits.csv" as an input.</p> <p> </p>
Laboratory bioassay of insecticides mixtures (neonicotinoids and ketoenols) against Bemisia tabaci Asia I
<p>Cotton leaves were dipped in serially diluted solutions of formulated insecticides for 10 s with slight agitation. The leaves with second instar nymphs that were dipped in double-distilled water containing 0.1 g L<sup>-1</sup> Triton X-100 only, served as control. Each bioassay including control used 3-4 replicates at a minimum of eight different concentrations and were maintained at the controlled growth condition. All the insecticides concentrations were selected to give a range of 0-100% mortality of <em>B. tabaci</em> nymphs. Two weeks later final mortality was assessed when the last nymphal instar had been reached on control plants. It was computed by comparing the number of second instar nymphs present at the time of treatment with the number remaining dead or unhatched on the day of mortality assessment. For bioassays with synergists (PBO and DEF), the cotton leaves containing the second instar nymphs of <em>B. tabaci</em> were dipped into synergist solutions (100 mg L<sup>-1</sup>) for 10 s at least 2 h before the imposition of insecticide treatments. Other procedures were same to those with insecticides only.</p>
Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS. Terra MODIS data are gap filled to better represent on the ground snow. The data was used in the a publication for The Cyrosphere titled Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, doi.org/10.5194/tc-2022-159. Geotiffs and PNG files for Landsat 8 are self describing. The .mat files for Terra MODIS contain three variables:</p> <p>snow_fraction: the gap filled snow fraction stored as uint8 with 255 as the NoData value and valid values between and including 0 to 100.</p> <p>mstruct: projection structure describing the standard MODIS tile projection structure. The data represent data from tile h08v05 and h09v05</p> <p>RefMatrix: affine spatial referencing matrix for the snow_fraction grid with the projection described by mstruct</p>
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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)
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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.