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3,871 results for “quantitative”
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
Quantitative electrostatic force tomography for virus capsids in interaction with an approaching nanoscale probe
<p>This repository contains the simulated data of a simple electrostatic model, based on the Poisson-Boltzmann equation, that quantifies the subnanometric electrostatic interactions between an AFM tip and a proteinaceous capsid (Zika Virus) from molecular snapshots. This allows us to describe the contributions of specific amino acids and atoms to the interaction force.</p> <p>The contains of this repository can be easily visualized through Jupyter Notebooks contained here:</p> <p>https://github.com/pyF4all/eTipVirusForce</p>
Sebastian+Simmons-High-frequency quantitative ultrasound to assess the acoustic properties of engineered tissues in vitro
<p>This dataset includes raw acquired ultrasound data, processing scripts, and statistical data for acoustic property characterization of cell-free and cell-seeded fibrin hydrogels.</p>
Quantitative spectroscopy of B-type supergiants [Dataset]
<p>This archive contains data used for the paper:</p> <p>Quantitative spectroscopy of B-type supergiants</p> <p>It contains reduced FOCES spectra - consult the paper for details on the reduction process.</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications.</p> </blockquote> <p><br> <br> <strong>Contact:</strong><br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong><br> Abaqus version R2018</p> <p><strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context:</strong><br> Data set supplementing journal paper:<br> [1] Ries, M.; Weber, F.; Possart, G.; Steinmann, P. & Pfaller, S., “A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects”, Composites Part A: Applied Science and Manufacturing, 2022, 107094.<br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content:</strong></p> <p>simulation folder denotation (“-” used instead of decimal points):<br> distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip</p> <p>with</p> <ul> <li> distance_particles: center distance of the nanoparticles in nm</li> <li> radius_particle: radius of the nanoparticles in nm</li> <li> thickness_ip: thickness of the interphase layers in nm</li> <li> num_ip: number of interphase layers</li> <li> length_box: box edge length in nm</li> <li> factor_el_length: factor scaling the element length on the arcs of the interphase layers (element length = factor_el_length * thickness_ip)</li> <li> fraction_box_length: matrix element length = length_box / fraction_box_length</li> <li> switch_mat_ip: if = 0: interphases are assigned their actual material properties, if = 1: interphases are assigned the material properties of the bulk</li> </ul> <p> <br> <br> each simulation folder contains the following file types:</p> <ul> <li> .cae: Abaqus model database, containing parts, meshes, loads, etc.</li> <li> .dat: Printed output from the analysis input file processor, as well as printed output of selected results written during the analysis</li> <li> .inp: Analysis input file</li> <li> .log: Log file, which contains start and end times for modules run by the current execution procedure</li> <li> .msg: Diagnostic or informative messages about the progress of the solution</li> <li> .odb: Output database containing all results data from an Abaqus analysis</li> <li> .sta: Status file with increment summaries</li> </ul> <p><strong>folder structure:</strong></p> <ul> <li>Standard_case:<br> simulation folders of the standard close (particle center distance: 5.1776 nm) and distant (particle center distance: 7.9481 nm) cases (particle radius: 2 nm, filler content 0.054 vol.%, number of interphase layers: 4, factor_el_length: 1.0) and further particle center distances</li> <li>Layers:<br> simulation folders with different numbers of interphase layers, i.e., different values for num_ip, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Mesh:<br> simulation folders with different mesh qualities, i.e., different values for factor_el_length, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Particle_size:<br> simulation folders with different particle sizes <ul> <li>2_nm: simulation folders with particle surface distance 2 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>4_nm: simulation folders with particle surface distance 4 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>8_nm: simulation folders with particle surface distance 8 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> </ul> </li> </ul>
Quantitative analysis data of the laminae
<p>The organic matter in the laminae was quantified using SVM. The data can be opened with ENVI software and ArcGIS software.</p>
Weamyl radar and precipitation data related to quantitative precipitation estimation
<p>This dataset contains radar data and precipitation data provided for research during the Weamyl project (NO Grants 2014-2021, under Project contract no. 26/2020) specifically used for quantitative precipitation estimation research.</p> <p>Radar data contains two radar products:</p> <ul> <li>Reflectivity (R): a base radar product, measured in dBZ, it is the product used for estimating precipitation. R data for the lowest 2 elevation angles. </li> <li>One-hour precipitation (OHP): a derivate radar product, it is the precipitation estimation that the radar makes automatically; to be used as comparison</li> </ul> <p>All radar data are in <a href="https://www.unidata.ucar.edu/software/netcdf/">netcdf </a>format. It is data from a WSR-98D Doppler weather radar situated in central Transylvania, provided in the form of a 557x800 grid with the cell size of ~1x1 km.</p> <p>Precipitation data contains 1-hour precipitation values collected from ground weather station, measured in mm; with the purpose to be used as ground truth. The precipitation data contains a csv file that contains the actual precipitation data, first value mentions the station code, the second the time and date and the third the 1-hour accumulated precipitation value, in mm.</p> <p>The second file provides the lat/long values for the ground weather stations, to be able to match the values of a station to a location on the grids from the radar data.</p>
Supplementary datasets for quantitative fate mapping
<p>Supplementary datasets for quantitative fate mapping:</p> <p>Dataset S1. All quantitative fate maps. Related to Figure 1. </p> <p>Dataset S2. inDelphi predicted mutant allele probabilities for hgRNAs in MARC1 mice and iPSC line. Related to Figure 4 and Figure 7. </p> <p>Dataset S3. Simulated phylogenies, single cell lineage barcodes, Phylotime reconstructed trees, fate map and set of MARC1 hgRNAs used for all experiments. Related to all Figures.</p>
Data from: Open access levels: a quantitative exploration using Web of Science and oaDOI data
<p>This is the raw data behind the publication (on PeerJ Preprints):</p> <p><strong>Open access levels: a quantitative exploration using Web of Science and oaDOI data</strong></p> <p>Across the world there is growing interest in open access publishing among researchers, institutions, funders and publishers alike. It is assumed that open access levels are growing, but hitherto the exact levels and patterns of open access have been hard to determine and detailed quantitative studies are scarce. Using newly available open access status data from oaDOI in Web of Science we are now able to explore year-on-year open access levels across research fields, languages, countries, institutions, funders and topics, and try to relate the resulting patterns to disciplinary, national and institutional contexts. With data from the oaDOI API we also look at the detailed breakdown of open access by types of gold open access (pure gold, hybrid and bronze), using universities in the Netherlands as an example. There is huge diversity in open access levels on all dimensions, with unexpected levels for e.g. Portuguese as language, Astronomy & Astrophysics as research field, countries like Tanzania, Peru and Latvia, and Zika as topic. We explore methodological issues and offer suggestions to improve conditions for tracking open access status of research output. Finally, we suggest potential future applications for research and policy development. We have shared all data and code openly.</p>
QR GWAS summary statistics for 39 quantitative traits in the UK Biobank
<p>Quantile regression (QR) GWAS summary statistics from the study "Genome-wide discovery for biomarkers using quantile regression at biobank scale". The preprint is available at <a href="https://doi.org/10.1101/2023.06.05.543699" target="_blank" rel="noopener">https://doi.org/10.1101/2023.06.05.543699</a>. </p> <p><strong>List of traits</strong></p> <p>A comma-delimited text file, QRGWAS.Traits_n39.csv, includes the list of 39 quantitative traits from the UK Biobank reported in the QR GWAS analyses above.</p> <p><strong>Summary statistics</strong></p> <p>The tab-delimited text files are QR GWAS summary statistics, which are bgzip compressed (.tsv.gz files) and tabix indexed (.tbi files).</p> <ul> <li>Column "CHR": chromosome</li> <li>Column "POS": based pair position</li> <li>Column "ID": variant ID</li> <li>Column "REF": non-effect allele</li> <li>Column "ALT": effect allele tested in GWAS</li> <li>Column "EAF": frequency of the effect allele</li> <li>Column "N": sample size</li> <li>Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.</li> <li>Column "P_LR": p-value of the linear regression (LR) association statistic</li> <li>Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).</li> </ul>
Figure data to "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice"
<p>This collection contains the data of the figures shown in the publication "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice" as txt files.</p> <p>The CST datasets "Fig1_Gap_CST.txt", "Fig1_Energy_CST.txt" and "Fig3_CST.txt" are already published in https://doi.org/10.5281/zenodo.7528316 and included here for the sake of completeness.</p>
Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)
Рис. 2. Река БоΛьшая Пёра, ниже устья реки Юхта Fig. 2. The Bolshaya Pyora River, below the mouth of the Yukhta River
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles) in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles)
Quantitative ethnobotany of multiple-use species and management of the Yangambi Biosphere Reserve in the Democratic Republic of the Congo
<p>The Yangambi Biosphere Reserve (YBR) is confronted to huge challenge for the livelihood of local communities and biodiversity or natural resources conservation. The lack of scientific information on the spatial distribution of useful woody species is a constraint to develop the sustainable management of forest resources. Hence the relevance of this study, carried out in the villages of Yaselia, Lilanda and Bagbanye, located on the outskirts of this protected area of YBR. It aims to identify the most useful woody species and analyse their socio-cultural use value, number of uses based on local community's commitments as well as to determine their abundance beyond village forests, to contribute to reforestation and conservation policies in the Yangambi landscape. To do this, we combined an ethnobotanical survey with a forest inventory. The results obtained showed that species such as Entandrophragma cylindricum, Petersianthus macrocarpus, Ricinodendron heudelotti, Scorodophloeus zenkeri, Pentaclethra macrophylla, Uapaca guineensis, Blighia welwitschii, Chrysophyllum lacourtianum, Dacryodes edulis, and Gilbertiondendron dewevrei, have high use and cultural value for local communities. Unfortunately, these species of high use and socio-cultural value are in low density in the village forests around the YBR. These results emphasize the necessity to implement as promptly as possible a management strategy including these useful species. This could be done through sustainable traditional agroforestry projects which will valorise existing resources and generate income for the local population to meet their livelihoods and avoid their incursion into the YBR which is the biodiversity sanctuary.</p>
Figure 2 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 2. Three-dimensional reconstructions of Diprotodon optatum (A), Zygomaturus trilobus (B), Neohelos stirtoni (C) and Propalorchestes sp. (D) showing the extent of the auditory, squamosal, parietal and frontal sinuses in blue, and brain endocast in red. Skulls are shown in dorsal (left) and lateral (right) views. Scale bars represent 10 cm. Bone is 70% transparent.
Figure 3 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 3. Three-dimensional reconstructions of Vombatus ursinus (A), Lasiorhinus latifrons (B) and Phascolarctos cinereus (C) showing the extent of the frontal sinuses in blue and brain endocast in red. Skulls are shown in dorsal (left) and lateral (right) views. Scale bars represent 10 cm. Bone is 70% transparent.
Figure 1 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 1. Three-dimensional digital reconstruction of Zygomaturus trilobus cranium, QVM1992 GFV73 from CT scans. Each fragment of the specimen was scanned separately and reconstructed to form the complete cranium on the right.
Figure 5 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 5. Frontal CT slices showing the braincase (BC), diploe (DIP) and parietal sinuses (PAS) inDiprotodon (A),Neohelos (B),Lasiorhinus (C) and Phascolarctos (D). Scale bars are 3 cm.
Dataset: Alpha Architect U.S. Quantitative Value ETF (QVAL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.