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990 results for “quantification”
Citation count error data for "Data inaccuracy quantification and uncertainty propagation for bibliometric indicators"
<p>This is the original collected data on citation count errors resulting from citation matching errors in Web of Science data for the publication "Data inaccuracy quantification and uncertainty propagation for<br>bibliometric indicators". The first column, <code>CITCOUNT_ALL</code>, gives the total (corrected) citation count for a publication, which is the citation count according to WoS plus the additionally manually identified citations (missed by WoS's algorithm). The second column, <code>CITCOUNT_WOS</code>, is the WoS citation count. The numeric difference between the two column values in one row is the number of additionally manually identified citations.</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
Dataset for: "Quantification of Geometric Errors Made Simple: Application to Main-Group Molecular Structures"
<p>This dataset contains geometric energy offset (GEO') values for a set of density functional theory (DFT) methods for the B2se set of molecular structures. The data was generated as part of a research project aimed at quantifying geometric errors in main-group molecular structures. The dataset is in XLSX format created with MS Excel (version 16.69), and contains multiple worksheets with GEO' values for different basis sets and DFT methods. The worksheet headings, such as "AVQZ AVTZ AVDZ VQZ VTZ VDZ" represent different basis sets of Dunning theory, and the naming convention "(A)VnZ = aug-cc-pVnZ" is being used to label the worksheets. The data is organized in columns, with the first column providing the molecular ID and the names of the DFT methods specified in the first row of each worksheet. The molecular structures corresponding to each of these IDs can be found in Figure S1 of the supplementary information of the underlying publication [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>]. The data have been generated from quantum-chemical calculations from the G16 and ORCA 5.0.0 packages, with further computational details, methodology, and data validation strategies (e.g., comparisons with higher-level quantum-chemical calculations) given in the supplementary information of the underlying publication [<em>J. Phys. Chem. A</em> 2022, 126, 7, 1300–1311] and its supporting information [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>].<br> The dataset is expected to be useful to researchers in the field of computational chemistry and materials science. All values are given in kcal/mol. The data is generated by the authors of the underlying publication and it is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The data is expected to be re-usable and the quality of the data is assured by the authors. The size of the data is 71 KB.</p>
Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
<p>Viruses show great diversity in their genome organisation. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (<em>Nicotiana tabacum</em>, <em>Nicotiana benthamiana</em>, and <em>Chenopodium quinoa</em>) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasise the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial.</p>
Validation of highly sensitive method based on UHPLC-ESI-MS/MS for the quantification of progestogens and androgens in plant material.
<p>We prepared a highly sensitive method for the quantification of progestogens and androgens in plant materials. This method is based on UHPLC-ESI-MS/MS. We show here the data used for the method validation. This includes the determination of linearity, recovery, precision, limits of detection and limits of quantification. </p> <p>The general procedure can be found in the txt or pdf file. </p> <p>The resulting data are collected in the excel file and can be found in the csv files, additonally.</p>
XRF spectra belonging to report on quantification method based on XRF analysis.
<p>Accumulated spectra. Details in supporting information </p> <p><a href="https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0283539.s003">S3 File. </a>XRF instrument report quantitative analysis.</p> <p>Report on quantification method based on XRF analysis.</p> <p><a href="https://doi.org/10.1371/journal.pone.0283539.s003">https://doi.org/10.1371/journal.pone.0283539.s003</a></p> <p>(DOCX)</p> <p>Belonging to publication</p> <p>Lagerqvist Alidoost A, Hacke M, Winther T, Sandström T (2023) A closer look at the Azzolino collection. PLOS ONE 18(4): e0283539. <a href="https://doi.org/10.1371/journal.pone.0283539">https://doi.org/10.1371/journal.pone.0283539</a></p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor.</p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p> <p>There is an additional count matrix in Anndata format created from full datasets.</p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation
<p>Supplementary material for the associated publication.</p>
ABA quantification data from 10.3389/fpls.2023.1251442
<p>Raw data from ABA quantification by HPLC MS-MS from the manuscript "Biostimulant activity of Galaxaura rugosa seaweed extracts against water deficit stress in tomato seedlings involves activation of ABA signaling" with DOI:10.3389/fpls.2023.1251442</p>
Product Reviews for Ordinal Quantification
<p>This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data.</p> <p>The data is extracted from the McAuley data set of product reviews in Amazon, where the goal is to predict the 5-star rating of each textual review. We have sampled this data according to three protocols that are designed for the evaluation of quantification methods.</p> <p>The first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ(50%), is a variant thereof, where only the smoothest 50% of all APP samples are considered. This variant is targeted at ordinal quantification, where classes are ordered and a similarity of neighboring classes can be assumed. 5-star ratings of product reviews lie on an ordinal scale and, hence, pose such an ordinal quantification task. The third protocol considers "real" distributions of labels. These distributions stem from actual products in the original data set.</p> <p>The data is represented by a RoBERTa embedding. In our experience, logistic regression classifiers work well with this representation.</p> <p>You can extract our data sets yourself, for instance, if you require a raw textual representation. The original McAuley data set is public already and we provide all of our extraction scripts.</p> <p>Extraction scripts and experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a></p> <p>Original data by McAuley: <a href="https://jmcauley.ucsd.edu/data/amazon/">https://jmcauley.ucsd.edu/data/amazon/</a></p>
Quantification of the value of selected Nature's Contributions to People provided by Low Trophic Species aquaculture
<p>This dataset was generated by the work on quantification of Nature’s Contributions to People (NCPs) provided by Low Trophic Species (LTS) aquaculture. The quantification and analysis of selected NCPs was performed based on selected indicators using data from the case studies within the AquaVitae project and literature reviews.</p>
Data for: Novel quantification of eggshell surfaces in Dromaius novaehollandiae with implications for the fossil eggshells of Oviraptorosauria (Dinosauria)
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Biological and chemical quantification of tadpole nurseries (phytotelmata)
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Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
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Intravital quantification reveals dynamic calcium concentration changes across B cell differentiation stages
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Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors
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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
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.