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720 results for “Identification data”
Data for: Morphological species delimitation in the Western Pond Turtle (Actinemys): Can machine learning methods aid in cryptic species identification?
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Data for [Identification of paleomagnetic remanence carriers in ca. 3.47 Ga dacite from the Duffer Formation, the Pilbara Craton]
<p>Data used in the manuscript [Identification of paleomagnetic remanence carriers in ca. 3.47 Ga dacite from the Duffer Formation, the Pilbara Craton] by Usui et al., Physics of the Earth and Planetary Interiors.</p>
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089</em></p> <p> </p>
IoT device identification - Multi user data (No fading)
<p>Artificial multi user observations without added fading generated as described in the associated paper Section III.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
Dataset used for "Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data"
<p>Each simulated dataset was generated using the AbSim R package (version 0.2.6) in a B cell single-lineage fashion. Each B cell clone simulation begins with a random selection from sets of IGHV, IGHD, and IGHJ germline sequences to produce a unique V(D)J recombination event. Then, clones are made by introducing mutations using a local nucleotide context-dependent model (S5F model) along a phylogenetic tree in which branching events occur stochastically. </p>
Data from: Modeling the mito-nuclear compatibility and its role in species identification
<p>Mitochondrial genetic material (mtDNA) is widely used for phylogenetic reconstruction and as a barcode for species identification. The utility of mtDNA in these contexts derives from its particular molecular properties, including its high evolutionary rate, uniparental inheritance, and small size. But mtDNA may also play a fundamental role in speciation -- as suggested by recent observations of coevolution with the nuclear DNA, along with the fact that respiration depends on coordination of genes from both sources. Here we study how mito-nuclear interactions affect the accuracy of species identification by mtDNA, as well as the speciation process itself. We simulate the evolution of a population of individuals who carry a recombining nuclear genome and a mitochondrial genome inherited maternally. We compare a null model fitness landscape that lacks any mito-nuclear interaction against a scenario in which interactions influence fitness. Fitness is assigned to individuals according to their mito-nuclear compatibility, which drives the coevolution of the nuclear and mitochondrial genomes. Depending on the model parameters, the population breaks into distinct species and the model output then allows us to analyze the accuracy of mtDNA barcode for species identification. Remarkably, we find that species identification by mtDNA is equally accurate in the presence or absence of mito-nuclear coupling and that the success of the DNA barcode derives mainly from population geographical isolation during speciation. Nevertheless, selection imposed by mito-nuclear compatibility influences the diversification process and leaves signatures in the genetic content and spatial distribution of the populations, in three ways. First, speciation is delayed and the resulting phylogenetic trees are more balanced. Second, clades in the resulting phylogenetic tree correlate more strongly with the spatial distribution of species and clusters of more similar mtDNA's. Third, there is a substantial increase in the intraspecies mtDNA similarity, decreasing the number of alleles substitutions per locus and promoting the conservation of genetic information. We compare the evolutionary patterns observed in our model to empirical data from copepods (<em>T. californicus</em>). We find good qualitative agreement in the geographic patterns and the topology of the phylogenetic tree, provided the model includes selection based on mito-nuclear interactions. These results highlight the role of mito-nuclear compatibility in the speciation process and its reconstruction from genetic data.</p>
Data from: Characterization of rhizome transcriptome and identification of a rhizomatous ER body in the clonal plant Cardamine leucantha
<p>The rhizome is a plant organ that develops from a shoot apical meristem but penetrates into belowground environments. To characterize the gene expression profile of rhizomes, we compared the rhizome transcriptome with those of the leaves, shoots and roots of a rhizomatous Brassicaceae plant, <i>Cardamine leucantha.</i> Overall, rhizome transcriptomes were characterized by the absence of genes that show rhizome-specific expression and expression profiles intermediate between those of shoots and roots. Our results suggest that both endogenous developmental factors and external environmental factors are important for controlling the rhizome transcriptome. Genes that showed relatively high expression in the rhizome compared to shoots and roots included those related to belowground defense, control of reactive oxygen species, and cell elongation under dark conditions. A comparison of transcriptomes further allowed us to identify the presence of an ER body, a defense-related belowground organelle, in epidermal cells of the <i>C. leucantha </i>rhizome, which is the first report of ER bodies in rhizome tissue.</p>
Remote near infrared identification of pathogens with multiplexed nanosensors - source data file for Nißler et al. 2020 (Nat. Commun.)
<p>source data file for Nißler et al. 2020 (Nat. Commun.)</p> <p>entitled: </p> <p>Remote near infrared identification of pathogens with multiplexed nanosensors</p>
Data for article: Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method
<p>Data to preproduce results presented in the article:<br> Title: <strong>Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method</strong><br> Authors: Markus Winklberger *, Christoph Kralovec, Christoph Humer, Peter Heftberger, Martin Schagerl<br> *Correspondence: markus.winklberger@jku.at<br> Journal: Sensors<br> Section: Physical Sensors<br> Special Issue: Piezoelectric Transducers Based Structural Health Monitoring</p> <p>Data saved in csv-files with ";" delimiter.</p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non-decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Data for the article "Identification of Néel vector orientation in antiferromagnetic domains switched by currents in NiO/Pt thin films"
<p>Data for the article "Identification of Néel vector orientation in antiferromagnetic domains switched by currents in NiO/Pt thin films" (<a href="https://arxiv.org/abs/2008.08507">[2008.08507] Identification of Néel vector orientation in antiferromagnetic domains switched by currents in NiO/Pt thin films (arxiv.org)</a>)</p>
Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
Chinook Salmon is an economically and ecologically important species, and populations from the west coast of North America are a major component of fisheries in the North Pacific Ocean. The anadromous life history strategy of this species generates populations (or stocks) that typically are differentiated from neighboring populations. In many cases, it is desirable to discern the stock of origin of an individual fish or the stock composition of a mixed sample to monitor the stock-specific effects of anthropogenic impacts and alter management strategies accordingly. Genetic stock identification (GSI) provides such discrimination, and we describe here a novel GSI baseline composed of genotypes from more than 8000 individual fish from 69 distinct populations at 96 single nucleotide polymorphism (SNP) loci. The populations included in this baseline represent the likely sources for more than 99% of the salmon encountered in ocean fisheries of California and Oregon. This new genetic baseline permits GSI with the use of rapid and cost-effective SNP genotyping, and power analyses indicate that it provides very accurate identification of important stocks of Chinook Salmon. In an ocean fishery sample, GSI assignments of more than 1000 fish, with our baseline, were highly concordant (98.95%) at the reporting unit level with information from the physical tags recovered from the same fish. This SNP baseline represents an important advance in the technologies available to managers and researchers of this species.
Underlying data for Slimani et al. Identification of dominant hydrogeochemical processes for groundwaters in the Algerian Sahara supported by inverse modeling of chemical and isotopic data
<p>The data hereafter underlie the paper by Slimani et al. doi:10.5194/hess-20-1-2016,</p> <p>appeared to Hydrol. Earth Syst. Sci., 20, 1-23, 2016.</p> <p> </p> <p> </p> <p>1. File Tableaux_data.xls</p> <p> </p> <p>This is an Excel sheet file. It contains:</p> <p>- raw analytical data, mostly in mg/L;</p> <p>- data converted in mmol/L;</p> <p>- data corrected from the defect of cations - anions balance; the correction is made proportionally.</p> <p>- the previous data completed with logarithms of activities, computed by Phreeqc, </p> <p>for calcium, sulfate, carbonate and water; those data are used to plot equilibrium diagrams for calcite and gypsum (figure 6);</p> <p>- for Phreatic aquifer only, saturation indexes for halite, anhydrite, calcite, dolomite and gypsum, along with distance from south to north, used in figure 7.</p> <p>2. Directory Phreeqc_res</p> <p>Contains the input file with all samples from CI, CT and Phr in a single file, and the selected output file.</p> <p>All calculations were made with version phreeqc-3.1.2 and database sit.dat.</p> <p>3. Directory Inverse models</p> <p>This directory contains inverse models for computing transformations:</p> <p>- from CI (average) to CT (average):</p> <p>- from CT (average) to Phr (pole I, average);</p> <p>- from pure water to Phr (pole II, sample P036);</p> <p>- for mixing Phr pole I and II, and try to explain a sample typical of medium mixing ratio, sample P068.</p>
Data Supporting: Identification of Nonlinearity Sources in a Flexible Wing
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Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting
<p><span>Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1,246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.</span></p>
Classification and identification of pinecones mulching on blueberry cultivation based on crop leaf characteristics and hyperspectral data
<p><span>Supplementary Figure S1: Spectra preprocessing before and after.; Table S1: The evaluation results of the classification model of leaf growth and physiology.; Table S2: The evaluation results of the classification model of VIs.; Table S3: The evaluation results of the classification model of VNIR.; Table S4: The evaluation results of the classification model of SWIR.</span></p>
Thesis data: Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels
<p>Starting dataset used within the thesis; Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels.</p> <p>Results of manual annotation of and extract of nonPatchTaggedCommitLinks within the NVD CVE dataset.</p>
Olink data for 'Identification of soluble biomarkers that associate with distinct manifestations of long COVID'
<p>Olink analysis output from plasma samples from healthy donors and donors with post-acute sequelae of SARS-CoV-2 infection. Data sourced from two cohorts - UK and Sweden based. UK patients with long covid start with CA. UK healthy controls start with CO. Swedish donors only include patients with long covid and start with KLIMP. Assays from the following panels were tested: Explore 384 Cardiometabolic, Explore 384 Cardiometabolic II, Explore 384 Inflammation, Explore 384 Inflammation II, Explore 384 Neurology, Explore 384 Neurology II, Explore 384 Oncology, Explore 384 Oncology II. </p> <p>Data relates to the manuscript titled 'Identification of soluble biomarkers that associate with distinct manifestations of long COVID'.</p>
Automated identification of small molecules in cryo-electron microscopy data with density- and energy-guided evaluation
<p>Data and turorials for ligand identification with EMERALD-ID. Each Zip file contains ligand models and data used to generate their respective figures in the EMERALD-ID manuscript. For specific details, consult the README file in each directory.</p>
Data from: Extraction and identification of a wide range of microplastic polymers in soil and compost
<p>Microplastic (MP) pollution is globally widespread, however their presence in soil systems is poorly understood due to complexity of soil and lack of standardised extraction methods. Datasets provided contain data from optimisation (recoveries) of MPs extraction protocol from soil and compost based on olive oil and density separation using zinc chloride, in case of low-density polyethylene and polyethylene terephthalate. Density separation was further used to extract five microplastic polymers (PET, PS, PE, PP and PVC), added to soil and compost at different concentrations, which is also included in the dataset. Additionally, identification data of extracted MPs from spiked soil and compost samples.</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)
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.