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720 results for “Identification data”
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Data from: Performance of DNA metabarcoding, standard barcoding and morphological approaches in the identification of insect biodiversity
<p>A repository containing the data and R code for the associated manuscript titled "Performance of DNA metabarcoding, standard barcoding and morphological approaches in the identification of insect biodiversity"</p> <p>Created by Romana K. Salis</p> <div> <h2>Content Overview</h2> <a href="https://github.com/rksalis/LNU_insectbarcoding/tree/main#content-overview"></a></div> <div> <h3>Rcode:</h3> <a href="https://github.com/rksalis/LNU_insectbarcoding/tree/main#rcode"></a></div> <p>analysis_070923.R</p> <div> <h3>Data files:</h3> <a href="https://github.com/rksalis/LNU_insectbarcoding/tree/main#data-files"></a></div> <p>literature_search_210624.csv barcoding_datasets.csv - barcoding datasets match category counts for the butterflies, bees and wasps</p> <p>barcoding_VB_thresholds.csv - barcoding datasets match category counts for the vietnamese butterfly with different similarity thresholds</p> <p>barcoding_VB_families.csv - barcoding datasets match category counts for the vietnamese butterfly families</p> <p>vietkordfinal_x.csv - intraspecific similarity and geographic distance</p> <p>vietnam_area.csv - intraspecific similarity and sampling area</p> <p>BINsvSp.csv - number of BINs and morphospecies</p> <p>Metabarcoding_Results_CommonSpecies.csv - metabarcoding results including the species only those also identified by morphology</p> <p>Metabarcoding_Results_BINSpecies.csv - metabarcoding results BIN species</p> <p>Metabarcoding_Results_BINs.csv - metabarcoding results all BINs</p>
Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Sracek O., Ettler V., Mihaljevič M., Kříbek B., Mapani B., Penížek V., Zádorová T., Vaněk A. (2024): Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia. <em>Hydrometallurgy</em> <strong>228</strong>, 106356.</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Accompanying data for the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification"
<p>Experimental Data accompanying the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ijimpeng.2024.105083" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijimpeng.2024.105083</a></p> <p>One (holed and double notched) specimen is dynamically loaded via an hydraulic tensile test machine (MTS-819, 20 kN) at 5 m/s. The specimen were cut from a 0.8 mm-thick DC04 (XES French standards) sheet in the rolling direction provided by ONERA. Reference image is captured using a high definition camera (29 Mpix, Prosilica GT from Stemmer) combined with the same objective lens than the one used for experiments. Deformed sample images are captured using the rotating mirror Ultra-high speed (HR-UHS) Cordin camera Model 580 at 68 kfps with a resolution of 3296 x 2472 pixels. The field of view is 35.8mm x 47.9mm leading to a pixel size of 14.49um. Ufreckles <a href="https://doi.org/10.5281/zenodo.1433776">10.5281/zenodo.1433775</a> is used to perform FE-based DIC using T3P1 linear triangular elements and a Tikhonov regularisation (over 3 elements). Eventually, kinematic data and load measurement are used to identify stress fields via Data-Driven stress Identification (DDI) method.</p> <p>Are provided:</p> <ul> <li>raw images, camera distortion modes and parameters, load net force and timeline</li> <li>kinematic fields obtained from Digital Image Correlation</li> <li>Stress fields identified using Data-Driven stress Identification</li> </ul> <p>Matlab Codes to produce results (working with Ufreckles)</p> <ul> <li>MultiSensor_DIC_script.m: in /Codes/ is the main script to run DIC</li> <li>Shape functions: in /Codes/shape_functions/ containing Zernike polynomial shape functions and deconvolution algorithm to get effective displacement from total displacement knowing camera distortions</li> </ul> <p> </p>
Identification data for discrimination between honey bee (Apis mellifera) lineages
<p><span>The data in dw.xml file can be used for discriminate between 4 lineages of honey bee </span><em>Apis mellifera</em><span>: A, C, M, O. </span></p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Nawrocka, A., Kandemir, İ., Fuchs, S., & Tofilski, A. (2018). Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie, 49(2), 172-184. <a href="https://doi.org/10.1007/s13592-017-0538-y" rel="noopener">https://doi.org/10.1007/s13592-017-0538-y</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> software or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a> https://github.com/DrawWing/IdentiFlyR. </p> <p><span> </span></p>
Identification data for discrimination between regions in lineage C of honey bee (Apis mellifera)
<p>The data in dw.xml file can be used for discriminate between 5 regions in lineage C of honey bee <em>Apis mellifera</em>: Greece, Croatia and Slovenia, Italy, Romania and Moldova and European part of Turkey. </p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Oleksa, A., Căuia, E., Siceanu, A., Puškadija, Z., Kovačić, M., Pinto, M. A., Rodrigues, P. J., Hatjina, F., Charistos, L., Bouga, M., Prešern, J., Kandemir, İ., Rašić, S., Kusza, S., Tofilski, A. (2023). Honey bee (<em>Apis mellifera</em>) wing images: a tool for identification and conservation. GigaScience, 12, giad019. <a href="https://doi.org/10.1093/gigascience/giad019" target="_blank" rel="noopener">https://doi.org/10.1093/gigascience/giad019</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a>. </p>
Training data for "Identification of allelic variants in SARS-CoV-2 from deep sequencing reads"
<p>Effectively monitoring global infectious disease crises, such as the COVID-19 pandemic, requires capacity to generate and analyze large volumes of sequencing data in near real time. These data have proven essential for monitoring the emergence and spread of new variants, and for understanding the evolutionary dynamics of the virus.</p> <p>Two sequencing platforms in combination with several established library preparation strategies are predominantly used to generate SARS-CoV-2 sequence data. However, data alone do not equal knowledge: they need to be analyzed. The Galaxy community developed analysis workflows to support the <strong>identification of allelic variants (AVs) in SARS-CoV-2 from deep sequencing reads</strong>.</p> <p>These workflows allow one to identify AVs and lineages in SARS-CoV-2 genomes with variant allele frequencies ranging from 5% to 100% (i.e., they detect variants with intermediate frequencies as well.</p> <p>In this tutorial we will see how to run these workflows for the different types of input data:</p> <ul> <li>Single end data derived from Illumina-based RNAseq experiments</li> <li>Paired end data derived from Illumina-based RNAseq experiments</li> <li>Paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols</li> <li>ONT fastq files generated with Oxford nanopore (ONT)-based Ampliconic (ARTIC) protocols</li> </ul> <p>To illustrate the tutorial, we took some example datasets (paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols) from COG-UK, the COVID-19 Genomics UK Consortium.</p>
Chemistry data identification report
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Data Set for Operando Identification of a Side-On Nickel Superoxide Intermediate and the Mechanism of Oxygen Evolution on Nickel Oxyhydroxide
<p>Dataset of the paper entitled "Operando Identification of a Side-On Nickel Superoxide Intermediate and the Mechanism of Oxygen Evolution on Nickel Oxyhydroxide"</p>
Data for: Identification of integrons and gene cassette-associated recombination sites in bacteriophage genomes
<p>Bacteriophages are versatile mobile genetic elements that play key roles in driving the evolution of their bacterial hosts through horizontal gene transfer. Phages co-evolve with their bacterial hosts and have plastic genomes with extensive mosaicism. In this study, we present bioinformatic and experimental evidence that temperate and virulent (lytic) phages carry integrons, including integron-integrase genes, attC/attI recombination sites and gene cassettes. Integrons are normally found in Bacteria, where they capture, express and re-arrange mobile gene cassettes via integron-integrase activity. We demonstrate experimentally that a panel of attC sites carried in virulent phage can be recognized by the bacterial class 1 integron-integrase (IntI1) and then integrated into the paradigmatic attI1 recombination site using an attC x attI recombination assay. With an increasing number of phage genomes projected to become available, more phage-associated integrons and their components will likely be identified in the future. The discovery of integron components in bacteriophages establishes a new route for lateral transfer of these elements and their cargo genes between bacterial host cells.</p>
Data for: On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI
<p>A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological disease. Performing {\em in vivo} studies on the human brain is complicated and can be highly risky for patients. In the present study, we show how a combination of Magnetic Resonance Imaging (MRI) and Computational Fluid Dynamics (CFD) can provide a non-invasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the Circle of Willis. The computational domain and boundary conditions are based on 4D flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to hemoglobin) models. We show that neglecting hemoglobin transport results in a significant underestimation of the arterial wall-mass transfer of oxygen. We identified the hypoxic regions along the arterial walls by introducing the critical thresholds that are obtained by comparison of the estimated range of Damk\"{o}hler number ($Da\subset\langle9;57\rangle$) with the local Sherwood number. Finally, we recommend additional validations of the combined MRI/CFD approach proposed here for larger groups of subject- or patient-specific brain vasculature systems.</p>
DATA FOR NON-INVASIVE (PHOTO) INDIVIDUAL FISH IDENTIFICATION OF MULTIPLE SPECIES
<p>This paper describes data from five studies focused on the individual fish identification of the same species. The lateral images of five fish species are present in the dataset. The dataset's primary purpose is to provide a data to develop a non-invasive and remote method of individual fish identification using fish skin patterns, which can serve as a substitute for the common invasive fish tagging. The lateral images of the whole fish body on the homogenous background for Sumatra barb, Atlantic salmon, Sea bass, Common carp and Rainbow trout are available with automatically extracted parts of the fish with skin patterns. A different number of individuals (Sumatra barb – 43, Atlantic salmon – 330, Sea bass – 300, Common carp – 32, Rainbow trout - 1849) were photographed by the digital camera Nikon D60 under controlled conditions. The photographs of only one side of the fish with several (from 3 to 20) repetitions were taken. Common carp, Rainbow trout and Sea bass were photographed out of the water. Atlantic salmon was photographed underwater, out of the water, and the eye of the fish was photographed by the microscope camera. Sumatra barb was photographed under the water only. For all species, except Rainbow trout, the data collection was repeated after a different period (Sumatra barb – four months, Atlantic salmon – six months, Sea bass – one month, Common carp – four months) to collect the data for a study of skin patter changes (aging). The development of the method for photo-based individual fish identification was performed on all datasets. The identification accuracy for all species for all periods was 100% using the nearest neighbour classification. Different methods for skin pattern parametrization were used. </p> <p>The dataset can be used to develop remote and non-invasive individual fish identification methods. The studies focused on the discrimination power of the skin pattern can benefit from it. The changes of skin patterns due to fish aging can be explored from the dataset.</p>
Data for: Species identification and population genetics of the Antarctic fish genera Lepidonotothen and Nototheniops (Perciformes, Notothenioidei)
<p>Accurate species identification is essential to assess biodiversity and species richness in ecosystems threatened by rapid and recent environmental changes, such as warming in most Antarctic waters. The <em>Lepidonotothen</em> species complex comprises demersal notothenioid fishes which inhabit the shelf areas of the Antarctic Peninsula, the Scotia Arc and sub-Antarctic islands with a circum-Antarctic distribution. Species determination in this group has often been problematic. In particular, whether <em>Lepidonotothen squamifrons</em> and <em>Lepidonotothen kempi </em>are valid as separate species has been questioned. In this study, we analysed the genetic variation among four nominal southern polar species within this complex (<em>L. kempi</em>, <em>L. squamifrons</em>, <em>Nototheniops larseni</em>, <em>Nototheniops nudifrons</em>) by means of three different markers (ND2 and tRNA mitochondrial genes and a panel of 16 nuclear microsatellites). We tested whether individuals morphologically assigned to <em>L. kempi</em> showed genetic separation from <em>L. squamifrons</em>. Our analyses indicated a lack of differentiation between <em>L. kempi</em> and <em>L. squamifrons</em>. However, a genetically distinct population was found for <em>L. squamifrons</em> at the Shag Rocks islands near South Georgia. Antarctic and sub-Antarctic islands are known to be home to many cryptic species and further studies will elucidate if the genetically differentiated population we found potentially originated from this context and can be considered as an incipient species. Our analysis contributes to further characterize the species composition of the most abundant fish suborder in the Southern Ocean, which is amongst the regions most threatened by climate change.</p>
Data for the publication of the SSI Weakness Identification Framework (SWIF)
<p>This is a supplementary data for an article submitted to a journal. There are several files, including:</p> <ul> <li><em>corpus.xml</em>: This is a dataset file for the weakness entry corpus retrieved from the CWE database.</li> <li><em>queries.xml</em>: This is an input file listing a list of SSI functional requirements used in the experiments.</li> <li><em>cdkg.xml</em>: This is the pre-defined cross-domain transfer knowledge graph (CTKG).</li> <li><em>SSI-Specific Weakness Justification.xlsx</em> - This is the justification for the determination of valid SSI-specific weakness, which is the full results for the evaluation of the validity of the identified SSI-specific weaknesses.</li> <li><em>Truth - Summary.xlsx</em>: This file is a spreadsheet that summarize the collected ground truth from the three participants.</li> <li><em>truth.csv:</em> This is a dataset file of the ground truth observed by an experienced system analyst.</li> </ul>
Data for: Targeted anatomical and functional identification of antinociceptive and pronociceptive serotonergic neurons that project to the spinal dorsal horn
<p>Spinally-projecting serotonergic neurons play a key role in controlling pain sensitivity and can either increase or decrease nociception depending on physiological context. It is currently unknown how serotonergic neurons mediate these opposing effects. Utilizing virus-based strategies, we identified two anatomically separated populations of serotonergic hindbrain neurons located in the lateral paragigantocellularis (LPGi) and the medial hindbrain, which respectively innervate the superficial and deep spinal dorsal horn and have contrasting effects on pain perception. Our tracing experiments revealed an unexpected high selectivity of serotonergic neurons of the LPGi for transduction with spinally injected AAV2retro vectors while medial hindbrain serotonergic neurons were largely resistant to AAV2retro transduction. Taking advantage of this selectivity, we employed intersectional chemogenetic approaches to demonstrate that activation of the LPGi serotonergic projections decreases thermal sensitivity, whereas activation of medial serotonergic neurons increases sensitivity to mechanical von Frey stimulation. Together these results suggest that there are functionally distinct classes of serotonergic hindbrain neurons that differ in their anatomical location in the hindbrain, their postsynaptic targets in the spinal cord, and impact on nociceptive sensitivity. At least the LPGi neurons give rise to rather global and bilateral projections throughout the rostrocaudal extent of the spinal cord suggesting that they contribute to widespread systemic pain control.</p>
Non - invasive identification of individuals of Atlantic salmon (aquarium data)
<p>Non - invasive identification of individuals of Atlantic salmon (aquarium data). There are four datasets. First dataset is a 330 fish individuals of atlantic Salmon, they are separated ito 6 part. Then, other Three datsets are tagged 30 fish out of those 330 fish. There are original data and processed data. data were collected in aquarium with water.</p>
Non-invasive identification of individuals of Atlantic salmon (tent data)
<p>Non-invasive identification of individuals of Atlantic salmon (tent data). 328 fish were sampled. 30 fish were tagged for next data collection. totally 4 data collection were done. The fish were photographed in tent out of water. The fish were photographed on the green background for simple fish localization.</p> <p>Each image was processed separately. First the green background was detected based on the defined value of the green in HSV color space. The backgound was used as the area of fish appearance. In the secodn step the fish was segmented on the background using the specific color of the fish in HSV color space. The fish was detected as the largest object. Some morphological operations were used to correct fish shape. The object was rotated to the horizontal position. The fish tail was removed by the algorithm because it was detected on some images and not detected on the rest. Therefore the algorithm detects the begining of back tail as the most narrow place at back part of the fish. The lenght of the fish and the height of the fish was used for the localization of the ROI. The ROI is shown on the image bellow. The ROI was selected to cover the fish body with most visible patter.</p> <p>Uploaded files - original and processd files for all data collections.</p>
Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data
<p>Flnc is software that can accurately identify full-length long noncoding RNAs (lncRNAs) from human RNA-seq data. lncRNAs are linear transcripts of more than 200 nucleotides that do not encode proteins. The most common approach for identifying lncRNAs from RNA-seq data which examines the coding abilities of assembled transcripts will result in a very high false-positive rate (30%-75%) of lncRNA identification. The falsely discovered lncRNAs lack transcriptional start sites and most of them are RNA fragments or result from transcriptional noise. Unlike the false-positive lncRNAs, true lncRNAs are full-length lncRNA transcripts that include transcriptional start sites (TSSs). To exclude these false lncRNAs, H3K4me3 chromatin immunoprecipitation sequencing (ChIP-seq) data had been used to examine transcriptional start sites of putative lncRNAs, which are transcripts without coding abilities. However, because of cost, time, and the limited availability of sample materials for generating H3K4me3 ChIP-seq data, most samples (especially clinical biospecimens) may have available RNA-seq data but lack matched H3K4me3 ChIP-seq data. This Flnc method solves the problem of lacking transcriptional initiation profiles when identifying lncRNAs.</p> <p>Flnc integrates seven machine-learning algorithms built with four genomic features. Flnc achieves state-of-the-art prediction power with a AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the common approach to over 85% on five independent datasets without requiring matched H3K4me3 ChIP-seq data. In addition to the stranded polyA-selected RNA-seq data, Flnc can also be applied to identify lncRNAs from stranded RNA-seq data of ribosomal RNA depleted samples or unstranded RNA-seq data of polyA-selected samples.</p>
Spectroscopic identification of water emission from a main-belt comet: Supporting data and code
<p>Reduced data, source files, and code used in the analysis of JWST spectroscopy of comet 238P/Read, by Kelley et al. (2023, Nature 619, 720. 10.1038/s41586-023-06152-y).</p>
Supporting data and code for: Phylogenetic identification of influenza virus candidates for seasonal vaccines
<p>The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75–0.89 (Area under the curve AUC 0.83–0.91) over 2016–2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent three years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.</p>
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.