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8,038 results for “validation.”
Data for validation STEC workflow
<p>Additional data<br> ========</p> <p>This archive contains additional data for the manuscript "Validation of a bioinformatics workflow for characterization of Shiga Toxin-Producing *Escherichia coli*, applied to a high-quality reference dataset, demonstrates high performance for using WGS for routine pathogen typing"</p> <p># Notes</p> <p>Samples were analyzed with anonymized file names. They can be linked back to the original sample name as indicated in the Excel sheet.</p> <p># Content</p> <p>## Validation results ('all_results.xlsx')</p> <p>This spreadsheet contains detailed results for the validation. It contains all workflow output, corresponding metadata and classification (TP, FN, TN or FP).</p> <p>## KMA output ('kma.tar')</p> <p>This folder contains the output from KMA for the various assays. They were executed in isolation for each of the assays (instead of with the workflow). </p> <p><br> ## Example reports ('report_EH1236_*.zip')</p> <p>Example output reports of the workflow for each of the three detection methods on the same sample (EH1236).</p> <p><br> ## Virulence gene custom database ('virulence_genes_db.fasta')</p> <p>This folder contains a FASTA file with the sequences that were used to evaluate the performance of the virulence gene detection.</p> <p>## Virulence gene detection ('virulence_gene_detection.tar')</p> <p>This folder contains the output of the virulence gene detection for the three detection methods.<br> This was executed separately because the custom virulence gene database is not included in the bioinformatics workflow.</p> <p>## Workflow reports ('workflow_reports_updated.tar')</p> <p>This folder contains the output of the workflow for all of the validation samples with the three detection methods. <br> All runs were executed in August 2020, with database updated to the latest available version. <br> A single archive is created for each sample, containing the output for the three bioinformatics approaches (BLAST+, KMA, SRST2).<br> BAM files were omitted from the archives due to their large sizes.</p> <p>## Workflow reports - validation ('workflow_reports_validation.tar')</p> <p>This folder contains the output reports used for the validation (with older database version, etc).<br> This does not include KMA results because they were validated per-assay (see KMA archive).</p> <p># Contact</p> <p>For further questions you can contact Bert Bogaerts (bert.bogaerts@sciensano.be)<br> </p>
KALIv2: calibration and validation experiment results
<p>The presented .zip archive consists of calibration and validation results we obtained while investigating the effect of calibration data length on the performance of different hydrological models.</p> <p>For more information, please visit our GitHub repository at <a href="https://github.com/hydrogo/KALIv2">https://github.com/hydrogo/KALIv2</a>.</p>
Canfield Joint Validation Closed Loop Data
<p>Validation trajectories for closed-loop tests of a hardware prototype Canfield joint. Each dataset has a video file (.wmv) showing the test, and a corresponding .csv file with XY coordinates. These datasets will be cited in a forthcoming paper on the validation of the Canfield joint. </p> <p> </p> <p>NOTE: The "quadrant trajectory track 24mm at 0.5mm-s" files are mislabeled. The rate for those is 1 mm/s, not .5 mm/s. </p>
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
<p>The .xlsx dataset includes all patients used for training, internal-external and external validation: these can be distinguished by looking at the ID (first column) in the dataset: those in format Axxxx-<Date> are the data used for the training, those in the format 20xx are the data used for the internal-external validation, while the remaining data were used for external validation.</p> <p>As regards the features: for the Target feature the value 1 stands for "Positive to COVID-19" while the value 0 stands for "Negative to COVID-19"; while for the Sex feature the value 1 stands for "Male" while the value 0 stands for "Female".</p> <p>The full article is available at: https://www.degruyter.com/view/journals/cclm/ahead-of-print/article-10.1515-cclm-2020-1294/article-10.1515-cclm-2020-1294.xml.</p> <p>A pre-print version of the article is also available on MedrXiv: https://www.medrxiv.org/content/10.1101/2020.10.02.20205070v1</p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong> The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. </p> <p><strong>Methods</strong> Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.</p> <p><strong>Results</strong> We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 15 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC 16 from 0.75 to 0.78; and specificity from 0.92 to 0.96. </p> <p><strong>Conclusions</strong> ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.</p>
Replication Package for "On Conclusion Validity of Empirical SE-studies with Sentiment Analysis Tools: Is Platform-Specific Retraining Enough?"
<p>This is the replication package for the paper "On Conclusion Validity of Empirical SE-studies with Sentiment Analysis Tools: Is Platform-Specific Retraining Enough?"</p>
Figure 9 from: Sheffield CS, Oram R, Heron JM (2020) Bombus (Pyrobombus) johanseni Sladen, 1919, a valid North American bumble bee species, with a new synonymy and comparisons to other "red-banded" bumble bee species in North America (Hymenoptera, Apidae, Bombini). ZooKeys 984: 59-81. https://doi.org/10.3897/zookeys.984.55816
Figure 9 Sternum 7 of male ABombus johanseni, and BB. lapponicus sylvicola.
Figure 8 from: Sheffield CS, Oram R, Heron JM (2020) Bombus (Pyrobombus) johanseni Sladen, 1919, a valid North American bumble bee species, with a new synonymy and comparisons to other "red-banded" bumble bee species in North America (Hymenoptera, Apidae, Bombini). ZooKeys 984: 59-81. https://doi.org/10.3897/zookeys.984.55816
Figure 8 Penis-valve head of male ABombus johanseni, and BB. lapponicus sylvicola.
Assessing Lumbar Paraspinal Muscle CSA and Fat Composition with T1 versus T2-Weighted MRI: Reliability and Concurrent Validity.
<p>Raw measurement data to support manuscript publication</p>
Figure 6 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 6 Linearity of IPA solutions.
Figure 5 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 5 Linearity of acetone solutions.
Figure 4 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 4 Chromatogram of specificity (spiked solution of quinabut API, acetone and 2-propanol).
Figure 3 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 3 Typical chromatogram of acetone and IPA standard solution.
Figure 2 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 2 Typical chromatogram of blank (water).
Figure 1 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 1 Chemical structure of quinabut.
Figure 7 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 7 Chromatogram of quinabut API sample.
Figure 8 from: Piponski M, Balkanov T, Logoyda L (2021) Development and validation of a fast and simple HPLC method for the simultaneous determination of bisoprolol and enalapril in dosage form. Pharmacia 68(1): 69-77. https://doi.org/10.3897/pharmacia.68.e50919
Figure 8 The calibration graph of bisoprolol fumarate.
Figure 9 from: Piponski M, Balkanov T, Logoyda L (2021) Development and validation of a fast and simple HPLC method for the simultaneous determination of bisoprolol and enalapril in dosage form. Pharmacia 68(1): 69-77. https://doi.org/10.3897/pharmacia.68.e50919
Figure 9 The calibration graph of enalaril maleate.
Figure 2 from: Piponski M, Balkanov T, Logoyda L (2021) Development and validation of a fast and simple HPLC method for the simultaneous determination of bisoprolol and enalapril in dosage form. Pharmacia 68(1): 69-77. https://doi.org/10.3897/pharmacia.68.e50919
Figure 2 Chemical structure of enalapril.
Figure 1 from: Piponski M, Balkanov T, Logoyda L (2021) Development and validation of a fast and simple HPLC method for the simultaneous determination of bisoprolol and enalapril in dosage form. Pharmacia 68(1): 69-77. https://doi.org/10.3897/pharmacia.68.e50919
Figure 1 Chemical structure of bisoprolol fumarate.
Data from: Sorting specimen-rich invertebrate samples with cost-effective NGS barcodes: validating a reverse workflow for specimen processing
Biologists frequently sort specimen-rich samples to species. This process is daunting when based on morphology, and disadvantageous if performed using molecular methods that destroy vouchers (e.g., metabarcoding). An alternative is barcoding every specimen in a bulk sample and then presorting the specimens using DNA barcodes, thus mitigating downstream morphological work on presorted units. Such a "reverse workflow" is too expensive using Sanger sequencing, but we here demonstrate that is feasible with an NGS barcoding pipeline that allows for cost-effective high throughput generation of short specimen-specific barcodes (313 bp of COI; lab cost <$0.50 per specimen) through Next Generation Sequencing of tagged amplicons. We applied our approach to a large sample of tropical ants, obtaining barcodes for 3290 of 4032 specimens (82%). NGS barcodes and their corresponding specimens were then sorted into molecular operational taxonomic units (mOTUs) based on objective clustering and Automated Barcode Gap Discovery (ABGD). High diversity of 88-90 mOTUs (4% clustering) was found and morphologically validated based on preserved vouchers. The mOTUs were overwhelmingly in agreement with morphospecies (match ratio 0.95 at 4% clustering). Because of lack of coverage in existing barcode databases, only 18 could be accurately identified to named species, but our study yielded new barcodes for 48 species, including 28 that are potentially new to science. With its low cost and technical simplicity, the NGS barcoding pipeline can be implemented by a large range of laboratories. It accelerates invertebrate species discovery, facilitates downstream taxonomic work, helps with building comprehensive barcode databases, and yields precise abundance information.
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.