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8,038 results for “validation.”
ade_validation
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A validated trigonelline-based method for the standardization and quality control of Trigonella foenum-graecum L.
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A validated trigonelline-based method for the standardization and quality control of Trigonella foenum-graecum L.
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Table 1 in Naming Species, A New Paradigm For Crisis Management In Taxonomy: Rapid Journal Validation Of Scientific Names Enhanced With More Complete Descriptions On The Internet
<p><b>Table 1.</b> Current status of the Carabidae of Costa Rica.</p><table><tbody><tr><th>Source</th><th>Tribes Genera</th><th>Species</th></tr></tbody><tbody><tr><th>Literature</th><td>36</td><td>70</td><td>253</td></tr><tr><th>Additional numbers from</th></tr><tr><th>USNM, UASM, and INBio collections</th><td>0</td><td>58</td><td>?</td></tr><tr><th>Additional predicted</th></tr><tr><th>(known to occur elsewhere in Central America)</th><td>0</td><td>31</td><td>?</td></tr><tr><th>Predicted</th><td>36</td><td>133</td><td>1,1001</td></tr></tbody></table>
Datasets employed for the validation of CarsiDock-Cov
<p>Datasets employed for the validation of CarsiDock-Cov.</p>
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Data from: Validation of the GARD™skin assay for assessment of chemical skin sensitizers – ring trial results of predictive performance and reproducibility
Proactive identification of chemicals with skin sensitizing properties is a key toxicological endpoint within chemical safety assessment. In order to meet recent legislations and increase animal welfare, considerable efforts have been made to develop non-animal approaches to replace current animal testing. Genomic Allergen Rapid Detection (GARD™) is a state-of-the-art technology platform, the most advanced application of which is the assay for assessment of skin sensitizing chemicals, GARD™skin. The methodology is based on a dendritic cell (DC)-like cell line, thus mimicking the mechanistic events leading to initiation and modulation of downstream immunological responses. Induced transcriptional changes are measured following exposure to test chemicals, providing a detailed evaluation of cell activation. These changes are associated with the immunological decision-making role of DCs in vivo and include among other phenotypic modifications, up-regulation of co-stimulatory molecules, induction of cellular and oxidative stress pathways and xenobiotic responses. Here, results from an inter-laboratory ring trial of GARD™skin, conducted in compliance with OECD guidance documents and comprising a blinded chemical test set of 28 chemicals, are summarized. The assay was found to be transferable to naïve laboratories, with an inter-laboratory reproducibility of 92.0%. The within-laboratory reproducibility ranged between 82.1-88.9%, while the cumulated predictive accuracy across the three laboratories was 93.8%. Based on these and previously published data, it is concluded that GARD™skin is a robust and reliable method for the identification of skin sensitizing chemicals and suitable for use as a stand-alone assay. These data form the basis for the regulatory validation of GARD™skin.
Data from: A probabilistic metric for the validation of computational models
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role.
Data from: Validation of diffusion tensor imaging measures of nigrostriatal neurons in macaques
Objective: Interpretation of diffusion MRI in the living brain requires validation against gold standard histological measures. We compared diffusion values of the nigrostriatal tract to PET and histological results in non-human primates (NHPs) with varying degrees of unilateral nigrostriatal injury induced by MPTP, a toxin selective for dopaminergic neurons. Methods: Sixteen NHPs had MRI and PET scans of three different presynaptic radioligands and blinded video-based motor ratings before and after unilateral carotid artery infusion of variable doses of MPTP. Diffusion measures of connections between midbrain and striatum were calculated. Then animals were euthanized to quantify striatal dopamine concentration, stereologic measures of striatal tyrosine hydroxylase (TH) immunostained fiber density and unbiased stereologic counts of TH stained nigral cells. Results: Diffusion measures correlated with MPTP dose, nigral TH-positive cell bodies and striatal TH-positive fiber density but did not correlate with in vitro nigrostriatal terminal field measures or in vivo PET measures of striatal uptake of presynaptic markers. Once nigral TH cell count loss exceeded 50% the stereologic terminal field measures reached a near zero floor effect but the diffusion measures continued to correlate with nigral cell counts. Conclusion: Diffusion measures in the nigrostriatal tract correlate with nigral dopamine neurons and striatal fiber density, but have the same relationship to terminal field measures as a previous report of striatal PET measures of presynaptic neurons. These diffusion measures have the potential to act as non-invasive index of the severity of nigrostriatal injury. Diffusion imaging of the nigrostriatal tract could potentially have diagnostic value in humans with Parkinson disease or related disorders.
FIGURE 1 in Taxonomic validity and phylogenetic relationships of a newly-described toothcarp, Aphanius mesopotamicus Coad, 2009 (Teleostei: Cyprinodontidae)
FIGURE 1. Sampling sites of Aphanius species in southwestern Iran
FIGURE 1 in Valid publication of Turpinia pomifera var. minor (Staphyleaceae)
FIGURE 1. Holotype of Turpinia pomifera var. minor (K.M.Feng 11588, KUN barcode 0198401).
FIGURE 2 in Valid publication of Turpinia pomifera var. minor (Staphyleaceae)
FIGURE 2. Isotype of Turpinia pomifera var. minor (K.M.Feng 11588, KUN barcode 0198396).
Acoustic video cameras multi-species multi-cameras Validation Dataset (VD) for Deep Learning applications
<p>This video dataset, called also VD (Validation Dataset), is designed to test/validate, on a real-world case, deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset is composed by around 40h of videos, to test the efficiency of the models in the frame of ecological studies and to assess their real-applicability on monitoring sites data. Two sheets are given as the ground truth in which all fish passages (for fish sizes larger than 20 cm) are annotated. No bounding boxes are given.</p>
Forecasting seizure risk in adults with focal epilepsy: a development and validation study - Data
<p><strong>Data for the article 'Forecasting seizure risk in adults with focal epilepsy: a development and validation study'</strong></p> <p>Includes deidentified individual data in the form of interictal epileptiform activity counts and electrographic seizures for the 18 patients in the development cohort.</p>
FIGURE 1 in Valid publication of the name Anaphalis hymenolepis (Asteraceae)
FIGURE 1. Holotype of Anaphalis hymenolepis Y. Ling (PE barcode 00493318!).
FIGURE 1 in Nomenclatural corrections in vascular plants, 1. Valid publication of Allium savranicum "Besser"
FIGURE 1. Lectotype specimen of Allium savranicum.
FIGURE 1 in Validation of the name Heliconia ×rauliniana (Heliconiaceae)
FIGURE 1. Holotype of Heliconia ×rauliniana (R. Burle Marx s.n., RB 159909).
FIGURE 1 in Validation of the name Centaurea ×forsythiana Levier (Asteraceae)
FIGURE 1. Photograph of the type of Centaurea ×forsythiana Levier (FI no. 239).
FIGURE 2 in Validation of the name Centaurea ×forsythiana Levier (Asteraceae)
FIGURE 2. Plant of Centaurea ×forsythiana Levier in the wild.
Figure 2 from: Kokanova-Nedialkova Z, Nedialkov P (2021) Validated UHPLC-HRMS method for simultaneous quantification of flavonoid contents in the aerial parts of Chenopodium bonus-henricus L. (wild spinach). Pharmacia 68(3): 597-601. https://doi.org/10.3897/pharmacia.68.e69781
Figure 2 Flavonoids in the MeOH extract from the aerial parts of C. bonus-henricus L. and detected glycosides of patuletin (A), spinacetin (B), 6-methoxykaempferol, and isorhamnetin (C).
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.