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8,038 results for “validation”
Fig. 3 in A new tribe in the Chironominae (Diptera: Chironomidae) validated by first immature stages of Xiaomyia Saether & Wang and a phylogenetic review
Fig. 3. Xiaomyia Saether & Wang, 1993, larva. A, head capsule, ventral view; B, dorsal surface of head; C, antenna; D, labrum; E, mandible; F, mentum, ventral view; G, ventromentum, detail, indicating variation of lateral teeth; H, ribbed lobe. Scale bars = 50 µm (A, B); 25 µm (C–H).
Fig. 8 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 8. Photos in Chemnitz (1784) referred by Röding (1798) firstly named the giant clam species "noae" (no. 494), "maxima" (no. 495), and Tridacna derasa (no. 497).
Fig. 6 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 6. Shell morphology of Tridacna maxima from Hongchia with prominent rib scales on right valve (A–F) and Tridacna noae from Naliao (G–L). R: rib; S: scale.
Fig. 4 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 4. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 18S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 2 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 2. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on Cytochrome c oxidase subunit 1(COI) gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 3 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 3. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 16S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 5 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 5. Neighbour joining tree of Tridacninae using Kimura 2-parameter model based on 28S rRNA gene sequence. Bootstrap values: 1,000; outgroup: Corculum cardissa.
Fig. 7 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 7. Mantle colour pattern and hyaline organs of Tridacna maxima (A, B) and Tridacna noae (C, D). E, Enlarged hyaline organs of T. maxima; F, Enlarged hyaline organs of T. noae. →: Hyaline organs.
An operational methodology for validating satellite-based snow albedo measurements using a UAV
<p>This dataset contains all data supporting the conclusions of the manuscript entitled "An operational methodology for validating satellite-based snow albedo measurements using a UAV", submitted to Frontiers in Remote Sensing on August 30, 2021.</p>
Figure 1–5 in Nomenclatural validation of new genera and species of the superfamily Psychopsoidea (Insecta: Neuroptera) from the mid-Cretaceous amber of Myanmar
Figure 1–5. Fiaponeura penghiani gen. & sp. nov., holotype male. 1. Habitus photo, dorsal. 2. Habitus drawing. 3. Photo of proximal part of forewing. 4. Photo of proximal part of hindwing. 5. Photo of foreleg tarsus. Scale bars: 1 = 2 mm; 2 = 2.5 mm; 3 = 0.5 mm; 4 = 0.2 mm; 5 = 0.05 mm.
Figure 6–14 in Nomenclatural validation of new genera and species of the superfamily Psychopsoidea (Insecta: Neuroptera) from the mid-Cretaceous amber of Myanmar
Figure 6–14. Burmopsychops limoae gen. & sp. nov. 6. Habitus photo, dorsal. 7. Forewing venation. 8. Hindwing venation. 9. Photo of head, dorsal. 10. Photo of hindleg tarsus. 11. Photo of proximal part of forewing. 12. Photo of proximal part of hindwing. 13. Photo of female genital segments, ventral. 14. Drawing of female genital segments, ventral. Abbreviation: T—tergite; e— ectoproct; gx—gonocoxite; gs—gonostylus. Scale bars: 6 = 2 mm; 7–8 = 1 mm; 9, 11–12 = 0.5 mm; 10 = 0.05 mm; 13–14 = 0.2 mm.
Fig. 2 in On the validity of Noah's giant clam Tridacna noae (Röding, 1798) and its synonymy with Ningaloo giant clam Tridacna ningaloo Penny & Willan, 2014
Fig. 2. Tridacna noae (Röding, 1798) on the reef in Coral Bay, Western Australia, 23°09'S 113°47'E, 14 August 2008 (photographed by: Tsun-Thai Chai).
Karte 1 in Thinobius opacus MULSANT & REY 1878, eine valide Art (Staphylinidae, Oxytelinae, Thinobiini)
Karte 1: Thinobius opacus MULSANT & REY: Verbreitung im westlichen Mittelmeerraum nach revidiertem Material.
Abb. 1-3 in Thinobius opacus MULSANT & REY 1878, eine valide Art (Staphylinidae, Oxytelinae, Thinobiini)
Abb. 1-3: Thinobius opacus MULSANT & REY: Sternit VIII, von Tanneron (1); Aedoeagus ventral, Paralectotypus (2); Aedoeagus lateral, Lectotypus (3). Massstab 0,2 mm.
Fig. 1 in Scientific Note Pimelodus microstoma Steindachner, 1877, a valid species of pimelodid catfish (Siluriformes: Pimelodidae) from the upper rio Paraná drainage
Fig. 1. Principal components analysis on covariance matrix of log-transformed measurements of Pimelodus microstoma from the upper Paraná (inverted triangles), syntypes of Pimelodus microstoma (dots), and Pimelodus blochii species-group (squares).
Figures 1–4 in Reinstatement of Carposina ottawana Kearfott, 1907 (Lepidoptera: Carposinidae) as a valid species
Figures 1–4. Genitalia and larvae of Carposina species. 1) Male genitalia. a) Carposina niponensis, b) C. ottawana. c) C. sasakii. 2) Female genitalia. a) C. ottawana. b) C. sasakii. 3) Female sternite of abdominal segment 8 showing difference in the shape of the posterior margin and cuticular folds along the midline. a) C. ottawana. b) C. sasakii. 4) Late instar larvae. a) C. ottawana. b) C. sasakii. bp: basal process of the valva, gn: gnathos, gn.s: spines of the gnathos, hrp: harpe, hrp.ex: anterior extensions of the harpe, jx: juxta, pm-A8: posterior margin of abdominal segment 8, sa: saccus, sig: signa, sf: sclerotized folds, tra: transtilla, un: uncus, *: kink on ductus bursae.
Figures 5–6 in Reinstatement of Carposina ottawana Kearfott, 1907 (Lepidoptera: Carposinidae) as a valid species
Figures 5–6. Molecular analysis of Carposina species. 5) Mixed model COI maximum likelihood tree. Japanese Carposina sasakii are strongly supported as distinct from C. sasakii ottawana from North America in both relationship and molecular distance. Nodes with 100% bootstrap support are indicated with gray circles. 6) Haplotype network of Carposina species based on COI data. Each circle or part of a circle represents a different sequence in the dataset. Each branch and each hash mark represent a single nucleotide change. Background shading was added to delineate geographic regions.
Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry
<p>A cleaned data set used to validate metabolism models in a muscuskeletal modeling software.<br> The dataset contains 240 rows and 18 columns. </p> <p>Labels:</p> <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time)<br> <br> There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
<p>Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.</p> <p>Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ("App") for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of "patient-intrinsic" information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.</p> <p>Results:<b> </b>Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 – -0.331) and calibration slope β=1.287 (1.207 – 1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).</p> <p>Conclusion: The app accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the app's potential use in diagnostic and antimicrobial stewardship are underway.</p>
GRILLIX simulation data for TCV-X21 divertor validation project
<p><strong>GRILLIX simulation data for TCV-X21 divertor validation project</strong></p> <p>This dataset contains simulation data from the GRILLIX high-performance edge simulation software. The simulation data is for the TCV-X21 divertor validation case.</p> <p>It contains two large-file types. These are stored separately from the main TCV-X21 repository, to reduce the repository size for the base validation repository.</p> <p>The first are "work files", which allow you to check the time-resolved dynamics of the GRILLIX TCV-X21 results.<br> The second are "checkpoints", the complete simulation state at a single time-point (as well as input files), which might be helpful if you are starting new TCV-X21 simulations.</p> <p>To use these files, you can use the TCV-X21 processing routines. For more details about the project, please check the arXiv version, available at https://arxiv.org/abs/2109.01618</p>
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.