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641 results for “data validation”
Fig. 3. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 3. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4, from the late Albian of Saint−Jouin−Bruneval, France. Detail of the orbital and narial regions. A. Photograph. B. Explanatory drawing. The lacrimal is broken in two and participates to the posterior and ventral borders of the bony naris aperture. The nasal is expanded posterolaterally, as in SMSS "SGS". A possible septomaxilla is present inside the naris, but its identification remains tentative. All bones are from the right side of the skull, except when stated otherwise by a (L).
Fig. 2. Ophthalmosaurid ichthyosaur Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 2. Ophthalmosaurid ichthyosaur Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4, from the late Albian of Saint−Jouin−Bruneval, France. Skull roof anatomy. A. Photograph. B. Explanatory drawing. The bones found in connection are coloured in pale grey and the bone placed manually at their supposed position are coloured in dark grey. C. Reconstruction. This reconstruction is designed to provide an overview of the skull roof of MNHN 2010.4; please refer to the description and the other figures for anatomical comparison or character state scoring in phylogenetic analysis. The anterior ramus of the nasal is developed, forming the dorsomedial part of the rostrum over a very long distance. The nasal is also developed posterolaterally, overlapping the postfrontal extensively. The frontal participates to three cranial openings: the internasal foramen anteriorly, the pineal foramen posteromedially, and the supratemporal fenestra posterolaterally.
Fig. 4. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 4. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4, from the late Albian of Saint−Jouin−Bruneval, France. Detail of the fronto−parietal region. A. Photograph. B. Explanatory drawing. The frontal forms a thickened posteromedial process that fully encloses the parietal foramen and separates it from the parietal.
Raw data for Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation
<p>Specification of affiliations:</p> <ul> <li>Hassan Ali - Centre of Polymer Systems</li> <li>Muhammad Yasir - Centre of Polymer Systems</li> <li>Hamza Ul Haq - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Ali Can Guler - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Muhammad Nouman Aslam Khan - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Vladimir Sedlarik - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
Raw data: Validity of fitness trackers when worn by older adults
<p>Co-authors of the dataset:</p> <p>Marina Dobnik <sup>2</sup> , Stefan Loefler <sup>3,4,6</sup> , Christian Hofer <sup>4</sup> and Nejc Šarabon <sup>2,5</sup></p> <p><sup>1</sup> University of Primorska, Andrej Marušič Institute, Muzejski trg 2, 6000 Koper, Slovenia; kaja.kastelic@iam.upr.si</p> <p><sup>2</sup> University of Primorska, Faculty of Health Sciences, Polje 42, 6310 Izola, Slovenia; <a href="mailto:nejc.sarabon@fvz.upr.si">nejc.sarabon@fvz.upr.si</a></p> <p><sup>3</sup> Physiko- & Rheumatherapie, Institute for Physical Medicine and Rehabilitation, 3100 St. Pölten, Austria; stefan.loefler@kern-reha.at</p> <p><sup>4</sup> Ludwig Boltzmann Institute for Rehabilitation Research, Neugebäudeplatz 1, 3100 St. Pölten, Austria; christian.hofer@rehabilitationresearch.eu</p> <p><sup>5</sup> InnoRenew CoE, Livade 6, 6310 Izola, Slovenia</p>
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.
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>
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>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
BIO4AFRICA_Co-definition of technologies to be transferred with local farmers and communities - Technology Validation Data (Findings)_D1.4_021122_1
<p>Validation Data (Findings) for Task 1.4: Co-definition of technologies to be transferred with local farmers and communities.</p>
BIO4AFRICA_Co-definition of technologies to be transferred with local farmers and communities - Technology Validation Data (Demographics)_D1.4_311022_1
<p>Validation Data (Demographics) for Task 1.4: Co-definition of technologies to be transferred with local farmers and communities.</p>
Data for Project 'Development and Initial Validation of the German Version of the Exergame Enjoyment Questionnaire (EEQ-G)'
<p>Data for Project 'Development and Initial Validation of the German Version of the Exergame Enjoyment Questionnaire (EEQ-G)', consisting of (1) the original and complete dataset (Data_EEQ-G_Validation_Study_for-publication.xlsx) and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Immerse Downstream User Toolbox - example data for FerryBox validation
<p>Example data for FerryBox Validation use case within </p> <p><a href="https://github.com/immerse-project/Downstream-Users-Toolbox">immerse-project/Downstream-Users-Toolbox: Analysis and assessment tools from IMMERSE WP8 (github.com)</a></p> <p>containing exemplary temperature and salinity data from a ship attached FerryBox (<a href="https://www.ferrybox.org/">https://www.ferrybox.org/</a>) and according surface fields of the the CMEMS Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast model <a href="https://resources.marine.copernicus.eu/product-detail/NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013/INFORMATION">NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013</a>, the high resolution NNEMO.v4.2_RC Southern North Sea configuration developed at <a href="https://www.hereon.de/">Helmholtz-Zentrum Hereon</a> in the context of IMMERSE, and the German Bight configuration operated at Hereon, using the <a href="http://ccrm.vims.edu/schismweb/">SCHISM unstructured grid moddeling framework</a> for an assesment of the model performance on those variables within the south eastern German Bight on 2018-11-05.</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.