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8,038 results for “validation”
Fig. 2 in The Australian Sinistral Flounder Arnoglossus aspilos praeteritus (Actinopterygii: Pleuronectiformes: Bothidae) Reassigned as a Valid Species of Engyprosopon
Fig. 2. Engyprosopon praeteritus (A, holotype, AMS IA. 4142, male, 57 mm SL; C, CSIRO A 1336, male, 69.0 mm SL; and D, CSIRO A 1335, female, 72.9 mm SL) and Arnoglossus aspilos (B, HUMZ 185345, male, 87.1 mm SL).
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
tobac-flow Validation data
<p>Validation results for the detection of cores and anvils using the tobac-flow method</p> <p>Validation was performed over 297 days in 2018. Other days were excluded due to missing ABI or GLM data. Validation was performed over a subset of the GOES-16 CONUS scan region.</p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
Databases of one-way slabs under concentrated loads: parameter analyses and validation of the proposed approach
<p>This database includes information about loading, geometry, failure loads, and failure modes of slabs under concentrated loads under different support conditions. Different datasets were organized for (i) identifying the relation between specific parameters on the governing failure mechanism of the tested slabs and (ii) for validating a proposed approach to predict the shear and punching capacity of slabs under concentrated loads using the ACI 318-19 code expressions.</p>
Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study
<p><strong>Supplementary Materials: </strong>The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, <strong>Table ST1</strong> – qMIDS<sup>V2 </sup>Gene panel primer sequences; <strong>Figure S1</strong> – qMIDS<sup>V1</sup> vs qMIDS<sup>V2</sup> 384-well assay format and protocols; <strong>Figure S2.</strong> Individual target gene expression pattern in 1761 samples; <strong>Figure S3.</strong> Various statistical methods used for gene selection analysis on 1761 clinical samples; <strong>Figure S4. </strong>Diagnostic performance comparison between qMIDS<sup>V2</sup> vs qMIDS<sup>V2* </sup>(with 4 less effective genes removed from the panel of 14 target genes of qMIDS<sup>V2</sup>); <strong>Figure S5</strong>. Effect of removing individual genes from the 14-target gene panel qMIDS<sup>V2</sup> (qV2) on diagnostic test performance based on the UK patient cohort data.</p>
DocTOR models and cross-validation dataset
<p>Dataset necessary for DocTOR utility.</p> <p>DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two.</p> <p>On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset.</p> <p>DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features.</p> <p>The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems.</p> <p>The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type).</p> <p>The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR</p>
Fig. 2 in A Reassessment Of The Taxonomic Validity Of Dynamoterror Dynastes (Theropoda, Tyrannosauridae)
Fig. 2. Frontal bones of tyrannosaurid theropods in right lateral view, scaled to same total length. A, the right frontal of Dynamoterror dynastes UMNH VP 28348 in lateral view. B, the right frontal of Teratophoneus curriei BYU 8120/9396 in lateral view. C, the braincase of Teratophoneus curriei UMNH VP 16690 (reversed) in lateral view. B and C are courtesy of Rod Scheetz and Mark Loewen. Abbreviations: cpos, caudal part of postorbital suture; dpos, depression between rostral and caudal parts of postorbital suture; gpos, groove between rostral and caudal parts of postorbital suture; rpos, rostral part of postorbital suture.
Fig. 1 in A Reassessment Of The Taxonomic Validity Of Dynamoterror Dynastes (Theropoda, Tyrannosauridae)
Fig. 1. Frontal bones of tyrannosaurid theropods in dorsal view, scaled to same total length. A, the right frontal of Dynamoterror dynastes UMNH VP 28348 in dorsal view. B, the right frontal of Daspletosaurus torosus SDNHM 32701 in dorsal view. Abbreviations: nt, notch between prefrontolacrimal and prefrontonasal processes; plp, prefrontolacrimal process; pnp, prefrontonasal process.
Dataset Methods for stratification and validation cohorts: a scoping review
<p>We searched PubMed, EMBASE and the Cochrane Library for reviews that described the tools and methods applied to define cohorts used for patient stratification or validation of patient clustering. We focused on cancer, stroke, and Alzheimer’s disease (AD) and limited the searches to reports in English, French, German, Italian and Spanish, published from 2005 to April 2020. Two authors screened the records, and one extracted the key information from each included review. The result of the screening process was reported through a PRISMA flowchart.</p>
Fig. 2 in The Influence Of Fixatives On The Validity Of Histological Preparations Of Olfactory Organ In Teleostei
Fig. 2. Cross-section through the olfactory organ of Misgurnus fossilis at developmental stage 36 (А, В), developmental stage 37 (C, D), stage of early larva (E, F), stage of late larva (G, H). Arrowheads indicate the artifacts "anlage of lamellae"; arrows indicate the rises of epithelium (artifacts); b — brain; e — eye; oe — olfactory epithelium; ol — olfactory lamella; o — olfactory pit; op — olfactory placode. Left images correspond to Bouin's solution, right images correspond to 10 % formalin fixation.
Fig. 1 in The Influence Of Fixatives On The Validity Of Histological Preparations Of Olfactory Organ In Teleostei
Fig. 1. Section levels through the olfactory organ of European weatherfish Misgurnus fossilis: A — cross-section (prolarva); B — frontal section (larva).
Laboratory validation of a smartphone-based sensor for diffuse optical volume properties
<p>This data set contains raw image data for laboratory validation of a diffuse-optical, smartphone-based sensor. The measurements were taken using scattering phantoms with known scattering and absorption coefficient. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the three scattering phantoms (One, Two, Three) or spatial calibration data using a 9mm x 9mm calibration pattern (calib). The raw images are located in the folder ./calib. Matlab code is contained in the filder ./matlab. It can be run on Matlab R2021b.</p> <p>For analyzing the raw data set, use "CameraBatchCalib.m". It wraps around the function "CameraAnalysisCalib.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfG.m". The resulting data is plotted for comparison with the nominal attenuation length of the scattering phantoms.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset and code. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022).</p> <p>The underlying fit function is based on the calculations from:</p> <p>Markus Allgaier and Smith, Diffuse optics for glaciology, Opt. Express 29, 18845–18864 (2021)</p> <p> </p>
Fishyscapes Lost & Found validation set
<p>Validation Set of 100 images for the Fishyscapes Benchmark Lost & Found dataset. The download contains novel annotations for the existing Lost & Found dataset.</p>
Validating marker-less pose estimation with 3D x-ray radiography
<p class="MsoNormal"><span>These data were generated to evaluate the accuracy of DeepLabCut (DLC), a deep learning marker-less motion capture approach, by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed three-dimensional kinematics in a common coordinate system. We used XMALab to track 11 XROMM markers, and we used the toolkit Anipose to filter and triangulate DLC trajectories of 11 corresponding markers on the forelimb and torso. We performed a parameter sweep of relevant Anipose and post-processing parameters to characterize their effect on tracking quality. We compared the median error of DLC+Anipose to human labeling performance and placed this error in the context of the animal's range of motion. </span></p>
Challenges of the New Accreditation in Digital Competences for Citizens (ACTIC): Update, Validation and Results
<p>The aim of this study is to explain the process used to update and validate the Catalan Accreditation of Information and Communications Technology Skills (ACTIC), conducted by the Open University of Catalonia and the Blanquerna Foundation of Ramon Llull University, at the initiative of the Catalan Department for Digital Policy and Public Administration, Government of Catalonia.</p> <p>Using a mixed method, the study is divided into 3 phases and presents the definitions of the 5 competence areas, 19 competences, 3 achievement levels and a total of 233 indicators. The definitions were validated by a group of experts. As a result of the study, three key dimensions have emerged that improve digital awareness among citizens while empowering them and providing them with a way of having their skills accredited and acknowledged in the workplace: (i) identity, centred around participatory, social, ethical and civic issues; (ii) skills learning among citizens; and (iii) technological development and digital autonomy of citizens.</p>
Running the validation assistant tool on pesticide dossiers in IUCLID
<p>Before submitting a dossier, <strong>applicants</strong> are recommended to use the Validation Assistant tool to check the dossier is technically complete. It is important to resolve all validation assistant quality warnings as this will support the Admissibility Check of the RMS/EMS. The <strong>RMS/EMS</strong> should also run the validation assistant report before declaring the admissibility of a dossier to make sure the dossier is complete.</p> <p>If the report shows a business rule failure (anything starting with BR, e.g. BR_PPP_033) this will prevent the applicant from successfully submitting the dossier and therefore must be resolved.</p> <p>This demonstration is supporting RMS/EMS and applicants on how to run the validation assistant tool and extract the report. </p>
Training and validation data used to produce the pre-trained model for the TomoTwin paper.
<p>This datasets represents the training and validation data that was used to produce the pre-trained model for the TomoTwin paper. Please see 10.5281/zenodo.6637357 for the raw tomograms.</p>
Gaia Data Release 3: BP/RP split-epoch validation dataset
<p>This dataset includes mean BP/RP spectra for about 43.6 thousand sources for which two mean spectra per source were generated using only a random selection of the available epoch spectra.</p> <p>More details about this dataset are given in Appendix D in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F., et al. A&A (2022). Results obtained from this dataset are published in the same paper and in "Gaia Data Release 3: The Galaxy in your preferred colours. Synthetic photometry from Gaia low-resolution spectra", Gaia Collaboration, Montegriffo, P., et al. A&A (2022).</p>
Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis
<p>This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.</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.