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42 results for “Structural validity”
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms
<p>This data set is described in detail in a paper submitted to AMTD:</p> <p><strong>Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms</strong></p> <p>by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and<br> Bernhard Mayer</p> <p> </p> <p>The subdirectory <em>boxcloud</em> includes synthetic reflectances for clearsky, 1D cloud and box cloud.</p> <p>The subdirectory <em>les_cloud</em> includes synthetic reflectances for the LES cloud scenario for low earth orbit (<em>leo</em>) and geostationary orbit (<em>geo</em>).</p> <p>All data are provided in <em>netcdf</em> format.</p> <p> </p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers
<p>This dataset is part of the Smart Tower Project and is intended to accompany academic publications entitled "Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers". It provides valuable data under high wind excitation conditions and is beneficial for researchers in the field of structural dynamics and Structural Health Monitoring (SHM). The dataset includes high-resolution accelerometer data and estimated modal parameters, along with part of the code script used for analysis.</p> <p>Dataset Components:</p> <ol> <li> <p><strong>Raw Accelerometer Data (April Month)</strong>: High-resolution time-series accelerometer data recorded during April, under high wind excitation conditions. The data is sampled at a rate of 250 Hz. The data is organized in dd/YYYYmmdd_HHMMSS.tdms</p> <ul> <li><strong>Format</strong>: TDMS</li> </ul> </li> <li> <p><strong>Modal Parameters</strong>: Resonance frequencies estimated over the entire monitoring period from 29/03/2023 to 25/06/2023.</p> <ul> <li><strong>Format</strong>: Parquet</li> </ul> </li> </ol>
Diagnostic Test Validity of Structural Vertebral Endplate Defects
ClinicalTrials.gov study NCT04808960. IPD Sharing: YES. Countries: 1. Publications: 9.
Dataset of the study "Factor Structure, Validity, and Reliability of the STarT Back Screening Tool in Italian Obese and Non-obese Patients With Low Back Pain"
<p>Dataset of the study "Factor Structure, Validity, and Reliability of the STarT Back Screening Tool in Italian Obese and Non-obese Patients With Low Back Pain" published in Frontiers in Psychology 20 October 2021 12:740851, doi: 10.3389/fpsyg.2021.740851</p>
DFT-NMR-Validated Full Structure Elucidation of Theionbrunonine C, An Unstable N-Oxide Theionbrunonine from Mostuea brunonis
<p>The structure elucidation of theionbrunonine C, a thioether-bridged dimeric monoterpene indole alkaloid (MIA), and more generally, one of the very few Sulfur-containing MIA, is reported after its isolation from Mostuea brunonis (Gelsemiaceae). This unstable structure had already been targeted for isolation in our former, molecular network-guided, investigation of this plant but this compound had degraded before sufficient spectroscopic data could have been acquired for a complete structure assignment. With this constraint in mind, the rapid acquisition of NMR data enabled retrieving sufficient spectroscopic information for full structure elucidation, although from a partial set of spectroscopic information (1H and 13C NMR; COSY, HSQC, and HMBC). In conjunction with biosynthetic considerations, the cursory examination of 13C NMR data unambiguously defined the complete stereostructure of 1, as further supported by DFT-NMR calculations and subsequent DP4 probability score.</p>
RNA 3D structural models used to train, test and validate lociPARSE
<p>This repository contains all the training, validation and test decoy sets to train and evalaute lociPARSE. It also contains training and benchmarks set-2 decoys from ARES.</p>
Data from: Genetic structuring among colonies of a pantropical seabird: Implication for subspecies validation and conservation
Appendix S1 <table> <tbody> <tr> <td>Table S1</td> <td>Details of field researchers and licences under which they took blood samples from white-tailed tropicbirds from populations in the years of study</td> </tr> <tr> <td>Table S2</td> <td>Morphometrics of 616 individual white-tailed tropicbirds from 11 populations. Population codes are as described in Table 1.</td> </tr> <tr> <td>Table S3</td> <td>Raw microsatellite genotypes for 382 individual White-tailed tropicbird from 13 populations. Population codes are as described in Table 1</td> </tr> <tr> <td>Table S4</td> <td>Details of mtDNA sequences </td> </tr> <tr> <td>Table S5</td> <td>Tests of bottleneck (P-values for one-tailed Wilcoxon's signed rank test for heterozygosity excess) based on 10 microsatellites in 13 populations of Phaethon lepturus</td> </tr> <tr> <td>Table S6</td> <td>Pairwise FST estimates based on nuclear microsatellite variation (above diagonal), and ΦST estimates based on mtDNA sequence (below diagonal) for 11 populations with sample sizes >5 ('Pop's) of Phaethon lepturus (see Table 1 for population codes)</td> </tr> </tbody> </table>
Case studies from doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models
<p>Case studies from "doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models"</p>
Case studies from: Sequence assignment validation in protein crystal structure models with checkMySequence
<p>Case studies from "Sequence assignment validation in protein crystal structure models with checkMySequence"</p>
Functionally Validated Structural Endpoints for Early AMD
ClinicalTrials.gov study NCT04112667. IPD Sharing: NO. Countries: 1. Publications: 4.
Validation of Structured Light Plethysmography
ClinicalTrials.gov study NCT02598336. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Validation of Structured Light Plethysmography - Health and Disease
ClinicalTrials.gov study NCT02626468. IPD Sharing: NO. Countries: 1. Publications: 2.
Validation of Structured Light Plethysmography: Asthma
ClinicalTrials.gov study NCT02543333. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Development and Validation of a Structured Tele-rehabilitation Programme of Brain Injured Patients
ClinicalTrials.gov study NCT06016374. IPD Sharing: NO. Countries: 1. Publications: 1.
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.