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82 results for “properties prediction”
Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning
<p>Dataset associated with the manuscript entitled "Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning". </p> <p>See Readme file (markdown format) for details on how the data is structured in the "database" file.</p>
Data from: Integrating soil properties into species distribution models enhances predictive accuracy for terricolous macrofungi
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Data from: In vitro properties of patient serum predict clinical outcome after high dose rate brachytherapy of hepatocellular carcinoma
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Data from: Simple measures of climate, soil properties and plant traits predict national scale grassland soil carbon stocks
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Data from: Boundary strength analysis: combining colour pattern geometry and coloured patch visual properties for use in predicting behaviour and fitness
1.Colour patterns are used by many species to make decisions that ultimately affect their Darwinian fitness. Colour patterns consist of a mosaic of patches that differ in geometry and visual properties. Although traditionally pattern geometry and colour patch visual properties are analysed separately, these components are likely to work together as a functional unit. Despite this, the combined effect of patch visual properties, patch geometry, and the effects of the patch boundaries on animal visual systems, behaviour and fitness are relatively unexplored. 2.Here we describe Boundary Strength Analysis (BSA), a novel way to combine the geometry of the edges (boundaries among the patch classes) with the receptor noise estimate (ΔS) of the intensity of the edges. The method is based upon known properties of vertebrate and invertebrate retinas. The mean and SD of ΔS (mΔS, sΔS) of a colour pattern can be obtained by weighting each edge class ΔS by its length, separately for chromatic and achromatic ΔS. This assumes those colour patterns, or parts of the patterns used in signalling, with larger mΔS and sΔS are more stimulating and hence more salient to the viewers. BSA can be used to examine both colour patterns and visual backgrounds. 3.BSA was successful in assessing the estimated conspicuousness of colour pattern variants in two species, guppies (Poecilia reticulata) and Gouldian finches (Erythrura gouldiae), both polymorphic for patch colour, luminance and geometry. 3D representations of the ΔS of patch edges (Fort Diagrams) of both species show that there is little or negative geometric correspondence between the chromatic and achromatic edges. All individuals have mΔS > 1.5 for both chromatic and achromatic measures, indicating the high within‐pattern contrast expected for display signals. In contrast from what one would expect from sexual selection, all guppies have mΔS less than expected from random contacts between all pairs of patch colour/luminance classes. The correlation between chromatic and luminance ΔS is negative in both species but zero when correlating all possible kinds of edges between the colours of each species and morph indicating non‐random colour geometry. 4.The pattern difference between chromatic and achromatic edges in both species reveals the possibility that chromatic and achromatic edges could function differently. The smaller than random expected mΔS values in guppies suggests an anti‐predator function because guppies are never found without predators. Moreover, mΔS could vary with predation intensity within and among species. BSA can be applied to any colour pattern used in intraspecific and interspecific behaviour. Seven predictions and four questions about colour patterns are presented. 5.In species which are very convex, both chromatic and luminance mΔS change with viewing angle; geometry of signalling is as important as signal geometry.
An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
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An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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Accurate Predictions of Volatile Plutonium Thermodynamic Properties: Dataset
<p>This dataset collects the input and output of the calculations discussed in the paper titled "Accurate Predictions of Volatile Plutonium Thermodynamic Properties", by Sophie Kervazo, Florent Réal, François Virot, André Severo Pereira Gomes and Valérie Vallet.</p> <p>The results for each atom/molecule are collected in a zip file with the following directory tree:</p> <ol> <li>geom-freq-b3lyp: inputs/outputs for B3LYP Gaussian optimization and frequency calculations (both harmonic and anharmonic)</li> <li>SO-CASPT2 <ul> <li>VTZP: inputs/outputs for VTZP SO-CASPT2 calculations</li> <li>VQZP: inputs/outputs for VQZP SO-CASPT2 calculations</li> </ul> </li> <li>2dcm <ul> <li>inp: input files for 2dcm-CCSD and 2dcm-EOM-EE and 2dcm-EOM-IP calculations</li> <li>out: output files for 2dcm-CCSD and 2dcm-EOM-EE and 2dcm-EOM-IP calculations</li> <li>xyz: XYZ geometry files</li> </ul> </li> </ol>
A New Rock Physics Model for Predicting the Elastic Properties of Sediments Hosting Nodule and Chunk-like Natural Gas Hydrate Morphologies
<p>MATLAB codes and well logs used in the manuscript are included.</p>
Data from: The microstructure of white feathers predicts their visible and near-infrared reflectance properties
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Data from: Boundary strength analysis: combining colour pattern geometry and coloured patch visual properties for use in predicting behaviour and fitness
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Data from: Evolutionary prediction of medicinal properties in the genus Euphorbia L.
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Data from: Prediction for electronic, vibrational and thermoelectric properties of chalcopyrite AgX(X=In,Ga)Te2: PBE + U approach
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Delta-X: NUMAR Predictive Model for Marsh Accretion Rates and Chemical Properties
This dataset provides input data and model code to run the Marsh Accretion Rates (NUMAR) process model used to predict soil accretion rates and chemical properties for marsh sites in the Mississippi River Delta. NUMAR is a modification of the NUMAN model by Chen and Twilley (1999) that was developed for mangrove environments. This dataset provides Python code, input data in comma separated values (CSV) format, and documentation for installing and running the model in Portable Document Format (PDF).
TNFα-stimulated gene 6 (TSG-6) is a key molecule for stemness and a predictive marker for biological properties of mesenchymal stem cells
GEO Series GSE109114. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiling predicts processing properties in short-term imbibition of wheat
GEO Series GSE116933. Triticum aestivum. 8 samples. Type: Expression profiling by array.
Mitotic chromosome binding predicts transcription factor properties in interphase
GEO Series GSE119784. Mus musculus. 60 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gene Regulatory Network inference in long-lived C. elegans reveals modular properties that are predictive of novel ageing genes
GEO Series GSE166512. Caenorhabditis elegans. 115 samples. Type: Expression profiling by high throughput sequencing.
Data from: A polymer dataset for accelerated property prediction and design
Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.
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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.