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114 results for “artificial neural networks”
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
Data from: Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas
<p><strong><span>General Information</span></strong></p> <p><span>This dataset was used in the study titled "Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas". The study employs citizen science data collected from the Infomedusa application to assess the presence of jellyfish on beaches along the Andalusian coast, along with environmental data to analyze the factors influencing jellyfish distribution. The study aims to employ machine learning techniques, specifically a Multi-Layer Perceptron (MLP) neural network, to classify user comments on the presence or absence of jellyfish and analyze how environmental factors such as sea surface temperature, wind direction, and wind speed influence jellyfish distribution.</span></p> <p><strong><span>Dataset Columns</span></strong></p> <ul> <li><strong><span>Fecha</span></strong><span>: Timestamp of the comment made by the user in the Infomedusa application.</span></li> <li><strong><span>Municipio</span></strong><span>: Name of the municipality where the beach mentioned in the comment is located.</span></li> <li><strong><span>Jellyfish</span></strong><span>: Binary variable indicating the presence (1) or absence (0) of jellyfish according to the user’s comment.</span></li> <li><strong><span>Comunidad</span></strong><span>: Autonomous community to which the municipality belongs.</span></li> <li><strong><span>Provincia</span></strong><span>: Province to which the municipality belongs.</span></li> <li><strong><span>Latitud</span></strong><span>: Geographical latitude of the municipality where the comment was made.</span></li> <li><strong><span>Longitud</span></strong><span>: Geographical longitude of the municipality where the comment was made.</span></li> <li><strong><span>Set</span></strong><span>: Set of grouped beaches for geographical analysis. Each set includes beaches close to each other and the nearest weather station.</span></li> <li><strong><span>Month</span></strong><span>: Month when the comment was made.</span></li> <li><strong><span>Longitud_sea</span></strong><span>: Longitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>Latitud_sea</span></strong><span>: Latitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>SST</span></strong><span>: Sea Surface Temperature at the nearest point in the sea to the municipality, obtained from the Copernicus Marine Environment Monitoring Service.</span></li> <li><strong><span>Wind_dir</span></strong><span>: Wind direction measured at the weather station closest to the municipality, provided by the Spanish Meteorological Agency (AEMET).</span></li> <li><strong><span>Wind_speed</span></strong><span>: Wind speed measured at the weather station closest to the municipality, provided by the AEMET.</span></li> </ul> <p><strong><span>Data Sources</span></strong></p> <ul> <li><strong><span>Infomedusa APP</span></strong><span>: Application developed by the Provincial Council of Malaga and Aula del Mar of Malaga to monitor the presence of jellyfish through citizen participation.</span></li> <li><strong><span>Copernicus Marine Environment Monitoring Service (CMEMS)</span></strong><span>: Provides data on sea surface temperature with an hourly temporal resolution and a spatial resolution of 0.0625° x 0.0625°.</span></li> <li><strong><span>Agencia Estatal de Meteorología (AEMET)</span></strong><span>: Provides daily data on wind direction and speed.</span></li> </ul>
Modeling fluid flow in ship systems for controller tuning using an artificial neural network
<p>Dataset used to develop ANN NARX models</p>
Data for "Convergent temperature representations in artificial and biological neural networks"
<p>Data for "Convergent Temperature Representations in Artificial and Biological<br> neural networks" by Haesemeyer M, Schier AF and Engert F, 2019</p> <p>The corresponding python code is available at:<br> <a href="https://github.com/haesemeyer/GradientPrediction">https://github.com/haesemeyer/GradientPrediction</a></p> <p>All zip files should be extracted in the same folder as the python files. This<br> will create a sub-folder structure for the model data.<br> ZIP File Contents (Note: These are used by the code and not necessarily useful<br> by themselves):<br> model_data.zip<br> Contains tensorflow checkpoints on all naive and fully trained models, test<br> errors during training as well as evolution weights where applicable.<br> model_cluster_assignments.zip<br> For the trained models in model_data.zip the response cluster assignment<br> for each individual unit.<br> zebrafish_data.zip<br> The zebrafish brain and behavior data used in the paper comparisons. This<br> archive also contains the temperature stimulus file stimFile.hdf5<br> training_data.zip<br> The generated training data used during predictive network training<br> test_data.zip<br> The generated test data used to evaluate predictive network training</p>
Battery Pack Temperature Change Prediction for Running BLDC 1500 W Motor using Artificial Neural Network
<p><span>The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model's effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems.</span></p>
First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (<em>D</em>, <em>H</em> and <em>Z</em>) in 2021. This reconstruction method has been proved to be effective.</p>
Artificial neural network model and metabolomics data of selected microbial strains
<p>Metabolomics data, metadata, sample R code, and a pre-trained artificial neural network model to predict group memberships of the bacterial strains in the dataset.</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
MetalHawk: Enhanced Classification Of Metal Coordination Geometries by Artificial Neural Networks
<p>The data used to train and validate the CSD-NN and PDB-NN models. These neural networks built in Scikit-learn and are trained to recognize the geometry and coordination number of a metal site from the geometric features including distances and angles of the six atoms closest to the metal. The sites are stored as .pdb files composed of all atoms falling within 10 angstroms of the central metal atom and they include both metal complexes deposited in the Cambridge Structural Database (CSD, Version 5.42-5.43) and bioinorganic sites deposited in the Protein Data Bank (PDB), retrieved through the MetalPDB interface (up to the end of 2018). The sites are divided in seven geometry classes: linear (LIN), trigonal planar (TRI), tetrahedral (TET), square planar (SPL), square pyramidal (SQP), trigonal bipyramidal (TBP) and octahedral (OCT).</p> <p>The number of metal sites in each file is the following:</p> <p>CSD_dataset_pdbs.zip - 110K </p> <p>CSD_validation_dataset_pdbs.zip - 1369</p> <p>PDB_dataset_pdbs.zip file - 2960</p> <p>PDB_validation_dataset_pdbs.zip - 106</p> <p>The file images_and_data_analysis.zip contains all data and code required to replicate the figures shown in the paper.</p> <p>The benchmark_fp.zip file contains code for the benchmark of Metalhawk and its comparison to Findgeo, another tool for coordination geometry classification.</p>
CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study
ClinicalTrials.gov study NCT00497640. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Evaluation With Artificial Neural Network of Pain Scales in Children (ANN)
ClinicalTrials.gov study NCT02682875. IPD Sharing: YES. Countries: 1. Publications: 1.
Data for assessment of damage to residential dwellings using artificial neural networks
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Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). <sup>1</sup>H spectra (43 MHz) were recorded as single scans.</p> <p> Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (<em>i</em>) Training data based on combinations of measured pure component spectra and (<em>ii</em>) Training data based on a spectral model.</p> <p><strong>Synthetic low-field NMR spectra</strong></p> <p>First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.</p> <p><em>X<sub>i</sub></em> (“pure component spectra dataset”)</p> <p><em>X<sub>ii</sub></em> (“spectral model dataset”)</p> <p><strong>Experimental low-field NMR spectra from MNDPA-Synthesis</strong></p> <p>This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.</p>
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
<p>This repository contains the cornerplots of relevant parameters for the 23 protoplanetary disks modeled in the manuscript "Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses" (Ribas et al. 2020).</p>
Dataset for "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market"
<p>This is the dataset for the manuscript "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market" submitted to the journal PLOS ONE. </p>
Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)
<p>This repository includes the input and output dataset, and python scripts used in the article, "Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)," of J. Chem. Phys. 156, 154108 (2022) [DOI: <a href="http://doi.org/10.1063/5.0087310">10.1063/5.0087310</a>] The repository also includes source data of figures in the article.</p>
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
<p>1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.</p> <p>2. In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.</p> <p>3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 seconds.</p> <p>4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 seconds. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.</p> <p>5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behaviour of the target animals.</p>
The Diagnostic Performance of BMO-MRW and RNFL Thickness and Their Combinational Index Using Artificial Neural Network
ClinicalTrials.gov study NCT03257020. IPD Sharing: YES. Countries: 1. Publications: 0.
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
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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.