Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
114
datasets available to search
ShareScore release 0.9.0
Dataset results
114 results for “artificial neural networks”
A Global Gridded Municipal Water Withdrawal Estimation Method Using Aggregated Data and Artificial Neural Network
<p>Global gridded municipal water withdrawal estimations for the following WST paper.</p> <p>Jiabao Yan, Shaofeng Jia; A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network. <em>Water Science Technology</em>, 2023; 87 (1): 251–274. <a href="https://doi.org/10.2166/wst.2022.399" target="_blank" rel="noopener">https://doi.org/10.2166/wst.2022.399</a></p> <p>The representative year of the data is 2015, and the unit of the data is in millimeters (mm).</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Retrieving monthly and interannual pHT in the East China Sea shelf using an artificial neural network: ANN-pHT-v1
<p><br> The reliability of the artificial neural network model was evaluated by independent sampled data from 3 cruises in 2018.</p> <p>Monthly water column pHT for the period 2000-2016 was obtained passing T, S, DO, N, P, and Si from the Finite-Volume Coastal Ocean Model with the European Regional Sea Ecosystem Model through the artificial neural network. The spatiotemporal resolution of monthly pHT is 1-10 km in the horizontal, 10 depth levels in the vertical, and 12 months. Seasonal pHT dynamics in the East China Sea shelf can be primarily attributed to temperature changes and the shifting balance of production and respiration processes.</p>
Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks
Open the record for dataset details and reuse information.
Artificial Neural Network Symbol Demapper for Coherent Optical Fiber Systems
<p>M-files and datasets that implement an artificial neural network (ANN) demapper targeted to the compensation of fiber nonlinearities in coherent optical transmission systems. </p> <p>The dataset contains simulation data of a 11-channel WDM fiber link with numerical propagation implemented by the split-step Fourier method over standard single-mode fiber with 100 km per span and inline optical amplification with 5 dB noise figure. The launched optical power is varied in the range of 0 to 5 dBm and the distance is swept up to 30 fiber spans. The transmitted signal is a root-raised cosine single-carrier 16QAM at 64 Gbaud. </p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
Open the record for dataset details and reuse information.
Data from: Performance-based Egress safety assessment of underground tunnels: Simulation and artificial neural network approaches
Open the record for dataset details and reuse information.
Supplementary Materials (An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population)
<p>Supporting information for an Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population</p>
Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm
<p>Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm.</p> <p>Abstract: </p> <p>Marine ecosystem models are important to identify the processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the network topology. With all three approaches, a direct approximation of the steady annual cycle was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement. In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction.</p> <p>Content:</p> <p>Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a></p> <p>zip-files with data: </p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for assessment of damage to residential dwellings using artificial neural networks
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture damage to residential home subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event. </p>
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks
<p>The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data. </p> <p>In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.</p> <p> </p>
Figure for "A quick battery charging curve prediction by artificial neural network"
Open the record for dataset details and reuse information.
Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Dataset for the article Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies
<p>Training and validation data sets along with the corresponding trained convolutional neural network in the article "Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies" by Sergio Baselga published in <em>Appl. Sci.</em> <strong>2024</strong>, <em>14</em>(23), 10859; https://doi.org/10.3390/app142310859</p>
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables</p>
Supplementary material 1 from: Behei N, Tryhubchak O, Pryymak B (2022) Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition. Pharmacia 69(3): 779-789. https://doi.org/10.3897/pharmacia.69.e86876
The results of the study of pharmaco-technological parameters of intermediates and amlodipine tablets with enalapril, data of the functions of desirability
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.