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.
86
datasets available to search
ShareScore release 0.7.1
Dataset results
86 results for “Power Modeling”
DTU 10MW reference turbine HAWC2 simulations for Model-free estimation of available power with deep learning training
<p>The time series of DTU 10MW HAWC2 model simulations of two channels: hub-height wind speed and produced power. They are generated to train model-free estimation of available power approach, using wind speed and its moving standard deviation as inputs. They include 3-hour length 100Hz simulations of 3 mean wind speeds (7 m/s, 9m/s and 11m/s) as well as 3 levels of turbulence intensity (TI = 7%, 10% and 20%). </p> <p>The dataset and the training algorithm can also be found here: <a href="https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master">https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master</a></p>
Viet Nam Technology catalogue for power generation and storage. Input for power system modelling
<p>The first Viet Nam Technology Catalogue was published in 2019. This new version includes all the technologies from the 2019 version that have been reviewed and updated where necessary. A main focus of the update has been to add new subcategories of technologies (roof-top solar PV, floating offshore wind, low wind speed turbines, improved flexibility of coal fired plants and pollution prevention technologies for coal power) as well as completely new technology descriptions and data sheets (tidal power, wave power, carbon capture and storage, coal CFB boilers and industrial cogeneration).</p> <p>This publication is developed under the Danish-Vietnamese Energy Partnership.</p> <p>The technologies described in this catalogue cover both very mature technologies and emerging technologies, which<br> are expected to improve significantly over the coming decades, both with respect to performance and cost. This<br> implies that the cost and performance of some technologies may be estimated with a rather high level of certainty<br> whereas, in the case of other technologies, both cost and performance today and in the future is associated with a<br> high level of uncertainty. All technologies have been grouped within one of four categories of technological<br> development described in the section on research and development indicating their technological progress, their<br> future development perspectives and the uncertainty related to the projection of cost and performance data.</p> <p>The technologies in the catalogue include the power production unit and the connection to the grid. This means<br> that the boundary for both cost and performance data are the generation assets plus the infrastructure required to<br> deliver the energy to the main grid. For electricity, this is the nearest substation of the transmission grid. This<br> implies that a MW of electricity represents the net electricity delivered, i.e. the gross generation minus the auxiliary<br> electricity consumed at the plant. Hence, efficiencies are also net efficiencies.</p> <p>The text and data have been edited based on Vietnamese cases to represent local conditions. For the mid- and long-<br> term future (2030 and 2050) international references have been relied upon for most technologies since Vietnamese<br> data is expected to converge to these international values. In the short run differences may exist, especially for the<br> emerging technologies. Differences in the short run can be caused by e.g. current rules and regulations and level of<br> market maturity of the technology. Differences in both the short and long run can be caused by local physical<br> conditions, e.g. seabed material and offshore conditions can affect costs of offshore wind farms and wind speed can<br> affect the dimensioning of rotor vs. generator which can influence the cost, or domestic coal quality can affect<br> efficiency and variable cost of coal-fired plants as well.</p> <p>Land use is assessed but the cost of land is not included in the total cost assessment since this depends on local<br> conditions.</p>
Dataset from Denmark for Modeling a Highly Renewable Power System
<p>This is a completed database of Danish electrical transmission system for modeling an electrical system with high penetration of renewable energy.</p>
RESTful API Testing with the Power of Large Language Models
<p>Sample data for <a href="https://ase2023.hotcrp.com/u/1/paper/519">RESTful API Testing with the Power of Large Language Models</a></p>
Automated patent extraction powers generative modeling in focused chemical spaces: Training data and model checkpoints release
<p>Training data and model checkpoints accompanying paper on "Automated patent extraction powers generative modeling in focused chemical spaces". If you use this data, please cite the following manuscript:</p> <pre>@article{subramanian2023automated, title={Automated patent extraction powers generative modeling in focused chemical spaces}, author={Subramanian, Akshay and Greenman, Kevin P and Gervaix, Alexis and Yang, Tzuhsiung and G{\'o}mez-Bombarelli, Rafael}, journal={Digital Discovery}, year={2023}, publisher={Royal Society of Chemistry} }</pre>
FIGURE 1 in Harnessing the power of AI language models for taxonomy and systematics: a follow-up to "Can ChatGPT be leveraged for taxonomic investigations? Potential and limitations of a new technology" by Davinack (2023)
FIGURE 1. Python code generated by ChatGPT that allows the preparation of a NEX file format. For Python tutorials, source code and installers, see https://www.python.org.
A Modified Mathematical Model to Calculate Power Received by Mechanically Ventilated Patients With Different Etiologies
ClinicalTrials.gov study NCT04046380. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
Open the record for dataset details and reuse information.
Data from: Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement
Open the record for dataset details and reuse information.
Data from: Predictive power of food web models based on body size decreases with trophic complexity
Food web models parameterized using body size show promise to predict trophic Interaction Strengths (IS) and abundance dynamics. However, this remains to be rigorously tested in food webs beyond simple trophic modules, where indirect and intraguild interactions could be important and driven by traits other than body size. We systematically varied predator body size, guild composition and richness in microcosm insect webs and compared experimental outcomes with predictions of IS from models with allometrically scaled parameters. Body size was a strong predictor of IS in simple modules (r2=0.92), but with increasing complexity the predictive power decreased, with model IS being consistently overestimated. We quantify the strength of observed trophic interaction modifications, partition this into density-mediated vs. behaviour-mediated indirect effects and show that model shortcomings in predicting IS is related to the size of behaviour-mediated effects. Our findings encourage development of dynamical food web models explicitly including and exploring indirect mechanisms.
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>
Dataset: Model-based Cognitive Communications for Low-power Wireless Networks
<p>Dataset: Model-based Cognitive Communications for Low-power Wireless Networks</p>
Toward nebular spectral modeling of magnetar-powered supernovae
<p>The set of models analyzed in Omand and Jerkstrand (2023). The zip files contain all data for the corresponding epoch and composition. All the outputs for each model are contained in the folder with the model's ID, using the system explained in the paper.</p>
Transmission line model of the lossless T-shaped power di- vider.
Open the record for dataset details and reuse information.
Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth-death models has made it possible to detect their signature based on extant-taxa phylogenies. Most birth-death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree ("single pulse" approach), in contrast to the paleontological record, where MEEs have a time-duration. Here, we explore the power of a Bayesian Birth-Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a "time-slice" approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on phylogenies size and MEE intensity. Comparisons of BDSKY with the single-pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
Supplementary data: "Physics-informed machine learning for power grid frequency modelling"
<p>This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" <a href="https://doi.org/10.48550/arXiv.2211.01481">(Preprint)</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">available at github</a>.</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files:</p> <ul> <li><em>tuning </em>: Results of hyper-parameter tuning.</li> <li><em>best_model </em>: Weights of the trained model with best hyper-parameters.</li> <li><em>best_model_<scaling-variation> </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling.</li> <li><em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised.</li> <li><em>shap_values_<parameter>_long.h5</em> : SHAP values for the prediction of the system parameters.</li> </ul>
Data from: Predictive power of food web models based on body size decreases with trophic complexity
Open the record for dataset details and reuse information.
Data from: Exploring the power of Bayesian birth-death skyline models to detect mass extinction events from phylogenies with only extant taxa
Open the record for dataset details and reuse information.
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.