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855 results for “model system”
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario
<p>The database is built over three main sources</p> <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> <p>Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset.</p> <p>Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset.</p> <p>The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above. </p> <p>Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.</p> <p> </p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"
<p>Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript.</p> <p>The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review.</p> <p>Please consult the README file for information on the specifications of the files.</p> <p>These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Finite Element model data for Academic Rotor bladed-disc system
<div> <div> <div> <p>A computational finite element based technique is proposed for developing a stochastic reduced order model for rotating bladed disc with spatial random inhomogeneities. The spatial inhomogeneities imply the system to be randomly mistuned. The formulation assumes the availability of a high fidelity finite element (FE) model for the tuned system. The corresponding FE matrices are antisymmetric on account of the Coriolis forces due to rotation. The spatial inhomogeneities, available from limited point measurements on the blades, are modelled as non-Gaussian random fields with arbitrary distributions. A low order stochastic computational model is developed by projecting the FE model onto a reduced dimensional state space defined in terms of specified observable nodal points and expressing the stochasticity through an arbitrary polynomial chaos (aPC) basis. This model enables probabilistic quantification of the variabilities in the system response and estimating failure probabilities. The methodology enables drastic reduction in the state space and stochastic dimensions, addresses the practical difficulties with having limited measurable data points, antisymmetric FE matrices, aPC representation in complex irregular geometries and carrying out probabilistic analyses on industrial systems, at significantly reduced computational costs. The methodology is illustrated through an academic rotor and an industrial rotor blade.</p> </div> </div> </div>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]
<p>This data package supports empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990–2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide a total of 1359 processed and harmonized data files in a format that is suitable as inputs to electricity system models. Four types of data files are included for each country: (i) a country file documenting national demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing fuel prices and CO2 emissions for each fuel, and (iv) load profiles describing 24-hour national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions. </p>
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Modelled basal melt rate underneath the Antarctic Lambert–Amery glacial system
<p>This is the modelled basal melt rates in six experiments using different geothermal heat flux (GHF) maps as forcing for an offline coupling between a forward model and an inverse model. The forward model consists of a thermomechanical steady state model using an improved shallow ice approximation in equilibrium with the subglacial hydrological system. The inverse model is solved using 3D full Stokes model.</p> <p>The six GHF maps are from Martos et al. (2017), Shen et al. (2020), An et al. (2015), Shapiro and Ritzwoller (2004), Purucker (2012) and Li et al. (2021).</p> <p>Each filename includes the family name of the first author of the GHF map that is used. For instance, basal_meltrate_an.nc provides the modelled basal melt rates using An et al. (2015) GHF. </p> <p>Files are provided in netcdf format. The netcdf files have 2D grids only. The unit of modelled basal melt rate is mm/yr. The value outside our modelled domain is given as 9.96921e36. The positive value inside our modelled domain represents basal melt rate, while negative represents basal freeze rate.</p> <p>For more information, please see the following paper that is currently accepted by The Cryosphere:</p> <p>H. Kang et al.: Evaluation of six geothermal heat flux maps for the Antarctic Lambert–Amery glacial system, The Cryosphere, 2022.</p> <p> </p>
Bulk and critical material demand for selected 'Starter Kit' energy system models - dataset
<p>This repository contains the data related to the Data in Brief article titled: <strong>Bulk and critical material demand for selected ‘Starter Kit’ energy system models.</strong></p> <p>The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.</p>
Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"
<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems. <em>Nat Hum Behav</em> (2023). https://doi.org/10.1038/s41562-022-01510-8 </p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”.</p> <p>Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux) </strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to. </p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> <p>https://zenodo.org/records/7102065/files/ages80to89_u4120_exgauss.zip<br>https://zenodo.org/records/7102065/files/optimal_square9.zip<br>https://zenodo.org/records/7102065/files/optimal_square8.zip<br>https://zenodo.org/records/7102065/files/optimal_square7.zip<br>https://zenodo.org/records/7102065/files/optimal_square6.zip<br>https://zenodo.org/records/7102065/files/optimal_square5.zip<br>https://zenodo.org/records/7102065/files/optimal_square4.zip<br>https://zenodo.org/records/7102065/files/optimal_square3.zip<br>https://zenodo.org/records/7102065/files/optimal_square2.zip<br>https://zenodo.org/records/7102065/files/optimal_square1.zip<br>https://zenodo.org/records/7102065/files/optimal_square10.zip<br>https://zenodo.org/records/7102065/files/model_metadata.csv<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4239_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4120_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3701_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1447_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4864_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3898_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_early.zip<br>https://zenodo.org/records/71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ps://zenodo.org/records/7102065/files/ages30to39_u1387_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u890_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5396_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5326_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3750_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u367_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3365_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3139_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2809_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2360_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u224_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1559_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1474_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1444_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_early.zip</p> <p> </p>
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 1: The ECG model-MAPPING BETWEEN SEMANTIC GRAPHS AND SENTENCES IN GRAMMAR INDUCTION SYSTEM
<p>The following Figure 1 shows a sample semantic graph that describes a<br> simple test world.<br> During the processing of the ECG, the base units of the graph are the ECG<br> atoms. An ECG atom corresponds to a primitive statements related to one<br> predicate. It has a structure of one-level deep tree, where the root of the tree<br> is the predicate and the concepts linked to it are the leaves. The child concept<br> of the root predicate may be not only a single concept but it can be another<br> ECG atom.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
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.