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.
239
datasets available to search
ShareScore release 0.7.1
Dataset results
239 results for “Energy system”
Seasonal analysis comparison of three air-cooling systems in terms of thermal comfort, air quality and energy consumption for school buildings in Mediterranean climates
<p>Efficient air-cooling systems for hot climatic conditions, such as Southern Europe, are required in the context of nearly Zero Energy Buildings, nZEB. Innovative air-cooling systems such as regenerative indirect evaporative coolers, RIEC and desiccant regenerative indirect evaporative coolers, DRIEC, can be considered an interesting alternative to direct expansion air-cooling systems, DX. The main aim of the present work was to evaluate the seasonal performance of three air-cooling systems in terms of air quality, thermal comfort and energy consumption in a standard classroom. Several annual energy simulations were carried out to evaluate these indexes for four different climate zones in the Mediterranean area. The simulations were carried out with empirically validated models. The results showed that DRIEC and DX improved by 29.8% and 14.6% over RIEC regarding thermal comfort, for the warmest climatic conditions, Lampedusa and Seville. However, DX showed an energy consumption three and four times higher than DRIEC for these climatic conditions, respectively. RIEC provided the highest percentage of hours with favorable indoor air quality for all climate zones, between 46.3% and 67.5%. Therefore, the air-cooling systems DRIEC and RIEC have a significant potential to reduce energy consumption, achieving the user’s thermal comfort and improving indoor air quality.</p>
Indicator values for the many-body localization of the Heisenberg spin chain at various energies, system sizes and disorder magnitude
<p>This data set accompanies the preprint <em>Scalable approach to many-body localization via quantum data </em>(arXiv: <a href="https://arxiv.org/abs/2202.08853">2202.08853</a>) and its code, available at <a href="https://github.com/GreschAl/MBLlearning">GitHub</a>.</p> <p>It consists of the numerically obtained indicator values for the many-body localization for the Heisenberg model (see preprint for details and background) for various values of the energy density <span class="math-tex">\(\epsilon = 0.05, 0.1, \dots, 0.9, 0.95\)</span> and for chain lengths <span class="math-tex">\(L = 10, 12, 14\)</span>. For each tuple <span class="math-tex">\((\epsilon,L)\)</span>, there exist two files, representing the training and the test set, respectively. Each such file contains various values of the disorder parameter <span class="math-tex">\(h = 0.5, 1, \dots, 14.5, 15\)</span> with <span class="math-tex">\(N = 1000\ (100)\)</span> sampled realizations of the disorder vector for each <span class="math-tex">\(h\)</span> for the training (test) set, followed by the three calculated values of the three indicators.</p> <p>The data is automatically processable by the code provided in the GitHub repository.</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>
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
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>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Dataset: Orion Energy Systems, Inc. (OESX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
CDR deployment in Europe, NEGEM-scenario results from Pan-European TIMES-VTT energy system modelling as reported in Markkanen et al. (2024)
<p>This dataset includes cumulative and yearly carbon dioxide removal (CDR) deployment in NEGEM-scenarios for Europe.</p> <p>The results originate from Pan-European TIMES-VTT energy system model and are published in Markkanen et al. (2024), manuscript submitted to Environmental Research Letters, Focus issue on Carbon Dioxide Removals on 31/05/2024. </p> <p>Regional coverage: EU-31. Temporal coverage: until 2060. </p> <p>Cumulative values are reported for the period 2025-2050. Yearly values are reported for 2010, 2020, 2030, 2040, 2050 and 2060.</p> <p>Negative emission technologies and practises (NETPs) included: bioenergy with carbon capture and storage (BECCS), biochar, direct air carbon capture and storage (DACCS), enhanced weathering (EW), forestry (A/R; afforestation and reforestation) and soil carbon sequestration (SCS). Additionally, sum of total CDR is reported, which is the sum of NETPs. For the yearly data, absolute CO2 emissions and net CO2 emissions are reported. </p> <p>Data covers six (6) NEGEM-scenarios, TEC, ENV and SEC, and their limited variants, which exclude the use of EW and SCS. Storylines and main assumptions for NEGEM-scenarios are reported in NEGEM Deliverable 8.2 Quantifying the NEGEM pathways and impact assessments with global TIMES-VTT and PET-VTT IAMs by <a href="https://www.negemproject.eu/wp-content/uploads/2023/11/NEGEM_D8.2_NEGEM-scenarios.pdf" target="_blank" rel="noopener">Lehtilä et al. (2023).</a></p>
H2020 OPERA Project: Mooring System Experimental data from MARMOK-A-5 Wave Energy Converter at BiMEP
<p>Funded under European Union's Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project’s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a condition monitoring system was deployed during the open-sea testing campaign of IDOM's MARMOK-A-5 wave energy converter, while this was deployed in the Biscay Marine Energy Platform (BiMEP) from October 2016 to June 2019.</p> <p>The dataset herein contains a collection of experimental results obtained during this extensive testing campaign, The campaign covers two deployment periods, where the first testing period includes polyesther tethers and the second testing campaing includes innovative elastomeric tethers as described in more detail in the project documentation. This experimental dataset aims to provide quantitative comparison data of the dynamic behavior of the system under these two different configurations.</p>
JRC-EU-TIMES - JRC TIMES energy system model for the EU
<p>JRC-EU-TIMES is designed for analysing the role of energy technologies and their innovation for meeting Europe's energy and climate change related policy objectives. This database contains a synchronised model version of the full JRC-EU-TIMES.and all input Excel files for the JRC-EU-TIMES model, owned by JRC. The TIMES code is not part of this download; it is owned by ETSAP. The TIMES code is open for anyone that requests the code after signing a letter of agreement. Other third party software is needed:VEDA software for data and result handling and GAMS for the optimisation.</p>
Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
<p>The dataset titled <strong>"Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</strong><strong>"</strong> was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</p>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Simulation systems of: "Free energies of membrane stalk formation from a lipidomics perspective"
<p><strong>Simulation systems of: </strong></p> <p>Free energies of membrane stalk formation from a lipidomics perspective</p> <p>Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub</p> <p>Nature Communications, 12, 6594 (2021), <a href="https://doi.org/10.1038/s41467-021-26924-2">https://doi.org/10.1038/s41467-021-26924-2</a></p> <p> </p> <p><strong>First published as a preprint manuscript in BioRxiv as:</strong></p> <p>Free energies of stalk formation in the lipidomics era</p> <p>Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub,</p> <p>BioRxiv, https://www.biorxiv.org/content/10.1101/2021.06.02.446700v1, 2021</p> <p>The archive contains</p> <ul> <li>starting conformations of double-membrane systems</li> <li>topologies</li> <li>MD parameter files</li> </ul> <p>Running the simulations requires a modified version of GROMACS, which implements the chain coordinate available at GitLab:</p> <p><a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a></p>
Experimental Data of Edge Energy Management System (EEMS)
<p>Readme>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> This data repository contains the experimental data of low-voltage and <br> industrial settings. Separate folders are provided for both data artefacts.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p>Low Voltage Residential Settings >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> Different sub-Folders are provided with respect to the different <br> combination of electrical load</p> <p>Each folder contains .txt file</p> <p>This File Contains Timestamp such that date and time are separated by "!".<br> After parsing time, Voltage and Current are separated by the "Current".<br> Then each voltage and current waveforms can be further parsed by "@@@"<br> because non-consecutive waveform signals are separated using this symbol in <br> project. Furthermore, the variable slab during data acquisition is provided <br> at the end of .txt file and can be parsed using the term "Const"</p> <p>This data parsing process is also provided in all phase calculation files.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p>Industrial Islanded Power System >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><br> This Folder contains the data acquired from 15 diesel gensets. Their <br> operational parameters are logged in MySQL database and are presented using<br> LAMP server. This MySQL data is provided as CSV file in this folder.</p> <p>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>></p> <p> </p>
Dataset for Service Restoration of Distribution Networks Considering Energy Storage System
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Techno-economic dataset for long-term energy systems modelling in Viet Nam
<p>Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam. This includes data on electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050.</p>
Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
<p>The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System.</p> <p>Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication.</p> <p><br> The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020.</p> <p>The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework.</p> <p>We encourage you to contribute to improving the datasets.</p>
Dataset of the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems"
<p>The dataset refers to the measurement and simulations of the supply system and rolling stock of line 10 B of Metro de Madrid. Simulations have been performed by changing the position of the reversible substation and computing the current flowing in the braking rheostat of the simulated rolling stock. The data refer to the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems" published in "The Open Transportation Journal".</p>
HEMStoEC: Home Energy Management Systems to Energy Communities DataSet
<p>The building sector is responsible for about 1/3 of all final energy consumed in the world. It is also responsible for about 30 % of CO<sup>2</sup> emissions from the end-use sector when accounting for indirect emissions from the use of electricity and heat in buildings. Focusing on EU, the use of electricity to satisfy the loads of lighting and most electrical appliances represents about 14.5 % of the energy consumed in residential sector, excluding heating and cooling systems, and 24.8 % including the latter. Therefore, we are in the presence of a sector that has a significant weight in the final energy consumption figures. Thus, innovative energy initiatives should contribute towards reducing energy consumption, reducing the effects on the climate, and achieving greater energy efficiency. These initiatives begin to emerge to a certain extent from small consumers, as they become more aware of environmental issues, either isolated or grouped in an energy community, where generated or stored energy is shared between stakeholders. In addition, energy markets go through a transition period and begin to give way, recognize, and promote the emerging role of prosumers (producers+consumers).</p> <p>It is within this context that this dataset is introduced. It allows, for a single prosumer, to:</p> <ol> <li>Test and validate different control strategies for home energy management systems;</li> <li>Design forecasting energy consumption models;</li> <li>Design forecasting PV energy generation models;</li> <li>Test and validate different non-invasive load monitoring (NILM) algorithms;</li> <li>Design forecasting thermal comfort models, as well as test and validate control strategies for Heating, Ventilation and Air Conditioning (HVAC) systems.</li> </ol> <p>Additionally, for a community of 4 houses, it allows to:</p> <ol> <li>Test and validate different control strategies for the community energy management system;</li> <li>Design forecasting community energy consumption models;</li> <li>Test and validate transfer learning strategies for NILM.</li> </ol> <p>The data, spanning more than three years, is stored in Matlab -v7 format, . This allows to be read by other languages, such as python.</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.