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.
50
datasets available to search
ShareScore release 0.9.0
Dataset results
50 results for “storage system”
Data and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
Data for: Multi-year field measurements of home storage systems and their use in capacity estimation
<p>The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, <a href="https://doi.org/10.1038/s41560-024-01620-9">DOI 10.1038/s41560-024-01620-9</a>. <br><br>In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, <a href="https://doi.org/10.48550/arXiv.2411.08025">DOI 10.48550/arXiv.2411.08025</a></p> <p>The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. </p> <p>Use the data as follows:<br><br>1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip)</p> <p>2. Uncompress the files so that the uncompressed folders have the same name as the .zip files.</p> <p>3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. </p> <p>In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.</p>
Dataset for "Sustainable Disposal and End-of-Life Treatment of Battery Energy Storage Systems: An Environmental and Economic Case Study"
This study investigates the environmental and economic impacts of end-of-life (EOL) treatment for a 2.8 MWh/2.5 MW battery energy storage system (BESS) based on lithium-ion batteries (LIBs). It focuses on recycling pre-treatment processes for battery systems and recycling procedures for components like cooling systems, fire extinguishing systems, inverters, and the reuse of BESS containers and substations. A life cycle assessment (LCA) was employed to evaluate key environmental impacts, including climate change, eutrophication, and resource use. The study reveals substantial environmental benefits, particularly from recovering secondary materials like aluminium and copper, with recycling pre-treatment contributing significantly to overall benefits. Additionally, the economic analysis projects profits, emphasizing the advantages of locally sourcing critical raw materials. The research highlights the need for more sustainable recycling practices and provides insights for improving environmental and economic strategies in BESS management, offering guidance for future research and policy development in battery waste processing.
Data supplement for "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition"
<p>This data supplements the publication "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition" by Thomas Kastner, Abhishek Chaudhary, Simone Gingrich, Alexandra Marques, U. Martin Persson, Giorgio Bidoglio, Gaëtane Le Provost, Florian Schwarzmüller, available here:</p> <p><a href="https://doi.org/10.1016/j.oneear.2021.09.006">https://doi.org/10.1016/j.oneear.2021.09.006</a></p> <p>For details, please refer to that publication.</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Net load profile for a 10 MWh hydrogen storage system
<p><i>Subject: <strong>Energy</strong></i></p><p><i>Specific subject area: <strong>Hydrogen Storage Modelling</strong></i></p><p><i>Data format: <strong>Raw</strong></i></p><p><i>Data source: <strong>Simulation </strong></i></p><p><i>Data format: <strong>Table</strong></i></p><p><i>Type of data: <strong>Timeseries </strong></i></p><p><i>Date format:<strong> YYYY-MM-GG hh:mm:ss</strong></i></p><p><i>Resolution: <strong>1 hour</strong></i></p><p><i>Units: <strong>Watt [W]</strong></i></p><p> </p><p><strong>Description</strong></p><p>This dataset encompasses 102 net-load, each serving as a guide for either charging or discharging a hydrogen energy storage system with a 0.4 roundtrip efficiency. These profiles correspond to a variety of power energy services.</p><p>Two primary characteristics distinguish each power energy service in the dataset:</p><ul><li>The length of the discharge period refers to the duration over which the stored energy is released.</li><li>The number of charge/discharge cycles within a year indicates how often the service is delivered.</li></ul><p>Although these characteristics vary among the different services, the energy fed to the storage remains constant at 10 MWh per cycle. The unique aspect is the rate at which this stored energy is delivered to fulfill varying power demands, which differs according to each service's specific profile.</p><p>In terms of interpreting the net-load profiles:</p><ul><li>A positive value in a profile signifies a phase of charging, indicating that excess generation is being stored.</li><li>A negative value, on the other hand, denotes a discharge phase, where the storage system delivers a power energy service.</li></ul>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>battery energy storag</span><span>e </span><span>systems</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Battery energy storage is the fastest growing form of power system </span><span>flexibility, and</span><span> will be critical to integrating large shares of variable renewable energy.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>671</span></span><span><span> datapoints from </span></span><span><span>18</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the </span><span>literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.</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>
A simplified palaeoceanography archiving system (PARIS) and GUI for storage and visualisation of marine sediment core proxy data vs age and depth.
<p>Scientific discovery can be aided when data is shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data (Wilkinson et al., 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i.e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project. Thanks to the relative simplicity of downcore palaeoclimate data, we demonstrate the power of this simple but powerful file format to function as a basic database in itself: we provide a Matlab-based GUI tool that allows users to search and visualise data by sediment core location, proxy type and species type. The adoption of similarily accessible, machine-readable file formats at other laboratories will promote data sharing within projects, while also allowing for the automation of submission of data to online database repositories with particular formatting and/or metadata requirements, thus reducing post-hoc workload.</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>
Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)
<p>All data used in paper "Piezomagnetic Fields Generated by the Gas Injection Process in Hutubi Ultralarge Underground Gas Storage System (China)".</p>
Survey of storage systems topics covered at top universities
<p>The CSV file contains our survey results with links to the specific courses.<br>The jupyter notebook contains the scripts used to generate the plot and table we use in the paper.<br>To run the jupyter notebook:<br>1. Install required dependencies using `pip install -r requirements.txt`<br>2. Start `jupyter lab`<br>3. Run the cells in order</p>
Surface science and liquid phase investigations of oxanorbornadiene/oxaquadricyclane ester derivatives as molecular solar thermal energy storage systems on Pt(111) [doi: 10.1063/5.0158124]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1063/5.0158124]</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
Data for heat transfer characterisation of inserts of ASTEP thermal storage system
<p>Dataset for heat transfer characterisation of Honeycomb inserts and Y-Inserts considered in ASTEP project.</p>
Challenges to Profitability for Energy Storage in High Renewable Energy Systems
<p>This figure illustrates the impact of renewable energy source (RES) penetration on electricity price spreads and the profitability of energy storage systems.</p> <p>In scenarios with a low share of RES, the price spread is significant. During low-demand periods, electricity is predominantly supplied by cost-effective RES, leading to lower prices. Conversely, during high-demand hours, traditional power plants with higher operational costs are required, resulting in elevated prices. Energy storage systems can capitalize on this large price spread by charging during low-price periods and discharging during high-price periods, thereby maximizing their profits.</p> <p>In contrast, with a high share of RES, both low and high-demand periods are largely covered by renewable sources. This extensive reliance on RES minimizes the price differential between these periods, resulting in a much smaller price spread. Consequently, the potential for storage systems to profit from price arbitrage is reduced, as the opportunities to buy low and sell high diminish.</p>
Importance of storage in renewable power systems
<p>This figure illustrates the renewable energy production from run-of-river hydro, wind, and photovoltaic (PV) sources, alongside the daily electricity demand curve. It highlights that during midday, the combined renewable energy production exceeds the demand, creating an opportunity to charge energy storage systems. Conversely, during the night, morning, and evening hours, renewable production falls short of meeting the demand. During these periods, the stored energy should be discharged to ensure a stable and reliable power supply. This emphasizes the critical role of energy storage in balancing supply and demand in renewable power systems.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
Data from "Evaluating the Risk of Data Loss Due to Particle Radiation Damage in a DNA Data Storage System"
<p>Raw sequencing data from irradiated DNA samples and controls.<br>Sequences for encoded files used in the irradiation studies.<br>Please see the publication's Source Data file for mapping to encoded files.</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.