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.
19
datasets available to search
ShareScore release 0.7.1
Dataset results
19 results for “Urban energy”
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. „Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling
<p>Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.</p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
SESMG model scenarios of the study "Indicators for the optimization of sustainable urban energy systems based on energy system modeling"
<p>This folder contains the model scenarios belonging to the publication "<strong>Indicators for the optimization of sustainable urban energy systems based on energy system modeling</strong>" (<a href="https://doi.org/10.1186/s13705-021-00323-3">https://doi.org/10.1186/s13705-021-00323-3</a>).</p> <p>The individual scenarios can be executed and evaluated with the <strong>Spreadsheet Energy System Model Generator (<a href="https://github.com/chrklemm/SESMG">SESMG</a>)</strong> <a href="https://doi.org/10.5281/zenodo.5412027">v0.0.4</a>, respectively <a href="https://doi.org/10.5281/zenodo.5520513">v0.2.0</a>.</p> <p>The file names are to be understood as follows:</p> <p><em>"scenario name"_"(dispatch) optimization criterion"_"scenario concretization"_"further scenario concretization"_"associated program version"</em>.xlsx.</p> <p>For example, the title name "<em>Scenario3_C_4MW_Biogas_SESMGv0.0.4.xlsx</em>" contains the following information:<br> - This file belongs to scenario 3 (see main publication for details).<br> - Dispatch optimized according to energy costs C (see main publication for details).<br> - The scenario contains 4 MW biogas CHP capacity (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.0.4.</p> <p>Another example. The title name "<em>optimization_C_80PercentDemand_70PercentEmissions_SESMGv0.1.1.xlsx</em>" contains the following information:<br> - This file belongs to the optimization scenario (see main publication for details).<br> - The primary optimization criterion is energy costs C (see main publication for details).<br> - Energy demand was capped at 80 percent and emissions at 70 percent of baseline (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.1.1.<br> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors would like to thank Prof. Dr. Peter Vennemann (Münster University of Applied Sciences) for the constructive discussion regarding this article. This research has been conducted within the R2Q project, funded by the German Federal Ministry of Education and Research (BMBF) - grant number 033W102A and the junior research group energy sufficiency funded by the German Federal Ministry of Education and Research (BMBF) as part of its Social-Ecological Research funding priority, funding number 01UU2004A. </p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Measures of urban form and mobility energy use indices for each census tract in the United States
<p>This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form. </p>
Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases
<p>Dataset associated to the article: Rodríguez-Álvarez, J.Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases. <em>Buildings </em><strong>2023</strong>, <em>13</em>, x.</p> <p>It contains filtered EPC datasets as xls and csv and shapefiles with the buildings' geometry and estimated energy loads</p>
Measures of urban form and mobility energy use indices for each census tract in the United States
Open the record for dataset details and reuse information.
Data from: home ranges, habitat selection, and energy expenditure of Strix varia (Barred Owls): understanding the full diel cycle matters for enhancing urban landscapes
Open the record for dataset details and reuse information.
Water, energy and carbon fluxes and ancillary meteorological measurements of four different urban landscapes in Phoenix, AZ during 2015
<p>Water, energy and carbon fluxes and ancillary meteorological measurements of four different urban landscapes in Phoenix, AZ during 2015. The measurements were done in three temporal not continuous deployment and in a permanent site as a reference. The urban landscape sites consisted in:</p> <ul> <li>A xeric landscape (XL), with measurements from 01/20/2015 to 03/13/2015.</li> <li>A parking lot (PL), with measurements from 05/19/2015 to 06/30/2015.</li> <li>A mesic landscape (ML), with measurements from 07/08/2015 to 09/18/2015.</li> <li>A reference suburban neighbourhood (REF), with measurements from 01/01/2015 to 12/13/2015.</li> </ul> <p>Water energy and carbon fluxes were processed using the software EdiRe. If additional data or information is needed, please contact the authors.</p> <p>The use of the datasets requires the citation of the next papers:</p> <p>- Templeton, N.P., Vivoni, E.R., Wang, Z-H., and Schreiner-McGraw, A.P. 2018. Quantifying Water and Energy Fluxes over Different Urban Land Covers in Phoenix, Arizona. Journal of Geophysical Research - Atmospheres. 123(4): 2111-2128.</p> <p>-Pérez-Ruiz, E. R., Vivoni, E. R. and Templeton, N. P. 2020. Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona. PLoS ONE 15(2): e0228537. https://doi.org/10.1371/journal.pone.0228537</p>
CoUDlabs_WP8_T811_UOS_001. Investigating geometrical effects on hydraulic energy losses during sewer to surface flow interactions during urban floods
<p>This document describes the dataset used in CO UD-labs JRA3 (WP 8) Task 8.1.1. This considers the hydraulic exchange (surcharge) from a piped drainage system to surface flood flow through a manhole. The dataset includes measurements of pressure, flow rate and depth from a physical scale model. The effect of changing the manhole lid properties on flow exchange (surcharge) and pressure in the experimental system is quantified over a range of flow rates. </p>
Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"
<p>This dataset contains the global multi-model urban climate and energy projections from Li et al. (2024), "Elevated urban energy risks due to climate-driven biophysical feedbacks", published in <em>Nature Climate Change</em>. It contains global monthly mean projections of urban 2-meter air temperature, and urban cooling and heating energy fluxes derived from 25 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Details about how this dataset was generated are described in the article. This dataset may be useful for multiple communities interested in future energy risks, climate change impacts and vulnerability, and climate-sensitive adaptation and energy planning.</p> <p>For more details, please refer to the README.md file included in the dataset.</p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Evaluating water and energy fluxes across three distinct land cover types in a desert urban environment
Urbanization impacts surface energy and water balances across multiple spatial and temporal scales, which can be particularly important in desert cities where resources are limited. Urban climate observations are limited, especially over a variety of locations that represent urban land cover. To help address the lack of observations over different urban land cover types, a mobile eddy covariance tower (ECT) was deployed at three different locations in the Phoenix metropolitan area, representing a xeric landscape (drip irrigated palo verde trees with gravel), a parking lot, and a mesic landscape (sprinkler irrigated turf grass). In this project, data obtained from the mobile ECT deployments will be coupled with data from an eddy covariance tower managed by CAP LTER in the Maryvale suburb of Phoenix, Arizona. Data is processed to obtain energy and water fluxes, which are controlled by land surface characteristics, over the four distinct land cover types.
Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
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.