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Landscape Phenology from Unmanned Aerial Vehicle Photography at Harvard Forest 2013
This data set contains orthophotos in the vicinity of the EMS tower at Harvard Forest, as well as the flight logs from the unmanned aerial vehicle (UAV) used to obtain the digital images used in orthophoto creation. Orthophotos were created by mosaicking approximately 200 JPEG images from each date of observation. The orthophotos cover the spatial extent of the 250 meter resolution MODIS pixel that contains the EMS tower. Land cover types in the area of photography include deciduous and evergreen forest, and wetlands. The research goal of data collection for this data set was to observe spatial variance in plant phenology. Therefore, photos were taken from before leaf out until after leaf drop. Orthophotos were collected approximately every 5 days during spring and weekly during fall; see filenames for specific dates. The nominal spatial resolution of the orthophotos is 6 cm, however due to various factors including inaccuracy of the onboard GPS, wind-blown motion of trees, the automated orthophoto mosaicking process, and user error in final georeferencing, image analysis has been conducted at 10 m resolution. The orthophotos are available as GeoTIFF files.
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Bidirectional and Unidirectional Charging Profiles of Electric Vehicles
<p>This dataset contains bidirectional and unidirectional charging profiles of Electric Vehicles (EVs) measured in laboratory environment at the Smart Grid Technology Lab of ie³ institute at TU Dortmund University. The dataset not only considers charging power and current but also harmonics/interharmonics emission of EV charging in both static and dynamic scenarios. Thus, it provides a solid foundation for the development of advanced EV charging algorithms and model validation. Raw data are available in csv format from the file <em>dataset_raw.zip</em> and a selection of merged measurements is provided in the file <em>dataset_merged.zip</em>.</p> <p>The following commercially available EV models are considered:</p> <ul> <li>Opel Corsa-e (2020)</li> <li>Fiat 500e (2022)</li> <li>Honda-e Advance (bidirectional, 2020)</li> <li>Nissan Leaf (bidirectional, 2020)</li> <li>VW ID.4 (2020)</li> <li>Hyundai Ioniq 5 (2021)</li> <li>Mitsubishi Eclipse Cross PHEV (bidirectional, 2022)</li> <li>Tesla Model Y SR (2022)</li> </ul> <p>The dataset is part of the deliverable D8.1 of DriVe2X project and is accompanied by a report including a description about data acquisition and measurement setup. The report is available from the project website's resources section. A more in-depth description of the tests and exemplary analysis is currently being prepared for publication.</p> <p><strong>References</strong></p> <ul> <li>DriVe2X project website: <a href="https://drive2x.eu/">Link</a></li> <li>CORDIS website: <a href="https://cordis.europa.eu/project/id/101056934">Link</a></li> <li>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></li> <li>Smart Grid Technology Lab: <a href="http://sgtl.et.tu-dortmund.de/">Link</a></li> </ul>
Unmanned Aerial Vehicles Dataset
<p><strong>Unmanned Aerial Vehicles Dataset:</strong></p> <p>The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Drone</td> </tr> <tr> <td>Training</td> <td>768</td> <td>818</td> </tr> <tr> <td>Validation</td> <td>384</td> <td>402</td> </tr> <tr> <td>Testing</td> <td>383</td> <td>400</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569</p> </blockquote>
Aerial Multi-Vehicle Detection Dataset
<p><strong>Aerial Multi-Vehicle Detection Dataset</strong>: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments. </p> <p>The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1] </p> <p>The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training. </p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Images</strong></td> <td><strong>Car</strong></td> <td><strong>Bus</strong></td> <td><strong>Truck</strong></td> </tr> <tr> <td>Training</td> <td>7239</td> <td>200301</td> <td>1601</td> <td>6247</td> </tr> <tr> <td>Validation</td> <td>904</td> <td>23397 </td> <td>193 </td> <td>727</td> </tr> <tr> <td>Testing</td> <td>905</td> <td>24715</td> <td>208</td> <td>770</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p>[1] Makrigiorgis, R., 2021. <em>Harpy Data Dataset</em>. [online] Kios.ucy.ac.cy. Available at: <https://www.kios.ucy.ac.cy/harpydata/> [Accessed 22 September 2022].</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442</p> </blockquote>
LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div> </div> <div> </div>
LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <p>This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 10m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div>
LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 10m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> </div> <div> </div>
Dataset for simulation studies of fleet vehicle selection in terms of pollutant emissions
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Szczepański E, Jachimowski R, Rudyk T. Simulation studies of fleet vehicle selection in terms of pollutant emissions. Combustion Engines. 2024;196(1):80-88. https://doi.org/10.19206/CE-169802 - published online: 2023-08-10, which discusses the application of simulation in solving the problem of vehicle selection and determining optimal approaches considering pollutant emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.csv: contains the input data used in the model, including data from the COPERT model.</li> <li>OutputOptimization.csv: contains output data</li> <li>OutputSummary.xlsx: contains output data</li> <li>imulation_model_xml.fsx: contains the code of the model in XML format.</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>
Data files: Electric vehicle charging dataset with 35,000 charging sessions from 12 residential locations in Norway
<p>Please refer to the data article where the data is described (Data-in-brief, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110883" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2024.110883</span></span></a>).</p> <p>The data article refers to the paper "A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study". The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in the paper, realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session.</p> <p>The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.</p>
Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major Cities in China
<div> <h1><strong>Background </strong></h1> </div> <div> <p>This dataset provides supporting data for the figures presented in our study on electric vehicle (EV) usage and charging behavior across major Chinese cities. The detailed analysis and raw data are thoroughly described in Zhan et al (2025). The study examines 1.69 million EVs, representing 42% of China's total EV fleet, from November 2020 to October 2021. The study provides insights into operational demands, infrastructure requirements, and energy consumption patterns by analyzing diverse vehicle types—including private cars, taxis, buses, and special purpose vehicles (SPVs). </p> </div> <div> <p>The purpose of this dataset is to enable researchers who do not have access to the same raw data to replicate, calibrate, or extend our findings using the processed data that underpins each figure. This resource is valuable for further research on EV infrastructure planning, energy consumption, and vehicle performance. This dataset is made available to help the research community leverage our findings and facilitate advancements in electric vehicle research and infrastructure planning. Please refer to Zhan et al (2025) for full details on the methodology and analysis. </p> </div> <div> <p> </p> <h1><strong>Data description </strong></h1> </div> <div> <p>This dataset includes the processed data underlying each figure in Zhan et al (2025), covering various aspects of EV usage, battery capacity, and charging behavior across seven major Chinese cities: Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing. The dataset is organized to correspond directly with the figures in the paper, facilitating its use for further analysis and model calibration. Each dataset is aligned with specific figures, providing essential data to help researchers without access to the original raw data. </p> </div> <div> <p> </p> <h2><strong>1. EV Type and Battery Energy Distribution Across Cities</strong></h2> </div> <div> <p><strong>Fig1a.Distribution of EV types across selected Chinese cities </strong></p> </div> <div> <p>File: Fig1a.Distribution of EV types across selected Chinese cities.csv </p> </div> <div> <p>Description: Distribution of EV types across seven cities, detailing the share of different vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Beijing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1b.Distribution of battery energy by vehicle types </strong></p> </div> <div> <p>File: Fig1b.Distribution of battery energy by vehicle types.csv </p> </div> <div> <p>Description: Distribution of battery energy across different vehicle types, represented as box plot statistics. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>2. Variations in Battery Energy</strong></h2> </div> <div> <p><strong>Fig1c.Variations of battery energy of buses </strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of buses across studied cities.csv </p> </div> <div> <p>Description: Battery energy variations for buses across the studied cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En </p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1d.Variations of battery energy of SPVs </strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of SPVs across studied cities.csv </p> </div> <div> <p>Description: Battery energy variations for special purpose vehicles (SPVs) across cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En </p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>3. Daily Driving Distance and Energy Consumption</strong></h2> </div> <div> <p><strong>Fig1e.Daily driving distance of different vehicle types </strong></p> </div> <div> <p>File: Fig1e.Daily driving distance of different vehicle types.csv </p> </div> <div> <p>Description: Cumulative distribution functions (CDFs) of daily driving distances for various vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>CDF Percentile </p> </div> <div> <p> </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Private car </p> </div> </div> </td> <td> <div> <div> <p>The value of private car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Official car </p> </div> </div> </td> <td> <div> <div> <p>The value of official car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>SPV </p> </div> </div> </td> <td> <div> <div> <p>The value of SPV daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Rental car </p> </div> </div> </td> <td> <div> <div> <p>The value of rental car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Bus </p> </div> </div> </td> <td> <div> <div> <p>The value of bus daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Taxi </p> </div> </div> </td> <td> <div> <div> <p>The value of taxi daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1f-1. Ratio of daily energy consumed over battery energy </strong></p> </div> <div> <p>File: Fig1f-1.The ratio of daily energy consumed over battery energy.csv </p> </div> <div> <p>Description: Ratio of daily energy consumption relative to battery energy for each vehicle type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1f-2. Number of charging events per day</strong></p> </div> <div> <p>File: Fig1f-2.The number of charging events per day.csv </p> </div> <div> <p>Description: Data on the number of daily charging events across vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>4. EV Usage Patterns and State of Charge (SOC)</strong></h2> </div> <div> <p><strong>Fig2a.Daily usage patterns of EVs </strong></p> </div> <div> <p>File: Fig2a.Daily usage patterns of EVs across different vehicle types and days.csv </p> </div> <div> <p>Description: Usage patterns of EVs by type and day, segmented into 15-minute intervals. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_day type_state </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_workday_driving as an example, it refers to the ratio of private cars parked to the total number of private cars on weekdays within a 15-minute period </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig2b. SOC levels before and after charging </strong></p> </div> <div> <p>File: Fig2b. SOC levels before and after charging by charging level by vehicle type.csv </p> </div> <div> <p>Description: SOC levels before and after charging events, classified by charging level and vehicle type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle_SOC_P </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_Start SOC_P1 as an example, it refers to SOC of private cars charging with P1 at the start of charging </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>5. Energy Consumption Rate (ECR) of Passenger Cars</strong></h2> </div> <div> <p><strong>Fig2c-top. ECR of passenger cars by month of the year </strong></p> </div> <div> <p>File: Fig2c-top.Energy consumption rate (ECR) of passenger cars by month of the year.csv </p> </div> <div> <p>Description: Monthly ECR of passenger cars in different cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Beijing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig2c-bottom.ECR of passenger cars as a function of temperature </strong></p> </div> <div> <p>File: Fig2c-bottom.ECR of passenger cars as a function of temperature.csv </p> </div> <div> <p>Description: Passenger vehicle ECR in relation to temperature across different cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Temperature </p> </div> </div> </td> <td> <div> <div> <p>Temperature of a city in a certain month </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>℃ </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ECR </p> </div> </div> </td> <td> <div> <div> <p>Average energy consumption rate of passenger cars of a city in a certain month </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>6. Charging Events and Load Distribution</strong></h2> </div> <div> <p><strong>Fig3-1.Number of vehicles being charged by level by time of day </strong></p> </div> <div> <p>File: Fig3-1.Number of vehicles being charged by level by time of day.csv </p> </div> <div> <p>Description: Number of vehicles charging at different power levels throughout the day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_P1_workday as an example, it refers to number of private cars being charged with P1 on weekdays within a 5-minute period </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig3-2.Daily charging load from electric vehicles </strong></p> </div> <div> <p>File: Fig3-2.Daily charging load from electric vehicles across different vehicle types and power level.csv </p> </div> <div> <p>Description: Charging load data across vehicle types and power levels, aggregated by time of day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_P1_workday as an example, it refers to charging load of private cars being charged with P1 on weekdays within a 5-minute period </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>7. Spatial Distribution of Max Charging Power </strong></h2> </div> <div> <p><strong>Fig4a. Annual maximum charging power within each hexagonal grid across Beijing, 4c Distributions of the three clusters of temporal charging profiles in Beijing, and 4d Share of clusters by city. </strong></p> </div> <div> <p><strong>FigS7-FigS12. Spatial distributions of charging power (kW): Max charging power and cluster distributions (City name). </strong></p> </div> <div> <p>File: max_power_cluster_cities.shp </p> </div> <div> <p>Description: This dataset covers the maximum charging power distribution across seven Chinese cities, using H3 grids with Resolution 8 (~0.74 km²). </p> </div> <div> <p>Cluster 0, 1, and 2 are defined based on the temporal profiles of charging power in the grids. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city </p> </div> </div> </td> <td> <div> <div> <p>Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>hex_id </p> </div> </div> </td> <td> <div> <div> <p>Hexagon ID of H3 system with Resolution 8. </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id </p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon. </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>max_power </p> </div> </div> </td> <td> <div> <div> <p>Maximum charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>Hexagons in EPSG: 4326 – WGS 84. </p> </div> </div> </td> <td> <div> <div> <p>Polygon </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>8. Temporal Patterns of Charging Power </strong></h2> </div> <div> <p><strong>Fig 4b Three unique clusters of daily temporal patterns of charging power (all cities) </strong></p> </div> <div> <p>File: clusters_tempo.csv </p> </div> <div> <p>Description: Temporal variations of charging power aggregated from all hexagons in each cluster. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id </p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon. </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>t </p> </div> </div> </td> <td> <div> <div> <p>Hourly index (0-23) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q25 </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q50 </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q75 </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Type </p> </div> </div> </td> <td> <div> <div> <p>Weekday/Weekend. </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h1><strong>Supplementary Information: </strong></h1> </div> <div> <h2><strong>S1. Accuracy and Quality of Data Collection: GPS Measurement Accuracy </strong></h2> </div> <div> <p><strong>FigSI1.Histogram of spatial errors in GPS Measurements </strong></p> </div> <div> <p>File: FigSI1.Histogram of spatial errors in GPS Measurements.csv </p> </div> <div> <p>Description: Analysis of the accuracy of GPS data used in the study. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of spatial error </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>m </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>S2. Charging Behavior Analysis </strong></h2> </div> <div> <h3><strong>Empirical Distributions of Charger Power Delivered: </strong></h3> </div> <div> <p><strong>FigSI2-1.Distributions of charger power delivered to cars </strong></p> </div> <div> <p>File: FigSI2-1.Empirical distributions of charger power delivered to cars.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for passenger cars. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>FigSI2-2.Empirical distributions of charger power delivered to buses </strong></p> </div> <div> <p>File: FigSI2-2.Empirical distributions of charger power delivered to buses.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for buses. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>FigSI2-3.Empirical distributions of charger power delivered to SPVs </strong></p> </div> <div> <p>File: FigSI2-3.Empirical distributions of charger power delivered to SPVs.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for special purpose vehicles (SPVs). </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Charging Power Preferences: </strong></h3> </div> <div> <p><strong>FigSI3.Distribution of charging power level preferences among different EV types </strong></p> </div> <div> <p>File: FigSI3.Distribution of charging power level preferences among different EV types.csv </p> </div> <div> <p>Description: Analysis of charging power level preferences for different EV types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P2 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P2 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P2 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P2 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P2 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P2 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Charging Event Durations </strong></h3> </div> <div> <p><strong>FigSI4.Average duration (hr) of charging events by type of charging energy for different vehicle types </strong></p> </div> <div> <p>File: Average duration (hr) of charging events by type of charging energy for different vehicle types.csv </p> </div> <div> <p>Description: Analysis of the average duration of charging events categorized by energy type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle type_charging duration_P </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_charging duration_P1 as an example, it refers to charging duration of private cars charging with P1 </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Vehicle Usage Patterns and Energy Metrics </strong></h3> </div> <div> <p><strong>FigSI5.Distributions of average daily driving distance by vehicle type </strong></p> </div> <div> <p>File: FigSI5.Distributions of average daily driving distance by vehicle type.csv </p> </div> <div> <p>Description: Distribution analysis of daily driving distances across different vehicle types and cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type </p> </div> </div> </td> <td> <div> <div> <p>Take Beijing_Private car as an example, it refers to average daily driving distance of private cars in Beijing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Battery Energy Distribution: </strong></h3> </div> <div> <p><strong>FigSI6.Distributions of nominal battery energy by vehicle type </strong></p> </div> <div> <p>File: FigSI6.Distributions of nominal battery energy by vehicle type.csv </p> </div> <div> <p>Description: Analysis of nominal battery energy distributions across vehicle types and cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type </p> </div> </div> </td> <td> <div> <div> <p>Take Beijing_Private car as an example, it refers to nominal battery energy of private cars in Beijing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div>
Data on the Swiss energy system and electric vehicles
<p>This repository gathers the data used in the paper:</p> <p>Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Giacomo Pareschi, Philipp Heer and Giovanni Sansavini. <strong>The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland. </strong><em>Energies.</em> 2021; 14(16):4812. <a href="https://doi.org/10.3390/en14164812">https://doi.org/10.3390/en14164812</a>.</p> <p>The linked code can be found <a href="https://gitlab.nccr-automation.ch/loris.dinatale/v2g-in-switzerland">here</a>.</p> <p>Small description of the different files:</p> <ul> <li><em>Car_trips.csv:</em> List of trips from different cars in Switzerland.<br> Data provided by Giacomo Pareschi and based on the result of the 2015 edition of MZMV (Bundesamt für Statistik / Bundesamt für Raumentwicklung, Verkehrsverhalten der Bevölkerung, Ergebnisse des Mikrozensus Mobilität und Verkehr 2015, Neuchâtel und Bern (2017), <a href="https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html">https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html</a> ). Each weekly profile is not representative and any result obtained with less than 50 profiles should be interpreted with extreme caution.</li> <li><em>ch.bfe.ladestellen-elektromobilitaet.json:</em> Data on the charging stations in Switzerland.<br> Online data from the Swiss Federal Office of Energy.</li> <li><em>cs_power_Home.csv</em> and<em> cs_power_Work.csv: </em>Own data on the charging powers of charging stations located at home or at work.</li> <li><em>energy_system_model_empa_results_sc_1.csv: </em>Swiss Energy System model used in our work to generate the fixed hydropower output profile.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>EVs_cap.csv:</em> Data on different EV brands, from own research.</li> <li><em>gCO2_eq_kWh_techs.csv: </em>CO2-equivalent greenhouse gas emission factors for different technologies, from own research.</li> <li><em>Heat_BEV_demand_2018.csv:</em> Electricity demand for heating and EVs in Switzerland.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>inflows_Beer.csv: </em>Data on the water inflows in the Swiss dams over the year.<br> Data provided by Michael Beer, from Beer, M. Abschätzung des Potenzials der Schweizer Speicherseen zur Lastdeckung bei Importrestriktionen. Z. Energiewirtschaft <strong>2018</strong>, 42, 1–12.</li> <li><em>MeteoSchweiz_pop_weight_2018.csv:</em> Temperature data in Switzerland taken from MeteoSwiss and population-weighted.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>Scenarios.csv </em>and <em>Scenarios+.csv: </em>Different scenarios for electricity production and consumption, generated in-house based on <ul> <li>the Energy Strategy 2050 (Kirchner, A.; Bredow, D.; Ess, F.; Grebel, T.; Hofer, P.; Kemmler, A.; Ley, A.; Piégsa, A.; Schütz, N.; Strassburg, S.; et al. Energy Perspectives, Die Energieperspektiven für die Schweiz bis 2050; Prognos AG: Basel, Switzerland, 2012), respectively</li> <li>the Energy Strategy 2050+ (Prognos AG and INFRAS AG and TEP Energy GmbH and Ecoplan AG. ENERGIEPERSPEKTIVEN 2050+ Kurzbericht. 2020).</li> </ul> </li> <li><em>transfer_15min_2018.csv: </em>Swiss power system model of electricity production and consumption in 2018.<br> Data provided by Dr. Martin Rüdisüli.</li> </ul> <p> </p>
Data for removal kinetics and breakthrough curves of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns [Dataset]
<p>This dataset describes the transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns. The experiments aimed at providing sustainable treatment options for relevant persistent, mobile and toxic (i.e., PMT substances) linked to vehicular traffic pollution. We assessed removal for 1H-benzotriazole, N'N-diphenylguanidine, and hexamethoxymethyl-melamine (PMT precursor) in batch and column experiments using pyrogenic carbonaceous adsorbents (e.g., GAC and biochar). Data contain kinetics batch experiments and breakthrough curves for the target contaminants.</p>
Augmented emission maps: several petrol and diesel (Euro 5 - 6d-Temp) vehicle-specific augmented emission maps
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">10.5281/zenodo</a> refers to a meta-data document that provides the full description of the standardized emission map</p>
Private vehicles GPS data
<p>The dataset provided here is an output of the Track & Know project, shared with the scientific community. It is an anonymized dataset of private vehicles. The dataset, containing anonymous GPS traces of private vehicles, was made accessible by the data owner to the partners of the Track & Know project, for activities relevant to the project. The proprietary dataset is not accessible to the public. It includes vehicle engine status. </p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03
<i>This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 15.07 GB of MP4 files, which were trimmed into 5136 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 59.54% of these extracted images are useful and 40.46% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-06
<i>This dataset was collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-06.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 17.33 GB of MP4 files, which were trimmed into 4251 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 100.0% of these extracted images are useful and 0.0% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.422 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01
<i>This dataset was collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 13.58 GB of MP4 files, which were trimmed into 2767 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 80.88% of these extracted images are useful and 19.12% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.335 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-07
<i>This dataset was collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-07.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 25.86 GB of MP4 files, which were trimmed into 5425 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.17% of these extracted images are useful and 0.83% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 20.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.313 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
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.