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Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Panoramic cityscape. South, Passeig de les Moreres, the cathedral, the city gate (Porta del Sol).
<u>File Name</u>: PM_072986_E_Solsona <br><u>Sublocation</u>: Passeig de les Moreres <br><u>Location</u>: Solsona <br><u>Province</u>: Catalunya, Lleida <br><u>Country</u>: Spain <br><u>Header</u>: None <br><u>Description</u>: Panoramic cityscape. South, Passeig de les Moreres, the cathedral, the city gate (Porta del Sol). <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage|Monuments; Cultural heritage|Thematic; Cultural heritage|Thematic|Cityscape/landscape/panoramic; Cultural heritage|Thematic|Historic site; Europe|Spain; Europe|Spain|Catalunya; Europe|Spain|Catalunya|Lleida; Europe|Spain|Catalunya|Lleida|Solsona; My photography; My photography|Architecture; My photography|Architecture|Cityscape/urbanism; Cultural heritage <br><u>Date of Generation</u>: 2012-06-12T09:14:54.073
Inter-Chemical Correlation results for the study: HHEARx2018-2512 (The Role of Environmental Endocrine Disruptors on the Health of Inner City Children)
Title: The Role of Environmental Endocrine Disruptors on the Health of Inner City Children <br>Species: Homo sapiens <br>Number of samples: 651 <br>Number of named analytes: 26 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=62 <br>
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
Dataset of The latent factor structure and assessment of childbirth-related PTSD in fathers and co-parents: psychometric characteristics of the City Birth Trauma Scale – French version (partner version)
<p>Little is known about the latent factor structure of CB-PTSD symptoms in co-parents (i.e., (a non-expecting mother or father). The City Birth Trauma Scale (City BiTS) was developed to assess childbirth-related posttraumatic stress disorder following childbirth (CB-PTSD), based on the PTSD criteria of the DSM-5. Still, no validated French questionnaire exists to assess CB-PTSD symptoms in co-parents. This study aimed (1) to establish the latent factor structure of CB-PTSD, and (2) to validate the French version of the City BiTS (partner version). </p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 282 co-parents who had an infant within the last 12 months. Sociodemographic data such as age, marital status, educational level, weeks of gestation, type of delivery, history of traumatic childbirth, or history of a traumatic event is available. </p>
Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID-19 CDMX – JULY 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the third dataset of the project, corresponding to July 2021, collected 15 months after the lockdown began in Mexico. Data collection was performed from July 19 to 31, 2021.</p>
Regional Datasets for Air Quality Monitoring in European Cities
<p>The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.</p>
The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"
<p>This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files:</p> <ul> <li>keywords with counts.txt - list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>
Inter-Chemical Correlation results for the study: HHEARx2016-1407 (Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study)
Title: Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study <br>Species: Homo sapiens <br>Number of samples: 157 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=2 <br>
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>
Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios
<p>This repository contains the data and results from the paper <strong>Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities</strong> published in <em>Nature Medicine</em> (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>).</p> <p>It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals. </p> <p>The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>.</p> <h3>Content</h3> <p>This repository contains three zip files, each with an internal codebook:</p> <ul> <li><em>data.zip</em>: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li><em>results_csv.zip</em>: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li><em>results_parquet.zip</em>: contains the same information as the <em>results_csv.zip</em> but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> <p>It is recommended to only download <em>results_csv.zip</em> for a quick exploration of the results, or only <em>results_parquet.zip</em> when the results are to be loaded into a software for deeper analysis.</p> <p> </p>
The Impact of the COVID-19 Pandemic On Cities. A Scoping Review Protocol
<p>The aim of the scoping review is to map out evidence based research on the Covid-19 pandemic impact on the European cities. The review questions touch three broad areas of interest:</p> <ol> <li>the aspects of urban life described and analysed in publications on the impact of the pandemic on cities</li> <li>the aspects of urban life that are described in terms of crisis, breakdown, turnaround, etc. (crisis, disruption, slump, shift…) in such studies</li> <li>theoretical and methodological approaches applied in such studies</li> </ol> <p>The search was conducted in June 2022, with the final body of literature consisting of 3,994 publication references from EBSCOhost, APA Psyc, Scopus, Web of Science, Proquest, Wiley, Sage, JSTOR, Tailor&Francis, Oxford Journals databases (Fig. 1). The following English words were searched for in titles, abstracts and keywords in the databases: (pandemic OR ‘Covid-19’) AND (city OR cities OR urban*). We used the following criteria for articles to be included in the study: 1) peer and non-peer-reviewed empirical papers in journals published in English from January 2019 to June 2022; 2) included studies where the impact of COVID-19 pandemic on European city/cities was an explicit variable of interest; 3) contained analysis of empirical data on cities or urban life retrieved or collected within and explicitly addressing the COVID-19 pandemic; 4) addressed the social, cultural, economic, political and socio-geographical aspects of a city. We excluded from our sample papers that were: 1) theoretical and opinion literature, media press releases, reports, MA dissertations and PhD theses; 2) secondary research papers (reviews, meta-analyses); 3) papers not in English; 4) studies about non-European cities; 5) studies which do not explicitly address the impact of the COVID-19 pandemic on cities; 6) studies addressing a city as a variable of secondary importance; 7) studies outside the scope of the COVID-19 pandemic, published before December 2019; 8) studies not addressing the social or human aspects of urban life.</p> <p>The final database of coded documents consisted of 138 empirical articles presenting findings on the impact of the COVID-19 pandemic on European cities. </p>
EVIDENT H2020– Environmental data for Sweden cities Dataset
<p>EVIDENT H2020- Environmental data for Swedish Cities Dataset</p> <p>Environmental data from 615 cities in Sweden</p> <p>Weather, in combination with residential characteristics and electricity consumption, might be useful to consider in association with other datasets. In the instance of EVIDENT, they will be examined in combination with electricity consumption to establish the correlation with weather and to examine if weather conditions contribute and should be considered for policy development.</p> <p>The data have been collected from 18.10.2021 to 4.05.2023 and refer to 615 Swedish cities. The collection has been carried out with agents created and by calling in API. In the file "Sweden_Cities_Avg_DaySect.xlsx", all cities have averaged from all measures. Also, the day has been divided into 3 sections and the averages apply to each section of the day.</p> <p>In each city, on average, there are 6 measurements per day. The source dataset is "swedish_cities_environmental.csv.". example</p> <table> <tbody> <tr> <td>country</td> <td>city</td> <td>temperature</td> <td>feels_like</td> <td>temp_min</td> <td>temp_max</td> <td>pressure</td> <td>humidity</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>wind_speed</td> <td>wind_deg</td> <td>sunrise</td> <td>sunset</td> <td>weather_description</td> </tr> </tbody> </table> <p>There are 2 more datasets, "swedish cities environmental_tranformDay.csv" and "swedish cities environmental_week.csv", and refer to transformations made in the original dataset.<br> The first file is about the day analysis, where the day has been divided into 3 sections and depending on the time of the measurement, a new column has been created in the Day section and can take values 0,1,2. In addition, there is the column day_hours which is the duration of the day in seconds from sunrise to sunset. Finally, there is pressure, humidity and wind speed. In the second file, the column weekday has been added and relates to the day of the week (e.g. Monday), and the daily analysis has been removed.</p> <p>More information can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in are described in deliverable D1.2 <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a> and deliverable <strong>D1.3</strong> <a href="https://evident-h2020.eu/wp-content/uploads/2022/03/EVIDENT_D1.3_Specification_of_Big_Data_Analytics.pdf">Specifications of Big Data Analytics</a>, in section 4 while the final design is reported in <strong>D3.2</strong> <a href="http://evident-h2020.eu/wp-content/uploads/2023/01/EVIDENT_D3.2_Implementation-of-preparatory-actions-for-RCT-surveys-and-serious-game.pdf">Implementation of preparatory actions for RCT, surveys and serious game</a>.</p>
Meteorological warnings issued by INMET for the Brazilian cities of Belém, Belo Horizonte, Porto Alegre, Rio de Janeiro, and São Paulo between 2021 and 2022
<p>The Brazilian National Institute of Meteorology (INMET, from the Portuguese "Instituto Nacional de Meteorologia'') is the Brazilian government agency responsible for monitoring, analysing and forecasting weather and climate. It provides meteorological warnings to be used by the local-level municipal authorities. </p> <p><strong>Data Content</strong></p> <p>INMET periodically publishes data on its website and provides them via XML RSS Feed. This dataset was collected from the RSS feeds mentioning the Brazilian cities of Belém located in the state of Pará, Belo Horizonte in Minas Gerais state, Porto Alegre in Rio Grande do Sul state, Rio de Janeiro in Rio de Janeiro state and São Paulo in São Paulo state from July/2021 to July/2022.</p> <p><strong>Data Structure</strong></p> <p>The description of columns collected from INMET warnings and stored in the warnings file (<em>inmet-meteorological-warnings-1658070001.csv</em>) is presented below. The warnings issued by INMET follow the Common Alerting Protocol (CAP). CAP provides an open, non-proprietary digital message format for all types of alerts and notifications [<em>Standard, OASIS (2010). Common Alerting Protocol Version 1.2. Jul, 1, pp. 1-47. <a href="http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html">http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html</a> </em>]. </p> <p>Columns:</p> <ul> <li><em>CITY</em>: Name of the city for which the warning was issued.</li> <li><em>STATE</em>: The Brazilian acronym for the state in which the city is located, for example, MG for Minas Gerais.</li> <li><em>CITYCODE</em>: Unique numeric code for the city for which the warning was issued.</li> <li><em>IDENTIFIER:</em> Unique identifier to INMET warning. </li> <li><em>RESPONSETYPE</em>: Reaction to the warning.</li> <li><em>URGENCY</em>: Urgency for taking action. For example, “Prepare”.</li> <li><em>SEVERITY</em>: Severity of the meteorological event. For example, “Future”</li> <li><em>CERTAINTY</em>: How likely is the event to happen? For example, “Observed” - Determined to have occurred or to be ongoing; “Likely” - (p > ~50%); “Possible” - Possible but not likely (p <= ~50%).</li> <li><em>WARNING</em>: Standardized type of warning. For example, "Aviso de Acumulado de Chuva", "Aviso de Tempestade", "Aviso de Declínio de Temperatura".</li> <li><em>TIMESTAMPDATEONSE</em>: Unix timestamp of the minimum time at which the event is expected to start.</li> <li><em>TIMESTAMPDATEEXPIRES</em>: Unix timestamp of the maximum at which the event is expected to occur or the warning expires.</li> <li><em>COLORRISK</em>: Event colour in hexadecimal following the INMET nomenclature, with yellow meaning potential danger, orange indicating danger, and red indicating great danger.</li> <li><em>BASESOURCE</em>: INMET RSS XML file from which the warning was extracted.</li> </ul>
Fruit-bearing plant species observations in Brazilian cities
<p>This data set, extracted from iNaturalist, compiles observations from the capitals of all 27 Brazilian federative units and specifically focuses on the species listed on https://doi.org/10.5281/zenodo.10212850. A backup of this data set was obtained from iNaturalist on August 22nd, 2023. The dataset features 47 columns, capturing details such as observation time, location, license, and taxonomic identification. It provides an extensive taxonomic breakdown, covering classifications from kingdom and phylum down to species, subspecies, and variety (in some cases). </p>
City of Seattle, Seattle Public Utilities, Annual Bull Trout Redd Surveys in Tributaries to Chester Morse Lake 1996-current, Cedar River Municipal Watershed, King County, WA
These data were collected during weekly annual redd surveys conducted by Seattle Public Utilities (SPU) in the Cedar River Municipal Watershed (CRMW), 1996 - current. Annual weekly bull trout redd surveys funded through the CRMW Habitat Conservation Plan (HCP) began in 2000 and ended in 2011 spawning year. To reinstate a monitoring program for the population, redd surveys in the most heavily used habitats by bull trout (termed the Core Zone), were opportunistically conducted in 2018. Weekly annual surveys in most of the Core Zone were reinstated in 2019. Approximately 77% of all redds observed 2000 - 2011 would have been observed during those years using the 2019 - 2022 spatial survey extent (SPU data on file). In 2023, the spatial and temporal coverage of surveys were on par with historical coverage, i.e., approximately 100% of all redds observed 2000 - 2011 would have been observed using the 2023 spatial survey extent. Information on redd location is used primarily to enable derivation of redd elevations. Redd elevation is required to estimate potential impacts to the spawning population and incubating embryos caused by reservoir inundation of stream spawning habitat after the spawning period during fall through spring. Redd weekly timing information is critical to accurately represent whether embryos remain in the gravel and are vulnerable to impacts of reservoir inundation as the reservoir is refilled starting in early spring. It is also vitally important that SPU understand timing and abundance of redds beyond the inundation zone to enable understanding of the overall impact to the population.
American Residential Macrosystems - Complete municipal ordinance documents across six U.S. cities, 2017-2019
These data files are the complete city codes, or municipal ordinances (n=156), across the metropolitan regions of Los Angeles, CA; Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis/St. Paul, MN. The documents were gathered for the specific purposes of a content analysis of how cities regulate residential landscapes; however, the documents include regulations on the books for the municipalities sampled for this project.
Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability
Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/, and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect
A Land-use/Land Cover Classification of Baltimore City in 1953
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
ScienceDex guides
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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.