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Little Rock Lake Experiment at North Temperate Lakes LTER: Major Ions 1996 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Parameters characterizing the major ions of the treatment and reference basins of Little Rock Lake are measured at one station in the deepest part of each basin at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include chloride, sulfate, calcium, magnesium, sodium, potassium, iron, and manganese Sampling Frequency: varies - Number of sites: 2
Historical Birge - Juday Lake Survey - major ions 1900 - 1943
Data collected by Birge, Juday, and collaborators, mostly in north-central Wisconsin, from 1900 through 1943; generally one sampling event per lake during the summer, but on some lakes, especially around Trout Lake Station, several sampling events for several successive years. This data set contains both surface data (depth of zero) and multi-depth data. Note that not all variables were measured on all lakes. Documentation: Johnson, M.D. (1984) Documentation and quality assurance of the computer files of historical water chemistry data from the Wisconsin Northern Highland Lake District (the Birge and Juday data).Wisconsin DNR Technical Report. Note: Values of -99999 in water quality data indicate trace amount of parameter was present. Number of sites: 663 (generally one sampling point per lake; occasionally, several sampling points per lake on multibasin, large lakes). Note: This data set was updated in 2013 to include multi-depth and additional surface data for a large subset of lakes. These additions expanded the number of sites from 605 to 663, and expanded the date range from 1925-1942 to 1900-1943 . Furthermore, 14 lakes in Minnesota were added to the data set contributing additional surface and multi-depth data. Another dataset was added in 2013 collected by Wisconsin limnologists Chauncey Juday and Edward Birge, this data set contains variables that are still commonly used in research. For example, temperature, dissolved carbon dioxide, color, pH, secchi disk, plankton, and silica. However, the data set also includes variables that are not commonly used, for example, crude protein, non-amino nitrogen, ether extract, and total organic and inorganic material. These data are characteristic of water chemistry analysis from the time in which they were compiled (5/31/1915 - 8/29/1938). The data set features data from 586 different lakes, primarily lakes in the Northern Highland Lakes District of Wisconsin. However, there is also data from lakes in southeastern and
North Temperate Lakes LTER Dane County Major Roads
Major roads in Dane County, Wisconsin
North Temperate Lakes LTER: Chemical Limnology of Lake Mendota: Major Ions 1940 - 1995
Chloride, Sodium and Sulfate concentrations in Lake Mendota. 1940 - 1987 data are annual averages collected and processed by Richard Lathrop Lathrop, R. C. (1988). Chloride and sodium trends in the Yahara lakes. Bureau of Research, Wisconsin Department of Natural Resources. 1988 - 1995 data are collected by the Wisconsins DNR and are also available in Storet.
Dictionary of FAO major fishing areas
<p>This JSON-formatted dictionary of fishing areas as organised by the Food and Agriculture Organization (FAO) of the United Nations (UN) is based on data published on the websites of the European Commission (<a href="https://fish-commercial-names.ec.europa.eu/fish-names/fishing-areas_en">English</a>, <a href="https://fish-commercial-names.ec.europa.eu/fish-names/fishing-areas_de">German</a>). If no German name could be determined, the English name is given instead.</p> <p>The concept and rationale of the FAO Major Fishing Areas is described on the <a href="https://data.apps.fao.org/catalog/dataset/cwp-fishing-area">FAO website</a>.</p> <p>This dictionary implements three hierarchical levels: Areas, Subareas and Divisions.</p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Data: DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype
<p>This data set includes all the raw data collected for the following article: "DEAE-Dextran enhances the lentiviral transduction of primary human mesenchymal stromal cells from all major tissue sources without affecting their proliferation and phenotype"</p>
Classification of majority opinions and headnotes written by U.S. Supreme Court Justice Antonin Scalia
<p>This is data to accompany an article by Linda L. Berger and Eric C. Nystrom, "A Rhetorical-Computational Analysis of Justice Antonin Scalia's 'Remarkable Influence': The Unexpected Importance of Deceptively Unanimous and Contested Majority Opinions," <em>Journal of Appellate Practice and Process</em> 20, no. 2 (2020).</p> <p>In "scalia-HN-with-ruletype.tsv," Berger classified each headnote from a Scalia-authored majority opinion as one of the following rhetorical types: argument, scalia rule, or preexisting rule. (See article for further explanation of these categories.) Organized by SCDB ID and headnote number.</p> <p>In "unanimity.tsv," Berger addressed each case with a Scalia-authored majority opinion, assessing the degree of unanimity, which may or may not be the same as that implied by the for/against vote in the case. Fields include case SCDB ID, majority-minority vote, and degree of unanimity.</p> <p>Both data files are in Tab-separated format. For further information, please contact the authors.</p>
Geological Maps in the Syrtis Major Region, Mars
<p>The dataset is an ArcGIS geodatabase for the geological maps within the Syrtis Major region illustrated in Voigt et al., 2024. The geodatabase includes contacts as line features and geologic units as point features. Related publication: J.R.C. Voigt, V.Z. Sun, C.E. Viviano,<span> </span>M. Stack (2024): Investigating Hydrated Silica in Syrtis Major, Mars: Implications for the Longevity of Water–Rock Interaction. Geophysical Research Letters. </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>
Dataset with Risk estimates of major currency pairs on the Forex market
<p>This dataset includes Value at Risk (VaR) and Expected Shortfall (ES) estimations of the major currency pairs on the Forex market. Notably, it provides daily VaR and ES estimates for the AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, and USDJPY FX assets for January 2021 to September 2022. The reported risk estimates were calculated by various parametric and non-parametric models, including Variance-Covariance (VS), Historical Simulation (HS), Monte Carlo (MC), and Garch(1,1) at both 95% and 99% confidence levels. To enable model evaluation, the last column of each CSV file, named pnl, provides the actual daily returns of the FX asset.</p> <p>The data and code used to create this dataset are available at <a href="https://doi.org/10.5281/zenodo.7411148">Zenodo</a> and <a href="https://marketplace.infinitech-h2020.eu/assets/portfolio-value-at-risk-estimation">INFINITECH Marketplace</a>, respectively.</p>
French Entity-Linking dataset between annotated tweets collected during major crises in France and French Wikipedia corpus
<p>Most of the available datasets are not particularly adapted to our target application: geolocate natural disasters from social networks. First, social media posts are largely underrepresented in these datasets, and the only Twitter dataset lacks Entity-Linking annotations. Second, none of the datasets focuses on a crisis or natural disaster event.</p> <p>To mitigate these issues, we extracted a collection of French tweets written during earthquakes and major floods that have occurred in France in recent years. We set up Label-Studio in order to annotate these tweets. A total of 4617 tweets were annotated, including 1678 tweets posted during earthquakes and 2939 during floods. For each annotated tweet, mentions were annotated using the set of labels described earlier in the paper as well as, when possible, the target Wikipedia title.</p> <p>Named “RéSoCIO” in reference to the research project in which it was carried out, the dataset resulting from this work contains a total of 12 828 annotated mentions and 1 513 distinct Wikipedia entities. 85% of mentions were associated with a Wikipedia page and 94 % if we ignore the RISKNAT and DAMAGES labels, which are often difficult to map to an existing entity.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entities</strong></td> </tr> <tr> <td>PERSON</td> <td>315</td> <td>263</td> <td>136</td> </tr> <tr> <td>ORG</td> <td>863</td> <td>790</td> <td>281</td> </tr> <tr> <td>GEOLOC</td> <td>4375</td> <td>4234</td> <td>701</td> </tr> <tr> <td>TRANSPORT</td> <td>250</td> <td>203</td> <td>101</td> </tr> <tr> <td>EVENT</td> <td>35</td> <td>21</td> <td>16</td> </tr> <tr> <td>FACILITY</td> <td>129</td> <td>94</td> <td>49</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>128</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>223</td> <td>200</td> <td>46</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>12828</strong></td> <td><strong>1322</strong></td> <td><strong>1513</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the Twitter dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entitie</strong>s</td> </tr> <tr> <td>PERSON</td> <td>1100102</td> <td>1098406</td> <td>557697</td> </tr> <tr> <td>ORG</td> <td>750925</td> <td>749504</td> <td>130394</td> </tr> <tr> <td>GEOLOC</td> <td>2729702</td> <td>2728296</td> <td>215924</td> </tr> <tr> <td>TRANSPORT</td> <td>161539</td> <td>160487</td> <td>53405</td> </tr> <tr> <td>EVENT</td> <td>798433</td> <td>798251</td> <td>86471</td> </tr> <tr> <td>FACILITY</td> <td>258835</td> <td>258513</td> <td>109867</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>127</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>4340621</td> <td>4339658</td> <td>682458</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>10146795</strong></td> <td><strong>10138230</strong></td> <td><strong>1836399</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the full dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p>
Sherbo et al. 2023 Data Package. Data associated with study assessing effects of dissolved organic matter on phytoplankton productivity in boreal lakes. The majority of data was collected in 2018 at the IISD Experimental Lakes Area in Northwestern Ontario
Allochthonous dissolved organic matter (DOM) structures many physical, chemical, and biological properties of lakes, including key variables that control productivity at the base of freshwater food webs. We examined phytoplankton biomass and productivity and their drivers, across eight pristine boreal lakes with DOM ranging from 3.5 to 9.5 mg DOC L-1. Increases in DOM were associated with significant increases in epilimnetic nitrogen, phosphorus and chlorophyll a (Chl a) concentrations suggesting that nutrients associated with DOM stimulate phytoplankton biomass and productivity. Such results were misleading; there was no significant relationship between Chl a and phytoplankton biomass measured via microscopy, and results did not incorporate the effects of DOM on thermocline and euphotic depth. Chl a:biomass and Chl a: carbon ratios indicated that increases in Chl a with DOM were driven by photo-acclimation to declining light availability. Increases. Further, increases in DOM led to large declines in thermocline (~50 %) and euphotic (~75 %) depths, and depth-integrated phytoplankton biomass and primary production (~70 %).
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
American Residential Macrosystems - Bird community data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"This dataset includes abundance of breeding bird species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens). Bird data was collected via standardized 10-min point counts during the breeding season in 2017 or 2018. "
Major Ion Concentrations in Surface Water Collected from Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, December 2003 – December 2015
This package includes data of concentrations of sodium, potassium, magnesium, calcium, chloride, and sulfate in surface water samples collected from Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) Program sites in Taylor Slough. These sites are TS/Ph1a (2003-2013), TS/Ph2 (2003-2012), and TS/Ph3 (2004-2015). Analyzed samples include composite samples, rainfall samples, and grab samples. Composite samples represent water collected over the course of 3 days by autosamplers programmed to draw 250 mL every 18 hours. Rainfall samples represent water collected by the autosamplers when a threshold of = 2.5 cm of rain per hour is passed. A 500 mL sample is collected 30 minutes after meeting the threshold. Composite and rainfall samples are retrieved every 3-4 weeks and returned to the Florida International University (FIU) Modesto A. Maidique campus. A grab sample is collected at each site during these visits. Cation and anion analysis were completed using ion chromatography on a Dionex DX-120. Sample preparation and analysis for major ions were completed in the Hydrogeology laboratory at FIU. This dataset is completed.
Water quality monitoring on the Altamaha River and major tributaries from September 2000 through November 2001
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through November 2001. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate) and dissolved organics (DOC, DON, DOP) were measured using standard methods. The concentrations of 6 elements (Ca, K, Mg, Na, Si, and Sr) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS).
Long-term water quality monitoring on the Altamaha River and major tributaries from September 2000 through April 2009
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through April 2009. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate, silicate), dissolved organics (DOC, DON, DOP) and total suspended solids were measured using standard methods. The concentrations of 20 elements (Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sr and Zn) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS). Total dissolved inorganic carbon (DIC) was measured using a custom automated DIC analyzer. Total alkalinity (TA)was determined by Gran titration and pH of surface water was measured using a glass electrode.
North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Major Ions 1981 - current
Parameters characterizing the major ions of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish) are measured at one station in the deepest part of each lake at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include chloride, sulfate, calcium, magnesium, sodium, potassium, iron, manganese, and specific conductance (northern lakes only). Lake Wingra has always been just a surface sample, but in the winter we have, at times, taken chloride samples from top to bottom to have a better understanding of road salt effects. Samples for conductivity are collected four times per year in the seven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, and unnamed lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog] in the Trout Lake area at the deepest part of the lake, sampling at the surface, mid water column, and the bottom. The sampling dates include February under ice, spring mixis, August stratified, and fall mixis. Conductivity is measured using a YSI Model 32 conductivity meter with YSI 3403 conductivity cell, reported as uS/cm at 25°C. 1981-1988: a Sybron Barnstead conductivity bridge was used. 1981-1986: conductivity was measured monthly. Sampling Frequency: quarterly (winter, spring and fall mixes, and summer stratified periods) More information on our lakes and where they are sampled can be found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-ntl&identifier=434. Number of sites: 11
ScienceDex guides
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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.