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Temperature, floral density, and Osmia pollen usage data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2023
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures ("trap-nests") were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees. In addition, pollen samples are taken from the nests of cavity-nesting bees and the constituent plant taxa identified by microscopic comparison with a reference pollen collection from the study area.
Data from: Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of cyp27c1 expression
<p>Code and Data associated with "Visual pigment chromophore usage in Nicaraguan Midas cichlids: Phenotypic plasticity and genetic assimilation of <em>cyp27c1</em> expression"</p> <h2><span>Abstract</span></h2> <p><span>The wide-ranging photic conditions found across aquatic habitats may act as selective pressures potentially driving rapid evolution and diversity in the visual system of teleost fishes. Fine-tuning of visual sensitivities in many fish species relies on regulating the two components of visual pigments, the opsin protein and the chromophore. Many studies have focused on opsin gene expression or opsin sequence divergence in fishes inhabiting contrasting habitats. However, variation in chromophore usage across photic habitats has received less attention. Species from the Nicaraguan Midas cichlid complex, <em>Amphilophus </em>cf <em>citrinellus </em>[Günther 1864], have independently colonized seven isolated crater lakes of varying photic conditions resulting in repeated examples of small adaptive radiations. Here, we investigate variation in <em>cyp27c1</em>, the main enzyme involved in chromophore exchange, in response to photic environments in the wild, we measure its genetic component using laboratory-reared fish and test the effect of different rearing light conditions on <em>cyp27c1</em> expression. We found that photic environments significantly predict variation in <em>cyp27c1</em> expression in wild populations and that this variation seems to be genetically assimilated in two populations. We found that light-induced <em>cyp27c1</em> expression is variable across populations (i.e., genotype-by-environment interactions) and correlated with local photic conditions thus highlighting <em>cyp27c1</em> as a key factor of visual ecology in cichlid fishes.</span></p> <p><span>Keywords: <em>cyp27c1 </em>gene expression, sensory ecology, visual plasticity, Neotropical cichlids </span></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>
Crowdsourcing vibration data stemming from different transportation usages
<p> </p> <p>Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.</p>
Supplementary dataset to publication: "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens"
<p>The dataset supplements the journal article "Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens" by H. Schomburg, J. Malchow, O. Sanders, J. Knöll and L. Schrader, that appeared in Smart Agricultural Technology 3 (2023), https://doi.org/10.1016/j.atech.2022.100095. The file archives trial1.zip and trial2.zip contain csv files with platform weighing system data measured from June 19, 2019 to July 22, 2019 (trial 1) and from September 9, 2019 to October 14, 2019 (trial 2) in a broiler chicken barn at Friedrich-Loeffler-Institut, Institute of Animal Welfare and Animal Husbandry, Celle. A detailed description of data structure is given in 00_hl_weighing_system_data_overview.txt.</p>
Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts".
<p>Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts". The data set consists of five folders: “Codon_usage_amoebae_and_viruses”, "Genome_annotations_amoebae", "Phylogenetic_trees_18S_amoebae", “Phylogenomic_trees_amoebae”, and "Viral_integration_detection_amoebae". The “Codon_usage_amoebae_and_viruses” folder contains for each amoeba host the calculated codon usage tables in the subfolder "codon_usage_table_host", the calculated codon usage preferences using different scores in the subfolder "codon_usage_scores_host", and the calculated codon usage preferences of giant viruses versus each host in the subfolder "codon_usage_scores_viruses_vs_host". The giant viruses in the subfolder "codon_usage_scores_viruses_vs_host" are organised by viral family and genus in separate sub-subfolders. The "Genome_annotations_amoebae" folder contains the generated genome annotations in different formats and the manually curated mitochondrial genome annotations for each amoeba host. The "Phylogenetic_trees_18S_amoebae" contains for the eukaryotic phyla <em>Discosea</em>, <em>Heterolobosea</em>, and <em>Tubulinea, </em>the 18S rRNA nucleotide alignments, distance matrices, and computed phylogenetic trees. The folder "Phylogenomic_trees_amoebae" contains for the eukaryotic clades <em>Amoebozoa</em> and <em>Discoba, </em>the protein alignment matrices and computed phylogenomic trees. The folder "Viral_integration_detection_amoebae" contains the MCP databases used (fasta file, alignment file, HMM profile and DIAMOND BLASTX database) and the MCP sequences detected in this study and the blast results of these. </p>
Analysis of internet usage and bioblitz frequency in the Global South
<p>This dataset was used to analyse factors contributing to the number of bioblitzes conducting in countries in the Global South.</p> <p>This is part of a review into the effectiveness of bioblitz as a method for collecting data on biodiversity.</p> <p>We modeled population and internet usage with the number of iNaturalist Bioblitzes in a country from our sample (Groom 2021). We only just looked at those global regions where citizen science has tended to have lower prevalence in the past (Africa, Asia, and Latin America, and the Caribbean) compared to other regions. We identified a total of 254 Bioblitz projects from iNaturalist in 37 countries, in Africa (30 projects in 13 countries); Asia (71 projects in 11 countries); and Latin America and the Caribbean (153 projects in 13 countries).</p> <p>We took the total population of each country from the mean of 2015-18 values in millions from <a href="https://population.un.org/wpp/Download/Standard/Population/">https://population.un.org/wpp/Download/Standard/Population/</a> (United Nations, Department of Economic and Social Affairs, Population Division (2019). <em>World Population Prospects 2019, Online Edition. Rev. 1.</em>). Internet usage was taken as the percentage of individuals using the internet in 2017 (<a href="http://data.un.org/">http://data.un.org/</a>).</p> <p>The natural log of the number of iNaturalist projects was modelled against the log of the population in millions and the internet usage using the lm package of R.</p> <pre><code>SUMMARY_DATA <- read.delim2("summary_data.tsv", row.names=1) model <- lm(log(projects) ~log(population) + internet, data=SUMMARY_DATA) summary(model) </code></pre> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Coefficient</p> </td> <td> <p>Std. Error</p> </td> <td> <p>t-Statistic</p> </td> <td> <p>Prob.</p> </td> </tr> <tr> <td> <p>log(population) in millions</p> </td> <td> <p>0.345</p> </td> <td> <p>0.0865</p> </td> <td> <p>3.99</p> </td> <td> <p>0.0003 ***</p> </td> </tr> <tr> <td> <p>internet usage as a percentage of individuals per country</p> </td> <td> <p>0.016</p> </td> <td> <p>0.0060</p> </td> <td> <p>2.62</p> </td> <td> <p>0.0130 *</p> </td> </tr> </tbody> </table> <p>Residual standard error: 0.9167 on 34 degrees of freedom</p> <p>Multiple R-squared: 0.3911, Adjusted R-squared: 0.3553 </p> <p>F-statistic: 10.92 on 2 and 34 DF, p-value: 0.0002176</p> <p>To view properties of the model to ensure it conformed to the assumptions of the model and was a good fit. Plots are included in the attached files.</p> <pre><code>par(mfrow = c(2,2)) plot(model)</code></pre> <p>To view other correlations in the data the following code can be used. The output is included in the attached files.</p> <pre><code>correlations <- cor(SUMMARY_DATA[,c(2,3,4,6,11,13,14)],method = c("spearman")) install.packages("corrplot") library("corrplot") corrplot(correlations, method="square") </code></pre> <p> </p>
Supplementary materials for the paper "Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach"
<p>These are materials necessary to replicate the study discussed in <em>Users' Privacy Concerns and Attitudes towards Usage-Based Insurance: an empirical approach</em>, accepted for publication at VEHITS 2022.</p>
Great Britain (GB) Domestic Electricity Usage by Low Carbon Technology by Season
<p><strong>Important</strong>: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.6576108">this short survey</a>.</p> <p> </p> <p>This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.</p> <p>For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.</p> <p>For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)</p> <p>We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.</p> <p>In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:</p> <ul> <li>daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT</li> <li>lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season</li> <li>lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season</li> </ul> <p>We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.</p>
Impedance-based forecasting of battery performance amid uneven usage
<p>Dataset of 88 commercial lithium-ion coin cells cycled under multistage constant current charging/discharging, with currents randomly changed between cycles to emulate realistic use patterns.</p> <p>raw-data.zip contains the following data:</p> <p>Variable Discharge: We subject 24 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V.</p> <p>Fixed Discharge: We subject an additional 16 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to the same cycling conditions as above, except now fixing the discharge current for all cells and cycles at 52.5mA (1.5C) instead of randomly changing the discharge current at each cycle.</p> <p>chemistry2-25C.zip contains the following data:</p> <p>Variable Discharge @ 25C: We subject 32 RS-Pro LiR2032 coin cells (of nominal capacity 1C = 40mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. The distributions of currents are varied across different cell batches. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V.</p> <p>Variable Discharge @ 35C: We repeat the experiment conducted above for 16 additional RSPro cells, except that now we cycle the cells at 35C instead of 25C.</p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
HPC-JEEP: Energy Usage on ARCHER2 and the DiRAC COSMA HPC services dataset
<p>This package contains the data and tools used to analyse the energy use on the ARCHER2 and DiRAC COSMA UK HPC facilities. This analysis was performed as part of the HPC-JEEP project. HPC-JEEP is funded by the UKRI DRI Net Zero Scoping project.</p>
Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects — Supplementary Material
<p><em>Background:</em> Stack Overflow (SO) is the largest Q&A website for software developers, providing a huge amount of copyable code snippets. Using those snippets raises various maintenance and legal issues. SO’s license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO’s license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution.</p> <p><em>Aim:</em> Our main goal was to analyze how often code from SO posts is used in public GitHub projects, but not attributed as required by the license. Further, we wanted to investigate if developers are aware of SO’s license and its implications, and to what degree they adhere to the attribution requirements defined in SO’s terms of service.</p> <p><em>Method:</em> We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub projects. We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results.</p> <p><em>Results:</em> For the different sets of projects that we analyzed, the amount of projects containing files with a reference to SO varied between 3.3% and 11.9%. We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required, i.e., using a link in a source code comment. About half of the surveyed developers admitted copying code from SO without attribution. Furthermore, about two thirds of them were not aware of the license of SO code snippets and its implications.</p>
Survey on the usage of Mathematical Modelling, Simulation and Optimization software
<p>This dataset contains the result of a survey we carried out in the context of the MSO4SC project in order to know which kinds of tools for simulation were using our stakeholders. The purpose was to prioritize functionalities depending on stakeholders' preferences. It was a survey with 41 questions grouped in 10 areas (impact of simulation software on their entities, usage of pre/post-processing, usage of visualization, etc...). The pdf file includes the list of questions for clarification. Such survey was answered by academia and industry from several European countries.</p>
Jupyter Usage in Institutions with Coordinates
<p>A dataset with the coordinates of several Institutions which are using Jupyter along with some metadata</p>
Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions
<p>Recently, GitHub introduced a new social feature, named reactions, which are pictorial characters similar to the emoji symbols widely used nowadays in text-based communications. Particularly, GitHub users can use a set of such symbols to react to issues and pull requests. However, little is known about the real usage and benefits of GitHub reactions. In this paper, we analyze the reactions provided by developers to more than 2.5 million issues and 9.7 million issue comments, in order to answer an extensive list of ten research questions about the usage and adoption of reactions. We show that reactions are being increasingly used by open-source developers. Moreover, we also found that issues with reactions usually take more time to be closed and have longer discussions.</p> <p>This dataset contains the data used in the paper "Beyond Textual Issues: Understanding the Usage and Impact of GitHub Reactions", accepted for SBES 2019.</p>
Wikimedia Commons photos by prominent users and their usage across the web
<p>Extract from the Wikimedia Commons database containing a list of users selected by the community for having uploaded high quality photos; list of 310k photos of theirs and of the subset of 59k photos sent to Infringement.Report for matching; list of domains whose matches were ignored as not useful for copyleft license enforcement. Domains were then matched for their rank in the Tranco list and the number of image usages found, and ranked by a mix of the two criteria.</p>
Survey Results: LP/MIP Solver Usage for Energy Modeling
<p>This report summarizes the responses to the recent solver benchmark survey conducted by Open Energy Transition. The survey aimed at better understanding energy modelers’ needs and pain points, and to gauge the usefulness of creating a new solver benchmark website for the energy modeling community. </p>
Geostrophic wind shear from CFSR v2 data for usage in WAsP
<p>The change of the geostrophic wind speed has an impact on boundary layer mixing that can be important for microscale flow modelling for wind energy purposes. The WAsP software is often used for this purpose. This dataset contains the climatological geostrophic wind shear and direction over the whole global on a 0.5 degree grid that has been used in WAsP 12. It was obtained from the 6-hourly CFSR v2 reanalysis for the period 2011 to 2017 (see https://doi.org/10.5065/D61C1TXF). The omni-directional geostrophic wind shear vector denotes how much the geostrophic wind speed is changing over a certain vertical distance. Because we are interested in geostrophic wind shear changes that contribute to turbulent mixing in the atmospheric boundary layer, it was estimated by using the data on pressure levels from the pressure level closest to the surface up to 500 hPa above that heights.p><p dir="ltr">More details about the implementation of the model in the WAsP software and a validation can be found in the corresponding technical report:<br>Floors, R. R., Troen, I., & Kelly, M. C. (2018). <i>Implementation of large-scale average geostrophic wind shear in WAsP12.1i>. DTU Wind Energy. DTU Wind Energy E No. 0169p><p><br>p><ul><li>meandgdz_2010_2017_CFSRv3.nc: version with coordinate reference system in the coordinates for usage in GIS programs. NaN values at the poles are filled with 0.0, i.e. assuming barotropic atmosphere, which avoids crashes in the pywasp code. A single sector variable has been added, indicating that these values are valid for all wind direction, as opposed to other files that have values for each wind direction sector (for example: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042). Naming conventions are in accordance with the windkit package (https://docs.wasp.dk/windkit/)</p> <p>Mirror of https://data.dtu.dk/articles/dataset/Geostrophic_wind_shear_from_CFSR_v2_data_for_usage_in_WAsP/21975482</p>
FIG. 15 in The exploitation of molluscs and other invertebrates in Alexandria (Egypt) from the Hellenistic period to Late Antiquity: food, usage, and trade
FIG. 15. — Spider conch (Lambis sp. Röding, 1798) shell from: A, Fouad site; and B, Theater Diana, probably used as a container. Inner side at the top and outer side at the bottom. Scale bar: 10 mm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.