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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Chakraborty, Debapriya;

    The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include: Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector. The dataset used in this report was created using the following public data sources: 2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping American Community Survey: https://www.census.gov/programs-surveys/acs Microsoft Excel.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ramadoss, Trisha; Tal, Gil; Davis, Adam;

    The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, we consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. We examine two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a "Small Vehicles" scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a "Large Vehicles" scenario with 4 million of each category. We find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Robertson, G. Philip; Hamilton, Stephen; Paustian, Keith; Smith, Pete;

    Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now. The dataset is a synthesis of literature values selected based on criteria described in the parent paper’s narrative. The files can be opened in Microsoft Excel or any other spreadsheet that can load Excel-format files.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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    DRYAD; ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: Datacite; ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Chakraborty, Debapriya; Bunch, David; Brownstone, David;

    The market for plug-in electric vehicles (PEVs) that primarily include battery electric vehicles (BEVs) and plug-in hybrid vehicles (PHEVs) has been rapidly growing in California for the past few years. Given the targets for PEV penetration in the state, it is important to have a better understanding of the pattern of technology diffusion and the factors that are driving the process. Using spatial analysis and Poisson count models we identify the importance of a neighborhood effect (at home locations) and a peer effect (at commute destinations) in supporting the diffusion of PEV technology in California. In the case of new BEV sales, we find that exposure to one additional BEV or PHEV within a 1-mile radius of a block group centroid is associated with a 0.2% increase in BEV sales in the block group. Interestingly, for new PHEV sales- the neighborhood effect of BEV sales is negative, suggesting that enhanced exposure to this type of technology (which is differentiated in distinctive ways from PHEVs) may impact new PHEV sales through a substitution effect. Specifically, higher BEV concentration in an area can have an overall negative effect on new PHEV sales. While the neighborhood effect at residential locations is important, a peer effect at commute destinations also has a notably important effect on new PEV sales. Both of these effects work in combination with socioeconomic, demographic, policy, and built environment factors in encouraging PEV adoption. These results suggest that policymakers should consider targeted programs and investments that can boost the impact of neighborhood and peer effects on PEV sales. The ReadMe sheet in the data file Data_DMV_2014_2016_PEV_new_sales__stock_and_other_variables gives detail of the variables in the datasheet. The dataset uploaded here does not have my identifiable information. Individual vehicle VIN numbers were aggregated to generate the count of EVs in each block group. The ReadMe file gives the information of the spatial unit of measurement for each variable (e.g., block group or census tract). Data on new plug-in vehicle sales are estimated from DMV's vehicle registration data. This vehicle registration data was then combined with data from the American Community survey, LODES data, and Smart Location Mapping data to account for other sources of dynamics in California's PEV market. The data was processed using STATA 16.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Latinopoulos, Charilaos;

    This dataset contains four files: - a trip diary that was synthesized for electric vehicle drivers in the area of Westfield Shopping Center, London. - a trip diary that was synthesized for electric vehicle drivers in the area of Canary Wharf, London. - A set of EV charging packages that were used to optimize charging prices for a Charging Service Provider - A readme file with details and variable definitions for the contained datasets The original trip sample that was used for the synthesis of the two travel diaries is part of the London Travel Demand Survey (LTDS) - https://tfl.gov.uk/corporate/about-tfl/how-we-work/planning-for-the-future/consultations-and-surveys

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DRYAD; ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Cuthbert, Ross N.; Wasserman, Ryan J.; Dalu, Tatenda; Kaiser, Horst; +5 Authors

    1. Predation is a pervasive force that structures food webs and directly influences ecosystem functioning. The relative body sizes of predators and prey may be an important determinant of interaction strengths. However, studies quantifying the combined influence of intra- and interspecific variation in predator-prey body size ratios are lacking. 2. We use a comparative functional response approach to examine interaction strengths between three size classes of invasive bluegill and largemouth bass towards three scaled size classes of their tilapia prey. We then quantify the influence of intra- and interspecific predator-prey body mass ratios on the scaling of attack rates and handling times. 3. Type II functional responses were displayed by both predators across all predator and prey size classes. Largemouth bass consumed more than bluegill at small and intermediate predator size classes, whilst large predators of both species were more similar. Small prey were most vulnerable overall, however differential attack rates among prey were emergent across predator sizes. For both bluegill and largemouth bass, small predators exhibited higher attack rates towards small and intermediate prey sizes, whilst larger predators exhibited greater attack rates towards large prey. Conversely, handling times increased with prey size, with small bluegill exhibiting particularly low feeding rates towards medium-large prey types. Attack rates for both predators peaked unimodally at intermediate predator-prey body mass ratios, whilst handling times generally shortened across increasing body mass ratios. 4. We thus demonstrate effects of body size ratios on predator-prey interaction strengths between key fish species, with attack rates and handling times dependent on the relative sizes of predator-prey participants. 5. Considerations for intra- and interspecific body size ratio effects are critical for predicting the strengths of interactions within ecosystems and may drive differential ecological impacts among invasive species as size ratios shift.

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    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2021
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      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Galtbalt, Batbayar; Lilleyman, Amanda; Coleman, Jonathan T.; Cheng, Chuyu; +6 Authors

    Abstract Background In-flight conditions are hypothesized to influence the timing and success of long-distance migration. Wind assistance and thermal uplift are thought to reduce the energetic costs of flight, humidity, air pressure and temperature may affect the migrants’ water balance, and clouds may impede navigation. Recent advances in animal-borne long-distance tracking enable evaluating the importance of these factors in determining animals’ flight altitude. Methods Here we determine the effects of wind, humidity, temperature, cloud cover, and altitude (as proxy for climbing costs and air pressure) on flight altitude selection of two long-distance migratory shorebirds, far eastern curlew (Numenius madagascariensis) and whimbrel (Numenius phaeopus). To reveal the predominant drivers of flight altitude selection during migration we compared the atmospheric conditions at the altitude the birds were found flying with conditions elsewhere in the air column using conditional logistic mixed effect models. Results Our results demonstrate that despite occasional high-altitude migrations (up to 5550 m above ground level), our study species typically forego flying at high altitudes, limiting climbing costs and potentially alleviating water loss and facilitating navigation. While mainly preferring migrating at low altitude, notably in combination with low air temperature, the birds also preferred flying with wind support to likely reduce flight costs. They avoided clouds, perhaps to help navigation or to reduce the risks from adverse weather. Conclusions We conclude that the primary determinant of avian migrant’s flight altitude selection is a preference for low altitude, with wind support as an important secondary factor. Our approach and findings can assist in predicting climate change effects on migration and in mitigating bird strikes with air traffic, wind farms, power lines, and other human-made structures.

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    Collection . 2021
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    Collection . 2021
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    DRYAD; ZENODO
    Dataset . 2021
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      DRYAD; ZENODO
      Dataset . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Stratmann, Tanja; van Oevelen, Dick; Martínez Arbizu, Pedro; Wei, Chih-Lin; +11 Authors

    In April and May 2019, we compiled the “BenBio” part of the “BenBioDen database” following the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses. In the first PRISMA step, the “Identification” step, we identified 1,373 articles in the Web of Science using the key words “marine meiofauna biomass”, “marine macrofauna biomass”, “marine megafauna biomass”, “marine meiobenth* biomass”, “marine macrobenth* biomass”, “marine megabenth* biomass”, “nematode biomass”, and “benthic ‘standing stock’”. We located an additional 201 publications based on expert knowledge. A search of the PANGAEA® Data Publisher (https://www.pangaea.de/) identified 1,488 datasets representing 148 publications using the key words “meiofauna biomass”, “macrofauna biomass” and “megafauna biomass”. Further 30 datasets were found in the EOL data archive (http://data.eol.ucar.edu/), through citations in review papers, and based on expert knowledge. After removing duplicates, we screened the titles and abstracts of 1,445 studies in PRISMA step 2 (“Screening”; Fig. 1A). This step excluded 951 studies because they did not report biomass values. In the Eligibility step, we assessed full texts of 494 studies for eligibility and excluded 110 studies because they did not report biomass, the publications or data were not accessible, or they did not report benthic biomass in appropriate units (g WW m-2, g DW m-2, g AFDW m-2, g or mol C m-2). Further reasons for excluding full texts included combining benthic biomass for several size classes, reporting benthic biomass for particular taxa rather than the whole size class, presenting biomass for faunal assemblages and/ or a group of sampling stations rather than for individual stations, not presenting primary research or lacking geographical details about sampling stations. We also excluded studies that estimated benthic biomass using modelling approaches, that conducted manipulative experiments, or did not report benthic biomass as single values, means or median values, but instead as ranges. The final “BenBio” part included 384 studies from which we extracted 11,792 georeferenced benthic biomass entries. The Benthos Density, i.e. “BenDen”, part of the “BenBioDen” database was established in July and August 2019 following the PRISMA Statement for systematic reviews and meta-analyses. In the Identification step, we found 2,515 articles in the Web of Science using the key words “meiofauna abundance”, “meiobenthos abundance”, “macrofauna abundance”, “macrobenthos abundance”, “megafauna abundance”, “megabenthos abundance”, “meiofauna Arctic Ocean”, “meiofauna Atlantic Ocean”, “meiofauna Black Sea”, “meiofauna Gulf of Mexico”, “meiofauna Indian Ocean”, “meiofauna Mediterranean Sea”, “meiofauna Pacific Ocean”, “meiofauna Southern Ocean”, “meiofauna Red Sea”, “meiofauna Pacific Ocean”, “megafauna Southern Ocean”, “megafauna Red Sea”, “megafauna Pacific Ocean”, “megafauna Mediterranean Sea”, “megafauna Indian Ocean”, “megafauna Black Sea”, “megafauna Gulf of Mexico”, “megafauna Atlantic Ocean”, “megafauna Arctic Ocean”, “macrofauna Arctic Ocean”, “macrofauna Atlantic Ocean”, “macrofauna Black Sea”, “macrofauna Southern Ocean”, “macrofauna Red Sea”, “macrofauna Pacific Ocean”, “macrofauna Gulf of Mexico”, “macrofauna Indian Ocean”, and “macrofauna Mediterranean Sea”. Expert knowledge identified a further 232 publications. Consulting PANGAEA® Data Publisher (https://www.pangaea.de/) identified 1,549 datasets from 172 publications using the key words “meiofauna abundance”, “macrofauna abundance” and “megafauna abundance”. Expert knowledge or unpublished datasets added a further 21 datasets. After removal of duplicates, the “Screening” step filtered 2,086 titles and abstracts and excluded 1,133 studies because they did not report benthic densities. The third PRISMA step assessed 953 studies and excluded 353 studies because they did not report metazoan meiobenthic, macrobenthic, or invertebrate megabenthic densities or they combined multiple size classes or sampling stations. We excluded other studies in the database that reported experimental studies, were inaccessible, or reported densities in a unit other than ind. m-2 or a unit that could be converted to ind. m-2, or reported densities for specific taxa instead of the entire size class. Studies were also excluded when they reported meta-studies or reviews rather than primary research, presented results of models, lacked sufficient geographical detail about sampling locations, or reported fauna associated with whale falls. The final “BenDen” part consisted of 600 studies from which we extracted 51,559 georeferenced benthic density records. For 12% (BioBen part) and 4% (BioDen part) of all data records, no exact sampling location in geographical coordinates (latitude, longitude) was indicated. For these cases, we approximated the coordinates of the sampling locations using Google Maps based on information about sampling area or based on maps presented in the original publications. We labelled these data records as ‘approximated location’. For studies that presented biomasses in several units, such as WM and DM, we report the data only once (preferred units: WM > DM > AFDM > C). The authors of this study intended to report all data records in the ‘raw’ units in which benthic fauna was measured initially. Whenever unknown conversion factors precluded calculating biomass back to ‘raw’ units, we noted this issue in the database using the label ‘converted data’ and listed references for the individual biomass conversion factors in the database. The authors of the various studies compiled in this database sometimes used different lower and upper limits (in mm) for mesh sizes of nets and/ or sieves to define the size class. Whenever an original study reported a lower and/ or upper limit mesh size, we included this information in the database as ‘sieve mesh size (mm) lower limit’ and ‘sieve mesh size (mm) upper limit’. Studies lacking this information were scored as NA. For those studies that reported data as mean or median ± error terms, we incorporated only mean or median values into the database. In all cases that did not report benthic biomasses and/ or densities in the text or in tables, but presented them in figures, we extracted biomass and/ or density values from these figures using ImageJ. Benthic fauna refers to all fauna that live in or on the seafloor, which researchers typically divide into size classes meiobenthos (32/ 64 µm – 0.5/ 1 mm), macrobenthos (250 µm – 1 cm), and megabenthos (> 1 cm). Benthic fauna play important roles in bioturbation activity, mineralization of organic matter, and in marine food webs. Evaluating their role in these ecosystem functions requires knowledge of their global distribution and biomass. We therefore established the BenBioDen database, the largest open-access database for marine benthic biomass and density data compiled so far. In total, it includes 11,792 georeferenced benthic biomass and 51,559 benthic density records from 384 and 600 studies, respectively. We selected all references following the procedure for systematic reviews and meta-analyses, and report biomass records as grams of wet mass, dry mass, or ash-free dry mass, or carbon per m2 and as abundance records as individuals per m2. This database provides a point of reference for future studies on the distribution and biomass of benthic fauna.

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    DRYAD; ZENODO
    Dataset . 2020 . 2021
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      DRYAD; ZENODO
      Dataset . 2020 . 2021
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    Authors: Gawryluk, Ryan M. R.; Tikhonenkov, Denis V.; Hehenberger, Elisabeth; Husnik, Filip; +2 Authors

    Evolution of the Rhodelphis heme biosynthetic pathway.Maximum likelihood phylogenetic trees were generated for all Rhodelphis heme biosynthetic proteins including: aminolevulinate synthase (ALAS), delta aminolevulinic acid dehydratase (HemB), porphobilinogen deaminase (HemC), uroporphyrinogen-III synthase (HemD), uroporphyrinogen-III decarboxylase (HemE), coproporphyrinogen-III oxidase (HemF), protoporphyrinogen-IX oxidase (HemY), ferrochelatase (HemH). Only the first step in heme synthesis, catalyzed by ALAS, is most similar to mitochondrial homologs, and is predicted to be mitochondrial. All others, with the exception of HemH, are plastid-type proteins. The atypical HemH protein is found in some, but not all red algae, and is closely related to HemH from myxobacteria. Untrimmed and trimmed alignments used to generate the trees are also included.Heme_Data_DRYAD.zipEvolution of the Rhodelphis plastid FeS cluster biosynthesis pathway.Maximum likelihood phylogenetic trees were generated for all Rhodelphis plastid iron-sulfur biosynthetic proteins including: SufB, SufC, SufD, SufE, SufS, ferredoxin, ferredoxin reductase, and NiFU. Untrimmed and trimmed alignments used to generate the trees are also included.FeScluster_Data_DRYAD.zipRhodelphis retain relic plastids including subunits of conserved plastid protein import complexes.Maximum likelihood phylogenetic trees were generated for Rhodelphis plastid Cpn60, along with conserved subunits of plastid protein import complexes. The presence of plastid import complexes is strong evidence for the retention of plastids in Rhodelphis cells. Untrimmed and trimmed alignments used to generate the trees are also included.Tic_Toc_Cpn_Data_DRYAD.zipUnaltered microscopic photographs of Rhodelphis cells.Raw photographs of Rhodelphis limneticus and R. marinus that correspond to photographs in Figure 1, along with Extended Data Figures 1 and 2 are presented.Raw_Rhodelphis_photos.zipIndividual and concatenated protein alignments for 151/253 datasetThis folder contains trimmed individual protein alignments that were used to construct the 151/253 concatenated supermatrix using SCaFoS. The resulting concatenated alignment - also included - was used to generate Figure 2a-b, along with Extended Data Figure 4.151-253_alignments.zipIndividual and concatenated protein alignments for 153/253 datasetThis folder contains trimmed individual protein alignments tthat were used to construct the 153/253 concatenated supermatrix using SCaFoS. The resulting concatenated alignment - also included - was used to generate Extended Data Figure 3.153-253_alignments.zipCoalescence recovers Rhodelphis as sister to red algae based on individual gene treesIndividual bootstrapped gene trees were generated with RAxML v8.1.6 and used to generate a species tree with ASTRAL-III under default parameters and 100 bootstrap replicates. Species trees were made from either all 253 single-gene trees from the 151/253 dataset, or b, the 50 trees with the highest relative tree certainty. The sister relationship of Rhodelphis and red algae is recovered with both datasets, and is in agreement with concatenated phylogenomic analyses. The resulting coalescent trees for each dataset, along with the gene trees used to generate the species tree, are included.Astral.zipConcatenation of alignments from the 50 single-gene datasets with highest relative tree certainty scores recovers Rhodelphis as sister to red algae.A maximum likelihood phylogenetic tree was generated with IQ-TREE based on a concatenated alignment of the 50 single gene trees with the highest relative tree certainty (151 taxa, 21,886 sites). The sister relationship of Rhodelphis and red algae still receives full statistical support with a highly reduced, phylogenetically well-supported dataset. This dataset represents Extended Data Figure 6.Top50_RTC.zipMaximum likelihood phylogenomic analyses of concatenated datasets demonstrate that Rhodelphis are sister to red algaeMaximum likelihood phylogenomic analyses were carried out for two very similar datasets: 153/253 (153 taxa, 253 proteins, 56,312 sites) and 151/253 (151 taxa, 253 proteins, 56,530 sites) using IQ-TREE under the LG+C60+F+G4 model. In each case, Rhodelphis were shown to be sister to red algae; in the 153/253 dataset, picozoa were shown to be sister to Rhodelphis + red algae. Unlike Bayesian analyses, maximum likelihood did not recover the monophyly of Archaeplastida, instead grouping Cryptista with green algae/plants and glaucophytes, though with only modest statistical support. The 153/253 tree corresponds to Extended Data Figure 3b, and the 151/253 tree corresponds to Extended Data Figure 4b.IQ-TREE_concat.zipBayesian phylogenomic analyses of concatenated protein datasets recover Rhodelphis as sister to red algaeBayesian phylogenomic analyses were carried out for two very similar datasets: 153/253 (153 taxa, 253 proteins, 56,312 sites) and 151/253 (151 taxa, 253 proteins, 56,530 sites) using PhyloBayes under the CAT+GTR+G4 framework. In each case, Rhodelphis were shown to be sister to red algae; in the 153/253 dataset, picozoa were shown to be sister to Rhodelphis + red algae. Unlike maximum likelihood analyses, Bayesian analyses recovered the monophyly of Archaeplastida.Phylobayes_concat.zipRhodelphis limneticus genome assemblies and predicted protein sequenecsThis folder contains R. limneticus genomic scaffolds from mixed culture (including the prey kinetoplastid Parabodo caudatus and bacteria) and manually isolated cells amplified by whole genome amplification (WGA). In each case, the assembly is based on a combination of Illumina HiSeq X and Oxford Nanopore minION reads that were assembled using SPAdes. Contaminant contigs were identified and removed using a combination of megablast and Autometa. For the WGA dataset, proteins were predicted using braker2; protein sequences are provided with soft repeat masking and no repeat masking.Rlimneticus_genome.zipRhodelphis limneticus filtered transcriptome assemblies and protein sequencesRNA-seq was performed on R. limneticus from mixed culture (including the prey kinetoplastid, Parabodo caudatus and bacteria) and from manually isolated single cells after amplification using the Smart-seq2 protocol. Paired-end reads were generated with the Illumina MiSeq platform and assembled with Trinity. In each case, contamination from prey organisms was subtracted using prey-only transcriptomes. Trinity contigs and Transdecoder protein sequences collapsed at 95% sequence identity with CD-HIT are included.Rlimneticus_transcriptome.zipRhodelphis marinus filtered transcriptome assembly and predicted proteinsRNA-seq was performed on R. marinus from mixed culture (including the prey kinetoplastid, Procryptobia sorokini and bacteria). Strand-specific paired-end reads were generated with the Illumina HiSeq 2500 platform and assembled with Trinity. In each case, contamination from prey organisms was subtracted using prey-only transcriptomes. Transdecoder protein sequences collapsed at 95% sequence identity with CD-HIT are included, along with Transdecoded mRNA sequences corresponding to the protein predictions.Rmarinus.zipFast site removal analyses demonstrate the robustness of the sister relationship of Rhodelphis and red algaeThe fastest evolving sites in the 151/253 and 153/253 concatenated datasets were estimated with AgentSmith and removed progressively in blocks of 3,000 amino acids. Maximum likelihood phylogenies were generated under the PROT+CAT+LG+F model with 100 rapid bootstrap replicates in RAxML 8.1.6. Here we include the individual phylogenies and alignments generated by AgentSmith. The support for various relationships is depicted in Figure 2c and Extended Data Figure 3c.FastSite.zip Rhodophyta (red algae) is one of three lineages of Archaeplastida, a supergroup that is united by the primary endosymbiotic origin of plastids in eukaryotes. Red algae are a diverse and species-rich group, members of which are typically photoautotrophic, but are united by a number of highly derived characteristics: they have relatively small intron-poor genomes, reduced metabolism and lack cytoskeletal structures that are associated with motility, flagella and centrioles. This suggests that marked gene loss occurred around their origin; however, this is difficult to reconstruct because they differ so much from the other archaeplastid lineages, and the relationships between these lineages are unclear. Here we describe the novel eukaryotic phylum Rhodelphidia and, using phylogenomics, demonstrate that it is a closely related sister to red algae. However, the characteristics of the two Rhodelphis species described here are nearly opposite to those that define red algae: they are non-photosynthetic, flagellate predators with gene-rich genomes, along with a relic genome-lacking primary plastid that probably participates in haem synthesis. Overall, these findings alter our views of the origins of Rhodophyta, and Archaeplastida evolution as a whole, as they indicate that mixotrophic feeding—that is, a combination of predation and phototrophy—persisted well into the evolution of the group.

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    DRYAD; ZENODO
    Dataset . 2019 . 2020
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    Authors: Kiani, Behdad; Ogden, Joan; Sheldon, F. Alex; Cordano, Lauren;

    California policy is incentivizing rapid adoption of zero emission electric vehicles for light duty and freight applications. In this project, we explored how locating charging facilities at California's highway rest stops, might impact electricity demand, grid operation, and integration of renewables like solar and wind into California's energy mix. Assuming a growing population of electric vehicles to meet state goals, we estimated state-wide growth of electricity demand, and identified the most attractive rest stop locations for siting chargers. Using a California-specific electricity dispatch model developed at ITS, we estimated how charging vehicles at these stations would impact renewable energy curtailment in California. We estimated the impacts of charging infrastructures on California's electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.

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    ZENODO
    Dataset . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Chakraborty, Debapriya;

    The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include: Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector. The dataset used in this report was created using the following public data sources: 2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping American Community Survey: https://www.census.gov/programs-surveys/acs Microsoft Excel.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Ramadoss, Trisha; Tal, Gil; Davis, Adam;

    The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, we consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. We examine two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a "Small Vehicles" scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a "Large Vehicles" scenario with 4 million of each category. We find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access.

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    ZENODO
    Dataset . 2023
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    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
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      Data sources: Datacite
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    Authors: Robertson, G. Philip; Hamilton, Stephen; Paustian, Keith; Smith, Pete;

    Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now. The dataset is a synthesis of literature values selected based on criteria described in the parent paper’s narrative. The files can be opened in Microsoft Excel or any other spreadsheet that can load Excel-format files.

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    DRYAD; ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Chakraborty, Debapriya; Bunch, David; Brownstone, David;

    The market for plug-in electric vehicles (PEVs) that primarily include battery electric vehicles (BEVs) and plug-in hybrid vehicles (PHEVs) has been rapidly growing in California for the past few years. Given the targets for PEV penetration in the state, it is important to have a better understanding of the pattern of technology diffusion and the factors that are driving the process. Using spatial analysis and Poisson count models we identify the importance of a neighborhood effect (at home locations) and a peer effect (at commute destinations) in supporting the diffusion of PEV technology in California. In the case of new BEV sales, we find that exposure to one additional BEV or PHEV within a 1-mile radius of a block group centroid is associated with a 0.2% increase in BEV sales in the block group. Interestingly, for new PHEV sales- the neighborhood effect of BEV sales is negative, suggesting that enhanced exposure to this type of technology (which is differentiated in distinctive ways from PHEVs) may impact new PHEV sales through a substitution effect. Specifically, higher BEV concentration in an area can have an overall negative effect on new PHEV sales. While the neighborhood effect at residential locations is important, a peer effect at commute destinations also has a notably important effect on new PEV sales. Both of these effects work in combination with socioeconomic, demographic, policy, and built environment factors in encouraging PEV adoption. These results suggest that policymakers should consider targeted programs and investments that can boost the impact of neighborhood and peer effects on PEV sales. The ReadMe sheet in the data file Data_DMV_2014_2016_PEV_new_sales__stock_and_other_variables gives detail of the variables in the datasheet. The dataset uploaded here does not have my identifiable information. Individual vehicle VIN numbers were aggregated to generate the count of EVs in each block group. The ReadMe file gives the information of the spatial unit of measurement for each variable (e.g., block group or census tract). Data on new plug-in vehicle sales are estimated from DMV's vehicle registration data. This vehicle registration data was then combined with data from the American Community survey, LODES data, and Smart Location Mapping data to account for other sources of dynamics in California's PEV market. The data was processed using STATA 16.

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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
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      Data sources: Datacite
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    Authors: Latinopoulos, Charilaos;

    This dataset contains four files: - a trip diary that was synthesized for electric vehicle drivers in the area of Westfield Shopping Center, London. - a trip diary that was synthesized for electric vehicle drivers in the area of Canary Wharf, London. - A set of EV charging packages that were used to optimize charging prices for a Charging Service Provider - A readme file with details and variable definitions for the contained datasets The original trip sample that was used for the synthesis of the two travel diaries is part of the London Travel Demand Survey (LTDS) - https://tfl.gov.uk/corporate/about-tfl/how-we-work/planning-for-the-future/consultations-and-surveys

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    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: Datacite; ZENODO
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    Authors: Cuthbert, Ross N.; Wasserman, Ryan J.; Dalu, Tatenda; Kaiser, Horst; +5 Authors

    1. Predation is a pervasive force that structures food webs and directly influences ecosystem functioning. The relative body sizes of predators and prey may be an important determinant of interaction strengths. However, studies quantifying the combined influence of intra- and interspecific variation in predator-prey body size ratios are lacking. 2. We use a comparative functional response approach to examine interaction strengths between three size classes of invasive bluegill and largemouth bass towards three scaled size classes of their tilapia prey. We then quantify the influence of intra- and interspecific predator-prey body mass ratios on the scaling of attack rates and handling times. 3. Type II functional responses were displayed by both predators across all predator and prey size classes. Largemouth bass consumed more than bluegill at small and intermediate predator size classes, whilst large predators of both species were more similar. Small prey were most vulnerable overall, however differential attack rates among prey were emergent across predator sizes. For both bluegill and largemouth bass, small predators exhibited higher attack rates towards small and intermediate prey sizes, whilst larger predators exhibited greater attack rates towards large prey. Conversely, handling times increased with prey size, with small bluegill exhibiting particularly low feeding rates towards medium-large prey types. Attack rates for both predators peaked unimodally at intermediate predator-prey body mass ratios, whilst handling times generally shortened across increasing body mass ratios. 4. We thus demonstrate effects of body size ratios on predator-prey interaction strengths between key fish species, with attack rates and handling times dependent on the relative sizes of predator-prey participants. 5. Considerations for intra- and interspecific body size ratio effects are critical for predicting the strengths of interactions within ecosystems and may drive differential ecological impacts among invasive species as size ratios shift.

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    DRYAD; ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2021
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      Data sources: Datacite; ZENODO
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    Authors: Galtbalt, Batbayar; Lilleyman, Amanda; Coleman, Jonathan T.; Cheng, Chuyu; +6 Authors

    Abstract Background In-flight conditions are hypothesized to influence the timing and success of long-distance migration. Wind assistance and thermal uplift are thought to reduce the energetic costs of flight, humidity, air pressure and temperature may affect the migrants’ water balance, and clouds may impede navigation. Recent advances in animal-borne long-distance tracking enable evaluating the importance of these factors in determining animals’ flight altitude. Methods Here we determine the effects of wind, humidity, temperature, cloud cover, and altitude (as proxy for climbing costs and air pressure) on flight altitude selection of two long-distance migratory shorebirds, far eastern curlew (Numenius madagascariensis) and whimbrel (Numenius phaeopus). To reveal the predominant drivers of flight altitude selection during migration we compared the atmospheric conditions at the altitude the birds were found flying with conditions elsewhere in the air column using conditional logistic mixed effect models. Results Our results demonstrate that despite occasional high-altitude migrations (up to 5550 m above ground level), our study species typically forego flying at high altitudes, limiting climbing costs and potentially alleviating water loss and facilitating navigation. While mainly preferring migrating at low altitude, notably in combination with low air temperature, the birds also preferred flying with wind support to likely reduce flight costs. They avoided clouds, perhaps to help navigation or to reduce the risks from adverse weather. Conclusions We conclude that the primary determinant of avian migrant’s flight altitude selection is a preference for low altitude, with wind support as an important secondary factor. Our approach and findings can assist in predicting climate change effects on migration and in mitigating bird strikes with air traffic, wind farms, power lines, and other human-made structures.

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    DRYAD; ZENODO
    Dataset . 2021
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      DRYAD; ZENODO
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    Authors: Stratmann, Tanja; van Oevelen, Dick; Martínez Arbizu, Pedro; Wei, Chih-Lin; +11 Authors

    In April and May 2019, we compiled the “BenBio” part of the “BenBioDen database” following the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses. In the first PRISMA step, the “Identification” step, we identified 1,373 articles in the Web of Science using the key words “marine meiofauna biomass”, “marine macrofauna biomass”, “marine megafauna biomass”, “marine meiobenth* biomass”, “marine macrobenth* biomass”, “marine megabenth* biomass”, “nematode biomass”, and “benthic ‘standing stock’”. We located an additional 201 publications based on expert knowledge. A search of the PANGAEA® Data Publisher (https://www.pangaea.de/) identified 1,488 datasets representing 148 publications using the key words “meiofauna biomass”, “macrofauna biomass” and “megafauna biomass”. Further 30 datasets were found in the EOL data archive (http://data.eol.ucar.edu/), through citations in review papers, and based on expert knowledge. After removing duplicates, we screened the titles and abstracts of 1,445 studies in PRISMA step 2 (“Screening”; Fig. 1A). This step excluded 951 studies because they did not report biomass values. In the Eligibility step, we assessed full texts of 494 studies for eligibility and excluded 110 studies because they did not report biomass, the publications or data were not accessible, or they did not report benthic biomass in appropriate units (g WW m-2, g DW m-2, g AFDW m-2, g or mol C m-2). Further reasons for excluding full texts included combining benthic biomass for several size classes, reporting benthic biomass for particular taxa rather than the whole size class, presenting biomass for faunal assemblages and/ or a group of sampling stations rather than for individual stations, not presenting primary research or lacking geographical details about sampling stations. We also excluded studies that estimated benthic biomass using modelling approaches, that conducted manipulative experiments, or did not report benthic biomass as single values, means or median values, but instead as ranges. The final “BenBio” part included 384 studies from which we extracted 11,792 georeferenced benthic biomass entries. The Benthos Density, i.e. “BenDen”, part of the “BenBioDen” database was established in July and August 2019 following the PRISMA Statement for systematic reviews and meta-analyses. In the Identification step, we found 2,515 articles in the Web of Science using the key words “meiofauna abundance”, “meiobenthos abundance”, “macrofauna abundance”, “macrobenthos abundance”, “megafauna abundance”, “megabenthos abundance”, “meiofauna Arctic Ocean”, “meiofauna Atlantic Ocean”, “meiofauna Black Sea”, “meiofauna Gulf of Mexico”, “meiofauna Indian Ocean”, “meiofauna Mediterranean Sea”, “meiofauna Pacific Ocean”, “meiofauna Southern Ocean”, “meiofauna Red Sea”, “meiofauna Pacific Ocean”, “megafauna Southern Ocean”, “megafauna Red Sea”, “megafauna Pacific Ocean”, “megafauna Mediterranean Sea”, “megafauna Indian Ocean”, “megafauna Black Sea”, “megafauna Gulf of Mexico”, “megafauna Atlantic Ocean”, “megafauna Arctic Ocean”, “macrofauna Arctic Ocean”, “macrofauna Atlantic Ocean”, “macrofauna Black Sea”, “macrofauna Southern Ocean”, “macrofauna Red Sea”, “macrofauna Pacific Ocean”, “macrofauna Gulf of Mexico”, “macrofauna Indian Ocean”, and “macrofauna Mediterranean Sea”. Expert knowledge identified a further 232 publications. Consulting PANGAEA® Data Publisher (https://www.pangaea.de/) identified 1,549 datasets from 172 publications using the key words “meiofauna abundance”, “macrofauna abundance” and “megafauna abundance”. Expert knowledge or unpublished datasets added a further 21 datasets. After removal of duplicates, the “Screening” step filtered 2,086 titles and abstracts and excluded 1,133 studies because they did not report benthic densities. The third PRISMA step assessed 953 studies and excluded 353 studies because they did not report metazoan meiobenthic, macrobenthic, or invertebrate megabenthic densities or they combined multiple size classes or sampling stations. We excluded other studies in the database that reported experimental studies, were inaccessible, or reported densities in a unit other than ind. m-2 or a unit that could be converted to ind. m-2, or reported densities for specific taxa instead of the entire size class. Studies were also excluded when they reported meta-studies or reviews rather than primary research, presented results of models, lacked sufficient geographical detail about sampling locations, or reported fauna associated with whale falls. The final “BenDen” part consisted of 600 studies from which we extracted 51,559 georeferenced benthic density records. For 12% (BioBen part) and 4% (BioDen part) of all data records, no exact sampling location in geographical coordinates (latitude, longitude) was indicated. For these cases, we approximated the coordinates of the sampling locations using Google Maps based on information about sampling area or based on maps presented in the original publications. We labelled these data records as ‘approximated location’. For studies that presented biomasses in several units, such as WM and DM, we report the data only once (preferred units: WM > DM > AFDM > C). The authors of this study intended to report all data records in the ‘raw’ units in which benthic fauna was measured initially. Whenever unknown conversion factors precluded calculating biomass back to ‘raw’ units, we noted this issue in the database using the label ‘converted data’ and listed references for the individual biomass conversion factors in the database. The authors of the various studies compiled in this database sometimes used different lower and upper limits (in mm) for mesh sizes of nets and/ or sieves to define the size class. Whenever an original study reported a lower and/ or upper limit mesh size, we included this information in the database as ‘sieve mesh size (mm) lower limit’ and ‘sieve mesh size (mm) upper limit’. Studies lacking this information were scored as NA. For those studies that reported data as mean or median ± error terms, we incorporated only mean or median values into the database. In all cases that did not report benthic biomasses and/ or densities in the text or in tables, but presented them in figures, we extracted biomass and/ or density values from these figures using ImageJ. Benthic fauna refers to all fauna that live in or on the seafloor, which researchers typically divide into size classes meiobenthos (32/ 64 µm – 0.5/ 1 mm), macrobenthos (250 µm – 1 cm), and megabenthos (> 1 cm). Benthic fauna play important roles in bioturbation activity, mineralization of organic matter, and in marine food webs. Evaluating their role in these ecosystem functions requires knowledge of their global distribution and biomass. We therefore established the BenBioDen database, the largest open-access database for marine benthic biomass and density data compiled so far. In total, it includes 11,792 georeferenced benthic biomass and 51,559 benthic density records from 384 and 600 studies, respectively. We selected all references following the procedure for systematic reviews and meta-analyses, and report biomass records as grams of wet mass, dry mass, or ash-free dry mass, or carbon per m2 and as abundance records as individuals per m2. This database provides a point of reference for future studies on the distribution and biomass of benthic fauna.

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    DRYAD; ZENODO
    Dataset . 2020 . 2021
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      DRYAD; ZENODO
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    Authors: Gawryluk, Ryan M. R.; Tikhonenkov, Denis V.; Hehenberger, Elisabeth; Husnik, Filip; +2 Authors

    Evolution of the Rhodelphis heme biosynthetic pathway.Maximum likelihood phylogenetic trees were generated for all Rhodelphis heme biosynthetic proteins including: aminolevulinate synthase (ALAS), delta aminolevulinic acid dehydratase (HemB), porphobilinogen deaminase (HemC), uroporphyrinogen-III synthase (HemD), uroporphyrinogen-III decarboxylase (HemE), coproporphyrinogen-III oxidase (HemF), protoporphyrinogen-IX oxidase (HemY), ferrochelatase (HemH). Only the first step in heme synthesis, catalyzed by ALAS, is most similar to mitochondrial homologs, and is predicted to be mitochondrial. All others, with the exception of HemH, are plastid-type proteins. The atypical HemH protein is found in some, but not all red algae, and is closely related to HemH from myxobacteria. Untrimmed and trimmed alignments used to generate the trees are also included.Heme_Data_DRYAD.zipEvolution of the Rhodelphis plastid FeS cluster biosynthesis pathway.Maximum likelihood phylogenetic trees were generated for all Rhodelphis plastid iron-sulfur biosynthetic proteins including: SufB, SufC, SufD, SufE, SufS, ferredoxin, ferredoxin reductase, and NiFU. Untrimmed and trimmed alignments used to generate the trees are also included.FeScluster_Data_DRYAD.zipRhodelphis retain relic plastids including subunits of conserved plastid protein import complexes.Maximum likelihood phylogenetic trees were generated for Rhodelphis plastid Cpn60, along with conserved subunits of plastid protein import complexes. The presence of plastid import complexes is strong evidence for the retention of plastids in Rhodelphis cells. Untrimmed and trimmed alignments used to generate the trees are also included.Tic_Toc_Cpn_Data_DRYAD.zipUnaltered microscopic photographs of Rhodelphis cells.Raw photographs of Rhodelphis limneticus and R. marinus that correspond to photographs in Figure 1, along with Extended Data Figures 1 and 2 are presented.Raw_Rhodelphis_photos.zipIndividual and concatenated protein alignments for 151/253 datasetThis folder contains trimmed individual protein alignments that were used to construct the 151/253 concatenated supermatrix using SCaFoS. The resulting concatenated alignment - also included - was used to generate Figure 2a-b, along with Extended Data Figure 4.151-253_alignments.zipIndividual and concatenated protein alignments for 153/253 datasetThis folder contains trimmed individual protein alignments tthat were used to construct the 153/253 concatenated supermatrix using SCaFoS. The resulting concatenated alignment - also included - was used to generate Extended Data Figure 3.153-253_alignments.zipCoalescence recovers Rhodelphis as sister to red algae based on individual gene treesIndividual bootstrapped gene trees were generated with RAxML v8.1.6 and used to generate a species tree with ASTRAL-III under default parameters and 100 bootstrap replicates. Species trees were made from either all 253 single-gene trees from the 151/253 dataset, or b, the 50 trees with the highest relative tree certainty. The sister relationship of Rhodelphis and red algae is recovered with both datasets, and is in agreement with concatenated phylogenomic analyses. The resulting coalescent trees for each dataset, along with the gene trees used to generate the species tree, are included.Astral.zipConcatenation of alignments from the 50 single-gene datasets with highest relative tree certainty scores recovers Rhodelphis as sister to red algae.A maximum likelihood phylogenetic tree was generated with IQ-TREE based on a concatenated alignment of the 50 single gene trees with the highest relative tree certainty (151 taxa, 21,886 sites). The sister relationship of Rhodelphis and red algae still receives full statistical support with a highly reduced, phylogenetically well-supported dataset. This dataset represents Extended Data Figure 6.Top50_RTC.zipMaximum likelihood phylogenomic analyses of concatenated datasets demonstrate that Rhodelphis are sister to red algaeMaximum likelihood phylogenomic analyses were carried out for two very similar datasets: 153/253 (153 taxa, 253 proteins, 56,312 sites) and 151/253 (151 taxa, 253 proteins, 56,530 sites) using IQ-TREE under the LG+C60+F+G4 model. In each case, Rhodelphis were shown to be sister to red algae; in the 153/253 dataset, picozoa were shown to be sister to Rhodelphis + red algae. Unlike Bayesian analyses, maximum likelihood did not recover the monophyly of Archaeplastida, instead grouping Cryptista with green algae/plants and glaucophytes, though with only modest statistical support. The 153/253 tree corresponds to Extended Data Figure 3b, and the 151/253 tree corresponds to Extended Data Figure 4b.IQ-TREE_concat.zipBayesian phylogenomic analyses of concatenated protein datasets recover Rhodelphis as sister to red algaeBayesian phylogenomic analyses were carried out for two very similar datasets: 153/253 (153 taxa, 253 proteins, 56,312 sites) and 151/253 (151 taxa, 253 proteins, 56,530 sites) using PhyloBayes under the CAT+GTR+G4 framework. In each case, Rhodelphis were shown to be sister to red algae; in the 153/253 dataset, picozoa were shown to be sister to Rhodelphis + red algae. Unlike maximum likelihood analyses, Bayesian analyses recovered the monophyly of Archaeplastida.Phylobayes_concat.zipRhodelphis limneticus genome assemblies and predicted protein sequenecsThis folder contains R. limneticus genomic scaffolds from mixed culture (including the prey kinetoplastid Parabodo caudatus and bacteria) and manually isolated cells amplified by whole genome amplification (WGA). In each case, the assembly is based on a combination of Illumina HiSeq X and Oxford Nanopore minION reads that were assembled using SPAdes. Contaminant contigs were identified and removed using a combination of megablast and Autometa. For the WGA dataset, proteins were predicted using braker2; protein sequences are provided with soft repeat masking and no repeat masking.Rlimneticus_genome.zipRhodelphis limneticus filtered transcriptome assemblies and protein sequencesRNA-seq was performed on R. limneticus from mixed culture (including the prey kinetoplastid, Parabodo caudatus and bacteria) and from manually isolated single cells after amplification using the Smart-seq2 protocol. Paired-end reads were generated with the Illumina MiSeq platform and assembled with Trinity. In each case, contamination from prey organisms was subtracted using prey-only transcriptomes. Trinity contigs and Transdecoder protein sequences collapsed at 95% sequence identity with CD-HIT are included.Rlimneticus_transcriptome.zipRhodelphis marinus filtered transcriptome assembly and predicted proteinsRNA-seq was performed on R. marinus from mixed culture (including the prey kinetoplastid, Procryptobia sorokini and bacteria). Strand-specific paired-end reads were generated with the Illumina HiSeq 2500 platform and assembled with Trinity. In each case, contamination from prey organisms was subtracted using prey-only transcriptomes. Transdecoder protein sequences collapsed at 95% sequence identity with CD-HIT are included, along with Transdecoded mRNA sequences corresponding to the protein predictions.Rmarinus.zipFast site removal analyses demonstrate the robustness of the sister relationship of Rhodelphis and red algaeThe fastest evolving sites in the 151/253 and 153/253 concatenated datasets were estimated with AgentSmith and removed progressively in blocks of 3,000 amino acids. Maximum likelihood phylogenies were generated under the PROT+CAT+LG+F model with 100 rapid bootstrap replicates in RAxML 8.1.6. Here we include the individual phylogenies and alignments generated by AgentSmith. The support for various relationships is depicted in Figure 2c and Extended Data Figure 3c.FastSite.zip Rhodophyta (red algae) is one of three lineages of Archaeplastida, a supergroup that is united by the primary endosymbiotic origin of plastids in eukaryotes. Red algae are a diverse and species-rich group, members of which are typically photoautotrophic, but are united by a number of highly derived characteristics: they have relatively small intron-poor genomes, reduced metabolism and lack cytoskeletal structures that are associated with motility, flagella and centrioles. This suggests that marked gene loss occurred around their origin; however, this is difficult to reconstruct because they differ so much from the other archaeplastid lineages, and the relationships between these lineages are unclear. Here we describe the novel eukaryotic phylum Rhodelphidia and, using phylogenomics, demonstrate that it is a closely related sister to red algae. However, the characteristics of the two Rhodelphis species described here are nearly opposite to those that define red algae: they are non-photosynthetic, flagellate predators with gene-rich genomes, along with a relic genome-lacking primary plastid that probably participates in haem synthesis. Overall, these findings alter our views of the origins of Rhodophyta, and Archaeplastida evolution as a whole, as they indicate that mixotrophic feeding—that is, a combination of predation and phototrophy—persisted well into the evolution of the group.

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    DRYAD; ZENODO
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    Authors: Kiani, Behdad; Ogden, Joan; Sheldon, F. Alex; Cordano, Lauren;

    California policy is incentivizing rapid adoption of zero emission electric vehicles for light duty and freight applications. In this project, we explored how locating charging facilities at California's highway rest stops, might impact electricity demand, grid operation, and integration of renewables like solar and wind into California's energy mix. Assuming a growing population of electric vehicles to meet state goals, we estimated state-wide growth of electricity demand, and identified the most attractive rest stop locations for siting chargers. Using a California-specific electricity dispatch model developed at ITS, we estimated how charging vehicles at these stations would impact renewable energy curtailment in California. We estimated the impacts of charging infrastructures on California's electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.

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    ZENODO
    Dataset . 2020
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