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  • Transport Research

  • Authors: Prada, Daniela Nieto;

    Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

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  • Authors: FRANCIS, C.; DAVIES, G.; EVANS, J.; Et Al.;

    Refrigerated road transport (RRT) vehicles are large users of energy, and reportedly have relatively high leakage of hydrofluorocarbon refrigerant gases, both of which contribute to global warming. The experience obtained from widespread research in leak reduction in stationary refrigeration systems can be instructive in combatting leakage in RRT systems, which has received less focus to date. This paper will take an integrated approach to develop and describe a preliminary model for sustainable RRT systems. It will first review lessons learned about refrigerant leakage in stationary systems in an effort to identify problematic/leak prone components common to transport refrigeration systems. This will then be followed by a survey of recent studies conducted in modelling transport refrigeration systems to advance energy efficiency. Initial results from the model illustrate the need to improve the efficiency of the refrigeration system, together with preventative maintenance of the box structure and refrigeration system as a whole.

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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: Vajedsamiei, Jahangir; Wahl, Martin; Schmidt, Andrea Lee; Yazdanpanahan, Maryam; +1 Authors

    These data were produced in two lab assays. The first assay was conducted in the period from August 29 to September 10, during which filtration and respiration of 18 mussels transplanted and grown for ca. four months under thermal history levels of + 0 °C and + 4 °C (using Kiel Outdoor Benthocosms, KOBs) were recorded in six temporally replicated (independent) trials using the Fluorometer and Oximeter-equipped Flow-through Setup (FOFS; Vajedsamiei et al., 2021). In each trial, filtration and respiration of three different transplants, randomly selected from the incubated samples were recorded in response to a constant mild temperature condition (20.8 °C) followed by two 24 h thermal fluctuation cycles. In the second assay, we recorded filtration and respiration rates of six batches of 5 or 6 mussels recruited and grown under the same thermal history levels in KOBs (three batches from each thermal history level) in temporally replicated trials of the same FOFS treatment, as explained earlier. The approach for aquiring data, associated measurement corrections and calculations of response variables, and the parameter names, prefices and suffices are described in the method paper: Vajedsamiei et al., 2021 doi:https://doi.org/10.1002/lom3.10414

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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2021
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2021
      License: CC BY
      Data sources: PANGAEA
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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: Virtanen, E. A.; Lappalainen, J.; Nurmi, M.; Viitasalo, M; +6 Authors

    Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087. Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. Additional details can be found from the published article: https://doi.org/10.1016/j.rser.2022.112087

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: O'Reilly, Ryan; Cohen, Jed; Reichl, Johannes;

    Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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  • A 2016 Honda Civic with a 4-cylinder 1.5-liter L15B7 turbocharged engine and continuously variable transmission (CVT) was benchmarked. The test method involved installing the engine and its CVT in an engine dynamometer test cell with the engine wiring harness tethered to its vehicle parked outside the test cell. Engine and transmission torque, fuel flow, key engine temperatures and pressures, and onboard diagnostics (OBD)/CAN bus data were recorded. The paper published as part of this work documents the test results for idle, low, medium and high load engine operation, as well as motoring torque, wide-open throttle torque and fuel consumption during transient operation using both EPA Tier 2 and Tier 3 test fuels. Particular attention is given to characterizing enrichment control during high load engine operation. Results have been used to create complete engine fuel consumption and efficiency maps and estimate CO2 emissions using EPA’s ALPHA full vehicle simulation model, over regulatory drive cycles. Within the published paper, the design and performance of the 1.5-liter Honda engine are compared to several other past, present, and future downsized-boosted engines and potential advancements were evaluated.

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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: David Frantz; Franz Schug; Dominik Wiedenhofer; André Baumgart; +8 Authors

    Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the West Coast CONUS, i.e. CA OR WA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      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: Murray, Christopher S; Baumann, Hannes;

    Whether marine fish will grow differently in future high pCO2 environments remains surprisingly uncertain. Long-term and whole-life cycle effects are particularly unknown, because such experiments are logistically challenging, space demanding, exclude long-lived species, and require controlled, restricted feeding regimes—otherwise increased consumption could mask potential growth effects. Here, we report on repeated, long-term, food-controlled experiments to rear large populations (>4,000 individuals total) of the experimental model and ecologically important forage fish Menidia menidia (Atlantic silverside) under contrasting temperature (17°, 24°, and 28°C) and pCO2 conditions (450 vs. 2,200 μatm) from fertilization to a third of this annual species' life span. Quantile analyses of trait distributions showed mostly negative effects of high pCO2 on long-term growth. At 17°C and 28°C, but not at 24°C, high pCO2 fish were significantly shorter [17°C: -5 to -9%; 28°C: -3%] and weighed less [17°C: -6 to -18%; 28°C: -8%] compared to ambient pCO2 fish. Reductions in fish weight were smaller than in length, which is why high pCO2 fish at 17°C consistently exhibited a higher Fulton's k (weight/length ratio). Notably, it took more than 100 days of rearing for statistically significant length differences to emerge between treatment populations, showing that cumulative, long-term CO2 effects could exist elsewhere but are easily missed by short experiments. Long-term rearing had another benefit: it allowed sexing the surviving fish, thereby enabling rare sex-specific analyses of trait distributions under contrasting CO2 environments. We found that female silversides grew faster than males, but there was no interaction between CO2 and sex, indicating that males and females were similarly affected by high pCO2. Because Atlantic silversides are known to exhibit temperature-dependent sex determination, we also analyzed sex ratios, revealing no evidence for CO2-dependent sex determination in this species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-12-25.

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2020
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 2020
      Data sources: PANGAEA
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  • The purpose of this work was to develop and validate a 48 V lithium-ion battery model for integration into EPA’s ALPHA vehicle simulation model and that can also be used within Gamma Technologies, LLC (Westmont, IL) GT-DRIVE™ vehicle simulations. These vehicle models allow simulation of energy flows and CO2 emissions for mild hybrid electric vehicles over EPA regulatory drive cycles and during real-world driving. The battery model is a standard equivalent circuit model with two-time constant resistance-capacitance (RC) blocks. Resistances and capacitances were calculated using test data from an 8 Ah, 0.4 kWh, 48 V (nominal) lithium-ion battery obtained from a Tier 1 automotive supplier, A123 Systems, and developed specifically for 48 V MHEV applications. The A123 Systems battery has 14 pouch-type lithium ion cells arranged in a 14 series and 1 parallel (14S1P) configuration. The RC battery model was validated using battery test data generated by a hardware-in-the-loop (HIL) system that simulated the impact of mild hybrid electric vehicle (MHEV) operation on the A123 systems 48 V battery pack over U.S. regulatory drive cycles. The HIL system matched charge and discharge data originally generated by Argonne National Laboratory (ANL) during chassis dynamometer testing of a 2013 Chevy Malibu Eco 115 V MHEV. All validation testing was performed at the Battery Test Facility (BTF) at the U.S. EPA National Vehicle and Fuel Emissions Laboratory (NVFEL) in Ann Arbor, Michigan. The simulated battery voltages, currents, and state of charge (SOC) of the HIL tests were in good agreement with vehicle test data over a number of different drive cycles and excellent agreement was achieved between RC model simulations of the 48 V battery and HIL battery test data.

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  • Authors: LeCroy, Chase; Dobbelaere, Cristina;

    Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

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  • Authors: Prada, Daniela Nieto;

    Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

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  • Authors: FRANCIS, C.; DAVIES, G.; EVANS, J.; Et Al.;

    Refrigerated road transport (RRT) vehicles are large users of energy, and reportedly have relatively high leakage of hydrofluorocarbon refrigerant gases, both of which contribute to global warming. The experience obtained from widespread research in leak reduction in stationary refrigeration systems can be instructive in combatting leakage in RRT systems, which has received less focus to date. This paper will take an integrated approach to develop and describe a preliminary model for sustainable RRT systems. It will first review lessons learned about refrigerant leakage in stationary systems in an effort to identify problematic/leak prone components common to transport refrigeration systems. This will then be followed by a survey of recent studies conducted in modelling transport refrigeration systems to advance energy efficiency. Initial results from the model illustrate the need to improve the efficiency of the refrigeration system, together with preventative maintenance of the box structure and refrigeration system as a whole.

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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: Vajedsamiei, Jahangir; Wahl, Martin; Schmidt, Andrea Lee; Yazdanpanahan, Maryam; +1 Authors

    These data were produced in two lab assays. The first assay was conducted in the period from August 29 to September 10, during which filtration and respiration of 18 mussels transplanted and grown for ca. four months under thermal history levels of + 0 °C and + 4 °C (using Kiel Outdoor Benthocosms, KOBs) were recorded in six temporally replicated (independent) trials using the Fluorometer and Oximeter-equipped Flow-through Setup (FOFS; Vajedsamiei et al., 2021). In each trial, filtration and respiration of three different transplants, randomly selected from the incubated samples were recorded in response to a constant mild temperature condition (20.8 °C) followed by two 24 h thermal fluctuation cycles. In the second assay, we recorded filtration and respiration rates of six batches of 5 or 6 mussels recruited and grown under the same thermal history levels in KOBs (three batches from each thermal history level) in temporally replicated trials of the same FOFS treatment, as explained earlier. The approach for aquiring data, associated measurement corrections and calculations of response variables, and the parameter names, prefices and suffices are described in the method paper: Vajedsamiei et al., 2021 doi:https://doi.org/10.1002/lom3.10414

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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2021
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2021
      License: CC BY
      Data sources: PANGAEA
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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: Virtanen, E. A.; Lappalainen, J.; Nurmi, M.; Viitasalo, M; +6 Authors

    Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087. Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. Additional details can be found from the published article: https://doi.org/10.1016/j.rser.2022.112087

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: O'Reilly, Ryan; Cohen, Jed; Reichl, Johannes;

    Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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  • A 2016 Honda Civic with a 4-cylinder 1.5-liter L15B7 turbocharged engine and continuously variable transmission (CVT) was benchmarked. The test method involved installing the engine and its CVT in an engine dynamometer test cell with the engine wiring harness tethered to its vehicle parked outside the test cell. Engine and transmission torque, fuel flow, key engine temperatures and pressures, and onboard diagnostics (OBD)/CAN bus data were recorded. The paper published as part of this work documents the test results for idle, low, medium and high load engine operation, as well as motoring torque, wide-open throttle torque and fuel consumption during transient operation using both EPA Tier 2 and Tier 3 test fuels. Particular attention is given to characterizing enrichment control during high load engine operation. Results have been used to create complete engine fuel consumption and efficiency maps and estimate CO2 emissions using EPA’s ALPHA full vehicle simulation model, over regulatory drive cycles. Within the published paper, the design and performance of the 1.5-liter Honda engine are compared to several other past, present, and future downsized-boosted engines and potential advancements were evaluated.

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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: David Frantz; Franz Schug; Dominik Wiedenhofer; André Baumgart; +8 Authors

    Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the West Coast CONUS, i.e. CA OR WA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
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      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      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: Murray, Christopher S; Baumann, Hannes;

    Whether marine fish will grow differently in future high pCO2 environments remains surprisingly uncertain. Long-term and whole-life cycle effects are particularly unknown, because such experiments are logistically challenging, space demanding, exclude long-lived species, and require controlled, restricted feeding regimes—otherwise increased consumption could mask potential growth effects. Here, we report on repeated, long-term, food-controlled experiments to rear large populations (>4,000 individuals total) of the experimental model and ecologically important forage fish Menidia menidia (Atlantic silverside) under contrasting temperature (17°, 24°, and 28°C) and pCO2 conditions (450 vs. 2,200 μatm) from fertilization to a third of this annual species' life span. Quantile analyses of trait distributions showed mostly negative effects of high pCO2 on long-term growth. At 17°C and 28°C, but not at 24°C, high pCO2 fish were significantly shorter [17°C: -5 to -9%; 28°C: -3%] and weighed less [17°C: -6 to -18%; 28°C: -8%] compared to ambient pCO2 fish. Reductions in fish weight were smaller than in length, which is why high pCO2 fish at 17°C consistently exhibited a higher Fulton's k (weight/length ratio). Notably, it took more than 100 days of rearing for statistically significant length differences to emerge between treatment populations, showing that cumulative, long-term CO2 effects could exist elsewhere but are easily missed by short experiments. Long-term rearing had another benefit: it allowed sexing the surviving fish, thereby enabling rare sex-specific analyses of trait distributions under contrasting CO2 environments. We found that female silversides grew faster than males, but there was no interaction between CO2 and sex, indicating that males and females were similarly affected by high pCO2. Because Atlantic silversides are known to exhibit temperature-dependent sex determination, we also analyzed sex ratios, revealing no evidence for CO2-dependent sex determination in this species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-12-25.

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2020
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 2020
      Data sources: PANGAEA
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  • The purpose of this work was to develop and validate a 48 V lithium-ion battery model for integration into EPA’s ALPHA vehicle simulation model and that can also be used within Gamma Technologies, LLC (Westmont, IL) GT-DRIVE™ vehicle simulations. These vehicle models allow simulation of energy flows and CO2 emissions for mild hybrid electric vehicles over EPA regulatory drive cycles and during real-world driving. The battery model is a standard equivalent circuit model with two-time constant resistance-capacitance (RC) blocks. Resistances and capacitances were calculated using test data from an 8 Ah, 0.4 kWh, 48 V (nominal) lithium-ion battery obtained from a Tier 1 automotive supplier, A123 Systems, and developed specifically for 48 V MHEV applications. The A123 Systems battery has 14 pouch-type lithium ion cells arranged in a 14 series and 1 parallel (14S1P) configuration. The RC battery model was validated using battery test data generated by a hardware-in-the-loop (HIL) system that simulated the impact of mild hybrid electric vehicle (MHEV) operation on the A123 systems 48 V battery pack over U.S. regulatory drive cycles. The HIL system matched charge and discharge data originally generated by Argonne National Laboratory (ANL) during chassis dynamometer testing of a 2013 Chevy Malibu Eco 115 V MHEV. All validation testing was performed at the Battery Test Facility (BTF) at the U.S. EPA National Vehicle and Fuel Emissions Laboratory (NVFEL) in Ann Arbor, Michigan. The simulated battery voltages, currents, and state of charge (SOC) of the HIL tests were in good agreement with vehicle test data over a number of different drive cycles and excellent agreement was achieved between RC model simulations of the 48 V battery and HIL battery test data.

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  • Authors: LeCroy, Chase; Dobbelaere, Cristina;

    Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

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