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  • Energy Research
  • 2021-2025
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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: YU, Yongqiang;

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

    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/ World Data Center fo...arrow_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/
    World Data Center for Climate
    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/ World Data Center fo...arrow_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/
      World Data Center for Climate
      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: Yong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); +1 Authors

    Additional file 1. Summary of sequence data from 24 samples.

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    figshare
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    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/
    figshare
    Dataset . 2021
    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/ figsharearrow_drop_down
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      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      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/
      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      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/
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  • Authors: Saberian, Soodeh;

    This paper evidenced sensitivity of US immigration judge decisions to temperature in the city of arbitration on date of adjudication. This note serves to correct errors noted since publication. Main results from both the main linear specifications are qualitatively unchanged, with estimated treatment effects similar in size to the original and retaining statistical significance at conventional levels. Some secondary results lose significance with erosion of sample size. We also acknowledge the additional finding by Spamann (2020) with respect to external validity. Immigration judges in the United States.

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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/

    Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

    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
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    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 . 2022
    License: CC BY
    Data sources: Datacite
    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 . 2022
    License: CC BY
    Data sources: 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/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      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 . 2022
      License: CC BY
      Data sources: Datacite
      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 . 2022
      License: CC BY
      Data sources: 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: Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; +2 Authors

    This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

    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/ UCL Discoveryarrow_drop_down
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    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    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/
    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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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/ UCL Discoveryarrow_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/
      UCL Discovery
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      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/
      UCL Discovery
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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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: Giri Ajay, Adhyanth; Morgan, Laurence; Wu, Yan; Bretos, David; +3 Authors

    This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.

    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
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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
    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 BY
    Data sources: ZENODO
    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 BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      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 BY
      Data sources: ZENODO
      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 BY
      Data sources: ZENODO
      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 BY
      Data sources: Datacite
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    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Shuai ZHANG;

    Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.

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    https://dx.doi.org/10.57760/sc...
    Dataset . 2022
    License: CC BY NC
    Data sources: Datacite
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      https://dx.doi.org/10.57760/sc...
      Dataset . 2022
      License: CC BY NC
      Data sources: Datacite
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    Authors: Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; +1 Authors

    Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

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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 . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Igneczi, Adam; Sole, Andrew; Livingstone, Stephen; Ng, Felix; +2 Authors

    This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"

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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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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: YU, Yongqiang;

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Yong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); +1 Authors

    Additional file 1. Summary of sequence data from 24 samples.

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  • Authors: Saberian, Soodeh;

    This paper evidenced sensitivity of US immigration judge decisions to temperature in the city of arbitration on date of adjudication. This note serves to correct errors noted since publication. Main results from both the main linear specifications are qualitatively unchanged, with estimated treatment effects similar in size to the original and retaining statistical significance at conventional levels. Some secondary results lose significance with erosion of sample size. We also acknowledge the additional finding by Spamann (2020) with respect to external validity. Immigration judges in the United States.

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    Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

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    Authors: Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; +2 Authors

    This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

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    UCL Discovery
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    B2FIND
    Dataset . 2022
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    Authors: Giri Ajay, Adhyanth; Morgan, Laurence; Wu, Yan; Bretos, David; +3 Authors

    This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.

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    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

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    Authors: Shuai ZHANG;

    Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.

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    https://dx.doi.org/10.57760/sc...
    Dataset . 2022
    License: CC BY NC
    Data sources: Datacite
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      https://dx.doi.org/10.57760/sc...
      Dataset . 2022
      License: CC BY NC
      Data sources: Datacite
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    Authors: Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; +1 Authors

    Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

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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 . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Igneczi, Adam; Sole, Andrew; Livingstone, Stephen; Ng, Felix; +2 Authors

    This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"

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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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