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      string: "[{\"name\":\"gri_osm\",\"versions\":[{\"name\":\"gri_osm.roads_and_rail_version_1\",\"description\":\"Extraction
        from GRI OSM Table for Roads and Rail, including Damages\",\"version\":\"roads_and_rail_version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"nismod/open-gira
        contributors and OpenStreetMap contributors\",\"data_title\":\"Road and Rail
        networks derived from OpenStreetMap\",\"data_title_long\":\"Road and Rail
        networks derived from OpenStreetMap\",\"data_summary\":\"\\nOpenStreetMap
        provides map data, including on road and railway networks.\\nThis dataset
        is a derived, processed extract from the global OpenStreetMap\\ndatabase,
        produced by researchers at the University of Oxford to support\\ninfrastructure
        systems analysis and climate risk and resilience assessments.\\n\\nThe data
        is produced from a snapshot of OpenStreetMap (the current version is \\ntaken
        from November 2022) by a reproducible pipeline which is under development\\nand
        made freely available at https://github.com/nismod/open-gira.\\n    \",\"data_citation\":\"\\nRussell
        T., Thomas F., nismod/open-gira contributors and OpenStreetMap contributors
        (2022)\\nGlobal Road and Rail networks derived from OpenStreetMap. [Dataset]
        Available at https://global.infrastructureresilience.org\\n    \",\"data_license\":{\"name\":\"ODbL-1.0\",\"path\":\"https://opendefinition.org/licenses/odc-odbl\",\"title\":\"Open
        Data Commons Open Database License 1.0\"},\"data_origin_url\":\"https://global.infrastructureresilience.org\",\"data_formats\":[\"Geopackage\"]}]},{\"name\":\"gridfinder\",\"versions\":[{\"name\":\"gridfinder.version_1\",\"description\":\"gridfinder
        - Predictive mapping of the global power system using open data\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Arderne,
        Christopher; Nicolas, Claire; Zorn, Conrad; Koks, Elco E\",\"data_title\":\"Gridfinder\",\"data_title_long\":\"Gridfinder
        data from 'Predictive mapping of the global power system using open data'\",\"data_summary\":\"\\nThree
        primary global data outputs from the research:\\n\\ngrid.gpkg: Vectorized
        predicted distribution and transmission line network, with existing OpenStreetMap
        lines tagged in the 'source' column\\ntargets.tif: Binary raster showing locations
        predicted to be connected to distribution grid. \\nlv.tif: Raster of predicted
        low-voltage infrastructure in kilometres per cell.\\n\\nThis data was created
        with code in the following three repositories:\\n\\nhttps://github.com/carderne/gridfinder\\nhttps://github.com/carderne/predictive-mapping-global-power\\nhttps://github.com/carderne/access-estimator\\n\\nFull
        steps to reproduce are contained in this file:\\n\\nhttps://github.com/carderne/predictive-mapping-global-power/blob/master/README.md\\n\\nThe
        data can be visualized at the following location:\\n\\nhttps://gridfinder.org\\n
        \   \",\"data_citation\":\"\\nArderne, Christopher, Nicolas, Claire, Zorn,
        Conrad, & Koks, Elco E. (2020).\\nData from: Predictive mapping of the global
        power system using open data [Data\\nset]. In Nature Scientific Data (1.1.1,
        Vol. 7, Number Article 19). Zenodo.\\nhttps://doi.org/10.5281/zenodo.3628142
        \   \\n\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"https://doi.org/10.5281/zenodo.3628142\",\"data_formats\":[\"Geopackage\",\"GeoTIFF\"]}]},{\"name\":\"isimp_drought\",\"versions\":[{\"name\":\"isimp_drought.version_1\",\"description\":\"ISIMP
        Drought v1 processor\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Lange,
        S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020)\",\"data_title\":\"ISIMP
        Drought\",\"data_title_long\":\"Annual probability of extreme heat and drought
        events, derived from Lange et al 2020\",\"data_summary\":\"\\nThe time series
        of extreme events given by Lange et al has been processed into an annual probability
        of occurrence by researchers at the University of Oxford, using the pipeline
        available online at https://github.com/nismod/infra-risk-vis/blob/45d8974c311067141ee6fcaa1321c7ecdaa59752/etl/pipelines/isimip/Snakefile
        - this is a draft dataset, used for visualisation in https://global.infrastructureresilience.org/
        but not otherwise reviewed or published.\\n\\nIf you use this, please cite:
        Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et
        al. (2020). Projecting exposure to extreme climate impact events across six
        event categories and three spatial scales. Earth's Future, 8, e2020EF001616.
        DOI 10.1029/2020EF001616\\n\\nThis is shared under a CC0 1.0 Universal Public
        Domain Dedication (CC0 1.0) When using ISIMIP data for your research, please
        appropriately credit the data providers, e.g. either by citing the DOI for
        the dataset, or by appropriate acknowledgment.\\n\\nAnnual probability of
        drought (soil moisture below a baseline threshold) or extreme heat (temperature
        and humidity-based indicators over a threshold) events on a 0.5\xB0 grid.
        8 hydrological models forced by 4 GCMs under baseline, RCP 2.6 & 6.0 emission
        scenarios. Current and future maps in 2030, 2050 and 2080.\\n\\nThe ISIMIP2b
        climate input data and impact model output data analyzed in this study are
        available in the ISIMIP data repository at ESGF, see https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input
        and https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output,
        respectively. More information about the GHM, GGCM, and GVM output data is
        provided by Gosling et al. (2020), Arneth et al. (2020), and Reyer et al.
        (2019), respectively.\\n\\nEvent definitions are given in Lange et al, table
        1. Land area is exposed to drought if monthly soil moisture falls below the
        2.5th percentile of the preindustrial baseline distribution for at least seven
        consecutive months. Land area is exposed to extreme heat if both a relative
        indicator based on temperature (Russo et al 2015, 2017) and an absolute indicator
        based on temperature and relative humidity (Masterton & Richardson, 1979)
        exceed their respective threshold value.\\n    \",\"data_citation\":\"\\nLange,
        S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020).
        Projecting exposure to extreme climate impact events across six event categories
        and three spatial scales. Earth's Future, 8, e2020EF001616. DOI 10.1029/2020EF001616
        \  \\n    \",\"data_license\":{\"name\":\"CC0\",\"path\":\"https://creativecommons.org/share-your-work/public-domain/cc0/\",\"title\":\"CC0\"},\"data_origin_url\":\"https://doi.org/10.5281/zenodo.7732393\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"jrc_ghsl_built_c\",\"versions\":[{\"name\":\"jrc_ghsl_built_c.r2022_epoch2018_10m_mszfun\",\"description\":\"\\nA
        Processor for JRC GHSL Built-Up Characteristics - \\nR2022 release, Epoch
        2018, 10m resolution, Morphological Settlement Zone and Functional classification\\n
        \   \",\"version\":\"r2022_epoch2018_10m_mszfun\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Joint
        Research Centre\",\"data_title\":\"GHS-BUILT-C MSZ and FC, R2022 E2018 10m\",\"data_title_long\":\"JRC
        Global Human Settlement Layer - Built-Up Characteristics (GHS-BUILT-C - MSZ
        & FC) - Release 2022 - Epoch 2018 - 10m resolution - Morphological Settlement
        Zone & Functional Classification\",\"data_summary\":\"\\nThe spatial raster
        dataset delineates the boundaries of the human settlements at\\n10m resolution,
        and describe their inner characteristics in terms of the\\nmorphology of the
        built environment and the functional use. The Morphological\\nSettlement Zone
        (MSZ) delineates the spatial domain of all the human settlements\\nat the
        neighboring scale of approx. 100m, based on the spatial generalization of\\nthe
        built-up surface fraction (BUFRAC) function. The objective is to fill the\\nopen
        spaces that are surrounded by large patches of built space. MSZ, open\\nspaces,
        and built spaces basic class abstractions are derived by mathematical\\nmorphology
        spatial filtering (opening, closing, regional maxima) from the BUFRAC\\nfunction.
        They are further classified according to the information regarding\\nvegetation
        intensity (GHS-BUILT-C_VEG_GLOBE_R2022A), water surfaces\\n(GHS_LAND_GLOBE_R2022A),
        road surfaces (OSM highways), functional use\\n(GHS-BUILT-C_FUN_GLOBE_R2022A),
        and building height (GHS-BUILT-H_GLOBE_R2022A).\\n\\nThe main characteristics
        of this dataset are listed below. The complete\\ninformation about the GHSL
        main products can be found in the GHSL Data Package\\n2022 report (10.33 MB):\\nhttps://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2022.pdf\\n
        \   \",\"data_citation\":\"\\nDataset:\\n\\nPesaresi M., P. Panagiotis (2022):
        GHS-BUILT-C R2022A - GHS Settlement\\nCharacteristics, derived from Sentinel2
        composite (2018) and other GHS R2022A\\ndata.European Commission, Joint Research
        Centre (JRC) PID:\\nhttp://data.europa.eu/89h/dde11594-2a66-4c1b-9a19-821382aed36e,\\ndoi:10.2905/DDE11594-2A66-4C1B-9A19-821382AED36E\\n\\nConcept
        & Methodology:\\n\\nSchiavina M., Melchiorri M., Pesaresi M., Politis P.,
        Freire S., Maffenini L.,\\nFlorio P., Ehrlich D., Goch K., Tommasi P., Kemper
        T. GHSL Data Package 2022,\\nJRC 129516, ISBN 978-92-76-53071-8 doi:10.2760/19817
        \\n    \",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"https://ghsl.jrc.ec.europa.eu/download.php?ds=builtC\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"jrc_ghsl_population\",\"versions\":[{\"name\":\"jrc_ghsl_population.r2022_epoch2020_1km\",\"description\":\"A
        Processor for JRC GHSL Population - R2022 release, Epoch 2020, 1Km resolution\",\"version\":\"r2022_epoch2020_1km\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Joint
        Research Centre\",\"data_title\":\"GHS-POP - R2022A\",\"data_title_long\":\"GHS-POP
        R2022A - extract from GHS population grid for 2020, 1km resolution\",\"data_summary\":\"\\nThe
        spatial raster dataset depicts the distribution of residential population,\\nexpressed
        as the number of people per cell. Residential population estimates\\nbetween
        1975 and 2020 in 5-year intervals and projections to 2025 and 2030\\nderived
        from CIESIN GPWv4.11 were disaggregated from census or administrative\\nunits
        to grid cells, informed by the distribution, density, and classification\\nof
        built-up as mapped in the Global Human Settlement Layer (GHSL) global layer\\nper
        corresponding epoch.\\n\\nThe complete information about the GHSL main products
        can be found in the GHSL\\nData Package 2022 report (10.33 MB):\\nhttps://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2022.pdf\\n
        \   \",\"data_citation\":\"\\nDataset:\\n\\nSchiavina M., Freire S., MacManus
        K. (2022): GHS-POP R2022A - GHS population\\ngrid multitemporal (1975-2030).European
        Commission, Joint Research Centre (JRC)\\nPID: http://data.europa.eu/89h/d6d86a90-4351-4508-99c1-cb074b022c4a,\\ndoi:10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A\\n\\nConcept
        & Methodology:\\n\\nFreire S., MacManus K., Pesaresi M., Doxsey-Whitfield
        E., Mills J. (2016)\\nDevelopment of new open and free multi-temporal global
        population grids at 250 m\\nresolution. Geospatial Data in a Changing World;
        Association of Geographic\\nInformation Laboratories in Europe (AGILE), AGILE
        2016\\n    \",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"https://ghsl.jrc.ec.europa.eu/download.php?ds=pop\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"natural_earth_raster\",\"versions\":[{\"name\":\"natural_earth_raster.version_1\",\"description\":\"A
        Test Processor for Natural Earth image\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Natural
        Earth Data\",\"data_title\":\"\",\"data_title_long\":\"\",\"data_summary\":\"\",\"data_citation\":\"\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"https://www.naturalearthdata.com/downloads/50m-natural-earth-2/50m-natural-earth-ii-with-shaded-relief/\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"natural_earth_vector\",\"versions\":[{\"name\":\"natural_earth_vector.version_1\",\"description\":\"A
        Test Processor for Natural Earth vector\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Natural
        Earth Data\",\"data_title\":\"\",\"data_title_long\":\"\",\"data_summary\":\"\",\"data_citation\":\"\",\"data_license\":{\"name\":\"Natural
        Earth\",\"path\":\"https://www.naturalearthdata.com/about/terms-of-use/\",\"title\":\"Natural
        Earth\"},\"data_origin_url\":\"https://www.naturalearthdata.com/downloads/10m-cultural-vectors/roads/\",\"data_formats\":[\"Geopackage\"]}]},{\"name\":\"storm\",\"versions\":[{\"name\":\"storm.global_mosaics_version_1\",\"description\":\"A
        Processor for WRI Aqueduct\",\"version\":\"global_mosaics_version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"University
        of Oxford\",\"data_title\":\"STORM tropical cyclone wind speed maps\",\"data_title_long\":\"STORM
        tropical cyclone wind speed return period maps as global GeoTIFFs\",\"data_summary\":\"\\nGlobal
        tropical cyclone wind speed return period maps.\\n\\nThis dataset is derived
        with minimal processing from the following datasets\\ncreated by Bloemendaal
        et al, which are released with a CC0 license:\\n\\n[1] Bloemendaal, Nadia;
        de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts,\\nJ.C.J.H. (Jeroen)
        (2020): STORM tropical cyclone wind speed return periods.\\n4TU.ResearchData.
        Dataset. https://doi.org/10.4121/12705164.v3\\n\\n[2] Bloemendaal, Nadia;
        de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert);\\nHaigh, I.D. (Ivan);
        Martinez, Andrew B.; et al. (2022): STORM climate change\\ntropical cyclone
        wind speed return periods. 4TU.ResearchData. Dataset.\\nhttps://doi.org/10.4121/14510817.v3\\n\\nDatasets
        containing tropical cyclone maximum wind speed (in m/s) return periods,\\ngenerated
        using the STORM datasets (see\\nhttps://www.nature.com/articles/s41597-020-0381-2)
        and STORM climate change\\ndatasets (see https://figshare.com/s/397aff8631a7da2843fc).
        Return periods were\\nempirically calculated using Weibull's plotting formula.
        The\\nSTORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a
        fixed set\\nof return periods at 10 km resolution in every basin and for every
        climate model\\nused here (see below).\\n\\nThe GeoTIFFs provided in the datasets
        linked above have been mosaicked into\\nsingle files with global extent for
        each climate model/return period using the\\nfollowing code:\\n\\nhttps://github.com/nismod/open-gira/blob/219315e57cba54bb18f033844cff5e48dd5979d7/workflow/rules/download/storm-ibtracs.smk#L126-L151\\n\\nFiles
        are named on the pattern:\\nSTORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif\\n\\nSTORM_MODEL
        is be one of constant, CMCC-CM2-VHR4, CNRM-CM6-1-HR, EC-Earth3P-HR\\nor HadGEM3-GC31-HM.
        The \\\"constant\\\" files are for the present day, baseline\\nclimate scenario
        as explained in dataset [1]. The other files are for 2050,\\nRCP8.5 under
        different models as explained in the paper linked from dataset [2].\\n\\nSTORM_RP
        is one of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500,\\n600,
        700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 or\\n10000.\\n\",\"data_citation\":\"\\nRussell,
        Tom. (2022). STORM tropical cyclone wind speed return periods as global\\nGeoTIFFs
        (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7438145\\n    \\nDerived
        from:\\n    \\n[1] Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh,
        I.D. (Ivan); Aerts,\\nJ.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind
        speed return periods.\\n4TU.ResearchData. Dataset. https://doi.org/10.4121/12705164.v3\\n\\n[2]
        Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert);\\nHaigh,
        I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change\\ntropical
        cyclone wind speed return periods. 4TU.ResearchData. Dataset.\\nhttps://doi.org/10.4121/14510817.v3\\n
        \   \",\"data_license\":{\"name\":\"CC0\",\"path\":\"https://creativecommons.org/share-your-work/public-domain/cc0/\",\"title\":\"CC0\"},\"data_origin_url\":\"https://doi.org/10.5281/zenodo.7438145\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"test_fail_processor\",\"versions\":[{\"name\":\"test_fail_processor.version_1\",\"description\":\"A
        test processor that fails\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"\",\"data_title\":\"\",\"data_title_long\":\"\",\"data_summary\":\"\",\"data_citation\":\"\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"http://url\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"test_processor\",\"versions\":[{\"name\":\"test_processor.version_1\",\"description\":\"A
        test processor for nightlights\",\"version\":\"version_1\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Nightlights
        Author\",\"data_title\":\"\",\"data_title_long\":\"\",\"data_summary\":\"\",\"data_citation\":\"\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"http://url\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"wri_aqueduct\",\"versions\":[{\"name\":\"wri_aqueduct.version_2\",\"description\":\"A
        Processor for WRI Aqueduct\",\"version\":\"version_2\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"Ward,
        P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A.
        D\xEDaz Loaiza, et al.\",\"data_title\":\"Aqueduct Flood Hazard Maps\",\"data_title_long\":\"World
        Resource Institute - Aqueduct Flood Hazard Maps (Version 2, updated October
        20, 2020)\",\"data_summary\":\"World Resource Institute - Aqueduct Flood Hazard
        Maps (Version 2 (updated\\nOctober 20, 2020)).  Inundation depth in meters
        for coastal and riverine\\nfloods over 1km grid squares. 1 in 2 to 1 in 1000
        year return periods.\\nBaseline, RCP 4.5 & 8.5 emission scenarios. Current
        and future maps in 2030,\\n2050 and 2080.\",\"data_citation\":\"\\nWard, P.J.,
        H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. D\xEDaz
        Loaiza, et al. 2020.\\nAqueduct Floods Methodology. Technical Note. Washington,
        D.C.: World Resources Institute. Available online at:\\nwww.wri.org/publication/aqueduct-floods-methodology.\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"http://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/index.html\",\"data_formats\":[\"GeoTIFF\"]}]},{\"name\":\"wri_powerplants\",\"versions\":[{\"name\":\"wri_powerplants.version_130\",\"description\":\"World
        Resources Institute - Global Powerplants\",\"version\":\"version_130\",\"status\":\"\",\"uri\":\"\",\"data_author\":\"World
        Resources Institute\",\"data_title\":\"WRI Global Power Plant Database\",\"data_title_long\":\"World
        Resources Institute Global Power Plant Database\",\"data_summary\":\"The Global
        Power Plant Database is a comprehensive, open source database of power plants
        around the world. It\\ncentralizes power plant data to make it easier to navigate,
        compare and draw insights for one\u2019s own analysis.\\nThe database covers
        approximately 35,000 power plants from 167 countries and includes thermal
        plants (e.g. coal,\\ngas, oil, nuclear, biomass, waste, geothermal) and renewables
        (e.g. hydro, wind, solar). Each power plant is\\ngeolocated and entries contain
        information on plant capacity, generation, ownership, and fuel type. It will
        be\\ncontinuously updated as data becomes available.\",\"data_citation\":\"Global
        Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm,
        Enipedia, World Resources\\nInstitute. 2018. Global Power Plant Database.
        Published on Resource Watch and Google Earth Engine;\\nhttp://resourcewatch.org/
        https://earthengine.google.com/\",\"data_license\":{\"name\":\"CC-BY-4.0\",\"path\":\"https://creativecommons.org/licenses/by/4.0/\",\"title\":\"Creative
        Commons Attribution 4.0\"},\"data_origin_url\":\"https://datasets.wri.org/dataset/globalpowerplantdatabase\",\"data_formats\":[\"Geopackage\"]}]}]"
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