JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "NOAA National Climatic Data Center, 151 Patton Avenue, 28801, Asheville, NC, USA", 
          "id": "http://www.grid.ac/institutes/grid.454206.1", 
          "name": [
            "NOAA National Climatic Data Center, 151 Patton Avenue, 28801, Asheville, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smith", 
        "givenName": "Adam B.", 
        "id": "sg:person.015710061746.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015710061746.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Center for Atmospheric Research, Boulder, CO, USA", 
          "id": "http://www.grid.ac/institutes/grid.57828.30", 
          "name": [
            "National Center for Atmospheric Research, Boulder, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Katz", 
        "givenName": "Richard W.", 
        "id": "sg:person.07365657053.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07365657053.04"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10584-011-0331-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018791977", 
          "https://doi.org/10.1007/s10584-011-0331-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-004-4808-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003220157", 
          "https://doi.org/10.1007/s11069-004-4808-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-4286-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000844355", 
          "https://doi.org/10.1007/978-1-4757-4286-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-010-9685-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045986092", 
          "https://doi.org/10.1007/s11069-010-9685-4"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013-02-03", 
    "datePublishedReg": "2013-02-03", 
    "description": "This paper focuses on the US Billion-dollar Weather/Climate Disaster report by the National Oceanic and Atmospheric Administration\u2019s National Climatic Data Center. The current methodology for the production of this loss dataset is described, highlighting its strengths and limitations including sources of uncertainty and bias. The Insurance Services Office/Property Claims Service, the US Federal Emergency Management Agency\u2019s National Flood Insurance Program and the US Department of Agriculture\u2019s crop insurance program are key sources of quantified disaster loss data, among others. The methodology uses a factor approach to convert from insured losses to total direct losses, one potential limitation. An increasing trend in annual aggregate losses is shown to be primarily attributable to a statistically significant increasing trend of about 5\u00a0% per year in the frequency of billion-dollar disasters. So the question arises of how such trend estimates are affected by uncertainties and biases in the billion-dollar disaster data.\u00a0The net effect of all biases appears to be an underestimation of average loss. In particular, it is shown that the factor approach can result in a considerable underestimation of average loss of roughly 10\u201315\u00a0%. Because this bias is systematic, any trends in losses from tropical cyclones appear to be robust to variations in insurance participation rates. Any attribution of the marked increasing trends in crop losses is complicated by a major expansion of the federally subsidized crop insurance program, as a consequence encompassing more marginal land. Recommendations concerning how the current methodology can be improved to increase the quality of the billion-dollar disaster dataset include refining the factor approach to more realistically take into account spatial and temporal variations in insurance participation rates.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11069-013-0566-5", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1050117", 
        "issn": [
          "0921-030X", 
          "1573-0840"
        ], 
        "name": "Natural Hazards", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "67"
      }
    ], 
    "keywords": [
      "National Climatic Data Center", 
      "Atmospheric Administration\u2019s National Climatic Data Center", 
      "National Flood Insurance Program", 
      "billion-dollar disasters", 
      "disaster loss data", 
      "tropical cyclones", 
      "National Oceanic", 
      "sources of uncertainty", 
      "Property Claim Services", 
      "insured losses", 
      "temporal variation", 
      "trend estimates", 
      "Flood Insurance Program", 
      "considerable underestimation", 
      "climate disasters", 
      "disaster reports", 
      "major expansion", 
      "disaster data", 
      "biases", 
      "key source", 
      "underestimation", 
      "net effect", 
      "average loss", 
      "trends", 
      "oceanic", 
      "factor approach", 
      "direct loss", 
      "cyclones", 
      "disaster dataset", 
      "source", 
      "uncertainty", 
      "variation", 
      "weather", 
      "disasters", 
      "loss data", 
      "land", 
      "dataset", 
      "data sources", 
      "data centers", 
      "estimates", 
      "loss dataset", 
      "aggregate loss", 
      "bias", 
      "data", 
      "current methodologies", 
      "attribution", 
      "US Department", 
      "crop losses", 
      "marginal lands", 
      "potential limitations", 
      "uses", 
      "loss", 
      "rate", 
      "years", 
      "methodology", 
      "center", 
      "crop insurance program", 
      "consequences", 
      "limitations", 
      "expansion", 
      "production", 
      "frequency", 
      "account", 
      "approach", 
      "quality", 
      "strength", 
      "accuracy", 
      "program", 
      "effect", 
      "questions", 
      "paper", 
      "insurance program", 
      "Department", 
      "report", 
      "services", 
      "recommendations", 
      "participation rates"
    ], 
    "name": "US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases", 
    "pagination": "387-410", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1014637390"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11069-013-0566-5"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11069-013-0566-5", 
      "https://app.dimensions.ai/details/publication/pub.1014637390"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T17:02", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_613.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11069-013-0566-5"
  }
]

Download the RDF metadata as:  json-ld nt turtle xml License info

This table displays all metadata directly associated to this object as RDF triples.

160 TRIPLES      21 PREDICATES      105 URIs      93 LITERALS      6 BLANK NODES