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/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Newcastle Australia", 
          "id": "https://www.grid.ac/institutes/grid.266842.c", 
          "name": [
            "Chemical Engineering, Faculty of Engineering and Built Environment, University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mohr", 
        "givenName": "S. H.", 
        "id": "sg:person.016015232155.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016015232155.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Newcastle Australia", 
          "id": "https://www.grid.ac/institutes/grid.266842.c", 
          "name": [
            "Chemical Engineering, Faculty of Engineering and Built Environment, University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Evans", 
        "givenName": "G. M.", 
        "id": "sg:person.012546401151.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012546401151.52"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-349-06028-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003819496", 
          "https://doi.org/10.1007/978-1-349-06028-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-349-06028-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003819496", 
          "https://doi.org/10.1007/978-1-349-06028-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1019856621335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008992848", 
          "https://doi.org/10.1023/a:1019856621335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2006.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026497895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007587132700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041296364", 
          "https://doi.org/10.1023/a:1007587132700"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-03", 
    "datePublishedReg": "2009-03-01", 
    "description": "Modeling oil production is of interest to society and hotly debated. Often anomalies have occurred which makes modeling oil production via a particular theory (e.g., Hubbert\u2019s bell curve) difficult. The empirical method described here allows for such historic anomalies to be incorporated while still using the underly theory. This method is explained using Hubbert\u2019s bell curve and Former Soviet Union oil production as an example.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11053-008-9083-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136218", 
        "issn": [
          "0961-1444", 
          "1520-7439"
        ], 
        "name": "Natural Resources Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "name": "An Empirical Method to Make Oil Production Models Tolerant to Anomalies", 
    "pagination": "1-5", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "28d606a2c38f36e88d1d79ec7f44c336086c415e0fa5d259e7cf4713dd1374a3"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11053-008-9083-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008571400"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11053-008-9083-8", 
      "https://app.dimensions.ai/details/publication/pub.1008571400"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:46", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8684_00000510.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11053-008-9083-8"
  }
]

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.

83 TRIPLES      21 PREDICATES      31 URIs      19 LITERALS      7 BLANK NODES