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/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institut f\u00fcr Physik, Universit\u00e4t Dortmund, D-44221 Dortmund, Germany, DE", 
          "id": "http://www.grid.ac/institutes/grid.5675.1", 
          "name": [
            "Institut f\u00fcr Physik, Universit\u00e4t Dortmund, D-44221 Dortmund, Germany, DE"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gl\u00fcck", 
        "givenName": "M.", 
        "id": "sg:person.01102507703.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01102507703.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institut f\u00fcr Physik, Universit\u00e4t Dortmund, D-44221 Dortmund, Germany, DE", 
          "id": "http://www.grid.ac/institutes/grid.5675.1", 
          "name": [
            "Institut f\u00fcr Physik, Universit\u00e4t Dortmund, D-44221 Dortmund, Germany, DE"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Reya", 
        "givenName": "E.", 
        "id": "sg:person.01366257165.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366257165.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institut f\u00fcr Theoretische Physik, Universit\u00e4t W\u00fcrzburg, Am Hubland, D-97074 W\u00fcrzburg, Germany, DE", 
          "id": "http://www.grid.ac/institutes/grid.8379.5", 
          "name": [
            "Institut f\u00fcr Theoretische Physik, Universit\u00e4t W\u00fcrzburg, Am Hubland, D-97074 W\u00fcrzburg, Germany, DE"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vogt", 
        "givenName": "A.", 
        "id": "sg:person.0616573130.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616573130.34"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1998-09", 
    "datePublishedReg": "1998-09-01", 
    "description": "Abstract. Dynamical parton densities, generated radiatively from valence-like inputs at some low resolution scale, are confronted with recent small-x data on deep inelastic and other hard scattering processes. It is shown that within theoretical uncertainties our previous (1994) dynamical/radiative parton distributions are compatible with most recent data and still applicable within the restricted accuracy margins of the presently available next-to-leading order calculations. Due to recent high precision measurements we also present an updated, more accurate, version of our (valence-like) dynamical input distributions. Furthermore, our perturbatively stable parameter-free dynamical predictions are extended to the extremely small-x region, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}\n$10^{-8} \\lesssim x \\lesssim 10^{-5}$\\end{document}, relevant to questions concerning ultra-high-energy cosmic ray and neutrino astronomy.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s100529800978", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1049394", 
        "issn": [
          "1434-6044", 
          "1434-6052"
        ], 
        "name": "European Physical Journal C", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "keywords": [
      "energy cosmic rays", 
      "parton distributions", 
      "recent high-precision measurements", 
      "dynamical parton distributions", 
      "high-precision measurements", 
      "neutrino astronomy", 
      "cosmic rays", 
      "low resolution scale", 
      "precision measurements", 
      "theoretical uncertainties", 
      "dynamical predictions", 
      "order calculations", 
      "input distribution", 
      "accuracy margin", 
      "resolution scale", 
      "partons", 
      "astronomy", 
      "rays", 
      "calculations", 
      "distribution", 
      "uncertainty", 
      "measurements", 
      "prediction", 
      "version", 
      "input", 
      "region", 
      "most recent data", 
      "data", 
      "scale", 
      "recent data", 
      "process", 
      "questions", 
      "margin"
    ], 
    "name": "Dynamical parton distributions revisited", 
    "pagination": "461-470", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1010724892"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s100529800978"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s100529800978", 
      "https://app.dimensions.ai/details/publication/pub.1010724892"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_286.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s100529800978"
  }
]

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.

108 TRIPLES      21 PREDICATES      59 URIs      51 LITERALS      6 BLANK NODES