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/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Dept of Ophthalmology, University of Lund, Malm\u00f6 General Hospital, S-21401, Malm\u00f6, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Malm\u00f6 and Lund, Sweden", 
            "Dept of Ophthalmology, University of Lund, Malm\u00f6 General Hospital, S-21401, Malm\u00f6, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heijl", 
        "givenName": "Anders", 
        "id": "sg:person.01042356506.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042356506.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dept of Mathematical Statistics, University of Lund, PO Box 118, S-22100, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Malm\u00f6 and Lund, Sweden", 
            "Dept of Mathematical Statistics, University of Lund, PO Box 118, S-22100, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lindgren", 
        "givenName": "Georg", 
        "id": "sg:person.012274436413.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012274436413.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dept of Mathematical Statistics, University of Lund, PO Box 118, S-22100, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Malm\u00f6 and Lund, Sweden", 
            "Dept of Mathematical Statistics, University of Lund, PO Box 118, S-22100, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Olsson", 
        "givenName": "Jonny", 
        "id": "sg:person.01237061656.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01237061656.70"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1987", 
    "datePublishedReg": "1987-01-01", 
    "description": "The distribution of results of perimetric reliability tests, false positive and false negative answers, fixation losses and short-term fluctuation, was studied in a material of 84 healthy normal subjects, randomly selected from the population, and in 45 patients with glaucoma. All subjects were tested with the 30\u20132 programme of the Humphrey Field Analyzer. We further investigated the influence of perimetric reliability on the measured visual field, as expressed by the visual field indices Mean Deviation and Pattern Standard Deviation. Sample multiple correlation coefficients were calculated for a number of combinations of reliability parameters and visual field indices.False negative answer were more common and short-term fluctuation higher in glaucomatous than in normal subjects. False positive answer and fixation losses did not differ between groups. Among glaucoma patients false negative answers were significantly more common than false positive answers. No such difference was found in the group of normals. The results showed no age-dependence. A large part of inter-individual field variability, particularly among normals, could be explained by perimetric reliability. false negative answers and short-term fluctuation had the largest predictive value.Normal inter-individual variation decreased considerably when stricter criteria for minimun results at reliability testing were applied. Naturally the number of fields meeting these criteria decreased at the same time. therefore, when establishing limits for normality, a compromise must be made resulting in reasonably narrow prediction limits without excluding a large percentage of normal and pathological fields from analysis.", 
    "editor": [
      {
        "familyName": "Greve", 
        "givenName": "E. L.", 
        "type": "Person"
      }, 
      {
        "familyName": "Heijl", 
        "givenName": "A.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-94-009-3325-5_75", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-94-010-7993-8", 
        "978-94-009-3325-5"
      ], 
      "name": "Seventh International Visual Field Symposium, Amsterdam, September 1986", 
      "type": "Book"
    }, 
    "keywords": [
      "fixation loss", 
      "normal subjects", 
      "normal inter-individual variation", 
      "pattern standard deviation", 
      "visual field indices", 
      "Humphrey Field Analyzer", 
      "healthy normal subjects", 
      "group of normals", 
      "largest predictive value", 
      "inter-individual variation", 
      "measured visual field", 
      "predictive value", 
      "computerized perimetry", 
      "false positive answers", 
      "field indices", 
      "visual field", 
      "strict criteria", 
      "Field Analyzer", 
      "subjects", 
      "pathological fields", 
      "mean deviation", 
      "large percentage", 
      "normals", 
      "patients", 
      "glaucoma", 
      "group", 
      "perimetry", 
      "false negative answers", 
      "distribution of results", 
      "such differences", 
      "correlation coefficient", 
      "criteria", 
      "prediction limits", 
      "loss", 
      "reliability testing", 
      "population", 
      "percentage", 
      "short-term fluctuations", 
      "index", 
      "differences", 
      "testing", 
      "number", 
      "results", 
      "test", 
      "multiple correlation coefficient", 
      "standard deviation", 
      "positive answer", 
      "program", 
      "combination", 
      "large part", 
      "compromise", 
      "time", 
      "reliability", 
      "variability", 
      "answers", 
      "analysis", 
      "same time", 
      "part", 
      "normality", 
      "parameters", 
      "values", 
      "deviation", 
      "number of combinations", 
      "sample multiple correlation coefficient", 
      "influence", 
      "analyzer", 
      "materials", 
      "variation", 
      "number of fields", 
      "distribution", 
      "limit", 
      "reliability test", 
      "fluctuations", 
      "field", 
      "negative answer", 
      "coefficient", 
      "reliability parameters", 
      "field variability", 
      "perimetric reliability tests", 
      "perimetric reliability", 
      "visual field indices Mean Deviation", 
      "field indices Mean Deviation", 
      "indices Mean Deviation", 
      "glaucoma patients false negative answers", 
      "patients false negative answers", 
      "inter-individual field variability", 
      "minimun results", 
      "narrow prediction limits"
    ], 
    "name": "Reliability parameters in computerized perimetry", 
    "pagination": "593-600", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023157561"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-94-009-3325-5_75"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-94-009-3325-5_75", 
      "https://app.dimensions.ai/details/publication/pub.1023157561"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_364.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-94-009-3325-5_75"
  }
]

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.

170 TRIPLES      23 PREDICATES      114 URIs      107 LITERALS      7 BLANK NODES