@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix org: <http://www.w3.org/ns/org#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://data.nasa.gov/dataset/7f4b8c29-4f60-427f-a923-123f5f68a5fa> a dcat:Dataset ;
    dct:description "In previous papers, we introduced the idea of a Virtual Sensor,  which is a mathematical model trained to learn the potentially  nonlinear relationships between spectra for a given image scene for the purpose of predicting values of a subset of those spectra when only partial measurements have been taken. Such models can be created for a variety of disciplines including the Earth and Space Sciences as well as engineering domains. These nonlinear relationships are induced by the physical characteristics of the  image scene. In building a Virtual Sensor a key question that arises is that of characterizing the stability of the model as the underlying scene changes. For example, the spectral relationships could change for a given physical location, due to seasonal weather conditions. This paper, based on a talk given at the American Geophysical Union (2005), discusses the stability of predictions through time and also demonstrates the use of a Virtual Sensor in making multi-resolution predictions. In this scenario, a model is trained to learn the nonlinear relationships between spectra at a low resolution in order to predict the spectra at a high resolution." ;
    dct:identifier "DASHLINK_156" ;
    dct:issued "2010-09-22"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "Characterizing Variability and Multi-Resolution Predictions" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Ashok Srivastava" ;
            vcard:hasEmail <mailto:ashok.n.srivastava@gmail.com> ] ;
    dcat:distribution <https://data.nasa.gov/dataset/7f4b8c29-4f60-427f-a923-123f5f68a5fa/resource/177b2486-b5a3-4561-962e-0b38e41c198c> ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/dataset/7f4b8c29-4f60-427f-a923-123f5f68a5fa/resource/177b2486-b5a3-4561-962e-0b38e41c198c> a dcat:Distribution ;
    dct:description "Paper" ;
    dct:format "PDF" ;
    dct:identifier "177b2486-b5a3-4561-962e-0b38e41c198c" ;
    dct:issued "2025-04-01T01:36:05.556600"^^xsd:dateTime ;
    dct:modified "2025-04-01T01:36:05.544399"^^xsd:dateTime ;
    dct:title "JPL2006.pdf" ;
    dcat:accessURL <https://c3.nasa.gov/dashlink/static/media/publication/JPL2006.pdf> ;
    dcat:mediaType "application/pdf" .

<https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> a org:Organization ;
    skos:prefLabel "NASA" ;
    foaf:name "NASA" .