@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/96fb38b8-0c2d-46d0-9604-24c171f48567> a dcat:Dataset ;
    dct:description """DATA MINING THE GALAXY ZOO MERGERS\r
\r
STEVEN BAEHR*, ARUN VEDACHALAM*, KIRK BORNE*, AND DANIEL SPONSELLER*\r
\r
Abstract. Collisions between pairs of galaxies usually end in the coalescence (merger) of the two\r
galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of\r
most galaxies, including our own Milky Way. With the onset of massive collection of astronomical\r
data, a computerized and automated method will be necessary for identifying those colliding\r
galaxies worthy of more detailed study. This project researches methods to accomplish that goal.\r
Astronomical data from the Sloan Digital Sky Survey (SDSS) and human-provided classifications\r
on merger status from the Galaxy Zoo project are combined and processed with machine learning\r
algorithms. The goal is to determine indicators of merger status based solely on discovering those\r
automated pipeline-generated attributes in the astronomical database that correlate most strongly\r
with the patterns identified through visual inspection by the Galaxy Zoo volunteers. In the end,\r
we aim to provide a new and improved automated procedure for classification of collisions and\r
mergers in future petascale astronomical sky surveys. Both information gain analysis (via the\r
C4.5 decision tree algorithm) and cluster analysis (via the Davies-Bouldin Index) are explored as\r
techniques for finding the strongest correlations between human-identified patterns and existing\r
database attributes. Galaxy attributes measured in the SDSS green waveband images are found to\r
represent the most influential of the attributes for correct classification of collisions and mergers.\r
Only a nominal information gain is noted in this research, however, there is a clear indication of\r
which attributes contribute so that a direction for further study is apparent.""" ;
    dct:identifier "DASHLINK_233" ;
    dct:issued "2010-10-13"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "DATA MINING THE GALAXY ZOO MERGERS" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Elizabeth Foughty" ;
            vcard:hasEmail <mailto:elizabeth.a.foughty@nasa.gov> ] ;
    dcat:distribution <https://data.nasa.gov/dataset/96fb38b8-0c2d-46d0-9604-24c171f48567/resource/ad2f17c1-f2b2-493c-a7b7-26a70d9cc121> ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/dataset/96fb38b8-0c2d-46d0-9604-24c171f48567/resource/ad2f17c1-f2b2-493c-a7b7-26a70d9cc121> a dcat:Distribution ;
    dct:description "DATA MINING THE GALAXY ZOO MERGERS" ;
    dct:format "PDF" ;
    dct:identifier "ad2f17c1-f2b2-493c-a7b7-26a70d9cc121" ;
    dct:issued "2025-03-31T17:32:19.054695"^^xsd:dateTime ;
    dct:modified "2025-03-31T17:32:19.038768"^^xsd:dateTime ;
    dct:title "Paper 11 .pdf" ;
    dcat:accessURL <https://c3.nasa.gov/dashlink/static/media/publication/Paper_11_.pdf> ;
    dcat:mediaType "application/pdf" .

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