<?xml version="1.0" encoding="utf-8"?><rdf:RDF  xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"  xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"  xmlns:skos="http://www.w3.org/2004/02/skos/core#"  xmlns:map="http://www.w3c.rl.ac.uk/2003/11/21-skos-mapping#"  xmlns:dct="http://purl.org/dc/terms/"  xmlns:dc="http://purl.org/dc/elements/1.1/"><skos:ConceptScheme rdf:about="http://www2.bui.haw-hamburg.de/tematres/vocab/">  <dc:title>DaLiCo Glossary</dc:title>  <dc:creator>Kristin Ameis, Christine Gläser, Hanna Käfer, Ulrike Spree</dc:creator>  <dc:contributor></dc:contributor>  <dc:publisher>The definitions of terms are based on the discussion within the project group. A list of used resources can be found at https://dalico.info/resources/</dc:publisher>  <dc:rights>This work is licensed under CC BY 4.0</dc:rights>  <dc:subject>data literacy</dc:subject>  <dc:description><![CDATA[ DaLiCo Glossary (Dataliteracy in Context Glossary) is a collection  of relevant key concepts in the field of data literacy (education)  developped in cooperation with the partners from the ERASMUS+ Project "Data Literacy in Context" (DaLiCo) (https://dalico.info/about/). It is structured as a thesaurus following the DIN ISO 25964 Thesauri and interoperability with other vocabularies - Part 1: Thesauri for Information retrieval.

The thesaurus draws on the following keys and abbreviations to denote relationships between terms:

<>: Indicates that this term is a “meta-term” meaning it is only used for hierarchical purposes. Deviating from the Thesaurus norm the metaterms below <DaLiCo Dimensions> are used to assign references to facets. 
BT: Broader Term – Indicates the “parent” of the term, in the hierarchical tree structure.
BTG: Broder Term Generic - is used when a generic is_a relation between the "parent" of the term exists. The generic relationship is the link between a class or category and its members or species.
NT: Narrower Term – Indicates the “child” of the term, in the hierarchical tree structure.
NTG: Narrower Term Generic - is used when a generic is_a "child" of relation exists. 
RT: Related Term – Indicates any terms that are related in meaning or in scope to the term being viewed.
USE: Use reference - Indicates that the current terms is "Non-preferred" and that it should not be used for indexing purposes.
UF: Used for - references to non-preferred equivalent term(s)
Translations of the terms into dutch, german, hungarian and spanish are referenced as specialized UF Relations.
UFDE - references the German translation
UFES - references the Spanish translation
UFHU - references the Hungarian translation
UFNE - refernces the Dutch translation

If you wish to receive a download in SKOS-format feel free to  get in touch with the contact mail. ]]></dc:description>  <dc:date>2021-08-05</dc:date>  <dct:modified>2024-12-03 16:47:19</dct:modified>  <dc:language>en-GB</dc:language>  </skos:ConceptScheme>  <skos:Concept rdf:about="http://www2.bui.haw-hamburg.de/tematres/vocab/xml.php?skosTema=1804"><skos:prefLabel xml:lang="en-GB">big data</skos:prefLabel><skos:altLabel xml:lang="en-GB">Big Data [DE]</skos:altLabel><skos:altLabel xml:lang="en-GB">datos masivos</skos:altLabel><skos:altLabel xml:lang="en-GB">große Datenmengen</skos:altLabel><skos:altLabel xml:lang="en-GB">nagyméretű adatok</skos:altLabel><skos:altLabel xml:lang="en-GB">omvangrijke data</skos:altLabel> <skos:definition xml:lang="en">Big data are data that are high volume, high velocity, and/or high variety; require new technologies and techniques to capture, store, and analyze; and are used to enhance decision making, provide insight and discovery, and support and optimize processes.
Source: Mills, S., S. Lucas, L. Irakliotis, M. Ruppa, T. Carlson and B. Perlowitz (2012). Demystifying Big Data: A Practical Guide to Transforming the Business of Government. Washington: TechAmerica Foundation. 
Online: http://breakinggov.com/documents/demystifying-big-data-a-practical-guide-to-transforming-the-bus/ 
 </skos:definition> <skos:example xml:lang="en-GB">High volume—the amount or quantity of data
High velocity—the rate at which data is created
High variety—the different types of data In short, “big data” means there is more of it, it comes more quickly, and comes in more form
Source: Russom, P. (2011). Big Data Analytics, TDWI Best Practices Report. Seattle: The Data Warehousing Institute, Fourth Quarter. 
Online: http://tdwi.org/research/2011/09/best-practices-report-q4-big-data-analytics.aspx </skos:example><skos:inScheme rdf:resource="http://www2.bui.haw-hamburg.de/tematres/vocab/"/><skos:related rdf:resource="http://www2.bui.haw-hamburg.de/tematres/vocab/xml.php?skosTema=2444"/><skos:related rdf:resource="http://www2.bui.haw-hamburg.de/tematres/vocab/xml.php?skosTema=2098"/><skos:related rdf:resource="http://www2.bui.haw-hamburg.de/tematres/vocab/xml.php?skosTema=2065"/><skos:broader rdf:resource="http://www2.bui.haw-hamburg.de/tematres/vocab/xml.php?skosTema=1805"/><skos:exactMatch> <skos:Concept rdf:about="https://www.wikidata.org/wiki/Q858810"/></skos:exactMatch>  <dct:created>2021-11-14 21:44:52</dct:created>  </skos:Concept></rdf:RDF>