DaLiCo Glossary Kristin Ameis, Christine Gläser, Hanna Käfer, Ulrike Spree 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/ This work is licensed under CC BY 4.0 data literacy : Indicates that this term is a “meta-term” meaning it is only used for hierarchical purposes. Deviating from the Thesaurus norm the metaterms below 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. ]]> 2021-08-05 2024-12-03 16:47:19 en-GB data understandingaz adatok megértésecomprensión de los datosdata-inzichtDatenverständnisDatenverstehen "The main goal of data understanding is to gain general insights about the data that will potentially be helpful for the further steps in the data analysis process, but data understanding should not be driven exclusively by the goals and methods to be applied in later steps. [...] Data understanding requires taking a closer look at the data." Source: Berthold, M. R., Borgelt, C., Höppner, F., & Klawonn, F. (2010). Data Understanding. Guide to Intelligent Data Analysis. Page 33. Online: doi:10.1007/978-1-84882-260-3_4  2022-08-23 10:37:092022-12-16 08:28:41