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 literacyadat-írástudásalfabetización de datosdatageletterdheidDatenkompetenz Data literacy in this glossary describes the understanding of data literacy in an academic and sciences context with a focus on generic competencies. In this context data literacy refers to knowledge and skills involved in collecting, processing, managing, evaluating, and using data for scientific inquiry. It focusses on the functional ability in data collection, processing, management, evaluation, and use. This coincides with the academic practice of producing, and using digital datasets during scientific research.  Data literacy serves as a ‚boundary concept‘ (Bowker/Star) partly shared between different research traditions and communities, weakly structured in common use however imposing stronger structures in the individual tailored use of a community of practice. Taking up a term coined by Pedersen and Caviglia (2019) data literacy is described as a compound competence ascribed to a community of practice rather than an individual consisting of some level of competence in metadata-management, statistics, data visualization and more generic competencies in problem-solving and reflexivity using different data. As such data literacy is closely related to data science but differs in the level of competence and the focus. While data science is a specific domain for trained specialists focussed on data analysis, data literacy is the set of competencies and apt to bridge between communities of practice and provide interfaces. This understanding calls for interdisciplinary collaboration that integrates different competencies and levels of skill. (DaLiCo 2022). Data Literacy is the cluster of all efficient behaviours and attitudes for the effective execution of all process steps for creating value or making decisions from data. Source: Schüller, K. (2020_07). Future Skills: A Framework for Data Literacy. Competence Framework and Research Report. Hochschulforum Digitalisierung. Online: http://doi.org/10.5281/zenodo.3946067 "Data Literacy is the ability to collect, manage, evaluate, and apply data; in a critical manner.” Source: Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., ... & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Online: https://dalspace.library.dal.ca/bitstream/handle/10222/64578/Strategies%20and%20Best%20Practices%20for%20Data%20Literacy%20Education.pdf Data literacy in this glossary describes the understanding of data literacy in an academic and sciences context with a focus on generic competencies. In this context data literacy refers to knowledge and skills involved in collecting, processing, managing, evaluating, and using data for scientific inquiry. It focusses on the functional ability in data collection, processing, management, evaluation, and use. This coincides with the academic practice of producing, and using digital datasets during scientific research.  Data literacy serves as a ‚boundary concept‘ (Bowker/Star) partly shared between different research traditions and communities, weakly structured in common use however imposing stronger structures in the individual tailored use of a community of practice. Taking up a term coined by Pedersen and Caviglia (2019) data literacy is described as a compound competence ascribed to a community of practice rather than an individual consisting of some level of competence in metadata-management, statistics, data visualization and more generic competencies in problem-solving and reflexivity using different data. As such data literacy is closely related to data science but differs in the level of competence and the focus. While data science is a specific domain for trained specialists focussed on data analysis, data literacy is the set of competencies and apt to bridge between communities of practice and provide interfaces. This understanding calls for interdisciplinary collaboration that integrates different competencies and levels of skill. (DaLiCo 2022). Data Analysis 2021-08-05 13:05:232021-12-15 08:12:25