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 DaLiCo DimensionsD Up to date there exists no unifying and accepted definition of neither the concept of data literacy nor about the competencies a data literate person should have. Rather, different communities of practice use slightly different terminologies. Within the ERASMUS+ Project DaLiCo we saw the need to define core competencies a data literate person in the field of higher education should have and the terms (labels) used to describe them. For this purpose the DaLiCo Dimensions describe a number of interconnected dimensions of data literacy. The DaLiCo Dimensions are informed by existing frameworks. As our main inspiration served three frameworks on data literacy, from Ridsdale, et al. (2015), Sternkopf and Mueller (2018) and Schüller (2020).  In order to describe behavior and competences in the field of data literacy verbs are used to mark relevant activities related to basic data literacy competencies and skills. The actual phrasings of the dimensions are based on a study by Guido Ongena (DaLiCo partner from Utrecht) on employee's data literacy. Sources: Ongena, Guido (2022). Data Literacy: Conceptualization, measurement, and nomological validity. ... Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., . . . Matwin, S. W. (2015). Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Halifax: Dalhouse University Schüller, K. (2020). Future Skills: A Framework for Data Literacy – Competence Framework and Research Report. Working Paper Nr 53. Hochschulforum Digitalisierung, Berlin Sternkopf, H., & Mueller, R. (2018). Doing Good with Data:Development of a Maturity Model for Data Literacy in Non-governmental Organizations. Proceedings of the 51st Hawaii International Conference on System Sciences, (pp. 5045–5054). Waikoloa Village, Hawaii     Up to date there exists no unifying and accepted definition of neither the concept of data literacy nor about the competencies a data literate person should have. Rather, different communities of practice use slightly different terminologies. Within the ERASMUS+ Project DaLiCo we saw the need to define core competencies a data literate person in the field of higher education should have and the terms (labels) used to describe them. For this purpose the DaLiCo Dimensions describe a number of interconnected dimensions of data literacy. The DaLiCo Dimensions are informed by existing frameworks. As our main inspiration served three frameworks on data literacy, from Ridsdale, et al. (2015), Sternkopf and Mueller (2018) and Schüller (2020).  In order to describe behavior and competences in the field of data literacy verbs are used to mark relevant activities related to basic data literacy competencies and skills. The actual phrasings of the dimensions are based on a study by Guido Ongena (DaLiCo partner from Utrecht) on employee's data literacy. Sources: Ongena, Guido (2022). Data Literacy: Conceptualization, measurement, and nomological validity. ... Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., . . . Matwin, S. W. (2015). Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Halifax: Dalhouse University Schüller, K. (2020). Future Skills: A Framework for Data Literacy – Competence Framework and Research Report. Working Paper Nr 53. Hochschulforum Digitalisierung, Berlin Sternkopf, H., & Mueller, R. (2018). Doing Good with Data:Development of a Maturity Model for Data Literacy in Non-governmental Organizations. Proceedings of the 51st Hawaii International Conference on System Sciences, (pp. 5045–5054). Waikoloa Village, Hawaii     2021-12-06 17:17:432021-12-06 18:20:18