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 lifecycle "The data life cycle is often described as a cycle because the lessons learned and insights gleaned from one data project typically inform the next. In this way, the final step of the process feeds back into the first." 1. Generation 2. Collection 3. Processing 4. Storage 5. Management 6. Analysis 7. Visualization 8. Interpretation Source: Stobierski, Tim (2021). 8 Steps in the data lifecycle. Business Insights. Havard Business School Online. Online: https://online.hbs.edu/blog/post/data-life-cycle  "DataONE consider [...] that the data management tasks depend on data lifecycle. [...]. DataOne has adopted [...] a lifecycle model specific to the field of scientific research. It focuses on "data". This cycle is useful because it makes possible to identify data flows and work processes for scientists. DataONE defines eight phases in the lifecycle of scientific data: Plan, Collect, Assure, Describe, Preserve, Discover, Integrate and Analyze." 1.  Plan 2. Collect 3. Assure 4. Describe 5. Preserve 6. Discover 7. Integrate 8. Analyze Source: M. E. Arass, I. Tikito and N. Souissi (2017). Data lifecycles analysis: Towards intelligent cycle.Intelligent Systems and Computer Vision (ISCV) 2017. pages 2-3. Online: 10.1109/ISACV.2017.8054938  Source: S. Allard (2012). Dataone : Facilitating escience through collaboration. Journal of eScience Librarianship, vol. 1, no. 1. page 3.  2021-11-14 21:44:52