{"tema_id":"1810","string":"data lifecycle","created":"2021-11-14 21:44:52","code":null,"notes":[{"@type":"Definition note","@lang":"en-GB","@value":"\"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.\"\n1. Generation\n2. Collection\n3. Processing\n4. Storage\n5. Management\n6. Analysis\n7. Visualization\n8. Interpretation\nSource: Stobierski, Tim (2021). 8 Steps in the data lifecycle.\u00a0Business Insights. Havard Business School Online. Online: https:\/\/online.hbs.edu\/blog\/post\/data-life-cycle\u00a0"},{"@type":"Definition note","@lang":"en-GB","@value":"\"DataONE consider [...] that the data management\ntasks depend on data lifecycle. [...].\nDataOne has adopted [...] a lifecycle model specific to the\nfield of scientific research. It focuses on \"data\". This cycle is\nuseful because it makes possible to identify data flows and\nwork processes for scientists. DataONE defines eight phases in the lifecycle of scientific data: Plan, Collect, Assure,\nDescribe, Preserve, Discover, Integrate and Analyze.\"\n1.\u00a0 Plan\n2. Collect\n3. Assure\n4. Describe\n5. Preserve\n6. Discover\n7. Integrate\n8. Analyze\nSource: 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. \nOnline: 10.1109\/ISACV.2017.8054938\u00a0\nSource: S. Allard (2012). Dataone : Facilitating escience through collaboration. Journal of eScience Librarianship, vol. 1, no. 1. page 3.\u00a0"}]}