Data Literacy ~ Data Curation
Data Curation
Ensure that data is reliably retrievable for future reuse, and to determine what data is worth saving and for how long) (Learn2Analyze, 2017).
You need to find data that is well-matched with analysis goals. Data seeking is not practical without first stating the goals and understanding the business, information, and data requirements. With known requirements, you can search for data. The datasets that you find should be evaluated for quality and trustworthiness, and sometimes to select the best fit among multiple datasets that are available (Wells, 2022).
The following questions have to be answered by data curators.
• Who owns the data?
• What requirements are imposed by others (such as funding agencies or
publishers)?
• Which data should be retained?
• For how long should data be maintained?
• How should it be preserved?
• What are the ethical considerations, related to it?
• What sort of risk management is needed?
• How is data accessed?
• How open should it be?
• How should the costs be borne?
• What alternatives to local data management exist? (Erway, 2013). (Cited in Koltay, 2015)
Relevant Links
These links are typically articles defining data literacy that include this theme in their definition.
, Learn2Analyze, 2017;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Dave Wells, Eckerson Group, 2022;, Tibor Koltay, Journal of Librarianship and Information Science, 2015;