Data literacy

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Data literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data also known as information.[1]

Background[edit]

As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business,[2] government[3] and society,[4] it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.[5]

Data literacy is distinguished from statistical literacy since it involves understanding what data mean, including the ability to read graphs and charts as well as draw conclusions from data.[6] Statistical literacy, on the other hand, refers to the "ability to read and interpret summary statistics in everyday media" such as graphs, tables, statements, surveys, and studies.[6]

Libraries and Data Literacy[edit]

As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills.[7]

A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed [8].

Resources created by librarians include MIT's Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh's Data Library and the University of Minnesota libraries’ Data Management Course for Structural Engineers.

See also[edit]

References[edit]

  1. ^ Baykoucheva, Svetla (2015). Managing Scientific Information and Research Data. Waltham, MA: Chandos Publishing. p. 80. ISBN 9780081001950.
  2. ^ Hey, A. J.; Tony Hey; Tansley, S.; Tolle, K., eds. (2009). The fourth paradigm: data-intensive scientific discovery. Microsoft.
  3. ^ "Open Data Philly". Retrieved 14 June 2013.
  4. ^ Na, L. & Yan, Z. (2013). "Promote Data-intensive Scientific Discovery, Enhance Scientific and Technological Innovation Capability: New Model, New Method, and New Challenges Comments on" The Fourth Paradigm: Data-intensive Scientific Discovery". Bulletin of Chinese Academy of Sciences. 1 (16).
  5. ^ Stanley, Deborah B. (2018-07-11). Practical Steps to Digital Research: Strategies and Skills For School Libraries. Santa Barbara, CA: ABC-CLIO. p. 275. ISBN 9781440856723.
  6. ^ a b Carlson, Jake; Johnston, Lisa (2015). Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers. West Lafayette, Indiana: Purdue University Press. p. 15. ISBN 9781557536969.
  7. ^ Koltay, Tibor (2015). "Data literacy for researchers and data librarians". Journal of Librarianship and Information Science. 49 (1): 3–14. doi:10.1177/0961000615616450.
  8. ^ Calzada-Prado, Francisco-Javier; Marzal, Miguel-Angel (2013). "Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents". Libri. 63 (2): 123-134. doi:10.1515/libri-2013-0010.