Data literacy

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Data literacy is the ability to read, understand, create, and communicate data as information. 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. [1]

Contents

Data literacy refers to the ability to understand, interpret, critically evaluate, and effectively communicate data in context to inform decisions and drive action. It is not a technical skill but a fundamental capability for everyone, encompassing the skills and mindset necessary to transform raw data into meaningful insights and apply these insights within real-world scenarios. [2]

Background

As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business, [3] government [4] and society, [5] 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. [6]

Data literacy is distinguished from statistical literacy since it involves understanding what data means, including the ability to read graphs and charts as well as draw conclusions from data. [7] 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. [7]

Role of libraries and librarians

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. [8]

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. [9]

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

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References

  1. ↑ Baykoucheva, Svetla (2015). Managing Scientific Information and Research Data. Waltham, MA: Chandos Publishing. p. 80. ISBN   9780081001950.
  2. ↑ Hanegan, Kevin (2021). Turning Data into Wisdom: How We Can Collaborate with Data to Change Ourselves, Our Organizations, and Even the World. Kevin Hanegan (published January 10, 2021). pp. 31, 232. ISBN   978-0578639871.
  3. ↑ Hey, A. J.; Tony Hey; Tansley, S.; Tolle, K., eds. (2009). The fourth paradigm: data-intensive scientific discovery. Microsoft.
  4. ↑ "Open Data Philly" . Retrieved 14 June 2013.
  5. ↑ 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).
  6. ↑ 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.
  7. 1 2 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.
  8. ↑ Koltay, Tibor (2015). "Data literacy for researchers and data librarians" (PDF). Journal of Librarianship and Information Science. 49 (1): 3–14. doi:10.1177/0961000615616450. S2CID   36467384.
  9. ↑ 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. hdl: 10016/27173 . S2CID   62074807.


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