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. As data collection and sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy.
Data literacy focuses on the ability to understand and build knowledge from data, and to communicate that meaning to others. It is related to other fields, including:
- Statistical literacy
- Media literacy
- Information literacy
- New literacies
- 21st-century skills
- When focused on understanding scientific data, data literacy emphasizes the numeric, statistical nature of data as information, including "... understanding what data mean, including how to read graphs and charts appropriately, draw correct conclusions from data, and recognize when data are being used in misleading or inappropriate ways."
- When focused on educating a population, data literacy involves "... the knowledge of what data are, how they are collected, analyzed, visualized and shared, and is the understanding of how data are applied for benefit or detriment, within the cultural context of security and privacy."
- When focused on citizen-design, data literacy is "... the ability to ask and answer real-world questions from large and small data sets through an inquiry process, with consideration of ethical use of data. It is based on core practical and creative skills, with the ability to extend knowledge of specialist data handling skills according to goals. These include the abilities to select, clean, analyse, visualise, critique and interpret data, as well as to communicate stories from data and to use data as part of a design process."
- When focused narrowly on the skills employers hope to find in their workers, data literacy means "... competence in finding, manipulating, managing, and interpreting data, including not just numbers but also text and images."
Libraries and Data Literacy
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.
- Hey, A. J.; Tony Hey; Tansley, S.; Tolle, K., eds. (2009). The fourth paradigm: data-intensive scientific discovery. Microsoft.
- "Open Data Philly". Retrieved 14 June 2013.
- 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).
- Carlson, J. R.; Fosmire, M.; Miller, C.; Sapp Nelson, M. (2011). "Determining Data Information Literacy Needs: A Study of Students and Research Faculty". Libraries Faculty and Staff Scholarship and Research. 23.
- Crusoe, D. (November 2016). "Data Literacy defined pro populo: To read this article, please provide a little information". The Journal of Community Informatics. 3.
- Wolff, A.; Gooch, D.; Cavero, J.; Rashid, U.; Kortuem, G. (November 2016). "Creating an understanding of data literacy for a data-driven society". The Journal of Community Informatics. 3.
- Harris, Jeanne. "Data Is Useless Without the Skills to Analyze It". Harvard Business Review. Retrieved 14 June 2013.
- "Become Data Literate in 3 Simple Steps".
- "Data Literacy".
- Wolff, A.; Cavero, J.; Kortuem, G. (November 2016). "Urban Data in the primary classroom: bringing data literacy to the UK curriculum". The Journal of Community Informatics. 3.
- Koltay, Tibor (2015). "Data literacy for researchers and data librarians". Journal of Librarianship and Information Science. 49 (1).
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