In this context data literacy refers to knowledge and skills involved in collecting, processing, managing, evaluating, and using data for scientific inquiry. It focusses on the functional ability in data collection, processing, management, evaluation, and use. This coincides with the academic practice of producing, and using digital datasets during scientific research.  Data literacy serves as a ‚boundary concept‘ (Bowker/Star) partly shared between different research traditions and communities, weakly structured in common use however imposing stronger structures in the individual tailored use of a community of practice.

Taking up a term coined by Pedersen and Caviglia (2019) data literacy is described as a compound competence ascribed to a community of practice rather than an individual consisting of some level of competence in metadata-management, statistics, data visualization and more generic competencies in problem-solving and reflexivity using different data. As such data literacy is closely related to data science but differs in the level of competence and the focus. While data science is a specific domain for trained specialists focussed on data analysis, data literacy is the set of competencies and apt to bridge between communities of practice and provide interfaces. This understanding calls for interdisciplinary collaboration that integrates different competencies and levels of skill. (DaLiCo 2022).

Source: Schüller, K. (2020_07). Future Skills: A Framework for Data Literacy. Competence Framework and Research Report. Hochschulforum Digitalisierung.
Online: http://doi.org/10.5281/zenodo.3946067

Source: Schüller, K. (2020_07). Future Skills: A Framework for Data Literacy. Competence Framework and Research Report. Hochschulforum Digitalisierung.
Online: http://doi.org/10.5281/zenodo.3946067

Source: Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., ... & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report.
Online: https://dalspace.library.dal.ca/bitstream/handle/10222/64578/Strategies%20and%20Best%20Practices%20for%20Data%20Literacy%20Education.pdf

Source: Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., ... & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report.
Online: https://dalspace.library.dal.ca/bitstream/handle/10222/64578/Strategies%20and%20Best%20Practices%20for%20Data%20Literacy%20Education.pdf

In this context data literacy refers to knowledge and skills involved in collecting, processing, managing, evaluating, and using data for scientific inquiry. It focusses on the functional ability in data collection, processing, management, evaluation, and use. This coincides with the academic practice of producing, and using digital datasets during scientific research.  Data literacy serves as a ‚boundary concept‘ (Bowker/Star) partly shared between different research traditions and communities, weakly structured in common use however imposing stronger structures in the individual tailored use of a community of practice.

Taking up a term coined by Pedersen and Caviglia (2019) data literacy is described as a compound competence ascribed to a community of practice rather than an individual consisting of some level of competence in metadata-management, statistics, data visualization and more generic competencies in problem-solving and reflexivity using different data. As such data literacy is closely related to data science but differs in the level of competence and the focus. While data science is a specific domain for trained specialists focussed on data analysis, data literacy is the set of competencies and apt to bridge between communities of practice and provide interfaces. This understanding calls for interdisciplinary collaboration that integrates different competencies and levels of skill. (DaLiCo 2022).