Data Literacy in the sciences can be described as the "knowledge and skills involved in collecting, processing, managing, evaluating, and using data for scientific inquiry. Although there are similarities in information literacy and digital literacy, science data literacy specifically focuses less on literature-based attributes and more on functional ability in data collection, processing, management, evaluation, and use. This emphasis on operational skills coincides with the practice-based production, operation, and use of digital datasets during scientific research."
Teaching data literacy in the sciences should have a focus on the following characteristics:
- Fundamentals of science data and data management,
- Data management in the context of research output, and
- Broader issues of science data including tools for management and visualization, as well as quality and publication practices.
Ignazio and Qin state in their seminal article that science data literacy training should be provided at different levels via different venues, and that the training needs to adapt to science disciplinary context, terminology, and workflow. At the undergraduate level, the goal of training for data literacy should be to train the future science workforce with a solid understanding and skill set in data management and use issues .