Science data literacy resources

A great set of examples of how big data can be mined and used for the public good. 

This post describes a course built around the philosophy that students should be working with real data and solving real problems. 

This collection of assessment techniques for mathematical thinking is interesting. I like the categories they have created and there are lots of examples. Many of them relate to working with data.

This is the blog associated with the Teaching with Data.org project. It focuses on developing quantitative literacy in the social sciences.

A cool sounding citizen science project.

This is a great example of how environmental sensors can be used to collect local data and feed into shared databases. It would be really interesting to think about what types of school science could be tied to this resource.

A press release from NSF about their new efforts related to big data. Lots of interesting stuff here.

Unfortunately, when you say big data in educational circles it applies only to data about educational outcomes not students tapping into scientific research data.

This looks like an interesting effort to make research data more accessible in Australia. I really like their slogan - ANDS enables the transformation of: Data that are: to Structured Collections that are: Unmanaged --> Managed Disconnected --> Connected Invisible --> Findable Single-use --> Reusable ...so that Australian researchers can easily publish, discover, access and use research data.

This is an interesting "dear colleague" letter from the NSF Geosciences Director that encourages researchers to engage in "data citation" so that the data is accessible to others.

This will be interesting to track. A joint call for proposals from NSF and NIH. From the call, "BIGDATA seeks proposals that develop and evaluate core technologies and tools that take advantage of available collections of large data sets to accelerate progress in science, biomedical research, and engineering."

This article discusses the important role that biostatistician play in dealing with the evolving world of big data in biological and biomedical research.

Sandra Porter's project on bioinformatics education. Their tagline is "using data to make sense of the world."

This article describes a range of features that are useful in data visual analysis. The language and examples are useful.

This is a very public effort to promote the discussion of computational thinking. In the video they make the following statement, "Many teachers may already be teaching these skills. Computational thinking provides shared vocabulary and a framework to unify teachers' efforts." which I think is a great way to connect with what is already going on.

I think this argument about pedagogical content knowledge has direct relevance to teaching scientific data literacy skills. We don't even have clear ideas about what our goals are - making it very difficult to build expertise in this area.

This page provides access to a talk by Jim Collins about changes in how science is done and how NEON (National Ecological Observatory Network) fits into that landscape.