As a member of the OpenWorm Foundation community committee (see previous post), we have been trying to find a means of engaging potential contributors within the context of the various projects. One type of activity is the Badge, a bite-sized  learning opportunity that we plan to use as both certifications of competency and concrete goals for the various projects. The OpenWorm Badge System is being spearheaded by Chee-Wai Lee, and is an emerging method in Educational Technology . More details about this will be shared to the community by Chee-Wai in the form of a tutorial at the upcoming OpenWorm Open House.
An example of how semantic data on phenotypes can be extracted from the scientific literature. PICTURE: Tagxedo.com, BLOGPOST: Phenoscape blog.
Each badge is designed to impart a specific skill. The OpenWorm badge system currently covers scientific topics (Muscle Model Builder, Hodgkin-Huxley) and research skills (Literature Mining). My contribution is the Literature Mining (LM) series. Literature mining is a technique used to organize the scientific literature, extract useful metadata (e.g. semantic data) from these sources, and identify secondary datasets for re-analysis . Learning skills in Literature Mining will be useful to a wide range of badge earners, particularly those interested in Bioinformatics and Open Science research. These are skills used extensively in the DevoWorm project, and we will be planning more badges on related topical areas in the future.
The first LM badge is focused on working with the scientific literature, while the second (LMII) badge introduces learners to open-access secondary datasets. The only prerequisite is that you must earn Badge I in order to earn Badge II. Both of these badges recently went live, and you may start working on them immediately.
Example of the badge curriculum for LMI. The badgelist system requires learners to complete each step one at a time, and then request feedback (if applicable) from the Admin (e.g. instructor).
 why not "byte-sized", you say? Well, the Literature Mining badges are almost byte-sized (seven requirements apiece), so you could say that we are headed in that direction!
 Ferdig, R. and Pytash, K. (2014). There's a badge for that. Tech and Learning, February 26.
 For examples of how Literature Mining can be useful, please see the Nature site for news on literature mining research.