The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.

James Mullenbach, Jonathan Gordon, Nanyun Peng, Jonathan May. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Our goal is to generate reading lists for students that help them optimally learn technical material.Existing retrieval algorithms return items directly relevant to a query but do not return results to help users read about the concepts supporting their query.This is because the dependency structure of concepts that must be understood before reading material pertaining to a given query is never considered.Here we formulate an information-theoretic view of concept dependency and present methods to construct a "concept graph" automatically from a text corpus.We perform the first human evaluation of concept dependency edges (to be published as open data), and the results verify the feasibility of automatic approaches for inferring concepts and their dependency relations.This result can support search capabilities that may be tuned to help users learn a subject rather than retrieve documents based on a single query.

Fifth International Conference on Knowledge Capture (K-CAP 2009)