Developing informatics tools to make immunology research data Findable, Accessible, Interoperable and Reusable

Immunological research produces complex datasets ranging from molecular level to individuals and populationsSeveral data repositories aim to serve the community by housing and making the datasets available for reuseHowevertheir usage is still limited due to lack of standardizations and accessibilityOne common issue is that the datasets in these repositories are not available to generalized search engines such as Google as expected because of missing standardized metadata modelsIn this projectwe propose to tackle this challenge by developing natural language processing and ontologybased methods and tools to extract and normalize the metadata of immunology datasetsin order to improve their discoverability by general and specific search enginesIt will provide a prototype framework that can automatically extract and normalize metadata of biomedical datasets and feed them into major search engines such as Google and PubMedthus greatly improving reuse of existing datasets to increase biomedical research productivity and reproducibility