AMIA Jt Summits Transl Sci Proc
December 2013
Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications.
View Article and Find Full Text PDFBiomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications.
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