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Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.


ABSTRACT: Biomedical 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. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

SUBMITTER: Pennington JW 

PROVIDER: S-EPMC3932456 | biostudies-other | 2014 Mar-Apr

REPOSITORIES: biostudies-other

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Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

Pennington Jeffrey W JW   Ruth Byron B   Italia Michael J MJ   Miller Jeffrey J   Wrazien Stacey S   Loutrel Jennifer G JG   Crenshaw E Bryan EB   White Peter S PS  

Journal of the American Medical Informatics Association : JAMIA 20131016 2


Biomedical 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 d  ...[more]

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