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Data federation, a process whereby data is collected from different databases without ever copying or transferring the original data, is part of the solution as well. It requires a shared semantic scheme and a supporting software framework to link the databases. databases. Most importantly for the success of CTIIP, data federation will make it possible to create integrative queries using data from TCIA and TCGA.  The software used to accomplish this data federation in this sub-project is Bindaas. Bindaas is middleware that is also used to build the backend infrastructure of caMicroscope. The team is extending Bindaas with a data federation capability that makes it possible to query data from TCIA and TCGA.

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All three research domains will clearly need an imaging archive that can be leveraged for integration across multiple data types and sources. For example, TCGA program has the goal of producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) software stack were created to manage well-curated, publicly-available collections of medical image data, including diagnostic images associated with the tissue samples sequenced by TCGA. TCIA currently supports over 40 active research groups including researchers who are exploiting the existing linkages between TCGA and TCIA. TCIA has recently released an API — an Application Programmatic Program Interface (API) that provides a REST API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas,and this API is being designed to support federation of multiple information repositories using the concepts of a data mashups.  This infrastructure can be expanded to include more data types and additional integration, and provide analytic and decision support, which will act as a foundation for a broader set of novel community research projects.Its mission is to create an open-source digital pathology image server that can host and serve digital pathology images for any of the major vendors without recoding. facilitating data integration. This image server would establish an informatics and IT infrastructure to implement pilot challenges for clinical and pre-clinical studies that integrate the (CKK: talk to Ulli about different names for the same? domains mentioned on this page) genomics, diagnostic imaging, and digital pathology domains.

Goals: data exploration, data connection, data mashup, make data available for analysis, make data accessible for image algorithms

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