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This data will likely contain a number of different components, however, the primary elements will focus on two structures.  One is the form definition that contains the layout and definition with rules for validation, bindings to pick lists, and skip patterns.  The second  is the schema definition that is required to capture and represent the data

Discovery includes service discovery, data discovery, and policy discovery. Service discovery allows primary users as well as secondary users to locate a service specification and instances based on attributes in the service metadata (for example, via a search for specific micro-array analysis services). Data discovery enables secondary users to find the types of data available in the ecosystem as well as summary-level information about available data sets. Policy discovery allows application developers to find and retrieve policies on services.

Link to use case: As institutions share de-identified glioblastoma data sets, they are available to others via data discovery. The treatment recommendation service used by the oncologist is able to discover these new data sets and their corresponding information models, and include that data for subsequent use in recommendation of treatment.

Conformance Testing

Human semantics include metadata about a service that is meant to be displayed via a user interface, for example, a description of the operations defined on a service. Computable semantics are metadata that are added to a service primarily in order to facilitate service orchestration and choreography, and to specify precisely the semantic meaning of data in order to allow interpretation and reasoning. Services in the ecosystem must have both types of metadata in order to facilitate tools for the platform and enable working interoperability.

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This group of requirements focuses on enabling developers of composite services and applications to discover, compose, and invoke services. This includes the discovery of published services based on service metadata and the generation of client APIs in multiple languages to provide cross-platform access to existing services. This also includes the ability to use an "analytical" service locally in the case where the data to be processed is too large to move to a remote service.

Discovery includes service discovery, data discovery, and policy discovery. Service discovery allows primary users as well as secondary users to locate a service specification and instances based on attributes in the service metadata (for example, via a search for specific micro-array analysis services). Data discovery enables secondary users to find the types of data available in the ecosystem as well as summary-level information about available data sets. Policy discovery allows application developers to find and retrieve policies on services.

Link to use case: As institutions share de-identified glioblastoma data sets, they are available to others via data discovery. The treatment recommendation service used by the oncologist is able to discover these new data sets and their corresponding information models, and include that data for subsequent use in recommendation of treatment.

Link to use case: all of the data management and access services in the use case are utilized by application developers to build the user interfaces that the clinicians use during the course of patient care.

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