Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

Abstract

Objective: As biomedical technology becomes increasingly sophisticated, researchers can probe ever more subtle effects with the added requirement that the investigation of small effects often requires the acquisition of large amounts of data. In biomedicine, these data are often acquired at, and later shared between, multiple sites. There are both technological and sociological hurdles to be overcome for data to be passed between researchers and later made accessible to the larger scientific community. The goal of the Biomedical Informatics Research Network (BIRN) is to address the challenges inherent in biomedical data sharing.

Materials and methods: BIRN tools are grouped into 'capabilities' and are available in the areas of data management, data security, information integration, and knowledge engineering. BIRN has a user-driven focus and employs a layered architectural approach that promotes reuse of infrastructure. BIRN tools are designed to be modular and therefore can work with pre-existing tools. BIRN users can choose the capabilities most useful for their application, while not having to ensure that their project conforms to a monolithic architecture.

Results: BIRN has implemented a new software-based data-sharing infrastructure that has been put to use in many different domains within biomedicine. BIRN is actively involved in outreach to the broader biomedical community to form working partnerships.

Conclusion: BIRN's mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.

Figure 1

A schematic of the layered architecture approach used in BIRN. The lowest infrastructure level of the architecture consists of the core infrastructure, that is, networks and associated protocols for generating, accessing, storing, and moving data. Above this layer are the Globus Toolkit generic data management services that are format and content agnostic. The BIRN services layer contains common standard services that are more specialized for biomedicine, but not for any specific biomedical domain. Finally, the top layer consists of application-specific services and tools that address the specific requirements of a particular research activity or specialized research domain. These are often supplied by the end users. One advantage of the layered approach is that it allows BIRN capabilities to be incorporated into the existing technology of the end user.

Figure 2

An example of federated data sharing using BIRN. The figure depicts two sites federating local storage systems and sharing with local and remote users. Each site (large circled) is composed of a storage server, data producing instruments, local users of data, and institutional firewalls. Typically, instruments rapidly produce large volumes of data and store the data on the local storage server. As data are stored, the system registers file locations at a location registry, often at a third-party location (small circled). When accessing data, client software queries the location registry to find the storage locations of the data. Users retrieve data from local or remote storage systems transparently. All communication between services and clients is secured with cryptographically strong methods that ensure authenticity, privacy, and integrity.

Figure 3

A schematic of the BIRN Mediator. Users are presented with a single database view that is achieved through a uniform semantically-consistent domain model of all of the data. No data are stored at the mediator; the data remain at the sources. The mediator reconciles the semantic discrepancies among the sources by using a set of declarative logical descriptions of the contents of the sources. The user poses queries to the mediator using terms from the domain model. The mediator then uses the source descriptions to identify the sources relevant to the user query and to rewrite the domain-level user query, expressed in terms of the domain model, into a source-level query, expressed in terms of the source schemas. The Mediator architecture is designed in a modular fashion, so that any mediation approach that produces source queries in a language supported by the query evaluation engine can be used by the system.

Figure 4

Knowledge processing in biomedicine is based on cycles of formulation—experimentation—interpretation. (A) BIRN's knowledge modeling methodology is based on a generic knowledge model for experimental design (EDlabel>) and specialized models for domain reasoning (DR). (B) We are developing technology to populate this modeling approach from the published literature (using a text mining infrastructure), from online databases (using BIRN's information integration technology) and raw data (using our modeling tools directly within database development systems).