Information Technology Supported Convergence

Modern information technology is transforming the collection, management, and sharing of scientific data in ways that greatly encourage convergence. Data-intensive science has evolved beyond the point at which all the information required for a research study can be centrally located, so interoperability across systems is required, with the additional benefit that data from different sources can be combined. Interoperability of heterogeneous data is a difficult challenge, requiring carefully specified metadata and well-conceptualized data management approaches like Digital Object Architecture. Scientific literature has become so complex and voluminous that it also must be managed in new ways, for example, using knowledge graphs to map connections as in Semantic Medline. In the commercial realm, systems like Google Knowledge Graph and the related Knowledge Vault have begun to appear. For more than a decade, it has been recognized that future science will depend heavily upon distributed resources, including data archives, distant experimental facilities, and domain-specific research tools to enable new scientific discoveries and education across disciplines and geography. Similar approaches will become valuable for the development of abstract theory, for example, the cooperative construction of rigorous modular theories, in fields as diverse as physics and sociology, as scientists around the world contribute concepts and connect them by means of computer-based online tools.