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Catalyzing Inquiry at the Interface of Computing and Biology
datasets dynamically. They must provide robust search capabilities so that researchers can find the datasets they need easily. Also, they are likely to have a major role in ensuring the data interoperability necessary when data collected in one context are made available for use in another.
Digital libraries that contain the intellectual legacy of biological researchers and provide mechanisms for sharing, annotating, reviewing, and disseminating knowledge in a collaborative context. Where print journals were once the standard mechanism through which scientific knowledge was validated, modern information technologies allow the circumvention of many of the weaknesses of print. Knowledge can be shared much more broadly, with much shorter lag time between publication and availability. Different forms of information can be conveyed more easily (e.g., multimedia presentations rich in biological imagery). One researcher’s annotations to an article can be disseminated to a broader audience.
High-speed networks that connect large-scale, geographically distributed computing resources, data repositories, and digital libraries. Because of the large volumes of data involved in biological datasets, today’s commodity Internet is inadequate for high-end scientific applications, especially where there is a real-time element (e.g., remote instrumentation and collaboration). Network connections ten to a hundred times faster than those generally available today are a lower bound on what will be necessary.
In addition to these components, cyberinfrastructure must provide software and services to the biological community. For example, cyberinfrastructure will involve many software tools, system software components (e.g., for grid computing, compilers and runtime systems, visualization, program development environments, distributed scalable and parallel file systems, human computer interfaces, highly scalable operating systems, system management software, parallelizing compilers for a variety of machine architectures, sophisticated schedulers), and other software building blocks that researchers can use to build their own cyberinfrastructure-enabled applications. Services, such as those needed to maintain software on multiple platforms and provide for authentication and access control, must be supported through the equivalent of help-desk facilities.
From the committee’s perspective, the primary value of cyberinfrastructure resides in what it enables with respect to data management and analysis. Thus, in a biological context, machine-readable terminologies, vocabularies, ontologies, and structured grammars for constructing biological sentences are all necessary higher-level components of cyberinfrastructure as tools to help manage and analyze data (discussed in Section 4.2). High-end computing is useful in specialized applications but, by comparison to tools for data management and analysis, lacks broad applicability across multiple fields of biology.
7.1.2Why Is Cyberinfrastructure Relevant?
The Atkins panel noted that the lack of a ubiquitous cyberinfrastructure for science and engineering research carries with it some major risks and costs. For example, when coordination is difficult, researchers in different fields and at different sites tend to adopt different formats and representations of key information. As a result, their reconciliation or combination becomes difficult to achieve—and hence disciplinary (or subdisciplinary) boundaries become more difficult to break down. Without systematic archiving and curation of intermediate research results (as well as the polished and reduced publications), useful data and information are often lost. Without common building blocks, research groups build their own application and middleware software, leading to wasted effort and time.
As a field, biology faces all of these costs and risks. Indeed, for much of its history, the organization of biological research could reasonably be regarded as a group of more or less autonomous fiefdoms. Unifying biological research into larger units of aggregation is not a plausible strategy today, and so the federation and loose coordination enabled by cyberinfrastructure seem well suited to provide the major advantages of integration while maintaining a reasonably stable large-scale organizational structure.
Furthermore, well-organized, integrated, synthesized information is increasingly valuable to biological research (Box 7.1). In an era characterized by data-intensive research observations, collecting,