Unfortunately, DKM systems, whether they are commercial off-the-shelf tools or in-house developed tools, can be costly to acquire, develop, and customize, as well as maintain. The ability to use interoperable terminologies and semantics between systems is a critical requirement to bring together and make effective use of disparate data and knowledge resources. Further, data- and knowledge-driven approaches are integral to fulfilling the NIH mandate for scientific rigor and transparency in the biomedical sciences. The ability to use data- and knowledge-driven approaches is increasingly providing competitive advantage for researchers and is necessary for informed and defensible decision-making. The goals vary across the institute, including enabling data-driven scientific discovery, informing health policies and funding decisions, and informing business operations. ![]() The ability to effectively curate, combine, and use scientific and operational data and knowledge resources (e.g., research data sets, databases, knowledge bases, content management systems ) is integral to the goals of each NIEHS division. ![]() Data within the DKM may be at any stage of its lifecycle. These systems may also provide critical analytical and visualization capabilities to support research and decision processes. Data and knowledge management (DKM) systems collect, manage, and provide controlled access to data and knowledge resources.
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