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Dr. Musen is Professor of Biomedical Informatics at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to open science, data stewardship, intelligent systems, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building and managing terminologies and ontologies. He is principal investigator of the National Center for Biomedical Ontology, one of the original National Centers for Biomedical Computing created by the U.S. National Institutes of Heath (NIH). He is principal investigator of the Center for Expanded Data Annotation and Retrieval (CEDAR). CEDAR develops new technology to ease the authoring and management of biomedical experimental data and metadata to make online datasets more findable, accessible, interoperable, and reusable. Dr. Musen chaired the Health Informatics and Modeling Topic Advisory Group for the World Health Organization’s revision of the International Classification of Diseases (ICD-11) and he currently directs the WHO Collaborating Center for Classification, Terminology, and Standards at Stanford University. Early in his career, Dr. Musen received the Young Investigator Award for Research in Medical Knowledge Systems from the American Association of Medical Systems and Informatics and a Young Investigator Award from the National Science Foundation. In 2006, he was recipient of the Donald A. B. Lindberg Award for Innovation in Informatics from the American Medical Informatics Association. He has been elected to the American College of Medical Informatics, the Association of American Physicians, the International Academy of Health Sciences Informatics, and the National Academy of Medicine. He is founding co-editor-in-chief of the journal Applied Ontology.
Semantic technology, which makes explicit the knowledge that drives computational systems, offers great opportunities to advance biomedical science and clinical medicine. Our laboratory studies the use of semantic technology in a range of application systems, emphasizing approaches that enhance the stewardship and dissemination of experimental datasets for open science.The Center for Expanded Data Annotation and Retrieval (CEDAR) investigates new computational approaches that use semantic technology to ease the creation of metadata to describe scientific experiments. Metadata are data about data—machine-actionable descriptions of experimental data, of the methods used to acquire the data, of the analyses that have been performed on the data, and of the provenance of all this information. Science suffers because much of the metadata that investigators create to annotate the datasets that they archive in public repositories are incomplete and nonstandardized. CEDAR studies new methods to assist the authoring of high-quality metadata. Our long-term goal is to aid the dissemination of scientific data and knowledge in machine-processable form, and to create intelligent agents that can help scientists to track experimental results online, to integrate datasets, and to use public data repositories to make new discoveries. We see CEDAR as the first step in the development of a new kind of technology to change the way in which scientific knowledge is communicated—not as prose journal articles but as computer-interpretable data and metadata. Automated systems one day will access such online "publications" to augment the capabilities of human scientists as they seek information about relevant studies and as they attempt to relate their own results to those of other investigators. Our laboratory has pioneered methods for the development of intelligent systems. An important element of this work has involved the use of ontologies—formal descriptions of application areas that are created in a form that can be processed by both people and computers. CEDAR uses ontologies to ensure that scientific metadata are represented in a standardized way. Our Protégé system for ontology development—now with more than 400,000 registered users—allows us to continue to explore new methods for ontology engineering and for the construction of intelligent systems and knowledge graphs.