Mark Musen (He/Him)

Professor, Stanford University

About Mark Musen

Mark is Stanford Medicine Professor of Biomedical Informatics Research at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Mark conducts research related to open science, intelligent systems, computational ontologies, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building and managing terminologies and ontologies. He served as 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. He directs the Center for Expanded Data Annotation and Retrieval (CEDAR), founded under the NIH Big Data to Knowledge Initiative. CEDAR develops semantic technology to ease the authoring of experimental metadata and the management of scientific datasets.

Areas of Expertise

Data Management Research

Subject Areas

Biological sciences Medicine & clinical sciences


Channels contributed to:

Behind the Paper

Recent Comments

Nov 23, 2022

It is time for research software to be treated as a first-class digital research object.  The FAIR principles described in this paper provide a means for technologies that have been applied to research data to help to make research software FAIR in a completely analogous manner.

Nov 23, 2022

ASL-BIDS is a fantastic example of the kind of community-based reporting guideline that we describe as an essential component of data FAIRness in our recent Research Data Community posting and in the associated paper in Scientific Data.  ASL-BIDS offers an excellent means to formalize the attributes of a class of  neuroimages stored in research repositories.  One can imagine embedding ASL-BIDS metadata within other, more comprehensive metadata to describe not only the images and the imaging techniques, but also the rationale for the experiment, the subjects, and the experimental protocol that led to the images in the first place.  This degree of metadata "richness"—which goes beyond the acquired images and includes a description of the complete experiment— is needed for datasets related to ASL images truly to to be FAIR in accordance with the FAIR principles.


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