Informed Consent Ontology
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Informed Consent Ontology (ICO)

Informed Consent Ontology (ICO) represents the documentations and processes involved in informed consent. ICO aims to support informed consent data integration and reasoning in the clinical research space.

As part of the consenting process in human subjects research, potential participants receive information about the purpose of the study, potential risks and benefits of participation, their rights, and the procedures to be undergone as part of the study. If the individual decides to participate, an informed consent document is signed and preserved as a record of voluntary participation in the research study. Following the OBO Foundry principles and aligned with the Basic Formal Ontology (BFO), the ICO logically represents the terms and their relations related to the informed consent process and content. ICO covers various topics related to informed consent, for example, informed consent forms, the components inside informed consent forms, and various informed consent processes.

ICO was initiated in 2012 with a support of a MCubed project at the University of Michigan. Since then, many researchers and funding resources have got together to move forward the development and application of the ICO ontology. The following (ICO introduction presentation), presented by Oliver He during the 2018 Ontology of Informed Consent workshop on February 26-27, 2018, at Little Rock, briefly introduces the collaborative ICO development background.

Relevant ICO websites:


Use the following URI to download this ontology

Note that the source ontology is an OWL file.



Lin Y, Harris MR, Manion FJ, Eisenhauer E, Zhao B, Shi W, Karnovsky A, He Y: Development of a BFO-based Informed Consent Ontology (ICO). In: The 5th International Conference on Biomedical Ontologies (ICBO): 2014; Houston, Texas, USA, October 8-9, 2014. CEUR Workshop Proceedings; 2013: Page 84-86. []


We thank Drs. Nicholas H. Steneck and Blake J. Roessler from the University of Michigan for their valuable discussions and feedback.

This research was initially supported by a University of Michigan interdisciplinary research award (MCubed), and then by the National Center for Advancing Translational Sciences of the NIH under Award Number 2UL1TR000433-06, and the NIH-NIDDK-funded Kidney Precision Medicine Project (KPMP, The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.