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J Biomed Semantics. 2016 Sep 14;7(1):53. doi: 10.1186/s13326-016-0100-2.

The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis.

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Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
Division of Systems Leadership and Effectiveness Science, University of Michigan School of Nursing, Ann Arbor, MI, 48109, USA.
Department of Biostatistics and Bioinformatics, Duke Medical Center, Duke University, Durham, NC, 27710, USA.
Department of Microbiology and Immunology, Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Department of Electrical Engineering and Computer Science, Department of Biomedical Engineering, and Department of Statistics, Michigan Institute of Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
Department of Philosophy and National Center for Ontological Research, University at Buffalo, Buffalo, NY, 14203, USA.
Department of Microbiology and Immunology, Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.



Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem.


The terms in OBCS including 'data collection', 'data transformation in statistics', 'data visualization', 'statistical data analysis', and 'drawing a conclusion based on data', cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed. The OBCS source code and documentation are available at: .


The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.


Biological statistics; Clinical outcomes statistics; Data integration; OBCS; Standardization; Statistical analysis

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