The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10588)


The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed—the CEDAR Workbench—is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.


Metadata Metadata authoring Metadata repository Ontologies 



CEDAR is supported by grant U54 AI117925 awarded by the National Institute of Allergy and Infectious Diseases through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative ( NCBO is supported by the NIH Common Fund under grant U54HG004028.


  1. 1.
    Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)CrossRefGoogle Scholar
  2. 2.
    OpenAIRE and CERN: Zenodo. Accessed 13 May 2017
  3. 3.
    Barrett, T., et al.: BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res. 40, D57–D63 (2012)CrossRefGoogle Scholar
  4. 4.
    Park, T.-R.: Semantic interoperability and metadata quality: an analysis of metadata item records of digital image collections. Knowl. Organ. 33, 20–34 (2006)Google Scholar
  5. 5.
    Park, J.-R., Tosaka, Y.: Metadata quality control in digital repositories and collections: criteria, semantics, and mechanisms. Cat. Classif. Q. 48(8), 696–715 (2010)Google Scholar
  6. 6.
    Zaveri, A., Dumontier, M.: MetaCrowd: crowdsourcing biomedical metadata quality assessment. In: Proceedings of Bio-Ontologies (2017)Google Scholar
  7. 7.
    Vasilevsky, N.A., et al.: On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ 1, e148 (2013)CrossRefGoogle Scholar
  8. 8.
    McQuilton, P., et al.: BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences. Database J. Biol. Databases Curation 2016 (2016). doi: 10.1093/database/baw075
  9. 9.
    Brazma, A., et al.: Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29(4), 365–371 (2001)CrossRefGoogle Scholar
  10. 10.
    Rocca-Serra, P., et al.: ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26(18), 2354–2356 (2010)CrossRefGoogle Scholar
  11. 11.
    González-Beltrán, A., Maguire, E., Sansone, S.-A., Rocca-Serra, P.: linkedISA: semantic representation of ISA-Tab experimental metadata. BMC Bioinform. 15, S4 (2014)CrossRefGoogle Scholar
  12. 12.
    Wolstencroft, K., et al.: RightField: embedding ontology annotation in spreadsheets. Bioinformatics 27(14), 2021–2022 (2011)CrossRefGoogle Scholar
  13. 13.
    Shankar, R., et al.: Annotare—a tool for annotating high-throughput biomedical investigations and resulting data. Bioinformatics 26(19), 2470–2471 (2010)CrossRefGoogle Scholar
  14. 14.
    Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016)CrossRefGoogle Scholar
  15. 15.
    Musen, M.A., et al.: The center for expanded data annotation and retrieval. J. Am. Med. Inform. Assoc. 22(6), 1148–1152 (2015)Google Scholar
  16. 16.
    O’Connor, M.J., Martínez-Romero, M., Egyedi, A.L., Willrett, D., Graybeal, J., Musen, M.A.: An open repository model for acquiring knowledge about scientific experiments. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS, vol. 10024, pp. 762–777. Springer, Cham (2016). doi: 10.1007/978-3-319-49004-5_49 CrossRefGoogle Scholar
  17. 17.
    Martínez-Romero, M., et al.: Supporting ontology-based standardization of biomedical metadata in the CEDAR workbench. In: Proceedings of International Conference on Biomedical Ontology (ICBO) (2017, in press)Google Scholar
  18. 18.
    Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 37, W170–W173 (2009)CrossRefGoogle Scholar
  19. 19.
    Miles, A., Matthews, B., Wilson, M.: SKOS core: simple knowledge organisation for the web. In: Proceedings of International Conference on Dublin Core and Metadata Applications (2005)Google Scholar
  20. 20.
    Martínez-Romero, M., et al.: Fast and accurate metadata authoring using ontology-based recommendations. In: Proceedings of AMIA Annual Symposium (2017, in press)Google Scholar
  21. 21.
    Bhattacharya, S., et al.: ImmPort: disseminating data to the public for the future of immunology. Immunol. Res. 58(2–3), 234–239 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Stanford Center for Biomedical Informatics ResearchStanford UniversityStanfordUSA

Personalised recommendations