Semantic Mediation to Improve Reproducibility for Biomolecular NMR Analysis

  • Michael R. Gryk
  • Bertram Ludäscher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)


Two barriers to computational reproducibility are the ability to record the critical metadata required for rerunning a computation, as well as translating the semantics of the metadata so that alternate approaches can easily be configured for verifying computational reproducibility. We are addressing this problem in the context of biomolecular NMR computational analysis by developing a series of linked ontologies which define the semantics of the various software tools used by researchers for data transformation and analysis. Building from a core ontology representing the primary observational data of NMR, the linked data approach allows for the translation of metadata in order to configure alternate software approaches for given computational tasks. In this paper we illustrate the utility of this with a small sample of the core ontology as well as tool-specific semantics for two third-party software tools. This approach to semantic mediation will help support an automated approach to validating the reliability of computation in which the same processing workflow is implemented with different software tools. In addition, the detailed semantics of both the data and the processing functionalities will provide a method for software tool classification.


Ontology Computational reproducibility Provenance 



This work was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number GM-111135.


  1. 1.
    Munafò, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J.J., Ioannidis, J.P.A.: A manifesto for reproducible science. Nat. Hum. Behav. 1, 1–9 (2017)CrossRefGoogle Scholar
  2. 2.
    Kanwal, S., Khan, F.Z., Lonie, A., Sinnot, R.O.: Investigating reproducibility and tracking provenance – a genomic workflow case study. BMC Bioinform. 18, 337 (2017)CrossRefGoogle Scholar
  3. 3.
    Vitek, J., Kalibera, T.: Repeatability, reproducibility, and rigor in systems research. In: Proceedings of the Ninth ACM International Conference on Embedded software (EMSOFT 2011), pp. 33–38 (2011)Google Scholar
  4. 4.
    Stodden, V., Miguez, S.: Best practices for computational science: software infrastructure and environments for reproducible and extensible research. J. Open Res. Softw. 2(1), e21 (2014)CrossRefGoogle Scholar
  5. 5.
    Verdi, K.K., Ellis, H.J., Gryk, M.R.: Conceptual-level workflow modeling of scientific experiments using NMR as a case study. BMC Bioinform. 8, 31 (2007)CrossRefGoogle Scholar
  6. 6.
    Ellis, H.J.C., Nowling, R.J., Vyas, J., Martyn, T.O., Gryk, M.R.: Iterative development of an application to support nuclear magnetic resonance data analysis of proteins. In: Proceedings of the International. Conference on Information Technology: New Generations, pp. 1014–1020 (2011)Google Scholar
  7. 7.
    Maciejewski, M.W., Schuyler, A.D., Gryk, M.R., Moraru, I.I., Romero, P.R., Ulrich, E.L., Eghbalnia, H.R., Livny, M., Delaglio, F., Hoch, J.C.: NMRbox: a resource for biomolecular NMR computation. Biophys. J. 112(8), 1529–1534 (2017)CrossRefGoogle Scholar
  8. 8.
    Piccolo, S.R., Frampton, M.B.: Tools and techniques for computational reproducibility. GigaScience 5(1), 30 (2016)CrossRefGoogle Scholar
  9. 9.
    Bowers, S., Ludäscher, B.: Towards a generic framework for semantic registration of scientific data. In: Semantic Web Technologies for Searching and Retrieving Scientific Data (SCISW) (2003)Google Scholar
  10. 10.
    McPhillips, T., Song, T., Kolisnik, T., Aulenbach, S., Belhajjame, K., Bocinsky, K., Cao, Y., Chirigati, F., Dey, S., Freire, J., Huntzinger, D., Jones, C., Koop, D., Missier, P., Schildhauer, M., Schwalm, C., Wei, Y., Cheney, J., Bieda, M., Ludäscher, B.: YesWorkflow: a user-oriented, language-independent tool for recovering workflow information from scripts. Int. J. Digit. Curation 10(1), 298–313 (2015)CrossRefGoogle Scholar
  11. 11.
    Madin, J.S., Bowers, S., Schildhauer, M., Jones, M.: Advancing ecological research with ontologies. Trends Ecol. Evol. 23, 159–168 (2008)CrossRefGoogle Scholar
  12. 12.
    Nowling, R.J., Vyas, J., Weatherby, G., Fenwick, M.W., Ellis, H.J.C., Gryk, M.R.: CONNJUR spectrum translator: an open source application for reformatting NMR spectral data. J. Biomol. NMR 50(1), 83–89 (2011)CrossRefGoogle Scholar
  13. 13.
    Bowers, S., Ludäscher, B.: Actor-oriented design of scientific workflows. In: Delcambre, L., Kop, C., Mayr, Heinrich C., Mylopoulos, J., Pastor, O. (eds.) ER 2005. LNCS, vol. 3716, pp. 369–384. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  14. 14.
    Rijgersberg, H., van Assem, M., Top, J.: Ontology of units of measure and related concepts. Semant. Web Interoper. Usabil. Appl. 4, 3–13 (2011)Google Scholar
  15. 15.
    Fenwick, M., Weatherby, G., Vyas, J., Sesanker, C., Martyn, T.O., Ellis, H.J.C., Gryk, M.R.: CONNJUR workflow builder: a software integration environment for spectral reconstruction. J. Biomol. NMR 62, 313–326 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Illinois, Urbana-ChampaignChampaignUSA
  2. 2.UCONN HealthFarmingtonUSA

Personalised recommendations