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SisGExp: Rethinking Long-Tail Agronomic Experiments

  • Sergio Manuel Serra da CruzEmail author
  • José Antonio Pires do Nascimento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9672)

Abstract

Reproducibility is a major feature of Science. Even agronomic research of exemplary quality may have irreproducible empirical findings because of random or systematic error. This work presents SisGExp, a provenance-based approach that aid researchers to manage, share, and enact the computational scientific workflows that encapsulate legacy R scripts. SisGExp transparently captures provenance of R scripts and endows experiments reproducibility. SisGExp is non-intrusive, does not require users to change their working way, it wrap agronomic experiments as a scientific workflow system.

Keywords

Provenance R scripts Workflows Precision agriculture 

References

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    Driemeier, C.E., et al.: Data analysis workflow for experiments in sugarcane precision agriculture. In: IEEE 10th International Conference on e-Science, vol. 1, pp. 163–168 (2014). http://doi.org/10.1109/eScience.2014.10
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    Nascimento, J.A.P., Cruz, S.M.S.: Rflow: uma arquitetura para execução e coleta de proveniência de workflows estatísticos. X Simpósio Brasileiro de Informática na Agricultura, 12pp. (2015). (In Portuguese)Google Scholar
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    Gandrud, C.: Reproducible Research with R and R Studio, 2nd edn. Chapman and Hall/CRC, Boca Raton (2015)zbMATHGoogle Scholar
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    McPhillips, T., et al.: YesWorkflow: a user-oriented, language-independent tool for recovering workflow information from scripts. Int. J. Digit. Curation 10(1), 298–313 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sergio Manuel Serra da Cruz
    • 1
    Email author
  • José Antonio Pires do Nascimento
    • 1
  1. 1.UFRRJ – Universidade Federal Rural do Rio de JaneiroSeropédicaBrazil

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