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Enriching Agronomic Experiments with Data Provenance

Enriching Agronomic Experiments with Data Provenance

Sergio Manuel Serra da Cruz, Jose Antonio Pires do Nascimento
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 18
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522513919|DOI: 10.4018/IJAEIS.2017070102
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MLA

Serra da Cruz, Sergio Manuel, and Jose Antonio Pires do Nascimento. "Enriching Agronomic Experiments with Data Provenance." IJAEIS vol.8, no.3 2017: pp.21-38. http://doi.org/10.4018/IJAEIS.2017070102

APA

Serra da Cruz, S. M. & do Nascimento, J. A. (2017). Enriching Agronomic Experiments with Data Provenance. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 8(3), 21-38. http://doi.org/10.4018/IJAEIS.2017070102

Chicago

Serra da Cruz, Sergio Manuel, and Jose Antonio Pires do Nascimento. "Enriching Agronomic Experiments with Data Provenance," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 8, no.3: 21-38. http://doi.org/10.4018/IJAEIS.2017070102

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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. The ability to reproduce agronomic experiments based on statistical data and legacy scripts are not easily achieved. We propose RFlow, a tool that aid researchers to manage, share, and enact the scientific experiments that encapsulate legacy R scripts. RFlow transparently captures provenance of scripts and endows experiments reproducibility. Unlike existing computational approaches, RFlow is non-intrusive, does not require users to change their working way, it wraps agronomic experiments in a scientific workflow system. Our computational experiments show that the tool can collect different types of provenance metadata of real experiments and enrich agronomic data with provenance metadata. This study shows the potential of RFlow to serve as the primary integration platform for legacy R scripts, with implications for other data- and compute-intensive agronomic projects.

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