Abstract
MotivationReproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the procedures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanopublications (NP), and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP, and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler.ResultsExecutable workflows were developed using Galaxy, which reproduced results that were consistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP, and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables, and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata.AvailabilitySOAPdenovo2 scripts, data, and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy.cbiit.cuhk.edu.hk; and the representations using the ISA, NP, and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. Contact: philippe.rocca-serra@oerc.ox.ac.uk and susanna-assunta.sansone@oerc.ox.ac.uk.
Highlights
Several reports have highlighted the practical difficulties in reproducing results from published experiments [1,2,3,4]
Scientists are coming under increasing pressure from funding agencies to disseminate their research data and methods
Research outputs may be assigned a Digital Object Identifier (DOI), a process overseen by DataCite [80], possibly facilitating discovery and citation
Summary
Several reports have highlighted the practical difficulties in reproducing results from published experiments [1,2,3,4]. Amongst the incentives tried by publishers are the lift on restrictions on the length of methods sections, the creation of data publication platforms, such as GigaScience [6] and Scientific Data [7], the provision of a statistical review of numerical results where appropriate and the requirement for data to be deposited in open-access repositories. These efforts have in part been driven by position statements from funding agencies, publishers and researchers advocating more widespread data sharing [8,9,10]. The NIH program Big Data to Knowledge (BD2K) constitutes a major initiative, aimed at making data dissemination and data preservation for all NIH funded work a reality, by mandating the creation of data access plans for all new grant applications [13]
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