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A checklist-based approach for quality assessment of scientific information

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A checklist-based approach for quality assessment of scientific information

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Abstract

The Semantic Web is becoming a major platform for disseminating and sharing scientific data and results. Quality of these information is a critical factor in selecting and reusing them. Existing quality assessment approaches in the Semantic Web largely focus on using general quality dimensions (accuracy, relevancy, etc.) to establish quality metrics. However, specific quality assessment tasks may not fit into these dimensions and scientists may find these dimensions too general for expressing their specific needs. Therefore, we present a checklist-based approach, which allows the expression of specific quality requirements, saving users from the constraints of the existing quality dimensions. We demonstrate our approach by two scenarios and share our lessons about different semantic web technologies that were tested during our implementation.

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... In addition, we used the minimal information model "Minim", also in Semantic Web format, to specify which elements in an RO we consider "must haves", "should haves" and "could haves" according to user-defined requirements [23]. A checklist service subsequently queries the Minim annotations as an aid to make sufficiently complete ROs [24]. ...
... When building an RO in myExperiment users are provided with a mechanism of quality insurance by our so-called checklist evaluation tool, which is built upon the Minim checklist ontology [23,44] and defined using Web Ontology Language. Its basic function is to assess that all required information and descriptions about the aggregated resources are present and complete. ...
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... Everything in this RO, as well as the RO itself, is uniquely identified and can be referred to. This list of 5 rules was implemented as a checklist and whether an RO is compliant with this checklist can be automatically assessed using the RO quality assessment tool [51]. ...
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Motivation: Reproducing 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. Results: Executable 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. Availability: SOAPdenovo2 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.
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