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2016 ; 2016
(ä): 1070-1079
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Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology
for Semantic Annotation of Study Description
#MMPMID28269904
Sahoo SS
; Valdez J
; Rueschman M
AMIA Annu Symp Proc
2016[]; 2016
(ä): 1070-1079
PMID28269904
show ga
Scientific reproducibility is key to scientific progress as it allows the
research community to build on validated results, protect patients from
potentially harmful trial drugs derived from incorrect results, and reduce
wastage of valuable resources. The National Institutes of Health (NIH) recently
published a systematic guideline titled "Rigor and Reproducibility " for
supporting reproducible research studies, which has also been accepted by several
scientific journals. These journals will require published articles to conform to
these new guidelines. Provenance metadata describes the history or origin of data
and it has been long used in computer science to capture metadata information for
ensuring data quality and supporting scientific reproducibility. In this paper,
we describe the development of Provenance for Clinical and healthcare Research
(ProvCaRe) framework together with a provenance ontology to support scientific
reproducibility by formally modeling a core set of data elements representing
details of research study. We extend the PROV Ontology (PROV-O), which has been
recommended as the provenance representation model by World Wide Web Consortium
(W3C), to represent both: (a) data provenance, and (b) process provenance. We use
124 study variables from 6 clinical research studies from the National Sleep
Research Resource (NSRR) to evaluate the coverage of the provenance ontology.
NSRR is the largest repository of NIH-funded sleep datasets with 50,000 studies
from 36,000 participants. The provenance ontology reuses ontology concepts from
existing biomedical ontologies, for example the Systematized Nomenclature of
Medicine Clinical Terms (SNOMED CT), to model the provenance information of
research studies. The ProvCaRe framework is being developed as part of the Big
Data to Knowledge (BD2K) data provenance project.