Abstract：
:In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientific fields exhibit distressingly low rates of reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and efficiency of reporting, which contributes to issues of reproducibility. Data provenance aids both reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers.
journal_name
Sci Datajournal_title
Scientific dataauthors
Pasquier T,Lau MK,Trisovic A,Boose ER,Couturier B,Crosas M,Ellison AM,Gibson V,Jones CR,Seltzer Mdoi
10.1038/sdata.2017.114subject
Has Abstractpub_date
2017-09-05 00:00:00pages
170114issn
2052-4463pii
sdata2017114journal_volume
4pub_type
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