Authors:
Armel Lefebvre
;
Marco Spruit
and
Wienand Omta
Affiliation:
Utrecht University, Netherlands
Keyword(s):
Knowledge Discovery, Reproducible Research, Bioinformatics, Research Objects, Software Development.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Interactive and Online Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
Calls for more reproducible research by sharing code and data are released in a large number of fields from
biomedical science to signal processing. At the same time, the urge to solve data analysis bottlenecks in the
biomedical field generates the need for more interactive data analytics solutions. These interactive solutions
are oriented towards wet lab users whereas bioinformaticians favor custom analysis tools. In this position
paper we elaborate on why Reproducible Research, by presenting code and data sharing as a gold standard
for reproducibility misses important challenges in data analytics. We suggest new ways to design interactive
tools embedding constraints of reusability with data exploration. Finally, we seek to integrate our solution
with Research Objects as they are expected to bring promising advances in reusability and partial
reproducibility of computational work.