A Computing Environment to Support Repeatable Scientific Big Data Experimentation of World-Wide Scientific Literature

A principal tenet of the scientific method is that experiments must be repeatable. This tenet relies on ceteris paribus (i.e., all other things being equal). As a scientific community, involved in data sciences, we must investigate ways to establish an environment where experiments can be repeated. We can no longer allude to where the data comes from, we must add rigor to the data collection and management process from which our analysis is conducted. This paper describes a computing environment to support repeatable scientific big data experimentation of world-wide scientific literature, and recommends a system that is housed at the Oak Ridge National Laboratory in order to provide value to investigators from government agencies, academic institutions, and industry entities. The described computing environment also adheres to the recently instituted digital data management plan, which involves all stages of the digital data life cycle including capture, analysis, sharing, and preservation, as mandated by multiple United States government agencies. It particularly focuses on the sharing and preservation of digital research data. The details of this computing environment are explained within the context of cloud services by the three layer classification of “Software as a Service”, “Platform as a Service”, and “Infrastructure as a Service”.