Smart Data Infrastructure: The Sixth Generation of Mediation for Data Science

Abstract:

In the emergent “fourth paradigm” (data-driven) science, the scientific method is enhanced by the integration of significant data sources into the practice of scientific research. To address Big Science, there are challenges in understanding the role of data in enabling researchers to attack not just disciplinary issues, but also the system-level, large-scale, and transdisciplinary global scientific challenges facing society.
Recognizing that the volume of data is only one of many dimensions to be considered, there is a clear need for improved data infrastructures to mediate data and information exchange, which we contend will need to be powered by semantic technologies. One clear need is to provide computational approaches for researchers to discover appropriate data resources, rapidly integrate data collections from heterogeneously resources or multiple data sets, and inter-compare results to allow generation and validation of hypotheses. Another trend is toward automated tools that allow researchers to better find and reuse data that they currently don’t know they need, let alone know how to find. Again semantic technologies will be required. Finally, to turn data analytics from "art to science", technical solutions are needed for cross-dataset validation, reproducibility studies on data-driven results, and the concomitant citation of data products allowing recognition for those who curate and share important data resources.