Using Visual Exploratory Data Analysis to Facilitate Collaboration and Hypothesis Generation in Cross-Disciplinary Research

Xiaogang Ma,Robert Downs,Robert Hazen,Peter Fox,Bhuwan Madhikarmi,Daniel Hummer,Chengbin Wang,Joshua Golden,Shaunna Morrison,Michael Meyer

doi:10.3390/ijgi6110368

Abstract

Massive open data resources are changing the way that people do science. To make use of those data resources, data science methods and technology can be leveraged by stakeholders of various disciplines. The objective of this paper is to present our experience of using visual exploratory data analysis as a method to facilitate collaboration and hypothesis generation in geoscience research. The research team consisted of both geoscientists and computer scientists. A use case-driven, iterative approach was applied to create a collaborative and communicative environment. Through several rounds of use case analysis and technological development, a data visualization pilot system was created for studying the co-relationships between chemical elements and mineral species. The exploratory data analyses conducted in those use case studies led to several research hypotheses for future work. This research illustrates the usefulness of exploratory data analysis for hypothesis generation in a data science process. Although the presented project is in geoscience, the discussed method and experience can also be translated into other disciplines.

Highlights

The open data movement is changing the way that people do science [1,2,3]
With abundant datasets made freely accessible through the open data movement, researchers can retrieve massive datasets from the open data environment on the Web [4]
Like many other disciplines, are facing opportunities and challenges raised by the open data environment

Summary

Introduction

The open data movement is changing the way that people do science [1,2,3]. A conventional process of scientific research begins with background study and hypothesis generation. Data will be collected in experiments and the results of data analysis will be used to approve or revise the hypothesis. With abundant datasets made freely accessible through the open data movement, researchers can retrieve massive datasets from the open data environment on the Web [4]. Researchers often struggle to develop hypotheses despite the abundance of data available to them. In this new era of science, methods and tools are desired to help researchers generate and test hypotheses

Objectives

Methods

Conclusion