A Data-driven Collaborative Research Experience in Extreme Biology and Biophysics

Abstract

In order to benefit from data and computational resources to accelerate research, scientists are having to redefine how they conduct their investigations. In increasing numbers, researchers in the natural sciences are coming together with colleagues who have different expertise and skill sets to work on multidisciplinary problems, with data science acting as an intermediary as well as a driving force for the research. In such settings, data aggregation, analysis and visualization methods and tools offer a collaborative space where team members can develop and exchange ideas towards their research goals. This allows researchers to go beyond current approaches in a discipline offering new perspectives and avenues to be explored. As part of a collaborative network of researchers in extreme biology and biophysics we are utilizing data science and data visualization platforms such as Jupyter (https://jupyter.org/) and Observable (https://observablehq.com/) to engage researchers with new ways to assess and solve problems. We are following principles of Linked Data to better design and annotate our data structures so as to align with community standards. With this approach we aim to introduce new analysis and data tools and visualizations that are transferable across projects. We believe that building a reliable data infrastructure goes a long way to facilitating discovery and fostering good quality research.