Transdisciplinary Foundations of Geospatial Data Science

Transdisciplinary Foundations of Geospatial Data Science

Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understand...

Foundations of Data Science ∗

Fostering transdisciplinary science.

The causes and prevention of cancer in humans are complex phenomena, and thereby require the application of scientific methods to address this inherent complexity. It is a common axiom that researchers from multiple disciplines may be required to address the complex etiology and prevention of human disease. There are, however, a number of ways that a group of researchers may bring their individ...

Evaluating transdisciplinary science.

The past two decades have seen a growing interest and investment in transdisciplinary research teams and centers. The Transdisciplinary Tobacco Use Research Centers (TTURCs) exemplify large-scale scientific collaborations undertaken for the explicit purpose of promoting novel conceptual and methodological integrations bridging two or more fields. Until recently, few efforts have been made to ev...

Science Policy for Transdisciplinary Research