In this chapter we revisit the seven domain data resources mentioned in Chapter 2 and discuss some data innovations that may appear in the future in these specific domains.


For many years, scientific research was divided into three separate areas: basic science, applied science, and theoretical research, all depending on collected data that can support or reject a new perspective for reason of self-consistency, insight, accuracy, unity, or compatibility. The motivation was a quadrant research methodology conceived to facilitate scientific development, and once popular in academic circles. The most famous was the four-quadrant configuration [43] conceived by a well-known development economist Vernon Ruttan, so called because it was based on Bohr's quadrant, Edison's quadrant, and Pasteur's quadrant, and used to understand the core characteristics of research (e.g., driving source, external restrictions, regression, availability) in advancing knowledge.

However, the basic methods of discovery and the generation of data used in the past to acquire cutting-edge knowledge can no longer satisfy our needs in extraterrestrial explorations, in probing the inner Earth, in saving the environment or even humanity itself. Evolution had been an inevitable force in all pursuits of knowledge throughout the whole history of scientific exploration. Today, it is innovations that can accumulate data that will facilitate academic ...

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