CLIMATE SCIENCE: Clearing up Cloudy Data

6 Dealing with Cloudy Data

Looking up, it’s easy to spot the clouds. The white fluff is strikingly contrasted by the blue sky. It’s not so easy from space, especially above the Earth’s poles. The clouds blend in against the vast expanses of snow and ice. This is a problem for scientists who use satellites to study clouds and climate. Recently though, UC Berkeley statistician Bin Yu, her graduate student Tao Shi, and thei...

Clearing Up Homicide Clearance Rates

Maximum Entropy: Clearing up Mysteries

There are several mystifications and a couple of mysteries pertinent to MaxEnt. The mystifications, pitfalls and traps are set up mainly by an unfortunate formulation of Jaynes’ die problem, the cause célèbre of MaxEnt. After discussing the mystifications a new formulation of the problem is proposed. Then we turn to the mysteries. An answer to the recurring question ’Just what are we accomplish...

Clearing up implicit knowledge: Implications for Knowledge Management, information science, psychology, and social epistemology

“Implicit knowledge” and “tacit knowledge” in Knowledge Management (KM) are important, often synonymous, terms. In KM they often refer to private or personal knowledge that needs to be made public. The original reference of “tacit knowledge” is to the work of the late scientist and philosopher, Michael Polanyi (Polanyi, 1969), but there is substantial evidence that the KM discourse has poorly u...

Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud

The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the ...