Machine Science

Late 20th and early 21st century science has been characterized by an enormous increase in the use of computational resources across an incredible variety of research-related activities. This trend has been driven by a combination of chip miniaturization coupled to exponential economies of scale in production, and has lead to appreciable shifts in the way in which knowledge is generated, shared and interpreted. A few of the more obvious knowledge-related challenges emerging from this evolving, computational research culture include:

  • Storing and Sharing data collected from a variety of high-throughput methods; raising questions of open-access and recognition/citation conventions suited to universally available data sets.
  • Analysis and visualization of large data sets, to include the use of a variety of increasingly powerful machine learning frameworks that are highly predictive but not always intuitive or comprehensible to the expert research community.
  • Automatic means of evaluating or crediting scientific contributions through publication-related indices: citation index, h-index; i-index.
  • The rise in the mobility and desirability of computer scientists and engineers capable of programming and maintaining the research-related computing infrastructure, but who are rarely trained in theoretical or experimental natural science.

In this proposal we seek to quantify through the use of suitable datasets and computational models: (1) citation and acknowledgment norms – evaluation Gold standards – associated with a growing dependence on “intelligent algorithms” and hardware; (2) The reduction in value placed on explanation versus prediction in high impact research science; (3) the shift towards increasingly quantitative evaluation of scientists for hiring, tenure and promotion; (4) the impact of computer scientists on the character of questions asked in the natural and social sciences and (5) the changing economic market for research associated with a relative increase in expenditure on computational infrastructure and an attendant reduction in salaries and faculty lines.