Data-Driven Science Policy

摘要

Acritical challenge for science policy decision makers is determining how to spend limited resources most productively. To do so, one must have a basic understanding of the inner workings of the science, technology, and innovation (STI) system, knowledge of where the most productive research is being done, and an awareness of how progress proceeds across numerous individuals and institutions. Advances in computational power, combined with the unprecedented volume and variety of data on science and technology developments, create ideal conditions for the development and application of data mining and modeling approaches that reveal the dynamics of research progress and can augment human judgment in allocating resources. STI studies use large-scale publication, patent, funding, news, social media, and other data to rigorously study the structure and evolution of the science and technology landscape; they use advanced visualizations to communicate the results of these studies; and they can empirically validate the results of various policy and funding strategies.