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Title: Choosing experiments to accelerate collective discovery

Scientists perform a tiny subset of all possible experiments. What characterizes the experiments they choose? What are the consequences of those choices for the pace of scientific discovery? We model scientific knowledge as a network and science as a sequence of experiments designed to gradually uncover it. By analyzing millions of biomedical articles published over 30 y, we find that biomedical scientists pursue conservative research strategies exploring the local neighborhood of central, important molecules. Although such strategies probably serve scientific careers, we show that they slow scientific advance, especially in mature fields, where more risk and less redundant experimentation would accelerate discovery of the network. Lastly, we also consider institutional arrangements that could help science pursue these more efficient strategies.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. Univ. of Chicago, IL (United States). Dept. of Medicine and Human Genetics; Univ. of Chicago and Argonne National Laboratory, Chicago, IL (United States); Univ. of Chicago, IL (United States). Inst. of Genomic and Systems Biology
  2. Univ. of California, Los Angeles, CA (United States)
  3. Univ. of Chicago and Argonne National Laboratory, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
  4. Univ. of Chicago and Argonne National Laboratory, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States). Dept. of Sociology
Publication Date:
OSTI Identifier:
1244548
Grant/Contract Number:
AC02-06CH11357; SBE 0915730; 1P50MH094267; U01HL108634-01; W911NF1410333
Type:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 112; Journal Issue: 47; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
Research Org:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC); National Science Foundation (NSF); National Institutes of Health (NIH); Air Force Office of Scientific Research (AFOSR)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING complex networks; computational biology; science of science; innovation; sociology of science