Citation: Arbesman S, Christakis NA (2011) Eurekometrics: Analyzing the Nature of Discovery. PLoS Comput Biol 7(6): e1002072. doi:10.1371/journal.pcbi.1002072
Editor: Philip E. Bourne, University of California San Diego, United States of America
Published: June 30, 2011
Copyright: © 2011 Arbesman, Christakis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: There were no funding sources for this research.
Competing interests: The authors have declared that no competing interests exist.
Until recently, the quantitative study of science has focused on studying patterns in publications , , such as citation counts to discern impact, and in coauthorship networks to discern collaboration. However, two major trends are converging that offer the field of scientometrics a novel opportunity to understand scientific discovery and also to influence how science is done. The first is the advent of vast computational resources and storage capacity available to scientists , , and the second is automated science , . These innovations offer the potential for a new type of scientometrics: quantitatively examining scientific discoveries themselves. This study of discoveries, rather than simply of scientific publications, offers the opportunity to understand science at a deeper level. We term this discovery-based approach to scientometrics as eurekometrics.
Eurekometrics aims to supplement the traditional bibliometric approach of scientometrics by examining the properties of scientific discoveries themselves rather than examining the properties of scientific publications. This is not simply a methodological development but a conceptual one. By using new types of data, we may be able to ask entirely different sorts of questions than we could before. For example, we are now able to examine both the material properties of phenomena that are discovered, such as their physical size, intrinsic entropy, or informational complexity, as well as the human properties of the phenomena, such as how much money, time, or effort it takes to discover them.
For instance, a traditional scientometric approach to understanding the nature of the genetic code and its elucidation would be to study the publications relevant to this area, looking at the citation network among these papers, for example. However, a eurekometric approach would instead examine the properties of the discoveries that were made during the deciphering of the code. In the 1960s, there was a large-scale push to elucidate what each triplet codon sequence coded for . Using a simple metric for informational entropy , one can examine the properties of each codon and find out whether or not, on average, the coding of those codons with less entropy can be found using more types of experiments . In other words, a simple eurekometric approach could examine whether or not those codons with less information can be more easily understood.
There are already examples of eurekometrics beyond the foregoing one. Using the properties and dates of discovery of mammalian species, minor planets, and chemical elements, a quantitative measurement of the decay in ease of scientific discovery has been made  (see Figure 1). By using measurements of the size of each item, a crude proxy for difficulty of discovery was developed. This allowed for insight into whether discovery becomes easier with time, and an analysis of how discoveries actually proceed over time. In addition, examination of the properties of scientific discoveries can be used to predict future discovery. For example, by examining the properties of previously discovered extrasolar planets, a prediction for the first potentially habitable planet similar to Earth has been made . A video visually displaying the location of minor planet discoveries from 1980 to 2010 relative to the Earth's orbit also offers eurekometric insight .
(A) Mean diameter (kilometers) of minor planets discovered, 1802–2008. (B) Mean physical size (g) of mammalian species discovered, 1760–2003. (C) Mean inverse of atomic weight of chemical elements discovered, 1669–2006. Adapted from .
Furthermore, there are examples of research that has begun to bridge the gap between bibliometrics and eurekometrics. Using gene interaction data from high-throughput experiments combined with citation data, an attempt was made to understand the relationship between the reliability of reported interactions and the popularity of a research field . These researchers also examined how the importance of a gene in interaction networks is related to its popularity in the literature .
With the increase of automated discovery and large-scale data collection, eurekometric research has the potential to explode. First, automated science will necessarily have the property of creating large amounts of discovery data. Illustrative examples of automated science include the Sloan Digital Sky Survey , Lincoln Near-Earth Asteroid Program , Gordon and Betty Moore Foundation Marine Microbial Genome Sequencing Project , and the Census of Marine Life . The initial output of these projects will not be publications, but findings. Each object, such as a newly discovered asteroid, need not have its own publication, but each object can be examined separately from a eurekometric perspective.
In addition, there is the potential in such areas as automated drug discovery , automated chemical synthesis path discovery , and automated theorem proving . In all these cases, the conceptually informed and rigorously quantifiable analysis of what is discovered, and when, will shed light on many things, e.g., where there is a relationship between the object of inquiry and human effort.
In addition, other types of research projects will provide potential for eurekometrics. For example, citizen science research, where interested laypeople provide much of the scientific labor, also has potential. Such projects include Galaxy Zoo , which examines stellar phenomena; Foldit , which studies protein folding; the Audobon Christmas Bird Count , which catalogues birds; and Valley of the Khans , which hunts for Genghis Khan's tomb. In addition to providing vast amounts of discovery data, these projects will allow us to understand the way collaborative approaches can create further discovery and the properties of discoveries that are best suited to citizen science.
Despite the great strides in automated discovery and digitization of data that is currently occurring, however, there are limits to eurekometrics. The most important limitation is how to determine what constitutes a “discovery.” Quantifying what constitutes a discovery is never an easy proposition: Is each publication a discovery? Or do only certain ones rise to meet that definition? Furthermore, even if we can list discoveries, it needn't necessarily be possible to quantify their properties. For example, while it's possible to quantify the properties of minor planets and extrasolar planets, it is not nearly as easy to quantify the properties of methodological innovations made in computational fields.
Scientometrics has for too long focused on understanding scientific progress at the level of the publication. Eurekometrics will allow us to understand the pace and determinants of scientific discovery in a way that simply examining the patterns in publications will not. For the first time, we will be able to explore how the properties of nature yield to human science.
- 1. Hood W, Wilson C (2001) The literature of bibliometrics, scientometrics, and informetrics. Scientometrics 52: 291–314.
- 2. Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316: 1036–1039.
- 3. Nature (2008) Community cleverness required. Nature 455: 1.
- 4. Lazer D, Pentland A, Adamic L, Aral S, Barabasi AL, et al. (2009) Computational social science. Science 323: 721–723.
- 5. Evans J, Rzhetsky A (2010) Machine science. Science 329: 399–400.
- 6. Waltz D, Buchanan BG (2009) Automating science. Science 324: 43–44.
- 7. Khorana HG, Buuchi H, Ghosh H, Gupta N, Jacob TM, et al. (1966) Polynucleotide synthesis and the genetic code. Cold Spring Harb Symp Quant Biol 31: 39–49.
- 8. Shannon CE (1998) The mathematical theory of communication. University of Illinois Press.
- 9. Arbesman S (2011) Quantifying the ease of scientific discovery. Scientometrics 86: 245–250.
- 10. Arbesman S, Laughlin G (2010) A scientometric prediction of the discovery of the first potentially habitable planet with a mass similar to earth. PLoS ONE 5: e13061. doi:10.1371/journal.pone.0013061.
- 11. Manley S (2010) Asteroid discovery from 1980–2010. Available: http://www.youtube.com/watch?v=S_d-gs0WoUw. Accessed 1 June 2011.
- 12. Pfeiffer T, Hoffmann R (2009) Large-scale assessment of the effect of popularity on the reliability of research. PLoS ONE 4: e5996. doi:10.1371/journal.pone.0005996.
- 13. Pfeiffer T, Hoffmann R (2007) Temporal patterns of genes in scientific publications. Proc Natl Acad Sci U S A 104: 12052–12056.
- 14. Kevork A, al E (2003) The first data release of the sloan digital sky survey. The Astronomical Journal 126: 2081.
- 15. Stokes GH, Evans JB, Viggh HEM, Shelly FC, Pearce EC (2000) Lincoln Near-Earth Asteroid Program (LINEAR). Icarus 148: 21–28.
- 16. Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, et al. (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304: 66–74.
- 17. Ausubel JH, Crist DT, Waggoner PE (2010) First Census of Marine Life 2010: highlights of a decade of discovery. Washington (D.C.): Census of Marine Life.
- 18. Caschera F, Gazzola G, Bedau MA, Bosch Moreno C, Buchanan A, et al. (2010) Automated discovery of novel drug formulations using predictive iterated high throughput experimentation. PLoS ONE 5: e8546. doi:10.1371/journal.pone.0008546.
- 19. Law J, Zsoldos Z, Simon A, Reid D, Liu Y, et al. (2009) Route designer: a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation. Journal of Chemical Information and Modeling 49: 593–602.
- 20. MacKenzie D (2004) Mechanizing proof: computing, risk, and trust (inside technology). Cambridge (Massachusetts): The MIT Press. 439 p.
- 21. Land K, Slosar A, Lintott C, Andreescu D, Bamford S, et al. (2008) Galaxy Zoo: the large-scale spin statistics of spiral galaxies in the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society 388: 1686–1692.
- 22. Cooper S, Khatib F, Treuille A, Barbero J, Lee J, et al. (2010) Predicting protein structures with a multiplayer online game. Nature 466: 756–760.
- 23. Dunn EH, Francis CM, Blancher PJ, Drennan SR, Howe MA, et al. (2009) Enhancing the scientific value of the Christmas Bird Count. The Auk 122: 338–346.
- 24. Ganapati P (2009) Gadgets join the search for the lost tomb of Genghis Khan. Available: http://www.wired.com/gadgetlab/2009/07/genghis-khan/. Accessed 1 June 2011.