Skip to main content

Theoretical and technological building blocks for an innovation accelerator


Modern science is a main driver of technological innovation. The efficiency of the scientific system is of key importance to ensure the competitiveness of a nation or region. However, the scientific system that we use today was devised centuries ago and is inadequate for our current ICT-based society: the peer review system encourages conservatism, journal publications are monolithic and slow, data is often not available to other scientists, and the independent validation of results is limited. The resulting scientific process is hence slow and sloppy. Building on the Innovation Accelerator paper by Helbing and Balietti [1], this paper takes the initial global vision and reviews the theoretical and technological building blocks that can be used for implementing an innovation (in first place: science) accelerator platform driven by re-imagining the science system. The envisioned platform would rest on four pillars: (i) Redesign the incentive scheme to reduce behavior such as conservatism, herding and hyping; (ii) Advance scientific publications by breaking up the monolithic paper unit and introducing other building blocks such as data, tools, experiment workflows, resources; (iii) Use machine readable semantics for publications, debate structures, provenance etc. in order to include the computer as a partner in the scientific process, and (iv) Build an online platform for collaboration, including a network of trust and reputation among the different types of stakeholders in the scientific system: scientists, educators, funding agencies, policy makers, students and industrial innovators among others. Any such improvements to the scientific system must support the entire scientific process (unlike current tools that chop up the scientific process into disconnected pieces), must facilitate and encourage collaboration and interdisciplinarity (again unlike current tools), must facilitate the inclusion of intelligent computing in the scientific process, must facilitate not only the core scientific process, but also accommodate other stakeholders such science policy makers, industrial innovators, and the general public. We first describe the current state of the scientific system together with up to a dozen new key initiatives, including an analysis of the role of science as an innovation accelerator. Our brief survey will show that there exist many separate ideas and concepts and diverse stand-alone demonstrator systems for different components of the ecosystem with many parts are still unexplored, and overall integration lacking. By analyzing a matrix of stakeholders vs. functionalities, we identify the required innovations. We (non-exhaustively) discuss a few of them: Publications that are meaningful to machines, innovative reviewing processes, data publication, workflow archiving and reuse, alternative impact metrics, tools for the detection of trends, community formation and emergence, as well as modular publications, citation objects and debate graphs. To summarize, the core idea behind the Innovation Accelerator is to develop new incentive models, rules, and interaction mechanisms to stimulate true innovation, revolutionizing the way in which we create knowledge and disseminate information.

Graphical abstract


  1. D. Helbing, S. Balietti, Eur. Phys. J. Special Topics 195, 101 (2011)

    ADS  Article  Google Scholar 

  2. A.A. Alsheikh-Ali, W. Qureshi, M.H. Al-Mallah, J.P.A. Ioannidis. PloS one 6, e24357 (2011)

    Article  Google Scholar 

  3. T. Opthof, L. Leydesdorff, A comment to the paper by waltman, et al., Scientometrics, 87, 467 (2011)

  4. T. Opthof, L. Leydesdorff, A comment to the paper by waltman, et al., Scientometrics 88, 1011 (2011)

    Google Scholar 

  5. L. Bornmann, L. Leydesdorff, P. Van den Besselaar, J. Informetrics 4, 211 (2010)

    Article  Google Scholar 

  6. V. Stodden, available at SSRN 1550193, (4773-10) (2010)

  7. H. Masum, Y.C. Zhang, First Monday 9, 7 (2004)

    Google Scholar 

  8. C. Cattuto, A. Barrat, A. Baldassarri, G. Schehr, V. Loreto, Proc. Nat. Acad. Sci. 106, 10511 (2009)

    Google Scholar 

  9. S.J. Kline, N. Rosenberg, The positive sum strategy: Harnessing Technol. Eco. Growth 14, 640 (1986)

    Google Scholar 

  10. R.R. Nelson, S.G. Winter, Res. Policy 6, 36 (1977)

    Article  Google Scholar 

  11. J. Caraça, B.Å. Lundvall, S. Mendonça, Technol. Forecasting Social Change 76, 861 (2009)

    Article  Google Scholar 

  12. L. D’Adderio, Inside the virtual product: How organizations create knowledge through software (Edward Elgar Pub, 2004)

  13. K. Knorr-Cetina, Epistemic cultures: How the sciences make knowledge (Harvard University Press, 1999)

  14. B. Alex, C. Grover, B. Haddow, M. Kabadjov, E. Klein, M. Matthews, R. Tobin, X. Wang, et al., Automating curation using a natural language processing pipeline, Genome Biology 9 (Suppl 2), S10 (2008)

    Google Scholar 

  15. G. La Rowe, S.A. Ambre, J.W. Burgoon, W. Ke, K. Börner, In Proceedings of the 11 th International Conference on Scientometrics and Informetrics, (2007), p. 25

  16. K. Börner, M. Conlon, J. Corson-Rikert, Y. Ding, VIVO: A Semantic Approach to Scholarly Networking and Discovery (Morgan & Claypool Publishers LLC, 2012)

  17. S. Bechhofer, M. Hauswirth, J. Hoffmann, M. Koubarakis, The Semantic Web: Research and Applications: 5th European Semantic Web Conference, ESWC 2008, Tenerife, Canary Islands, Spain, vol. 5021 (Springer, 2008)

  18. V. Belak, M. Karnstedt, C. Hayes, Procedia-Social Behavioral Sci. 22, 37 (2011)

    Article  Google Scholar 

  19. P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, T. Ideker, Genome Res. 13, 2498 (2003)

    Article  Google Scholar 

  20. A.S. Elnashai, S. Hampton, H. Karaman, J.S. Lee, T. Mclaren, J. Myers, C. Navarro, M. Şahin, B. Spencer, N. Tolbert, J. Earthquake Eng. 12 (S2), 100 (2008)

    Article  Google Scholar 

  21. G. Kampis, L. Gulyás, Z. Szászi, Z. Szakolczi, S. Soós, In Applied Social Network Analysis Conference (2009), p. 27

  22. C. Goble, D. De Roure, The impact of workflow tools on data-centric research in data intensive computing, edited by A.J.G. Hey, S. Tansley, K.M. Tolle, The fourth paradigm: data-intensive scientific discovery (Microsoft Research Redmond, WA, 2009), p. 137

  23. B. Ludäscher, I. Altintas, S. Bowers, J. Cummings, T. Critchlow, E. Deelman, D.D. Roure, J. Freire, C. Goble, M. Jones, et al., Scientific Data Management: Challenges, Existing Technology, and Deployment, Computational Science Series (2009), p. 476

  24. P. Nowakowski, E. Ciepiela, D. Hareżlak, J. Kocot, M. Kasztelnik, T. Bartyński, J. Meizner, G. Dyk, M. Malawski, Procedia Computer Sci. 4, 608 (2011)

    Article  Google Scholar 

  25. F. Leisch, Sweave, dynamic generation of statistical reports using literate data analysis (2002)

  26. P. Fox, J. Hendler, The Fourth Paradigm: Data Intensive Scientific Discovery, edited by T. Hey, S. Tansley, K. Tolle, Microsoft External Research (2009), p. 145

  27. P. Groth, T. Gurney, Proceedings of the WebSci10: Extending the Frontiers of Society On-Line (2010)

  28. B. Mons, M. Ashburner, C. Chichester, E. Van Mulligen, M. Weeber, J. Den Dunnen, G.J. Van Ommen, M. Musen, M. Cockerill, H. Hermjakob, et al., Genome Biol. 9, R89 (2008)

    Article  Google Scholar 

  29. G. William Baxt, F. Joseph Waeckerle, A. Jesse Berlin, L. Michael, Ann. Emerg. Med. 32, 310 (1998)

    Article  Google Scholar 

  30. Science UK House of Commons and Technology Committee, Peer Review in Scientific Publications (2011)

  31. F. Prinz, T. Schlange, K. Asadullah, Nature Rev. Drug Discovery 10, 712 (2011)

    Article  Google Scholar 

  32. B.A. Huberman, Nature 482, 308 (2012)

    ADS  Article  Google Scholar 

  33. D. Taraborelli, Soft peer review: Social software and distributed scientific evaluation (2008)

  34. J. Priem, D. Taraborelli, P. Groth, C. Neylon, Altmetrics: a manifesto. Web. http://altmetrics. org/manifesto (2010)

  35. C. Neylon, S. Wu, Article-level metrics and the evolution of scientific impact, PLoS biology 7, e1000242 (2009)

    Google Scholar 

  36. J. Priem, B.H. Hemminger, First Monday 15, 9 (2010)

    Google Scholar 

  37. J. Priem, K.L. Costello, Proc. Amer. Soc. Inf. Sci. Technol. 47, 1 (2010)

    Article  Google Scholar 

  38. J. Letierce, A. Passant, J.G. Breslin, S. Decker, In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010), p. 279

  39. J. Letierce, A. Passant, J. Breslin, S. Decker (2010)

  40. J.G. Breslin, A. Passant, S. Decker, Social Semantic Web (Springer, 2009)

  41. A. Passant, J. Breslin, S. Decker, Web Engineering (2010), p. 263

  42. A. Passant, P. Ciccarese, J.G. Breslin, T. Clark. CLM+].:, 523 (2009)

  43. T. Groza, S. Handschuh, J.G. Breslin, S. Decker, Int. J. Virtual Comm. Social Networking (IJVCSN) 1, 37 (2009)

    Article  Google Scholar 

  44. T. Groza, S. Handschuh, K. Möller, S. Decker, The Semantic Web: Research and Applications (2007), p. 518

  45. T. Groza, K. Möller, S. Handschuh, D. Trif, S. Decker, The Semantic Web (2007), p. 197

  46. T. Groza, S. Handschuh, J.G. Breslin, In Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT’08. IEEE/WIC/ACM International Conference on, vol. 1 IEEE (2008), p. 475

  47. T. Groza, S. Handschuh, S. Decker, J. Data Semantics XV (2011), p. 1

  48. P. Groth, A. Gibson, J. Velterop, Inf. Services Use 30, 51 (2010)

    Google Scholar 

  49. A.K. Agrawal, C. Catalini, A. Goldfarb, Technical report, National Bureau of Economic Research (2011)

  50. S. Brown, Intellectual Property Magazine (2011)

  51. Y. Wu, Res. Policy 39, 835 (2010)

    Article  Google Scholar 

  52. R.D. Putnam, Bowling alone (Simon & Schuster, 2001)

  53. R.R. Nelson, S.G. Winter, An evolutionary theory of economic change (Belknap Press, 1982)

  54. J.A. Schumpeter, The theory of economic development. an inquiry into profits, capital, credit, interest, and the business cycle. new brunswick (1934)

  55. T. Buecheler, R.M. Füchslin, R. Pfeifer, manuscript available from the author (submitted) (2011)

  56. P. Belleflamme, T. Lambert, A. Schwienbacher, available at SSRN 1578175 (2011/32) (2010)

  57. B. Shneiderman. Science 319, 1349 (2008)

    Article  Google Scholar 

  58. X. Su, T.M. Khoshgoftaar, Adv. Artificial Intell. (2009)

  59. A. Kittur, E.H. Chi, B. Suh, In Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems ACM (2008), p. 453

  60. A. Irwin, Citizen science: a study of people, expertise and sustainable development (Routledge, 1995)

  61. D. Hull, S.R. Pettifer, D.B. Kell, PLoS Comput. Biol. 4, e1000204 (2008)

    Article  Google Scholar 

  62. A.H. Renear, C.L. Palmer, Science 325, 828 (2009)

    ADS  Article  Google Scholar 

  63. C.L. Borgman, J. Amer. Soc. Inf. Sci. Technol. 63, 1059 (2012)

    Article  Google Scholar 

  64. S. Cincotti, D. Sornette, P. Treleaven, S. Battiston, G. Caldarelli, C. Hommes, Eur. Phys. J. Special Topics 214, 361 (2012)

    Google Scholar 

  65. M. Batty, et al., Eur. Phys. J. Special Topics 214, 481 (2012)

    Google Scholar 

  66. M. Ajmone-Marsan, D. Arrowsmith, W. Breymann, O. Fritz, M. Masera, A. Mengolini, D. Helbing, A. Carbone, Eur. Phys. J. Special Topics 214, 547 (2012)

    Google Scholar 

  67. A. Vespignani, et al., Eur. Phys. J. Special Topics 214, 347 (2012)

    Google Scholar 

  68. G. Deffuant, I. Alvarez, O. Barreteau, B. de Vries, B. Edmonds, N. Gilbert, N. Gotts, F. Jabot, A. Janssen, M. Hilden, O. Kolditz, D. Murray-Rust, Ch. Rougé, P. Smits, Eur. Phys. J. Special Topics 214, 519 (2012)

    Google Scholar 

  69. D. Helbing, Futurict – new science and technology to manage our complex, strongly connected world (2011)

  70. D. Helbing, et al., Eur. Phys. J. Special Topics 214, 11 (2012)

    ADS  Google Scholar 

  71. D. Helbing, Eur. Phys. J. Special Topics 214, 41 (2012)

    ADS  Google Scholar 

  72. T.S. Kuhn, Selected Studies in Scientific Tradition and Change (University of Chicago Press, London, 1977), 1979

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to F. van Harmelen.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

van Harmelen, F., Kampis, G., Börner, K. et al. Theoretical and technological building blocks for an innovation accelerator. Eur. Phys. J. Spec. Top. 214, 183–214 (2012).

Download citation

  • Revised:

  • Published:

  • Issue Date:

  • DOI:


  • European Physical Journal Special Topic
  • Complex World
  • Link Open Data
  • Citation Score
  • Knowledge Channel