Theoretical and technological building blocks for an innovation accelerator

Abstract

Abstract. 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 a e-mail: frank.van.harmelen@gmail.com 184The European Physical Journal Special Topics 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, c...

Cloud–Based Evaluation Framework for Big Data

“…The framework can be seen as an initial low-level building block of the Innovation Accelerator, as envisioned by van Harmelen et al [11] as an outline for revolutionising the scientific process.…”

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Modeling the Structure and Dynamics of Science Using Books

“…Other scientific communities rely extensively on predictive models to simulate events such as weather, seismic hazards (UNAVCO Facility, 2010), or epidemics (Colizza et al, 2006). Recent efforts have sought to forecast science and technology in the form of an "innovation accelerator" (Van Harmelen et al, 2012). However, the heterogeneous and partially commercial datasets required to model science remain scattered; cultures of algorithm and model sharing are slow to evolve; and a unified theory that interlinks validated models of science does not yet exist.…”

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“…Several such initiatives exists, such as Mendeley or Vivo, but perhaps the one with the largest current momentum is ResearchGate. [6] Our belief is that potentially the best way to channel such efforts would be to implement something like the Innovation Accelerator concept put forward in [4,5,1]. In short of that, however, we turn our scientific interest to using existing tools and analysing data currently available.…”