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Passive Vs Active Knowledge Exchange

Research funders are increasingly concerned with measurable socio-economic impact of investment in research, and on increasingly shorter timescales. Innovation, and “open innovation” are the policy priorities of the moment and optimising the flow of ideas along the lab-2-market spectrum is essential for re-use of results, fueling open innovation, and boosting socio-economic impact or public funded research.

The presentation showcases two complimentary strategies that Project Managers can employ pre- and/or post-award in order to optimise the exploitation and impact of research project: passive and active knowledge transfer.

Passive Knowledge Transfer relies on maximum disclosure of research output (other than commercially exploitable research via patents and other IPR) in the interest of optimal reproducibility, independent validation and re-use by both academic and non-academic users, without necessarily targeting specific users. Tools of the
trade include standard public & academic dissemination means (research articles, online media publications, newsletters, generic policy briefs).

Additional transparency of the research workflow can be achieved by integrating “open science” (open notebooks, open data, open research software and open access to research publications) as well as Virtual Research Environments (VREs) in the methodology of the proposed work.

Ensuring that the proposal partners are suitably trained in best practices of open science, makes proposal grant more competitive at evaluation and the resulting maximum access to research outputs does contribute to better return on investment for funders (Beagrie 2016) and economic growth objectives of public s e.g. Blue Growth
(Houghton & Swan 2011, Marine Knowledge 2020 Roadmap).

Active Knowledge Transfer, or the pro-active translation of research into policy or commercial context, is the more classical and better known approach (also referred to as extension services, or researchers providing advice e.g. to fisheries and aquaculture governance bodies and private sector).

Horizon2020 COLUMBUS Consortium proposes and tests a methodology for categorizing the diverse output of research into verifiable “knowledge outputs” , and documenting the execution of an transfer plan to very specific and identified potential users, in order to transfer knowledge along the lab-2-market spectrum.

The presentation will demonstrate how Open Science and detailed knowledge transfer plans complement each other, enhance grant proposal evaluation pre- and post-award, and can address Blue Growth policy objectives.

Concepts presented are developed by FP7/H2020 FOSTER+ (www.fosteropenscience.eu), H2020 COLUMBUS
(www.columbusproject.eu) and H2020 SeaCHANGE (www.seachangeproject.eu)

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Passive Vs Active Knowledge Exchange

  1. 1. Passive vs Active Knowledge Exchange: boosting grant proposal impact (EGU General Assembly 2017) Ivo Grigorov, Georgia Bayliss-Brown, David Murphy, Thomas Thøgersen & Patrizio Mariani Danish Technical University, DTU Aqua, Denmark AquaTT Ltd, Olympic House, Pleasants Street, Dublin, Ireland LAB MARKET Policy SMEs Research
  2. 2. MARKETLAB Policy SMEs Research Access to research Transfer of outputs Translate in Societal Context
  3. 3. MARKETLAB Policy SMEs Research Access Transfer Translate Access for SMEs not optimal Funders seek better ROI Opportunities through awareness
  4. 4. Horizon 2020 Proposal Template 2. IMPACT (min 3/5 points): 2.1 Expected impact to WorkProgram - expected impacts set out in the work programme - delivering innovations to the markets - socially important impacts 2.2 Maximise Impact - plan for results dissemination and exploitation - research data management for verification & re-use - knowledge strategy management & Open Access/Data Impact
  5. 5. MARKETLAB Policy SMEs Research Access Transfer Translate Access for SMEs not optimal Funders seek better ROI Opportunities through awareness
  6. 6. But is ACCESS really a problem Based on: Winning Horizon 2020 with Open Science, http://dx.doi.org/10.5281/zenodo.12247
  7. 7. “ two out of five studies substantially reproduced research findings, although not all experiments met thresholds of statistical significance” Nature 541, 19 Jan 2017, doi:10.1038/541269a eLife elifesciences.org/collections/reproducibility-project-cancer-biology Stanley Flegler/Visuals Unlimited, Inc./Science Photo Library “
  8. 8. Mostly due to current methods capture and data malpractice, approximately 50% of all research data and experiments is considered not reproducible, and the vast majority (likely over 80%) of data never makes it to a trusted and sustainable repository. Investment by Europe in data-generating research of €120 Billion between 2014-2020, the annual capital destruction is consequently very substantial. “ Source: Realising the European Open Science Cloud, EC DG Research & Innovation 2016 http://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud _2016.pdf#view=fit&pagemode=none
  9. 9. … And what is so new with Knowledge Transfer Based on: Horizon 2020 Blue Growth project COLUMBUS www.columbusproject.eu
  10. 10. COLUMBUS testing existing methods, at scale: "Knowledge Output" A unit of knowledge or learning generated by or through research activity (new methods, processes, adaptations, insights, alt. applications) “Target User" The individual(s) identified in Knowledge Output Pathway to whom specific Knowledge Output is transferred. “End User" The individual(s) who will apply the Knowledge Output at the end of the Knowledge Output Pathway. “Eventual Impact" Measurable end benefit of the application of the Knowledge Output, contributing to “Blue Growth” incl. the implementation of MSFD, CFP, other policy.
  11. 11. MARKETLAB Policy SMEs Research Access Transfer Translate Access for SMEs not optimal Funders seek better ROI Opportunities through awareness
  12. 12. … is there real impact at proposal EVALUATION(results inlc.2017 calls) Based on: Winning Horizon 2020 with Open Science, http://dx.doi.org/10.5281/zenodo.12247
  13. 13. “Open Science is applied to optimise reproducibility” Excellence Impact Implementation“data storage & access not considered” “Open Access is an essential principle in the project, but there is not enough information on data management or IPR.” “new knowledge delivered to SMEs by depositing research outputs in OpenAIRE2020 and EMODNet” “data accessibility is unclear!” Source: quotes from EC Evaluators 2014-2017
  14. 14. Access Transfer Translate #EGU17 Thank you! Ivo Grigorov ivgr@aqua.dtu.dk / @OAforClimate https://www.slideshare.net/ivogrigorov/passive-amp-active-knowledge-exchange

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