Literature DB >> 29795961

An efficient system to fund science: from proposal review to peer-to-peer distributions.

Johan Bollen1,2,3, David Crandall1,3, Damion Junk1, Ying Ding1,2, Katy Börner1,4,2,3.   

Abstract

This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications.

Entities:  

Keywords:  Bibliometrics; Collective intelligence; Funding; PageRank; Peer review

Year:  2016        PMID: 29795961      PMCID: PMC5963719          DOI: 10.1007/s11192-016-2110-3

Source DB:  PubMed          Journal:  Scientometrics        ISSN: 0138-9130            Impact factor:   3.238


  15 in total

1.  Peer review, unmasked.

Authors:  Trisha Gura
Journal:  Nature       Date:  2002-03-21       Impact factor: 49.962

2.  Statistical analysis of the National Institutes of Health peer review system.

Authors:  Valen E Johnson
Journal:  Proc Natl Acad Sci U S A       Date:  2008-07-28       Impact factor: 11.205

3.  National Institutes of Health. Zerhouni's parting message: make room for young scientists.

Authors:  Jocelyn Kaiser
Journal:  Science       Date:  2008-11-07       Impact factor: 47.728

4.  Improving the peer-review process for grant applications: reliability, validity, bias, and generalizability.

Authors:  Herbert W Marsh; Upali W Jayasinghe; Nigel W Bond
Journal:  Am Psychol       Date:  2008-04

5.  Wellcome Trust makes it personal in funding revamp.

Authors:  Natasha Gilbert
Journal:  Nature       Date:  2009-11-12       Impact factor: 49.962

6.  Research funding: Global challenges need global solutions.

Authors:  Subra Suresh
Journal:  Nature       Date:  2012-10-18       Impact factor: 49.962

7.  Funding: Australia's grant system wastes time.

Authors:  Danielle L Herbert; Adrian G Barnett; Nicholas Graves
Journal:  Nature       Date:  2013-03-21       Impact factor: 49.962

8.  Chance and consensus in peer review.

Authors:  S Cole; J R Cole; G A Simon
Journal:  Science       Date:  1981-11-20       Impact factor: 47.728

Review 9.  Peer review: a flawed process at the heart of science and journals.

Authors:  Richard Smith
Journal:  J R Soc Med       Date:  2006-04       Impact factor: 18.000

10.  The impact of funding deadlines on personal workloads, stress and family relationships: a qualitative study of Australian researchers.

Authors:  Danielle L Herbert; John Coveney; Philip Clarke; Nicholas Graves; Adrian G Barnett
Journal:  BMJ Open       Date:  2014-03-28       Impact factor: 2.692

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  7 in total

1.  Forecasting innovations in science, technology, and education.

Authors:  Katy Börner; William B Rouse; Paul Trunfio; H Eugene Stanley
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-11       Impact factor: 11.205

2.  Recommendations for Reviewers of Biomedical Imaging Grant Applications.

Authors:  Mark D Pagel
Journal:  Mol Imaging Biol       Date:  2019-08       Impact factor: 3.488

Review 3.  What do we know about grant peer review in the health sciences?

Authors:  Susan Guthrie; Ioana Ghiga; Steven Wooding
Journal:  F1000Res       Date:  2017-08-07

4.  Comprehensive Researcher Achievement Model (CRAM): a framework for measuring researcher achievement, impact and influence derived from a systematic literature review of metrics and models.

Authors:  Jeffrey Braithwaite; Jessica Herkes; Kate Churruca; Janet C Long; Chiara Pomare; Claire Boyling; Mia Bierbaum; Robyn Clay-Williams; Frances Rapport; Patti Shih; Anne Hogden; Louise A Ellis; Kristiana Ludlow; Elizabeth Austin; Rebecca Seah; Elise McPherson; Peter D Hibbert; Johanna Westbrook
Journal:  BMJ Open       Date:  2019-03-30       Impact factor: 2.692

5.  Grant writing and grant peer review as questionable research practices.

Authors:  Stijn Conix; Andreas De Block; Krist Vaesen
Journal:  F1000Res       Date:  2021-11-08

6.  Fund behavioral science like the frameworks we endorse: the case for increased funding of preliminary studies by the National Institutes of Health.

Authors:  Michael W Beets; Christopher Pfledderer; Lauren von Klinggraeff; Sarah Burkart; Bridget Armstrong
Journal:  Pilot Feasibility Stud       Date:  2022-09-28

7.  Factors influencing the scientific performance of Momentum grant holders: an evaluation of the first 117 research groups.

Authors:  Balázs Győrffy; Andrea Magda Nagy; Péter Herman; Ádám Török
Journal:  Scientometrics       Date:  2018-07-20       Impact factor: 3.238

  7 in total

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