Community review: a robust and scalable selection system for resource allocation within open science and innovation communities

Abstract

Resource allocation is essential to the selection and implementation of innovative projects in science and technology. With large stakes involved in concentrating large fundings over a few promising projects, current winner-take-all models for grant applications are time-intensive endeavours that mobilise significant researcher time in writing extensive project proposals, and rely on the availability of a few time-saturated volunteer experts. Such processes usually carry over several months, resulting in high effective costs compared to expected benefits. Faced with the need for a rapid response to the Covid19 pandemic in 2020, we devised an agile community review system to allocate micro-grants for the fast prototyping of innovative solutions. Here we describe and evaluate the implementation of this community review across 147 projects from the OpenCOVID19 and Helpful Engineering open research communities. The community review process uses granular review forms and requires the participation of grant applicants in the review process. Within a year, we organised 7 rounds of review, resulting in 614 reviews from 201 reviewers, and the attribution of 48 micro-grants of up to 4,000 euros. We show that this system is fast, with a median process duration of 10 days, scalable, with a median of 4 reviewers per project independent of the total number of projects, and fair, with project rankings highly preserved after the synthetic removal of reviewers. We investigate the potential bias introduced by involving applicants in the process, and find that review scores from both applicants and non-applicants have a similar correlation of r=0.28 with other reviews within a project, matching previous observations using traditional approaches. Finally, we find that the ability of projects to apply to several rounds allows to both foster the further implementation of successful early prototypes, as well as provide a pathway to constructively improve an initially failing proposal in an agile manner. Overall, this study quantitatively highlights the benefits of a frugal, community review system acting as a due diligence for rapid and agile resource allocation in open research programs, with particular implications for decentralised communities.

Education-based grant programmes for bottom-up distance learning and project catalysis: Antimicrobial Resistance in Sub-Saharan Africa

“…Similarly the funding granted for these international programmes can be more efficiently spent on capacity building rather than on flights. Prior to this study, the researchers conducted another study focusing on an agile funding allocation scheme to projects determined by applicants (14). Following this scheme, here we used the travel spending funds towards a microgrant allocation to allow the applicants to determine a project within their cohort that should receive seed funding for capacity building beyond education, as well as motivate their own ideas with de novo project creation.…”

“…For presenting the modules within the "Massive Open Online Course" (MOOC), and the grant review round, we used a citizen science and project creation network called "Just One Giant Lab" (14,16). This allowed participants to create their own projects in addition to taking part in our syllabus available here (15).…”

“…(Fig2) Applicants were required to mark at least four other applicants, therefore giving a democratic and local outlook on the most relevant and innovative solutions by curricula through experts and existing creators. This followed a community review methodology as used in the OpenCOVID19 programme 2020-2021 (14). (14)…”

“…This followed a community review methodology as used in the OpenCOVID19 programme 2020-2021 (14). (14)…”

“…In order to ensure fairness after submission and review and prevent gamification, the reviewers' scores across the questions in each form were normalised to their overall average review mark across their five reviews using the community review method. The distribution of scores per question shown by the heatmap indicates independence of questions asked in the form, and allowed for detection of odd reviewer behaviour (14). (Fig5b).…”