ThinkLab: Startup Wanting to Fix Science

In 2014, fresh out of a successful trading algorithm startup, internet entrepreneur Jesse Spaulding began to think about the academic environment in which science is conducted from a tech enterprise’s perspective. Jesse believes that traditional journals, which focus on publishing single articles, withhold data, methods and software, avoid publication of negative results, act as detriment to science overall. ThinkLab intends to build on a new incentive structure for open and collaborative scientific publishing. It moves dramatically away from other models of open and collaborative publishing by incentivising researchers to engage with it.

Financial Incentives

ThinkLab offers two financial incentives for scientists to join. First, it aims to offer research grants, provided by private donors and philanthropists, for scientists to undertake new research projects on the ThinkLab platform. These projects adhere to the highest level of openness and transparency as the scientists are required to share everything online in real time for other researchers to see and comment upon. The researchers provide regular updates, publishing everything ranging from the initial proposal, data, to their results; moreover, contributors are able to comment, ask questions, and engage in discussion. This result is what Spaulding calls “massively collaborative open science”, wherein anyone can question what is happening in science and anyone can have an opinion. To most researchers, this idea may sound somewhat difficult to comprehend but Spaulding is passionate in his belief that this level of collaboration and transparency will contribute to eliminating publication bias in science.

Second, Thinklab offers a points-based Reviewer Reward system, i.e., participation in discussion on the ThinkLab platform helps individuals earn points. Thus, different levels of participation help participants earn different levels of points. For example, publishing a proposal earns 100 points, whereas writing a review comment earns one point. In addition, comments are awarded “peer assessed points,” based on the helpfulness and appropriateness of the comments. The peer assessment points aim to discourage poor quality contributions and reward high quality and come from comment ratings provided by readers. A complex system of ranking and adjustments is then performed, followed by an algorithmic calculation, and on the basis of these adjustments participation points are awarded. Spaulding hopes that individuals will be able to earn up to $100 for a good comment and $400 for a full review of a research proposal—certainly a significant step up from reviewing for free!

Increasing Collaborations Through Open Science

Through financial incentives, ThinkLab intends to encourage the collaboration and transparency that is missing from academic science, which is also being addressed by many startups. Collaborative and open access research aims to both speed up the publication and peer review process by making research instantly accessible to anyone who wants to read it and simultaneously enhance the visibility of research. However, such an open model raises concerns for scientists such as the potential for ideas to be stolen, as well as for unqualified, non-experts to clutter the discussion with unhelpful comments. ThinkLab acknowledges these concerns and a large part of the Reviewer Rewards model aims to minimise these problems.

ThinkLab is still a brand new platform. Currently it has just one active project, no external funders and is still looking for co-founders to expand its operations; however, private funding for science is an area that is rapidly growing as public funding shrinks, and is being discussed in even the most traditional of scientific venues as a viable way forward for scientific research. Moreover, the ThinkLab project is taking a radical approach to incentivising scientists and has the potential to succeed where other open and collaborative platforms have failed to draw researchers away from the traditional publish-or-perish model of science that is governed by impact factors.


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