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. 2014 Dec 10;9(12):e110329.
doi: 10.1371/journal.pone.0110329. eCollection 2014.

To Crowdfund Research, Scientists Must Build an Audience for Their Work

Affiliations

To Crowdfund Research, Scientists Must Build an Audience for Their Work

Jarrett E K Byrnes et al. PLoS One. .

Abstract

As rates of traditional sources of scientific funding decline, scientists have become increasingly interested in crowdfunding as a means of bringing in new money for research. In fields where crowdfunding has become a major venue for fundraising such as the arts and technology, building an audience for one's work is key for successful crowdfunding. For science, to what extent does audience building, via engagement and outreach, increase a scientist's abilities to bring in money via crowdfunding? Here we report on an analysis of the #SciFund Challenge, a crowdfunding experiment in which 159 scientists attempted to crowdfund their research. Using data gathered from a survey of participants, internet metrics, and logs of project donations, we find that public engagement is the key to crowdfunding success. Building an audience or "fanbase" and actively engaging with that audience as well as seeking to broaden the reach of one's audience indirectly increases levels of funding. Audience size and effort interact to bring in more people to view a scientist's project proposal, leading to funding. We discuss how projects capable of raising levels of funds commensurate with traditional funding agencies will need to incorporate direct involvement of the public with science. We suggest that if scientists and research institutions wish to tap this new source of funds, they will need to encourage and reward activities that allow scientists to engage with the public.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Crowdfunding donation patterns.
The daily time series of donations during the firth three rounds of #SciFund.
Figure 2
Figure 2. Total dollars raised plotted against the number of contributors.
Line represents best fit from model described in the text. Shaded grey area represents the 95% confidence interval around the fit relationship.
Figure 3
Figure 3. Factors affecting number of contributors to a project.
Plot shows the number of contributors plotted against the number of Facebook friends. Size of points shows the number of page views before achieving success. Color shows the number of project page views after goals were reached with blue representing no views to red representing many views. Line represents best fit from generalized linear model between x and y. Shaded grey area represents the 95% confidence interval around the fit relationship.
Figure 4
Figure 4. Relationship between Facebook “Likes”, number of Twitter followers, and project page views before a project hit its goal.
Line represents best fit from model described in the text. Shaded grey area represents the 95% confidence interval around the fit relationship. Point size is proportional to the number of Twitter followers.
Figure 5
Figure 5. Relationship between Facebook “Likes” and the number of page views after a project has achieved its funding goal.
Line represents best fit from model described in the text. Shaded grey area represents one standard error around the fit relationship.
Figure 6
Figure 6. Relationship between monthly blog posts and number of Twitter followers.
Line represents best fit from model described in the text. Shaded grey area represents on standard error around the fit relationship.
Figure 7
Figure 7. Relationship between pre-goal page views, press contacts, number of people emailed, and effort times engagement on Twitter.
Line represents best fit from model between press and pre-goal page views. Shaded grey area represents the 95% confidence interval around the fit relationship.
Figure 8
Figure 8. How online engagement leads to a crowdfunded research project.

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