Network Analysis of Science Crowdfunding
Readers will remember when I announced Ethan Perlstein‘s plan to crowdfund his scientific research. Well, since then, Ethan has been combining two of my interests: alternative ways of funding science and network science. In his attempt to achieve his goal of raising $25,000, Ethan has been attempting to understand what conditions and connections yield the most money. And network analysis is one component of this.
Some of his analyses have looked at the statistical properties of the donations so far, confirming that donations do not come in at a constant rate (there is often a burst in the beginning and end, with some stagnation in the middle). In addition, Ethan recently emailed me an analysis based on his Facebook friends, and who donated and who did not:
Yellow indicates a donor, while blue means a non-donor, and the node size corresponds to degree—the number of connections to others in the network data. As you can see, it’s a bit messy. There aren’t clusters of donation so it’s hard to determine the pattern of influence, if any. Nonetheless, it is gratifying to note that there is a generally high level of donation (about 10%).
When it comes to donation amount, there doesn’t seem to be much in the way of a relationship between donation and network degree, though it does seem that only those with many connections donate high amounts (of course, many other large donations come from those outside the Facebook network):
Whether or not there are clear results in all of these analyses, we need more of this. Those who are trying to crowdfund projects should continue to be open about how this process works and how it doesn’t. This is a great first step in trying to best understand how to fund scientific research in a broad-based way.
Top image:Noah Sussman/Flickr/CC
Samuel Arbesman is an applied mathematician and network scientist. He is a senior scholar at the Ewing Marion Kauffman Foundation and author of the book The Half-Life of Facts. His research and essays explore how to quantify all aspects of society.
Follow @arbesman on Twitter.