The second round of #SciFund is over. It was a huge success and as far as I can tell the most successful attempt so far at crowdfunding science. Almost half of the projects got funded. Unfortunately, mine wasn’t one of them.
My project went really well I think considering I have never done this before, I am a new scientist and I don’t have much of an online presence. However, I do feel a little sad. I wanted to do better and in order to do that I have to figure out what, if anything, went wrong. Inevitably, I turned to the data collected during the second round of #SciFund (I also analysed the data collected from the first round but that was just for reference, it bears no significance here). I decided to perform a simple analysis, something that I can then tell everyone else how to do, and see what is important for a successful #SciFund project.
The data analysed was the financial Goal of project, which I like to think is linked to how ambitious the project is. The amount Raised, Percent Funded and whether the project was Fully Funded or Not which are indicators of how successful the project was. The number of Contributors, Contributions and Mean Donation are simple statistics about how the project was funded and could be said to be linked to its popularity. And finally, the number of Tweets, Facebook Likes and Video Views which I like to think are indicators of exposure.
The best thing about the analysis I carried out is that it was dead easy, anyone should be able to do it. I took the data, put it in a lovely LibreOffice spreadsheet and fed it to Wolfram Alpha. Uploading files is a pro feature which means you have to register but fortunately there is a free trial period of two weeks. I won’t bore you with all of the analysis, I’ll just give some highlights and my own, possibly unsafe conclusions along with a small hypothesis.
First, I’ll throw some pretty pictures in your face. Here is one with the distributions of the data:
And here is another one with everything plotted against everything (I like to call it shotgun-science):
The really interesting bit comes next though. Apart from being extremely easy to use, Wolfram Alpha is also super helpful. You just give it the data, ask it which variable to focus on (which is the dependent variable), set the confidence, it runs a regression analysis and then very helpfully tells you in english, not in math, what the analysis tells us. Here is an example:
This shows the effect of all variables on the amount Raised. Some are intuitive and some are not. The most important thing to notice though is that ambition, popularity and exposure of the project do not seem to have a statistically significant effect on the amount of funds raised. The same can be said about the less absolute Percent Funded variable:
What does this mean for a researcher that wants to crowdfund a science project? Well, so far it is obvious that having an ambitious project is not an obstacle and that popularity and exposure are not surefire ways of successfully funding a scientific project. This is also backed by the data of the first round of #SciFund. So what is important when crowdfunding science? My hypothesis is that right now, at this stage of the crowdfunding culture and particularly when talking about science, the most important factor is having a supporting community. This could translate into Twitter followers, Facebook friends, number of blog/site readers per month and a host of other things. Also, this notion seems to be backed by the analysis carried out on the data collected after the last round of #SciFund.
Maybe this will change as the crowdfunding phenomenon grows, but for now, it seems that the best preparation one can make is to nurture a community around their work that will support them when they decide to crowdfund their research.