Monthly Archives: May 2012
Crowdfunding for Science 101 [guest post]
Ethan and I have been watching the emergence of crowdfunding in science with great interest. We meant to blog about it, but our rate of blog idea generation is >> our rate of blog writing. So, when Mary Rogalski, a graduate student at Yale who is participating in #SciFund (one of the crowdfunding sites being run by ecologists) asked if we might be interested in blogging about this new phenomena, we thought this was an opportune time for us to recruit a knowledgeable guest blogger! When you’re done reading her post, wander over to #SciFund and check out Mary’s project and the other intrepid young scientists experimenting with this new venue.
Now, introducing Mary Rogalski….
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You may have heard of crowdfunding – it’s sort of a combination of venture capitalism and social networking. Artists, musicians, and video game developers have netted thousands or even millions of dollars by gathering small donations from the interested public. In fact, crowdfunding is now a multibillion dollar industry.
Until recently I was peripherally aware of this flurry of activity, but it was only after I heard of scientists using crowdfunding to support their research that I began to pay attention. If you’ve ever applied for research grants you know how competitive the process can be. This only seems to have intensified as we tighten our belts to deal with the ongoing recession.
Two students in my lab recently raised $7,000 for their master’s project by crowdfunding through the group Petridish. Impressed with their success, I decided to investigate the possibilities. A friend shared an article in Nature that discussed crowdfunding, featuring the #SciFund Challenge. #SciFund caught my eye for two reasons. First, unlike some crowdfunding campaigns, participants receive funds even if they fail to reach their funding target. Second, #SciFund’s mission to teach scientists to more effectively engage with the general public resonates with my own career goals.
I submitted a short description of my research to the #SciFund organizers, Jai Ranganathan and Jarrett Byrnes, and was deemed worthy of joining round 2 of the #SciFund Challenge! I quickly found that crowdfunding requires a lot of time and energy. Overall I would say that I have spent close to 40 hours creating my project description and video, and an hour or two per day over the past three weeks promoting my project.
A short video serves as the centerpiece of a #SciFund campaign. In only 2-3 minutes I had a lot of information to convey. I study ecological and evolutionary responses to pollution exposure over long time scales. I work in lakes, using the sediment record to reconstruct changes in heavy metal contamination and cyanobacteria blooms over the past century. Zooplankton resting egg banks in these same sediments provide a means of examining ecological and evolutionary trends over the same time scales. I will hatch Daphnia from resting eggs to see which species were better able to tolerate polluted conditions. Later I will examine evolutionary responses over time.
I struggled to explain my project in three minutes – not to mention, I had never made a video before! I decided that people would be most interested in the fact that I can “resurrect” animals from the past to see how they were affected by environmental conditions that they experienced. In focusing on the “how” of my research, I think I might have sacrificed a bit too much of the “why”. Why do we even care about long-term effects of pollution? (I can give you lots of reasons, but they didn’t end up in the video!) Considering it’s my first attempt at making such a video, I do like how it turned out.
During the month of April, the 75 participants in the #SciFund Challenge created draft videos and written descriptions of our research. We reviewed each other’s work, focusing on creating clear, compelling language.
When the Challenge launched on May 1, we were coached on how to best spread the word about our projects. First I alerted my close friends and family about my crowdfunding campaign. Once I received some traction, I reached out to my broader social networks, asking my friends and colleagues to spread the word. From here, outreach is only limited by your own creativity and time investment. Before beginning my crowdfunding adventure my exposure to the world of science media was limited. I felt overwhelmed by the number and diversity of blogs out there, not to mention newspapers, journals, Facebook groups, and scientists that Tweet. I also felt awkward promoting myself, especially before doing the research that I propose. In the end I just jumped right in and did my best to wade through what for me represents a wealth of new opportunities to reach out to the public.
With the #SciFund Challenge coming to an end on May 31, I can reflect on my experience. First, I have been overwhelmed and humbled by the support that my project has received from friends and family. Crowdfunding also turned out to be a great networking opportunity. I have connected with other ecologists through Twitter, a form of social media that I had completely avoided until now. I even found out that there is another paleolimnologist in my own department at Yale! We are going for a coffee next week to chat about our research. These interactions began because of my search for research funds, but the end result has been so much richer.
So, will I continue to crowdfund my research? Do I think it is the wave of the future for science funding? Could crowdfunding ever replace NSF? I think the answers to these questions are yes, maybe and probably not. However, that elusive crowd of people interested in my research, outside of my friends and family, will take years to cultivate. As I build my career as a scientist I will implement the lessons I have learned from crowdfunding and continue reaching out to audiences outside of academia. My new blog is a start!
I think that crowdfunding may not be for everyone, and that some types of science might be a tougher sell. Major research programs requiring hundreds of thousands of dollars will likely not be easily supported in this way. But who am I to say? Perhaps crowdfunding could take off and replace traditional sources of science research funding. Only time will tell!
Mary Rogalski PhD Candidate, 2014 Yale School of Forestry & Environmental StudiesWhy your science blog should provide full feeds
People find blog posts in different ways. Some visit the website regularly, some subscribe to email updates, and some subscribe using the blog’s feed. Feeds can be a huge time saver for processing the ever increasing amount of information that science generates, by placing much of that information in a single place in a simple, standardized, format. It also lets you consume one piece of information at a time and keeps your inbox relatively free of clutter (for more about why using a feed reader is awesome see this post).
When setting up their feeds bloggers can choose to either provide the entire content of the post, or just a small teaser that contains just the first few sentences of the post. In this post I am going to argue that science bloggers should choose to provide full posts.
The core reason is that we are are doing this to facilitate scientific dialog, and we are all very busy. In addition to the usual academic work load of teaching, doing research, and helping our departments and universities function, we are now dealing with keeping up with a rapidly expanding literature plus a bloom of scientific blogs, tweets, and status updates (and oh yeah, some of us even have personal lives). This means that we are consuming a massive amount of information on a daily basis and we need to be able to do so quickly. I squeeze this in during small windows of time (bus rides home, gaps between meetings, while I’m running my toddler’s bath) and often on a mobile device.
I can do this easily if I have full feeds. I open my feed reader, open the first item, read it, move on to the next one. My brain knows exactly what format to expect, cognitive load is low, and the information is instantly available. If instead I encounter a teaser, I first have to make a conscious decision about whether or not I want to click through to the actual post, then I have to hit the link, wait for the page to load (which can still be a fairly long time on a phone), adjust to a format that varies widely across blogs, often adjust the zoom and rotate my screen (if I’m reading on my phone), read the item, and then return to my reader. This might not seem like a huge deal for a handful of items, but multiply the lost time by a few hundred or a few thousand items a week and it adds up in a hurry. On top of that I store and tag full-text, searchable, copies of posts for all of the blogs I follow in my feed reader so that I can find posts again. This is handy when I remember there is a post I want to either share with someone or link to, but can’t remember who wrote it.
So, if your blog doesn’t provide full feeds this means three things. First, I am less likely to read a post if it’s a teaser. It costs me extra time, so the threshold for how interesting it needs to be goes up. Second, if I do read it I now have less time to do other things. Third, if I want to find your post again to recommend it to someone or link to it, the chances of my doing so successfully are decreased. So, if your goal is science communication, or even just not being disrespectful of your readers’ time, full feeds are the way to go.
This all goes for journal tables of contents as well. As I’ve mentioned before, if the journal feed doesn’t include the abstracts and the full author line, it is just costing the papers readers, and the journal’s readers time, and therefore making the scientific process run more slowly than it could.
So, bloggers and journal editors, for your readers sake, for sciences sake, please turn on full feeds. It will only take you two minutes. It will save science hundreds of hours. It will probably be this most productive thing you do for science all week.
Characterizing the species-abundance distribution with only information on richness and total abundance [Research Summary]
This is the first of a new category of posts here at Jabberwocky Ecology called Research Summaries. We like the idea of communicating our research more broadly than to the small number of folks who have the time, energy, and interest to read through entire papers. So, for every paper that we publish we will (hopefully) also do a blog post communicating the basic idea in a manner targeted towards a more general audience. As a result these posts will intentionally skip over a lot of detail (technical and otherwise), and will intentionally use language that is less precise, in order to communicate more broadly. We suspect that it will take us quite a while to figure out how to do this well. Feedback is certainly welcome.
This is a Research Summary of: White, E.P., K.M. Thibault, and X. Xiao. 2012. Characterizing species-abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology. http://dx.doi.org/10.1890/11-2177.1*
The species-abundance distribution describes the number of species with different numbers of individuals. It is well known that within an ecological community most species are relatively rare and only a few species are common, and understanding the detailed form of this distribution of individuals among species has been of interest in ecology for decades. This distribution is considered interesting both because it is a complete characterization of the commonness and rarity of species and because the distribution can be used to test and parameterize ecological models.
Numerous mathematical descriptions of this distribution have been proposed and much of the research into this pattern has focused on trying to figure out which of these descriptions is “the best” for a particular group of species at a small number of sites. We took an alternative approach to this pattern and asked: Can we explain broad scale, cross-taxonomic patterns in the general shape of the abundance distribution using a simple model that requires only knowledge of the species richness and total abundance (summed across all species) at a site?
To do this we used a model that basically describes the most likely form of the distribution if the average number of individuals in a species is fixed (which turns out to be a slightly modified version of the classic log-series distribution; see the paper or John Harte’s new book for details). As a result this model involves no detailed biological processes and if we know richness and total abundance we can predicted the abundance of each species in the community (i.e., the abundance of the most common species, second most common species… rarest species).
Since we wanted to know how well this works in general (not how well it works for birds in Utah or trees in Panama) we put together a a dataset of more than 15,000 communities. We did this by combining 6 major datasets that are either citizen science, big government efforts, or compilations from the literature. This compilation includes data on birds, trees, mammals, and butterflies. So, while we’re missing the microbes and aquatic species, I think that we can be pretty confident that we have an idea of the general pattern.
In general, we can do an excellent job of predicting the abundance of each rank of species (most abundant, second most abundant…) at each site using only information on the species richness and total abundance at the site. Here is a plot of the observed number of individuals in a given rank at a given site against the number predicted. The plot is for Breeding Bird Survey data, but the rest of the datasets produce similar results.
Observed-predicted plot for nearly 3000 Breeding Bird Survey communities. Since there are over 100,000 points on this plot we’ve color coded them by the number of points in the vicinity of the focal point, so red areas have lots of points nearby and blue areas have very few points. The black line is the 1:1 line.
The model isn’t perfect of course (they never are and we highlight some of its failures in the paper), but it means that if we know the richness and total abundance of a site then we can capture over 90% of the variation in the form of the species-abundance distribution across ecosystems and taxonomic groups.
This result is interesting for two reasons:
First, it suggests that the species-abundance distribution, on its own, doesn’t tell us much about the detailed biological processes structuring a community. Ecologists have know that it wasn’t fully sufficient for distinguishing between different models for a while (though we didn’t always act like it), but our results suggest that in fact there is very little additional information in the distribution beyond knowing the species richness and total abundance. As such, any model that yields reasonable richness and total abundance values will probably produce a reasonable species-abundance distribution.
Second, this means that we can potentially predict the full distribution of commonness and rarity even at locations we have never visited. This is possible because richness and total abundance can, at least sometimes, be well predicted using remotely sensed data. These predictions could then be combined with this model of the species-abundance distribution to make predictions for things like the number of rare species at a site. In general, we’re interested in figuring out how much ecological pattern and process can be effectively characterized and predicted at large spatial scales, and this research helps expand that ability.
So, that’s the end of our first Research Summary. I hope it’s a useful thing that folks get something out of. In addition to the science in this paper, I’m also really excited about the process that we used to accomplish this research and to make it as reproducible as possible. So, stay tuned for some follow up posts on big data in ecology, collaborative code development, and making ecological research more reproducible.
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*The paper will be Open Access once it is officially published but ,for reasons that don’t make a lot of sense to me, it is behind a paywall until it comes out in print.
