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.
On the value of fundamental scientific research
Jeremy Fox over at the Oikos Blog has written an excellent piece explaining why fundamental, basic science, research is worth investing in, even when time and resources are limited. His central points include:
- Fundamental research is where a lot of our methodological advances come from.
- Fundamental research provides generally-applicable insights.
- Current applied research often relies on past fundamental research.
- Fundamental research often is relevant to the solution of many different problems, but in diffuse and indirect ways.
- Fundamental research lets us address newly-relevant issues.
- Fundamental research alerts us to relevant questions and possibilities we didn’t recognize as relevant.
- Fundamental research suggests novel solutions to practical problems.
- The only way to train fundamental researchers is to fund fundamental research.
I don’t have a lot to add to what Jeremy has already said, except that I strongly agree with the points that he has made and think that in an era where much of ecology has direct applications to things like global change we need to guard against the temptation to justify all of our research based on its applications.
When I think about the value of fundamental research I always recall a scene from an early season of The West Wing where a politician (SAM) and a scientist (MILLGATE) are discussing how to explain the importance of something akin to the Large Hadron Collider. It loses a little something as a script (complements of Unofficial West Wing Transcript Archive), but nonetheless:
SAM
What is it?MILLGATE
It’s a machine that reveals the origin of matter… By smashing protons together at very high speeds and at very high temperatures, we can recreate the Big Bang in a laboratory setting, creating the kinds of particles that only existed in the first trillionth of a second after the universe was created.SAM
Okay, terrific. I understand that. What kind of practical applications does it have?MILLGATE
None at all.SAM
You’re not in any way a helpful person.MILLGATE
Don’t have to be. I have tenure.SAM
Doctor.MILLGATE
There are no practical applications, Sam. Anybody who says different is lying.…
ENLOW
If only we could only say what benefit this thing has, but no one’s been able to do that.MILLGATE
That’s because great achievement has no road map. The X-ray’s pretty good. So is penicillin. Neither were discovered with a practical objective in mind. I mean, when the electron was discovered in 1897, it was useless. And now, we have an entire world run by electronics. Haydn and Mozart never studied the classics. They couldn’t. They invented them.SAM
Discovery.MILLGATE
What?SAM
That’s the thing that you were… Discovery is what. That’s what this is used for. It’s for discovery.
The episode is “Dead Irish Writers” and I’d highly recommend watching the whole thing if you want to feel inspired about doing fundamental research.
Sometimes it’s important to ignore the details [Things you should read]
Joan Strassman has a very nice post about why it is sometimes useful to step back from the intricate details of biological systems in order to understand the general processes that are operating. Here’s a little taste of the general message
In this talk, Jay said that MacArthur claimed the best ecologists had blurry vision so they could see the big patterns without being overly distracted by the contradictory details. This immediately made a huge amount of sense to me. Biology is so full of special cases, of details that don’t fit theories, that it is easy to despair of advancing with broad, general theories. But we need those theories, for they tell us where to look next, what data to collect, and even what theory to challenge. I am a details person, but love the big theories.
The whole post is definitely worth a read.
Why I will no longer review for your journal
I have, for a while, been frustrated and annoyed by the behavior of several of the large for-profit publishers. I understand that their motivations are different from my own, but I’ve always felt that an industry that relies entirely on both large amounts of federal funding (to pay scientists to do the research and write up the results) and a massive volunteer effort to conduct peer review (the scientists again) needed to strike a balance between the needs of the folks doing all of the work and the corporations need to maximize profits.
Despite my concerns about the impacts of increasingly closed journals, with increasingly high costs, on the dissemination of research and the ability of universities to support their core missions of teaching and research, I have continued to volunteer my time and effort as a reviewer to Elsevier and Wiley-Blackwell. I did this because I have continued to see valuable contributions made by these journals and I felt that this combined with the contribution that I was making to science by helping improve the science published in high profile places made supporting these journals worthwhile. I no longer believe this to be the case and from now on I will no longer be reviewing for any journal that is published by Elsevier, Springer, or Wiley-Blackwell (including society journals that publish through them).
Why have I changed my mind? Because of the pursuit/support by these companies of the Research Works Act. This act seeks to prevent funding agencies from requiring that the results of research that they funded be made publicly available. In other words it seeks to prevent the government (and the taxpayers that fund it), which pays for a very large fraction of the cost of any given paper through both funding the research and paying the salaries of reviewers and editors, from having any say in how that research is disseminated. I think that Mike Taylor in the Guardian said most clearly how I feel about this attempt to exert legislative control requiring us to support corporate profits over the dissemination of scientific research:
Academic publishers have become the enemies of science
This is the moment academic publishers gave up all pretence of being on the side of scientists. Their rhetoric has traditionally been of partnering with scientists, but the truth is that for some time now scientific publishers have been anti-science and anti-publication. The Research Works Act, introduced in the US Congress on 16 December, amounts to a declaration of war by the publishers.
You should read the entire article. It’s powerful. There are lots of other great articles about the RWA including Michael Eisen in the New York Times, a nice post by INNGE, and a interesting piece by Paul Krugman (via oikosjeremy). I’m also late to the party in declaring my peer review strike and less eloquent than many of my peers in explaining why (see great posts by Michael Taylor, Gavin Simpson, and Timothy Gowers). But I’m here now and I’m letting you know so that you can consider whether or not you also want to stop volunteering for companies that don’t have science’s best interests in mind.
If you’d like to read up on the publisher’s side of this argument (they have costs, they have a right to recoup them) you can see Springer’s official position or an Elsevier Exec’s exchange with Michael Eisen. My problem with all of these arguments is that there is nothing in any funding agency’s policy that requires publishers to publish work funded by that agency. This is not (as Springer has argued) an “unfunded mandate”, this is a stake holder that has certain requirements related to the publication of research in which they have an interest. This is just like an author (in any non-academic publishing situation) negotiating with a publisher. If the publisher doesn’t like the terms that the author demands, then they don’t have to publish the book. Likewise, if a publisher doesn’t like the NIH policy then they should simply not agree to publish NIH funded research.
To be clear, I am not as extreme in my position as some. I still support and will review for independent society journals like Ecology and American Naturalist even though they aren’t Open Access and even though ESA has made some absurd comments in support of the same ideas that are in RWA. The important thing for me is that these journals have the best interests of science in mind, even if they are often frustratingly behind the times in how they think and operate.
And don’t worry, I’ve still got plenty of journal related work to keep me busy, thanks to my new position on the editorial board at PLoS ONE.
UPDATE: The links to the INNGE and Timothy Gowers post have now been fixed, and here are links to a couple of great posts by Casey Bergman that I somehow left out: one on how to turn down reviews while making a point and one on the not so positive response he received to one of these emails.
UPDATE 2: A great collection of posts on RWA. There are a lot of really unhappy scientists out there.
UPDATE 3: A formal Boycott of Elsevier. Almost 1000 scientists have signed on so far.
UPDATE 4: Wiley-Blackwell has now distanced itself from RWA and said that “We do not believe that legislative initiatives are the best way forward at this time and so have no plans to endorse RWA. Instead we believe that research funder-publisher partnerships will be more productive.” In addition, it was announced that a bill that would do the opposite of RWA has now been introduced. Hooray for collective action!
Am I teaching well given the available research on teaching
Figuring out how to teach well as a professor at a research university is largely a self-study affair. For me the keys to productive self-study are good information and self-reflection. Without good information you’re not learning the right things and without self-reflection you don’t know if you are actually succeeding at implementing what you’ve learned. There have been some nice posts recently on information and self-reflection about how we teach over at Oikos (based on, indirectly, on a great piece on NPR) and Sociobiology (and a second piece) that are definitely worth a read. As part of a course I’m taking on how to teach programming I’m doing some reading about research on the best approaches to teaching and self-reflection on my own approaches in the classroom.
One of the things we’ve been reading is a great report by the US Department of Education’s Institute of Education Sciences on Organizing Instruction and Study to Improve Student Learning. The report synthesizes existing research on what to do in the classroom to facilitate meaningful long-term learning, and distills this information into seven recommendations and information on how strongly each recommendation is supported by available research.
Recommendations
- Space learning over time. Arrange to review key elements of course content after a delay of several weeks to several months after initial presentation. (moderate)
- Interleave worked example solutions with problem-solving exercises. Have students alternate between reading already worked solutions and trying to solve problems on their own. (moderate)
- Combine graphics with verbal descriptions. Combine graphical presentations (e.g., graphs, figures) that illustrate key processes and procedures with verbal descriptions. (moderate)
- Connect and integrate abstract and concrete representations of concepts. Connect and integrate abstract representations of a concept with concrete representations of the same concept. (moderate)
- Use quizzing to promote learning.
- Use pre-questions to introduce a new topic. (minimal)
- Use quizzes to re-expose students to key content (strong)
- Help students allocate study time efficiently.
- Teach students how to use delayed judgments of learning to identify content that needs further study. (minimal)
- Use tests and quizzes to identify content that needs to be learned (minimal)
- Ask deep explanatory questions. Use instructional prompts that encourage students to pose and answer “deep-level” questions on course material. These questions enable students to respond with explanations and supports deep understanding of taught material. (strong)
(Quoted directly from the original report via a Software Carpentry blog post)
This is a nice summary, but it’s definitely worth reading the whole report to explore the depth of the thought process and learn more about specific ideas for how to implement these recommendations.
How am I doing?
Recently I’ve been teaching two courses on programming and database management for biologists. Because I’m not a big believer in classroom lecture, for this type of material, a typical day in one of these courses involves: 1) either reading up on the material in a text book or viewing a Software Carpentry lecture before coming to class; 2) a brief 5-10 minute period of either re-presenting complex material or answering questions about the reading/viewing; and 3) 45 minutes of working on exercises (during which time I’m typically bouncing from student to student helping them figure out things that they don’t understand). So, how am I doing with respect the the above recommendations?
1. Space learning over time. I’m doing OK here, but not as well as I’d like. The nice thing about teaching introductory programming concepts is that they naturally build on one another. If we learned about if-then statements two weeks ago then I’m going to use them in the exercises about loops that we’re learning about this week. I also have my advanced class use version control throughout the semester for retrieving data and turning in exercises to force them to become very comfortable with the work-flow. However, I haven’t done a very good job of bringing concepts back, on their own, later in the semester. The exercise based approach to the course is perfect for this, I just need to write more problems and insert them into the problem-sets a few weeks after we cover the original material.
2. Interleave worked example solutions with problem-solving exercises. I think I’m doing a pretty good job here. Student’s see worked examples for each concept in either a text book or video lecture (viewed outside of class) and if I think they need more for a particular concept we’ll walk through a problem at the beginning of class. I often use the Online Python Tutor for this purpose which provides a really nice presentation of what is going on in the program. We then spend most of the class period working on problem-solving exercises. Since my classes meets three days a week I think this leads to a pretty decent interleaving.
3. Combine graphics with verbal descriptions. I do some graphical presentation and the Online Python Tutor gives some nice graphical representations of running programs, but I need to learn more about how to communicate programming concepts graphically. I suspect that some of the students that struggle the most in my Intro class would benefit from a clearly graphical presentation of what is going happening in the program.
4. Connect and integrate abstract and concrete representations of concepts. I think I do this fairly well. The overall motivation for the course is to ground the programming material in the specific discipline that the students are interested in. So, we learn about the general concept and then apply it to concrete biological problems in the exercises.
5. Use quizzing to promote learning. I’m not convinced that pre-questions make a lot of sense for material like this. In more fact based classes they are helping to focus students’ attention on what is important, but I think the immediate engagement in problem-sets that focus on the important aspects works at least as well in my classroom. I do have one test in the course that occurs about half way through the Intro course after we’ve covered the core material. It is intended to provide the “delayed re-exposure” that has been shown to improve learning, but after reading this recommendation I’m starting to think that this would be better accomplished with a series of smaller quizzes.
6. Help students allocate study time efficiently. I spend a fair bit of time doing this when I help students who ask questions during the assignments. By looking at their code and talking to them it typically becomes clear where the “illusion of knowing” is creeping in and causing them problems and I think I do a fairly good job of breaking that cycle and helping them focus on what they still need to learn. I haven’t used quizzes for this yet, but I think they could be a valuable addition.
7. Ask deep explanatory questions. One of the main focuses in both of my courses is an individual project where the students work on a larger program to do something that is of interest to them. I do this with the hope that it can provide the kind of deep exposure that this recommendation envisions.
So, I guess I’m doing OK, but I need to work more on representation of material both through bringing back old material in the exercises and potentially through the use of short quizzes throughout the semester. I also need to work on alternative ways to present material to help reach folks whose brains work differently.
If you are a current or future teacher I really recommend reading the full report. It’s a quick read and provides lots of good information and food for thought when figuring out how to help your students learn.
Thanks for listening in on my self-reflection. If you have thoughts about this stuff I’d love to hear about it in the comments.
A new database for mammalian community ecology and macroecology
There are a number of great datasets available for doing macroecology and community ecology at broad spatial scales. These include data on birds (Breeding Bird Survey, Christmas Bird Count), plants (Forest Inventory & Analysis, Gentry’s transects), and insects (North American Butterfly Association Counts). However, if you wanted to do work that relied on knowing the presence or abundance of individuals at particular sites (i.e., you’re looking for something other than range maps) there has never been a decent dataset to work with for mammals.
Announcing the Mammal Community Database (MCDB)
Over the past couple of years we’ve been working to fill that gap as best we could. Since coordinated continental scale surveys of mammals don’t yet exist [1] we dug into the extensive mammalogy literature and compiled a database of 1000 globally distributed communities. Thanks to Kate Thibault‘s leadership and the hard work of Sarah Supp and Mikaelle Giffen, we are happy to announce that this data is now freely available as a data paper on Ecological Archives.
In addition to containing species lists for 1000 locales, there is abundance data for 940 of the locations, some site level body size data (~50 sites) and a handful of reasonably long (> 10 yr) time-series as well. Most of the data is restricted to the particular mode of sampling that an individual mammalogist uses and as a result much of the data is for small mammals captured in Sherman traps.
Working with data compilations like this is always difficult because the differences in sampling intensity and approaches between studies can make it very difficult to compare data across sites. We’ve put together a detailed table of information on how sampling was conducted to help folks break the data into comparable subsets and/or attempt to control for the influence of sampling differences in their statistical models.
The joys of Open Science
We’ve been gradually working on making the science that we do at Weecology more and more open, and the MCDB is an example of that. We submitted the database to Ecological Archives before we had actually done much of anything with it ourselves [2], because the main point of collecting the data was to provide a broadly useful resource to the ecological community, not to answer a specific question. We were really excited to see that as soon as we announced it on Twitter
We just published a new data set of 1000 mammal communities esajournals.org/doi/abs/10.189… Check it out and do something cool with it.
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(@weecology) December 28, 2011folks started picking it up and doing cool things with it [3]. We hope that folks will find all sorts of uses for it going forward.
Going forward
We know that there is tons more data out there on mammal communities. Some of it is unpublished, or not published in enough detail for us to include. Some of it has licenses that mean that we can’t add it to the MCDB without special permission (e.g., there is a lot of great LTER mammal data out there). Lots of it we just didn’t find while searching through the literature.
If folks know of more data we’d love to hear about it. If you can give us permission to add data that has more restrictive licensing then we’d love to do so [4]. If you’re interested in collaborating on growing the database let us know. If there’s enough interest we can invest some time in developing a public portal.
The footnotes [5]
[1] We are anxiously awaiting NEON’s upcoming surveys, headed up by former Weecology postdoc Kate Thibault.
[2] We have a single paper that is currently in review that uses the data.
[3] Thanks to Scott Chamberlain and Markus Gesmann. You guys are awesome!
[4] To be clear, we haven’t been asking for permission yet, so no one has turned us down. We wanted to get the first round of data collection done first to show that this was a serious effort.
[5] Because anything that David Foster Wallace loved has to be a good thing.
NSF Pre-proposal guidelines/instructions
Since I have now spent far too much time on multiple occasions trying to track down the instructions for the new pre-proposals for NSF DEB and IOS grants I’m going to post the link here under the assumptions that other folks will be looking for this information as well (and also finding it difficult to track down).
http://www.nsf.gov/pubs/2011/nsf11573/nsf11573.htm#prep
Happy post-holiday grant writing to all.
UPDATE 1: Also note that the Biosketches are different for the pre-proposals (changes noted in bold-italics)
Biographical Sketches (2-page limit for each) should be included for each person listed on the Personnel page. It should include the individual’s expertise as related to the proposed research, professional preparation, professional appointments, five relevant publications, five additional publications, and up to five synergistic activities. Advisors, advisees, and collaborators should not be listed on this document, but in a separate table (see below).
UPDATE 2: Though it is not explicitly clear from the link above, Current & Pending Support should NOT be included in pre-proposals (thanks to Alan Tessier for clearing this up).
Stay Classy Wiley
I logged into one of my reviewer accounts at a Wiley journal this morning and was greeted by a redirect that took me to a page with the following message:
CONSENT
We appreciate your involvement with this publication, which is published by a John Wiley & Sons company. The publisher would like to contact you by email/post with details of publications and services that may be of interest to you, specific to your subject area, from companies in the John Wiley & Sons group (only) worldwide. Your information will never be passed to any third party companies and as part of any communications you will be given the opportunity to unsubscribe from receiving further contact. Please indicate whether you wish to receive this information by answering the CONSENT question below.
Asking someone who is already working for you for free if it’s OK to also try to sell them stuff while they’re doing it seems like a pretty good definition of classless to me.
