Monthly Archives: January 2012
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.
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!
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.
- 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)
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.
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  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 , 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.—
(@weecology) December 28, 2011
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 . 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 
 We are anxiously awaiting NEON’s upcoming surveys, headed up by former Weecology postdoc Kate Thibault.
 Because anything that David Foster Wallace loved has to be a good thing.