Note from Jeremy: This is a guest post from Mark Vellend.
I was not at the ESA meeting this year, but a handful of advance copies of my book, The Theory of Ecological Communities, were, and Margaret Kosmala was kind enough to send me a photo of the first buyers. I’d like to be able to play it cool and say this was just another ho-hum moment in the life of a scientist, but it wasn’t. I stared at the photo for a good while with a huge smile on my face. Maybe that was just because smiling is contagious and it was instinctual to smile back at the two people smiling at me through the screen. But there was also a sense of deep gratification. Following in the footsteps of some of my scientific heroes, my name was on the cover of a green and yellow book, the book was now born, and at least two people other than my Mom and Dad were willing to pay money for it. Success!
Writing a book is a teeny bit like having a child, but also not like it at all. The similarities: long gestation period, intense anticipation for its arrival, major investment in its success, worry about its uncertain future, and sometimes wondering what you’ve gotten yourself into. The differences: I (gender: male) actually did most of the work this time getting it to parturition, books are decidedly precocial (no diapers, bottles, tantrums, lunch boxes, or swimming lessons), I’m not sure anything I do now will influence its future, and although one might say the journey was difficult at times (f*$%ing index!), it’s not even in the same universe…I’ll just stop there instead of pretending that words can do justice to the difference on this point (just received stink eye from across the room). I guess I’m just trying to say that there’s a bit of emotion involved.
This post is the last (I think) in a short series based on thoughts that grew out of the process of writing the book. The others (here, here, and here) focused largely on scientific issues that flowed directly out of the contents of the book. In addition to the little story and handful of thoughts above, I figured I’d now step back from the content of the book, and share some thoughts on writing books in general. (Pretty thin cover story for shamelessly advertising a just-released book now available from amazon.com, I know.) Before diving into this project, I had a short-lived but intense bout of wondering why anyone would write a really long document that people need to pay for in an age when nobody reads anything they can’t download for free. Now I can think of several reasons:
Yesterday I presented what I tongue-in-cheek (or arrogantly – take your pick) called “10 commandments for good data management”. In that post I laid out what I believe to be best practices for managing and analyzing scientific data. Key points were to separate a data entry copy from an analysis copy of the data and to organize them differently, to use row-column organization of raw data, to use a star schema, and to denormalize data before analysis.
Here I present a worked example. It is from a hypothetical survey of raptors (data actually generated by a computer simulation). It records abundances for a number of species of raptors at a number of sites and on a number of days. The sites are unimaginatively named alpha, beta, gamma, etc. Dates are American (mm/dd/yyyy) format. Species names are real species names for raptors in North America. Abundances are made up. There is also data on temperature for each of those sites for each of those days. And some ancillary information on sites (including lat/lon coordinates). It is a constellation schema in the terms of yesterdays post. One fact table is abundance with dimensions of time, site, and taxa. The other fact table is measure with dimensions of time and site. It also has a number of errors in the data entry of the types typically seen.
Today at 10:20 am Central time I’m giving a keynote talk on science blogging at the 2016 American Fisheries Society meeting. It’s part of the #SocialFish symposium, which runs all day. Come on by if you’re at the meeting, or follow via Twitter if you’re not! It’ll be a mix of old thoughts and new thoughts. There will be zombie jokes. And I’ll be comparing myself to an alligator gar.
Me. More or less.
(image source: Wikimedia Commons)
Usually when I am asked to give a few words to describe myself I say macroecologist or large-scale-ecologist. And I might on other days say biodiversity scientist or global change scientist. But a lot of days I would say “ecoinformatician”. Ecoinformatics is the subset of bioinformatics that applies to ecology – that is to say informatic (data) techniques applied to ecology. Some of you may know that I spent 9 years in business before returning to my PhD. But not many know that most of what I was doing was business informatics. Helping companies understand their data. It wasn’t planned. I just have always liked seeing what the data has to tell me. But it turned out to be great training as ecology dived into informatics just as I hit graduate school.
Not surprisingly given my background, I spend a lot of time being asked to make recommendations on how to work with data. I’ve also been involved in some very large data projects like BIEN. Here I don’t want to focus on the large (often social) issues of really big projects (my slides from ESA 2015 on the next 100 years of ecoinformatics are on figshare if you’re interested). Here I want to focus on the much smaller single person or lab-scale project. This post is attempts to summarize what I have learned to be best practices over both my business informatics and ecoinformatics careers. I am intentionally going to stay tool or software agnostic. In this post I really want to emphasize a frame of mind and mental approach that can be implemented in literally dozens of different software packages. In a second post tomorrow, I will give a worked example in R since I know that has the highest popularity in ecology. Continue reading
Also this week: myths about NSF grant applications, Stephen Heard vs. poetry, Ben Kerr vs. poetry, log(history), and more.
You’ve probably already seen this because it went viral on social media: the syllabus for PSY 607: Everything is F*cked (ht @dandrezner).
Ok, the title is trolling, but in a good way–it actually looks like a really good psychology course to me. It’s good mental exercise to consider the possibility that your entire field might have gone off the rails, even if you don’t really believe it has.
Unlike social psychologists, ecologists don’t have a widespread collective sense that their entire field is f*cked, and I don’t think they should. In case it needs saying, ecology is not f*cked! But it’s still amusing and interesting to imagine what the syllabus for an advanced ecology course would look like if ecology were f*cked. So here’s an opening bid for the syllabus of ECO 607: Everything is F*cked. Suggest edits/additions in the comments!
How did I not know about this until now?!
The above is from the 3rd (2014) Canadian edition of Manuel Molles & James Cahill’s Ecology textbook. “Zombie ideas”! In a textbook!
Note from Meg: This guest post (which starts below the break) is a follow up to my post on life as an anxious scientist, where I talked about having an anxiety disorder and some of my strategies for managing it. The post below was written by a graduate student who wishes to remain anonymous. It summarizes that student’s experience with an anxiety disorder, and includes information that I think will be useful to students and advisors. My plan is to have a follow up post in the future with more thoughts on the topic.
Brian, Meg, and I all skipped the ESA meeting this year. So you tell us–how was it? What’s new and exciting in ecology this year?
Below the fold: brief comments on choice of site. What are your favorite past sites? Where would you like to see ESA meet in future?
Also this week: Olympian statistical machismo, successful scientist vs. failure, grant success rate vs. length, Zen Faulkes vs. Reddit, and more