Dynamic Ecology has had a couple of recent posts relating to peer review roles (reviewer, associate editor) that seem to have struck a nerve. I want to provide some thoughts on the two fundamental roles of peer-review: gatekeeping and editing.
I am currently attending a Festschrift this week for Michael Rosenzweig. Make no mistake, he is still actively doing science, but with 50+ years of scientific career, it seems like a good time to reflect on what an impressive career he has had. Just for full disclosure upfront, he was my PhD adviser, so I’m hardly the most unbiased reporter, but of course that gives me a close perspective.
Mike was awarded the Ecological Society of America’s Eminent Ecologist award in 2008 and he has well over 100 papers, many massively cited, and three books, so I imagine many are familiar with his published work, and it would take too much space to summarize it anyway. I want to offer several more reflective and in some cases more personal thoughts. Take them as a reflection of my respect and appreciation for Mike or my musings on the ingredients of a good scientific career as you wish.
I returned this weekend from the IBS 2017 meeting in Tucson. It was a great meeting. The organizers moved it on fairly short notice from Brazil to Tucson due to concerns about Zika. This resulted in a lot of extra work for the organizers, but it didn’t show. It was a well-run meeting. And it was my favorite type of a meeting a few hundred people organized around a fairly specific topic.
I’m not going to repeat individual talks – check out the twitter feed for many great talks (#ibstucson). As is usual with me, such meetings inspire big-picture musings. This one probably more than most, since the last time I was able to attend IBS was the inaugural meeting in Mesquite Nevada in 2003. I noticed a lot of differences in the 14 year gap.
Jeremy had a post on Monday musing on a propensity for researchers that start out doing basic research and end up mixing applied research in later in their careers. I think the core observation is, on average of course, not by individual, correct. And there were a lot of spirited explanations of why this is in the comments. His framing of a single trade-off dimension between basic and applied is extremely common, and embedded in the funding of many nations’s scientific agencies (e.g. in the US, NSF only funds basic research while the US Department of Agriculture funds applied research).
But I’ve always found that trade-off limiting. Among other things, it implies something cannot be both basic and applied, something which I reject (and Don S gave a pretty spirited rebuttal of in the comments as well). I have found the notion of two trade-off axes put forth by Donald Stokes, in his book Pasteur’s Quadrant: Basic Science and Technological Innovation to be a more useful framing (also see a decent summary of the book in Wikipedia).
I define a serial bully as somebody who repeatedly bullies new victims and never gets caught or stopped*. I don’t have exact statistics at my fingertips, but it is a definite 90/10 scenario (90% of the bullying is done by 10% of the people) – and it is that small fraction that are the serial bullies. Every campus has a PhD adviser (or three) who repeatedly abuses and victimizes his/her students. And you might have a senior colleague in your department who bullies everybody junior to her/him just because they can. Or you may have met a researcher who will do anything, ethical or not, to “win” at research, leaving behind a trail of people feeling used or abused. And although there are many unique aspects to sexual harassment, it most certainly involves bullying-like abuse of power against someone and it most certainly shares the trait that most offenders repeat over and over without getting called on it (as recent shameful cases to make the news show – just e.g. the Marcy case).You may or may not apply the word bully to all of these cases. But what all these have in common is somebody who is harming other people over and over again with little regard for the consequences, because, well, there usually are no consequences. And that is what I want to talk about.
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.
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
I attended the BES Macroecology meeting in Oxford last Thursday and Friday. It was a great meeting. Check out a storify of the conference tweets for details. I suppose it says something about me, but everytime I get >24 hours of all macroecology, I get reflective on trends I see. As I noted last year, macroecology is in a self-aware and self-reflective adolesence. And this was evident again this year. A great deal of the conversation was on topics like “what is macroecology?”, “is macroecology working?”, “should we move past pattern to to process?”. and “how does macroecology relate to conservation and the public/policy dialogue?”. For those of you who hang around macroecology, these seem like perennial conversations. There was a great conversation with many enlightening thoughts shared on all of these topics.What follows are my own thoughts on the state of macroecology (as observed in the non-self-reflective science talks in the conference and building on the self-reflective thoughts others shared).
Last week the 2015 ISI Impact Factors were announced. Hopefully this was not a date circled on your calendar. But if you were on a editorial board you could not escape a quick announcement of your journal’s new impact factor, whether it gained or lost in rank relative to other journals, and cheers and (email) back-slaps all around or solemn faces and vows to do better. And in my experience authors will now switch allegiance in which journals they submit to so as to follow those ranked highest in impact factor. Is this justified?
This post has evolved substantially over its writing. It started from a good post over on EEB and Flow by Marc Cadotte arguing that ecology needed a more robust culture of critique to weed out bad papers, and arguing that comments/critiques to the journals that published the original papers was an important way to do this. Despite strongly agreeing with the first part, I instinctively disagreed with the later part. (And have been thinking about critique letters a lot lately in my role as Editor-in-Chief at an ecology journal just as Marc has)*. But unpacking why I don’t like critique letters has led to a lot of musings on how ecology works, how the human mind works, and my own answer to the specific question of how best to steer the field away if you see a bad paper. And just maybe along the way I stumbled on a strategy or two for killing zombie ideas!