Jabberwocky Ecology

Is it OK to cite preprints? Yes, yes it is.

Should you cite preprints in your papers and should journals allow this? This is a topic that gets debated periodically. The most recent round of Twitter debate started last week when Martin Hunt pointed out that the journal Nucleic Acids Research wouldn’t allow him to cite them. A couple of days later I suggested that journals that don’t allow citing preprints are putting their authors’ at risk by forcing them not to cite relevant work. Roughly forty games of Sleeping Queens later (my kid is really into Sleeping Queens) I reopened Twitter and found a roiling debate over whether citing preprints was appropriate at all.

The basic argument against citing preprints is that they aren’t peer reviewed. E.g.,

and that this could lead to the citation of bad work and the potential decay of science. E.g.,

There are three reasons I disagree with this argument:

  1. We already cite lots of non-peer reviewed things in ecology
  2. Lots of fields already do this and they are doing just fine.
  3. Responsibility for the citation lies with the citer

We already cite non-peer reviewed things in ecology

As Auriel Fournier, Stephen Heard, Michael Hoffman, TerryMcGlynn and ATMoody pointed out we already cite lots of things that aren’t peer reviewed including government agency reports, white papers, and other “grey literature”.

We also cite lots of other really important non-peer reviewed things like data and software. We been doing this for decades. Ecology hasn’t become polluted with pseudo science. It will all be OK.

Lots of other fields already do this

One of the things I find amusing/exhausting about biologists debating preprints is ignorance of their history and use in other fields. It’s a bit like debating the name of an actor for two hours when you could easily look it up on Google.

In this particular case (as Eric Pedersen pointed out) we know that citation of preprints isn’t going to cause problems for the field because it hasn’t caused issues in other fields and has almost invariably become standard practice in fields that use preprints. Unless you think Physics and Math are having real issues it’s difficult to argue that this is a meaningful problem. Just ask a physicist

You are responsible for your citations

Why hasn’t citing unreviewed work caused the wheels to fall off of science? Because citing appropriate work in the proper context is part of our job. There are good preprints and bad preprints, good reports and bad reports, good data and bad data, good software and bad software, and good papers and bad papers. As Belinda Phipson, Casey Green, Dave Harris and Sebastian Raschka point out it is up to us as the people citing research to make professional judgments about what is good science and should be cited. Casey’s take captures my thoughts on this exactly:

TLDR

So yes, you should cite preprints and other unreviewed things that are important for your work. That’s called proper attribution. It has worked in ecology and other fields for decades. It will continue to work because we are scientists and evaluating the science we cite is part of our jobs. You can even cite this blog post if you want to.

Thanks to everyone both linked here and not for the spirited discussion. Sorry I wasn’t there, but Sleeping Queens is a pretty awesome game.

UPDATE: For those of you new to this discussion, it’s been going on for a long time even in biology. Here is Graham Coop’s excellent post from nearly 4 years ago.

UPDATE: Discussion of why it’s important to put preprint citations are in the reference list

Data Analyst position in ecology research group

The Weecology lab group run by Ethan White and Morgan Ernest at the University of Florida is seeking a Data Analyst to work collaboratively with faculty, graduate students, and postdocs to understand and model ecological systems. We’re looking for someone who enjoys tidying, managing, manipulating, visualizing, and analyzing data to help support scientific discovery.

The position will include:

  • Organizing, analyzing, and visualizing large amounts of ecological data, including spatial and remotely sensed data. Modifying existing analytical approaches and data protocols as needed.
  • Planning and executing the analysis of data related to newly forming questions from the group. Assisting in the statistical analysis of ecological data, as determined by the needs of the research group.
  • Providing assistance and guidance to members of the research group on existing research projects. Working collaboratively with undergraduates, graduate students and postdocs in the group and from related projects.
  • Learning new analytical tools and software as needed.

This is a staff position in the group and will be focused on data management and analysis. All members of this collaborative group are considered equal partners in the scientific process and this position will be actively involved in collaborations. Weecology believes in the importance of open science, so most work done as part of this position will involve writing open source code, use of open source software, and production and use of open data.

Weecology is a partnership between the White Lab, which studies ecology using quantitative and computational approaches and the Ernest Lab, which tends to be more field and community ecology oriented. The Weecology group supports and encourages members interested in a variety of career paths. Former weecologists are currently employed in the tech industry, with the National Ecological Observatory Network, as faculty at teaching-focused colleges, and as postdocs and faculty at research universities. We are also committed to supporting and training a diverse scientific workforce. Current and former group members encompass a variety of racial and ethnic backgrounds from the U.S. and other countries, members of the LGBTQ community, military veterans, people with chronic illnesses, and first-generation college students. More information about the Weecology group and respective labs is available on our website. You can also check us out on Twitter (@skmorgane, @ethanwhite, @weecology, GitHub, and our blog Jabberwocky Ecology.

The ideal candidate will have:

  • Experience working with data in R or Python, some exposure to version control (preferably Git and GitHub), and potentially some background with database management systems (e.g., PostgreSQL, SQLite, MySQL) and spatial data.
  • Research experience in ecology
  • Interest in open approaches to science
  • Experience collecting or working with ecological data

That said, don’t let the absence of any of these stop you from applying. If this sounds like a job you’d like to have please go ahead and put in an application.

We currently have funding for this position for 2.5 years. Minimum salary is $40,000/year (which goes a pretty long way in Gainesville), but there is significant flexibility in this number for highly qualified candidates. We are open to the possibility of someone working remotely. The position will remain open until filled, with initial review of applications beginning on May 5th. If you’re interested in applying you can do so through the official UF position page. If you have any questions or just want to let us know that you’re applying you can email Weecology’s project manager Glenda Yenni at glenda@weecology.org.

Postdoctoral research position in the Temporal Dynamics of Communities

The Weecology lab group run by Morgan Ernest and Ethan White at the University of Florida is seeking a post-doctoral researcher to study changes in ecological communities through time. This position will primarily involve broad-scale comparative analyses across communities using large time-series datasets and/or in-depth analyses of our own long-term dataset (the Portal Project). Experience with any of the following is useful, but not required: long-term data, macroecology, paleoecology, quantitative/theoretical ecology, and programming/data analysis in R or Python. The successful applicant will be expected to collaborate on lab projects on community dynamics and develop their own research projects in this area according to their interests.

Weecology is a partnership between the Ernest Lab, which tends to be more field and community ecology oriented and the White Lab, which tends to be more quantitative and computationally oriented. The Weecology group supports and encourages students interested in a variety of career paths. Former weecologists are currently employed in the tech industry, with the National Ecological Observatory Network, as faculty at teaching-focused colleges, and as postdocs and faculty at research universities. We are also committed to supporting and training a diverse scientific workforce. Current and former group members encompass a variety of racial and ethnic backgrounds from the U.S. and other countries, members of the LGBTQ community, military veterans, people with chronic illnesses, and first-generation college students. More information about the Weecology group and respective labs is available on our website. You can also check us out on Twitter (@skmorgane, @ethanwhite, @weecology), GitHub, and our blog Jabberwocky Ecology.

This 2-year postdoc has a flexible start date, but can start as early as June 1st 2017. Interested students should contact Dr. Morgan Ernest (skmorgane@ufl.edu) with their CV including a list of three references, a cover letter detailing their research interests/experiences, and one or more research samples (a PDF or link to a scientific product such as a published paper, preprint, software, data analysis code, etc). The position will remain open until filled, with initial review of applications beginning on April 24th.

Fork our course: A semester-long Data Carpentry course for biologists

This is post is co-authored by Zack Brym and Ethan White

Over the last year and a half we have been actively developing a semester-long Data Carpentry course designed to be easily customized and integrated into existing graduate and undergraduate curricula.

Data Carpentry for Biologists contains course materials for teaching scientists how to work more effectively with data. The course provides introductions to data management and relational databases, data manipulation and analysis, and data visualization. It covers the same general types of material as a two-day Data Carpentry workshop, but expands the materials and opportunities for practice into a full-length university course. The teaching material uses R and SQLite, with some corresponding materials for Python as well. To help students understand the direct applications to their interests, the examples and exercises focus on biological questions and working with real data. The course emphasizes using best practices to produce reusable and reproducible data analysis.

Active-learning Teaching Materials

Learning computing requires active practice by working through programming problems. Just diving in to computing is challenging for most scientists, so the course instruction is designed to combine short live-coding introductions to concepts followed immediately by the students working on a related exercise. Additional exercises are assigned later for practice. This follows the “I do”, “We do”, “You do” approach to teaching, which leverages the benefits of active-learning and flipped classrooms without leaving students who are less comfortable with the material feeling lost. The bulk of class time is spent working on assigned exercises with the instructor moving around the room helping guide students through things they don’t understand and engaging with students who are thinking about advanced applications of what they’ve learned.

This approach is the result of lots of reading about effective teaching methods and Ethan’s experience teaching this and related courses over the last six years at Utah State University and the University of Florida. It seems to work well for both students that get the material easily and those that find it more challenging. We’ve also tried to make these materials as useful as possible for self-guided students.

Open course development

Software Carpentry and Data Carpentry have shown how powerful collaborative lesson development can be and we’re interested in bringing that to the university classroom. We have designed the course materials to be modular and easy to modify, and the course website easy to clone and set up. All of the teaching materials and associated website files are openly available at the Data Carpentry for Biologists repository on GitHub under CC-BY and MIT licenses. The course materials are all written in Markdown and everything runs on Jekyll through GitHub Pages. Making your own version of the course should take less than an hour. We’ve developed documentation for how to create your own version of the course and how to contribute to development. Exercises and assignments are modular and changing exercises and assignments simply involves reordering items in a list. Adding a new exercise involves creating a new Markdown file and then adding its title to the list of exercises for an assignment.

Get Involved

If you teach, or want to teach, a course like this, we’d love to get you involved. Here are some useful links for getting started.

–   I want to teach the course.
–   I have some feedback.
–   I want to contribute to the project.

We want to be sure getting involved is as easy as possible. We’ve worked hard to provide documentation and help resources for students and instructors. Students can find all they need to know at our student start guide. Instructors have access to course content and site design documentation.

If your having trouble finding something or getting something to work, or simply have some feedback about the course please open a new issue at GitHub or send us an email.

Acknowledgements

Development of this course was generously support by  the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4563 to Ethan White and the National Science Foundation as part of a CAREER award to Ethan White.

New release of the EcoData Retriever

EcoData Retriever logoWe are very exited to announce the newest release of the EcoData Retriever, our software for automating the downloading, cleaning, and installing of ecological and environmental data. Instead of hours or days trying to get complicated datasets like the Breeding Bird Survey ready for analysis, the Retriever lets you simply click a button or run a single command from R or the command line, and your computer does the rest.

bbs_install_animated

It’s been over a year since the last retriever release and there are lots of new features and improvements to be excited about.

  • We’ve added 21 new datasets, including major ecological and environmental datasets like eBird, Vertnet, and the Global Wood Density Database, and the PRISM climate data.
  • To support all of these datasets we’ve added support for additional data types including greater than memory archive files, and we’ve also improved the ability to control where downloaded files are stored and how they are clustered together.
  • We’ve significantly improved documentation and now have a new automatically built documentation site at Read The Docs.
  • We’ve also made a lot of under the hood improvements.

This is also the first release that has been overseen by Weecology’s new software engineer, Henry Senyondo. We’re excited to have Henry on the team, and now that he’s around development of both the EcoData Retriever and other lab software projects will be happening more quickly.

A big thanks to the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative for funding this development through Grant GBMF4563 and to the National Science Foundation for funding as part of a CAREER award to Ethan White.

UPDATE: Led by Dan McGlinn we also released a new version of the ecoretriever R interface for the Retriever last fall. This makes using the Retriever from R as simple as:

data <- ecoretriever::fetch("BBS")

GEB adds unlimited data references section to papers

In a big step forward for allowing proper credit to be provided to all of the awesome folks collecting and publishing data, the journal Global Ecology & Biogeography has just announced that they will start supporting an unlimited set of references to datasets used in a paper.

A growing concern in the macroecological community has been that many papers whose data are used in meta-analyses or data-compilation papers have not been getting citation credit because most journals require these papers to only be listed in the supplemental material (which is not indexed by most indexing services). GEB is proud to support the inclusion of a second list of references within the main paper for all data papers used… To our knowledge, GEB is the first journal in the ecological field to do this. And we’ll be working with Wiley to further improve options in this area.

These references will be included immediately following the traditional references section in both the html and pdf versions of the paper. You can see an example in Olds et al. (2016).

What this means is that when you combine data from dozens or hundreds of studies to conduct a synthetic analysis, you can cite all of the sources in a way that will provide citation credit to those collecting the data1. It also means that scientists using large data compilations can cite the original data sources as well as the compilation itself2.

This is important for encouraging the publication of data, since one of the common reasons that scientists don’t publish data is a lack of credit, and citation only in non-indexed supplementary materials sections is a common concern.

Facilitating proper citation of all data sources is something the community has been requesting and it’s great to see GEB taking the lead in this area. Since Wiley, the publisher of GEB, is the largest publisher of ecology journals, it should be straightforward to implement this new approach widely. If other journals follow GEB’s lead, we will enter a new era where citation of data can be as complete as possible, allowing proper credit to everyone who collects and publishes data.

1GEB will need to make sure that this section gets properly picked up by the indexers, and tweak the presentation as necessary if it isn’t.
2Provided that the compilation provides a method for compiling a citation list of all associated sources.

How technology can help scientists with chronic illnesses (or Technology FTW!)

This is a guest post by Elita Baldridge (@elitabaldridge)

I am currently the remotely working member of Weecology, finishing up my PhD in the lower elevation and better air of Kansas, while the rest of my colleagues are still in Utah, due to developing a chronic illness and finally getting diagnosed with fibromyalgia.  The relocation is actually working out really well.  I’m in better shape because I’m not having to fight the air too, and I’m finally making real progress toward finishing my dissertation again.

I ruthlessly culled everything that wasn’t directly working on my dissertation.  I was going to attend the Gordon Conference this year, as I had heard fantastic things about it for years, but had not been ready to go yet, but I had to drop that because I wasn’t physically able to travel.  I did not go to ESA, because I couldn’t travel.  There are working groups and workshops galore, all involving travel, which I cannot do.  Right now, the closest thing that we have to bringing absent scientists to an event is live tweeting, which is not nearly as good as hearing a speaker for yourself, and is pretty heartbreaking if you had to cancel your plans to attend an event because you were too infirm to go.The tools that I’m using to do science remotely are not just for increasing accessibility for a single chronically ill macroecologist.  They are good tools for science in general.  I’m using GitHub to version control my code, and Dropbox to share data and figures.  Ethan can see what I’m working on as I’m doing it, and I’ve got a clear record of what I was doing and what decisions that I made. While my cognitive dysfunction may be a bit more extreme of a problem, I know that we’ve all stayed up too late coding and broken something we shouldn’t have and the ability to wave the magic Git wand and make any poor decisions that I made while my brain was out to lunch go away is priceless.

Open access?  Having open access to papers is really important when you are going to be faced shortly with probably not having any institutional access anymore.  Also, important for everyone else who isn’t at a major university with very expensive subscriptions to all the journals.  Having open access to data and code is crucial when you can’t collect your own data and are going to be doing research from your home computer on the cheap because you can’t rely on your body to work reliably at any given point in time.

Video conferencing is working well for me to meet with the lab, but could also be great for attending conferences and workshops.  This would not only be good for a certain macroecologist, but would also be good to include people from smaller universities, etc. who would like to participate in these type of things too, but can’t otherwise due to the travel.  I did my master’s degree at Fort Hays State University, and I still love it dearly.  This type of increased accessibility would have been great for me while I was a perfectly healthy master’s student.  Fort Hays is a primarily undergraduate institution in the middle of Kansas, about four hours away from any major city, and it does not have some of the resources that a larger university would have.  No seminar series, no workshops, not much travel money to go to workshops or conferences, which doesn’t mean that good science can’t still be happening.

Many of my labmates are looking for post-docs, or are already in postdoc positions at this point.  I’m very excited for all of them, and await eagerly all the stories of the exciting new things they are doing.  Having a chronic illness limits what I am capable of doing physically.  I am not going to be able to move across the country for a post-doc.  That does not mean that I do not want to play science too.  I’ve got my home base set up, and I can reach pretty far from here.  I still want to be a part of living science, I don’t want to have to get to the party after everyone else has gone home.

And I wonder, why can I not do these things?  Is it not the future?  Do we not have the internet, with video chat?  I get to meet with Ethan and talk science at our weekly meetings every week.  I go to lab meetings with video chat, and get to see what my labmates are doing, and crack jokes, and laugh at other people’s jokes.  It wouldn’t be hard to get me to conferences and working groups either.

With technology, I get to be a part of living, breathing science, and it is a beautiful thing.