The story behind the paper: Xiao et al. 2016

This is the story behind “Comparing process-based and constraint-based approaches for modeling macroecological patterns” by my former PhD student Xiao Xiao, James O’Dwyer, and myself.

Background

I was on sabbatical in the fall of 2013 and was doing a lot of reading, and I reread “An integrative framework for stochastic, size-structured community assembly” by James O’Dwyer, Jessica Green, and colleagues. A couple of months earlier Xiao Xiao, Dan McGlinn, & I had submitted a paper on “A strong test of the Maximum Entropy Theory of Ecology“, where we had tested John Harte and colleague’s new maximum entropy based model by looking at four different predictions of the model simultaneously. In rereading O’Dwyer et al. I realized that their size-structured neutral theory would probably be able to predict a similar set of ecological distributions to those predicted by the maximum entropy model. We’d already conducted the first three levels in McGill’s hierarchy of model testing (see McGill 2003 and McGill et al. 2006) for Harte et al.’s maximum entropy model (checking the general form of the predictions, comparing to null hypotheses, and testing multiple complex predictions) and this would let us complete the last level by comparing the fit to realistic alternative models.

Getting to work

The math in O’Dwyer et al. is pretty advanced and I knew James through shared interests in ecological theory, so I emailed him and Xiao to see if it might it be mathematically tenable to use James’ model to make the same predictions we’d been testing and, if so, if he and Xiao were interested in working together on trying to do this.

What resulted was a very interdisciplinary collaboration, combining shared expertise in mathematical modeling, computing, analysis of large ecological datasets, and knowledge of the foundations of multiple models/theories. It was regular for two of the three people to have a detailed conversation that the third collaborator didn’t follow the details of but always felt comfortable interjecting to make sure that the big picture goals of the project stayed on track. In particular, I remember a \~100 message long email exchange where James and Xiao were working on getting the two theories to make identical predictions. They were on-boarding each other with the details of the two theories and then exchanging ideas in math that I wasn’t even trying to keep up with. I’d occasionally jump in to provide some relevant empirical details and information on other related theory/ideas to help keep things moving in the right direction, but generally just got to watch in awe as two folks with amazing theory skills did their thing. Xiao was constantly running and sharing new analyses which really helped make all of our interactions cohesive by grounding them in graphs and real values.

Reviews, revisions, and the speed of scientific dialog

During the review process John Harte pointed out that there was a second generation model from the maximum entropy theory that was expected to improve the areas where the version we were analyzing was performing poorly. We’d known about this work for a couple of years since we’d been actively sharing ideas and results with the Harte Lab throughout this research. We knew that this paper was already in review, but it didn’t seem like we could reasonably analyze work that they hadn’t made publicly available yet. So, we’d acknowledged in our paper that new models based on this general theory could improve it’s performance and planned to potentially come back later and analyze the new model in a second paper.

Aside: this is a perfect example of the advantages of preprints for facilitating a rapid scientific dialog. If this second generation paper had been posted as a preprint at the time it was initially submitted for review we would have been able to cite and analyze the new theory from earlier on in the process of working on our paper. In fact, we probably wouldn’t have had any choice because good reviewers would have pointed us to the preprint and told us that we needed to address it.

Without a preprint and with the paper still in review we could have easily told the editor that we couldn’t address the new model yet, and in fact the editor explicitly gave us that option. This would have made for a quick and easy acceptance since all the other comments involved only writing, but it arguably wasn’t in the best interests of moving science forward quickly. The new model would either be published first, or shortly after our paper, which would mean that the answer to the overarching question would have been very much up in the air. So, it would be better to add the new model to our analyses, but it would take a lot more work to do so. We would have to implement a new model from scratch, integrate it into our code base, and then rerun all of our fairly time-consuming analyses. Xiao was a newly minted PhD and James was an untenured assistant professor, so the best career strategy for them would have been to just get the paper in as is. This was particularly true for Xiao who was going to have to do the majority of the work getting the new model implemented, so we left the decision in her hands and made it clear that everyone was happy with either choice. She decided that the extra work was worth it to better answer the core question now and not only added the 2nd generation maximum entropy model, but also a more advanced version of the size-structured neutral theory model that she and James had been working on. This also broadened the scope of inference for the paper because we had now evaluated two models from each theory instead of just a single model.

Sharing the Long-term Portal Project Data: 37 years of Rodent, Plant, Ant, and Weather Data

It is with great glee that I can announce the latest release of the Portal Project Database. For those of you who just want to go play with the data – here’s the link to the Data Paper we just published in Ecology.

But I would encourage you to read on, as there is more data-related news below.

But first, a story.

As some of you know, I manage a long-term ecological study: the Portal Project. It was started by Jim Brown, Diane Davidson, and Jim Reichman back in 1977 to study competition and plant/animal interactions. That original team moved on (intellectually) and eventually retired. Tom Valone and I inherited the mantel of responsibility for the site. Jim Brown believed in sharing data with whomever asked for it, and in 2009 we formalized that philosophy by publishing all of the data from 1977-2003 that we felt was in good enough shape to document and share. We chose to release the data as an Ecology Data Paper, using Ecological Archives. Partly that was because I had great previous experiences publishing data through Ecology, and partly because I wanted something permanent. I’ve seen many people talk about their “publically available data” that was either not actually publically available, stored on a now-defunct personal website, or had so many data owner imposed hoops to jump through that it was effectively not public. I wanted the data to be available even if I died (a little grim, I know, but a real consideration when we talk about data archiving).

But we kept collecting data, which meant in 2013 we realized we had an additional 10 years of data we could share. We also had cleaned up and documented additional data that we wanted to add. So we started the process of publishing the next chunk of data. But how should we do this? Should we just add on to the existing Data Paper (assuming Ecological Archives allowed this it would be awkward since the title of the original data paper included the words 1977-2003)? We also decided to add all the graduate students who had been funded to collect the data for the project from 2003-2013, but tracking down people from the 1970s and 80s seemed unfeasible.  The short version of the story is that we opted for a separate data paper for 2003-2013, but Ecological Archives wanted a new Data Paper with all the years of data in one place – so that’s what we ended up doing. Our new Data Paper contains all the data in the original Data Paper, plus the new years of data, plus old ant and weather data that we felt we now understood well enough to let loose in the world.

It should come as no surprise to those who follow this blog that we here at Weecology are interested in open science. I love Ecological Archives as a permanent repository¹ – the data is safely in the public sphere even if I die, change universities, forget to update my website, or hand the research over to someone who doesn’t share my ideals. But publishing new data papers is a big ordeal that I only want to do every few years. If we want to make data available more rapidly (and we do), we needed another mechanism for delivery to the public.

Thus begins the Portal Project GitHub Database experiment.

What is GitHub?

Github is a web-based repository typically used for version control and management of software projects. We have created a repository on GitHub (https://github.com/weecology/PortalData) where we can create new releases of data after it has undergone our quality control processes. Here’s a screenshot of what this page looks like:

Version 1.0.0 (which is currently available) matches what is available on Ecological Archives and can be reached through this link: https://github.com/weecology/PortalData/releases or by clicking the release button on the main page of the repository (see above).

When will new data be released?

Our aim is to release a new vetted and updated version approximately every 6 months. However, you can also get our most up-to-date data from GitHub. You can find it on the main page (see figure above). As part of this process, we have moved our data entry and quality control processes to center around the Portal Data repository. Yes, that’s right, you’ll be able to access our new data as soon as we’ve entered it from our field datasheets. New data has not gone through the same level of quality control – so user beware. That data will be less stable than the release data.

Why GitHub?

GitHub met a variety of our data publishing and data management needs. I won’t go into everything here, but the big one is version control. Every time we make a change to the data files, it is documented. This has not been the case in the past. Though we did try to keep records, it relied on someone making a change in the database and then remembering to write it down somewhere. Now with our new setup, any changes will be automatically documented by commit messages (descriptions of changes that accompany any modification to a file on GitHub). It’s also publicly available, so users can use our history of changes as well, maybe to track down why results differ between two different downloads. How can you do this? Select one of the folders in the current repo – let’s randomly pick the rodent folder and look at the history of the rodent data file (Portal_rodent.csv)

This gives you all the commit messages that are associated with changes to this file. Maybe one of these catches your eye. You can see exactly what got changed by clicking on it.

The red shows you a row that has a deletion. The green a row that is “new”.

How do we feel about this shift to GitHub?

We were very nervous about this initially. While the White Lab has some serious Git-Fu skills, the Ernest Lab views itself as field ecologists and GitHub is not exactly intuitive to us. We worried we would screw up the data. We worried we were adding complexity to an already complex quality control process. But so far we are really happy about our new system. By integrating data entry into the data publishing process, it insures that we are always providing updated data, even if we’re slow on official releases. Version control is allowing us to document all the changes being made to the database – and everyone involved with the project has a chance to see the changes and comment on them if they have concerns. And everyone in our group (and now the world) has access to the most up to date data (and can choose between extremely current but still being vetted for errors or less current but more stable and less error prone). We’re not alone in taking this step to using GitHub for data management; other examples of projects that have moved to GitHub include the Biomass and Allometry Database for woody plants (BAAD) and the Open Tree of Life.

I want to end by saying that I don’t currently intend to stop submitting major updates to Ecological Archives or some other permanent repository. What GitHub provides is more transparency on how the data is being managed (both for people within and outside our group) and faster data streaming to other scientists than we’re capable of doing through Ecological Archives. But what it doesn’t do is provide the data in a stable way for ecologists in the future – and that is something we take very seriously! So if you only want to use our data via Data Papers, never fear, you now have all the data through 2013 and more will come eventually. But in the meantime, you might want to check out our data repository.

1 I might love it a little less right now since my data files are ‘Wiley Property’ housed on Wiley servers, but that’s a separate blog post.

Trait Databases: What is the End Goal?

For the past few years I’ve been involved in a collaboration to put together a broad-coverage life history database for mammals, reptiles, and birds. The project started because my collaborator, Nathan Myhrvold, and I both had projects we were interested in that involved comparing life history traits of reptiles, mammals, and birds, and only mammals had easily accessible life history databases with broad taxonomic coverage. So, we decided to work together to fix this. To save others the hassle of redoing what we were doing, we decided to make the dataset available to the scientific community. While this post started out as a standard “Hey, check out this new publication from our group” post (Here it is, by the way: Myhrvold, N.P., ^†E. Baldridge, B. Chan, D. Sivam, D.L. Freeman, S.K.M. Ernest. 2015. An Amniote Life-history Database to Perform Comparative Analyses with Birds, Mammals, and Reptiles. Ecology 96:3109), I’ve realized that there’s something more important that needs to be discussed: what is the future of trait databases?

Trait databases are all the rage these days, for good reason. Traits are interesting from evolutionary and ecological perspectives: How and why do species differ in traits, how do traits evolve, how quickly do traits change in response to changing environment, and what impacts do these differences have on community assembly and ecosystem function. They have the potential to link individual performance with local, regional, and even global processes. There’s lots of trait data out there, but most of it has been buried in papers, books, theses, gray literature, field guides, etc. This has led to the explosion of compendiums compiling trait data. Some of these are published as Data Papers (e.g.: Mammals: Jones et al 2009 , Plankton: Kremer et al 2014) or on-line databases (e.g. AnAge, FishBase), which are open for everyone to use. Many of these open datasets are generated by a small number of scientists to address some particular question. Some are quasi-open/quasi-private resources generated by consortiums of scientists (TRY).

There are a variety of issues regarding these trait compendiums, not least of which is these trait compendiums pull data from numerous sources, but how do data generators get credit and what type of credit is reasonable? This is a doozy that I don’t have an answer to. Instead, my focus today is on the eventual endgame of trait databases. No trait database currently being produced has all the trait data of interest for every species. This means we have a bunch of incomplete data products running around. So, every few years, a bigger – more complete, but still incomplete – trait dataset is produced for some group of species. Sometimes the bigger dataset replicates the effort of the smaller one, sometimes it incorporates the smaller compilation whole-cloth, sometimes they have little overlap in sources whatsoever. Data compilations vary in the ease of use and accessibility. Some databases are widely known, some are known only to a few insiders. I could keep going. Clearly this state of affairs is less than optimal for rapid progress in studying traits.

So what’s the end game here? What should we be doing? In my opinion, what we need is a centralized trait database where people can contribute trait data and where that data is easily accessible by anyone who wants to use it for research (not just to the contributing members of the database). It would also be nice if people who contribute significant amounts of data (no, I’m not going to define that here) could get specific credit for that contribution – maybe as a Data Paper or E-Publication. To encourage people to not just download data, add to it, and then sit on the expanded dataset, embargoes could be put in place to allow people to add their data to the dataset but have the data protected for a limited period of time to allow that researcher to get first crack at the publications using that entry. It’d be really nice if people who use the database could easily download all the references for the data they used so it can be easily incorporated into a literature cited section. The central database could get credit (let’s face it, it needs to be able to justify the funding that such an endeavor would require) by having people register papers published using data from the database. They could then keep track of numbers of pubs and citations to those pubs to help track the database’s impact.

Right about now, my Paleo brethren may be thinking “this sounds suspiciously familiar”. I’ve pretty much lifted this list right off of the Paleobiology Database website (https://paleobiodb.org/#/faq). While ecologists have been running our every database for itself experiment on Trait Databases, the Paleobiologists have been experimenting with collaborative open databases for fossil records. I’m an outsider, so I don’t really know how the database is perceived within the paleo community, but from the outside I have been a big fan of the database, the work that has emerged from its existence, and the community that surrounds it. Which is why I’ve wondered if ecology could some something similar.

But if we’re going to do this, I think we need to copy something else from the Paleobiology Database: a focus on individual records. Currently, many trait databases focus on a species-level value; what is the average number of offspring per litter? Seed Mass? Average body size? This is a logical place to start building a database if many of the questions are focused on comparing central tendencies across species. But our understanding of traits and the questions we want to ask have evolved. Having any info is still better than no info, but often we need info on variability across individuals within a species or we want to know how the trait might vary with changes in the environment. For this, we need record-level data. By this, I mean that instead of pooling observations to obtain an average for a species, we now often want to know that the average litter size for a species at location X is 3 but 8 at location Y. For some species, traits are especially sensitive to temperature or some other environmental variable – so knowing if the body size was measured at 28C or 32C can be important. This data could then be summarized in whatever way the user needed (species-averages, region-specific averages, etc). This, of course, is the hard part, because while we have an increasing number of trait compilations, they have either jettisoned the record information, or little of the record info is associated with the datapoint except maybe the citation name (I say this knowing I’m guilty of this). It also involves doing some form of georeferencing if we want the location info to be useable (like they’ve been doing for museum records). This means we would need to basically uncompile the compilations – find the original citations, extract as much info as we can from them, and then re-enter them as part of a more sophisticated database. This is an extraordinary amount of work that (to be clear) I am not volunteering for.

There are undoubtedly some in the trait community who are about to explode because they’ve been thinking “but we’re doing what you are talking about!”. There are indeed already some bigger initiatives out there (AnAge, FishBase, TRY) but they are either not community-based (i.e. run by a closed group), taxon-centric, or a nightmare of open and closed policies that make extracting data needlessly burdensome, or some unfortunate combo of the above. The one that seems closest to the Paleobiology Database model is TraitBank at the Enyclopedia of Life. Its goal, however, is different from the record-based trait database that I outlined above. Its goal is to have a webpage (and trait data) for every species on the planet, so this still seems to be a species average approach. As I mentioned before, some info is better than no info, so this alone would be a huge benefit to trait research, but still carries the restrictions of species-average values. On the plus side, data in the database is available for everyone to use and each data entry has the specific reference listed with it. But I don’t think it’s had broad buy-in from the trait community. TraitBank only lists 50 data sources and 327 “content partners” (websites/databases that have agreed to share their data via Encyclopedia of Life pages). Admittedly, these sources are some of the biggest data aggregations around, but it’s inconceivable that they cover the wide array of trait info for all of life. Without broad buy-in from the trait community, both using it for research and contributing their data to it, I don’t see this working in the way I’ve outlined above.

So where does this leave us? Well, things are currently in a muddle with respect to trait data, but there’s also tremendous opportunity for someone who can envision the type of database the field needs, sell broad swaths of the trait data community on its importance, and figure out how to build both the database and the community to support and use it. This may involve better community buy-in with TraitBank and/or some new initiative working on a record-level product that would allow a finer-level of question to be asked. The question is how does this happen and is there enough will in the trait community to give up on the current idiosyncratic ad hoc approach and contribute to something with broad trait and taxonomic coverage with an open data policy?

On Ecological Rants and Microcosms

Recently, over at the blog Ecological Rants the eminent ecologist Charles Krebs wrote a post about the ills of simplification in ecology. The post focuses specifically on how ecology has been ‘led astray’ by simplified models and lab studies. This has recently been picked up on Dynamic Ecology by Jeremy Fox who responded generally to the post but specifically to the affront to microcosms. I strongly recommend you check them out for yourself and not just rely on my version of events.

I went on record a long time ago (in blog years I think 2011 was a century ago) that I believe that we need a multitude of approaches, so I don’t plan on wading into the microcosm debate. That we’re still having this debate exhausts me. Instead, I want to focus on a different angle in Kreb’s post. Here’s the specific section:

“If we assume equilibrial dynamics in our communities and ecosystems, we fly in violation of almost all long term studies of populations, communities, and ecosystems. The problem lies in the space and time vision of our science. Our studies are too short to show even a good representation of dynamics over a 100 year time scale, and the problems of landscape ecology highlight that what we see in patch A may be greatly influenced by whether patches B and C are close by or not. We see this darkly in a few small studies but are compelled to believe that such landscape effects are unusual or atypical. This may in fact be the case, but we need much more work to see if it is rare or common. And the broader issue is what use do we as ecologists have for ecological predictions that cannot be tested without data for the next 100 years?”

I agree with a lot of this paragraph, though my perspective on it is different. I agree that our focus for much of the past 60 years in community ecology has been on equilibrial dynamics at a specific spatial scale with limited understanding on the impact context (i.e. what patches are near what other patches) can have on the local community. Does this make it difficult for us to predict what will happen in the dynamic world we actually live in? Yes. But unlike Krebs I don’t see the past few decades of research as a waste. We’ve learned a great deal about the fundamentals of ecological systems – species interactions, food web structure, biodiversity, niche partitioning, colonization, extinction, etc etc etc – all with the help of microcosms and simplified theory (and field studies and macroecology). We needed those decades of work to understand the basics of how communities are structured under idealized conditions.

Left: A child’s line drawing of SpongeBob’s Squidward. Right: Squidward.Does the drawing capture the essence of squidward? I’m biased, but I say yes. But how does a child get to being able to create a reasonable facsimile of something without first learning how pencils work, how they respond to hand movement, and how to simplify an image but still make it recognizable to others? I think this is also true with ecology. How do we know how to reasonably abstract a complicated system down to its most important components without first understanding what the components are and how to convey them in simple understandable ways?

Now, our challenge is to take what we have learned and apply it to the more complicated scenarios that are happening in nature (i.e. how does our Squidward change as he interacts with the dynamic setting of Bikini Bottom*). How do ecosystems change through time? What is the role of species interactions, context-dependence, and processes at different spatial and temporal scales in driving (or ameliorating) changes in food webs, niche partitioning, etc? These are pressing questions for our society as we try to predict how nature will respond to human perturbations, but these are also important for the basic development of our science. Some of this work will be done through detailed case studies out in the field, but some (hopefully) will be done with the help of theory, controlled experiments, and data-intensive approaches like macroecology to generate generalizations that help us know how to think and predict likely responses and scenarios.

The danger that I think Krebs is concerned about is that we become so attached to our clean, simplified view, our polished theories, that we refuse to engage with the more complicated scenarios. For example, if long-term studies suggest that the focus on equilibrial communities is misplaced, it would be to our detriment to continue to focus only on equilibrial communities in our theories and experiments. However, I don’t think this is happening (or if it was, I think momentum is shifting). Landscape ecology, metacommunity theory, biogeography, are all areas where people have been actively studying the very spatial issues Krebs bemoans us neglecting. I think he is more accurate about community ecology shying away from rigorously thinking about temporal dynamics, but I have a whole post on that planned, so I’ll spare you my rant. That we are starting to think about these more complex issues is what makes ecology exciting right now (and frustrating and really really hard). We have a grasp (tenuous, maybe, but a grasp nonetheless) on the fundamental, general concepts that bridge across ecosystems and organisms. We have more data, better tools, and better theoretical constructs than at any time in the past. Now is the time to tackle these more complex questions and to do so will require all the scientific approaches available to us – that includes field ecology, macroecology, theory, and, yes, microcosms.

*Yes I have been forced to watch too much SpongeBob lately.

Sciencing with a chronic illness: Tips, tricks, & technology

*This is a guest post by Elita Baldridge.**

This is the third in a series of posts about my experiences completing a PhD with a chronic illness (Part 1, Part 2, and background information).

Not only is this about the tools that I used to complete my PhD, but I am optimistic that these tools/coping mechanisms will allow me to be a scientist that gets paid for doing science.

The tips & tricks:

Remote work: Working remotely accommodated the variability in my functioning levels, and allowed me to be as productive as possible without having to allocate most of my energy to getting to/working at a physical location or trying to conserve enough energy so that I could make it home on the bus (since I can’t drive anymore).

Ergonomics: Finding what triggered more discomfort and what allowed me to work for longer periods of time really helped make it possible for me to finish up.

• Laptop of Science & a desktop, running Synergy to run one mouse and keyboard for both computers.
• Monitor risers to prevent fatigue.
• Kneeling chair to avoid obnoxious pressure points on hips, back and arms.
• Wrist rests galore.
• Kinesis Freestyle 2 keyboards, one for desk work with the dual machine setup, one for a reclining setup with just the Laptop of Science.)

Travel: Travel is dreadful. It involves a lot of discomfort while traveling, plus a lot of discomfort for weeks after. The thing that I am traveling for had better provide enough benefits to me that it is actually worth it because it is truly, truly unpleasant (of the crying and vomiting from pain variety). Remote attendance is vastly preferred.

However, if I really, really must:

• Grabber 12+ hour Peel N’ Stick body warmers, which make it possible to function on a reasonably human level most of the time.
• Cane or forearms crutches
• Wheelchair service in airports/Redcap on trains. (Voice of Experience: When you are asked if you can get to places on your own, up stairs, etc., select “no” if the answer is “yes, but it will be exceptionally unpleasant and there may be crying, whimpering, or falling over”.)
• Rest day after travel/accommodations really close to wherever you are supposed to be.
• Electric blanket for hotel (as full body heat pad)\
• Small travel blanket (for padding uncomfortable chair backs, etc.)

The technology:

Version control: Using version control (I used GitHub) allowed for a more efficient workflow between me & dissertation collaborators (mostly Ethan, but also Xiao Xiao), plus I was insulated against the effects of cognitive dysfunction through commit messages, issues, and the ability to revert commits.

Kubi: A teleconferencing robot that allowed me to turn my (remote) head and look at people when they were speaking through whatever teleconferencing system we were using. I cannot say enough good things about how much this made me feel more like a part of whatever was going on.

Web conferencing: We tended to use browser based options Google Hangouts or Firefox Hello for this, but Skype is another option as well, I just had some difficulty getting it to behave well on my laptop.

Live-streaming: For my defense, I wanted to make the presentation a demonstration of making a talk accessible, and also how easy this can be. Full details of the accommodations & accessibility statement that I used for my defense are available on the event announcement. I used Google Hangouts on Air to live-stream my defense, then close captioned the talk afterward with the editor available on YouTube. This was all straightforward and took very little time. Handouts were available in advance of the talk, and an accessibility statement was provided with my defense announcement.

Data Carpentry receives Moore Foundation funding

For the last 5 years I’ve been actively involved in training efforts through Software Carpentry and Data Carpentry to train researchers in best practices for software development and data analysis. These are concepts that are fundamental to the research we do in my gropu and my commitment to open and reproducible research.

As one of the founding members of the Data Carpentry Steering Committee, I am excited to announce that Data Carpentry has received a grant from the Gordon and Betty Moore Foundation that will help support our work over the next two years.

For those of you who aren’t familiar with Data Carpentry, we are a non-profit organization whose goal is to help teach scientists the skills they need to manage and analyze the increasingly large amounts of data that are being generated across the sciences. We do this through a combination of 2 day workshops at universities (if you’re interested in a workshop at your university request one here), and online resources including lesson material and forums. Data Carpentry is both similar to, and associated with, Software Carpentry, but with an emphasis on teaching material that is specific to particular scientific disciplines and focused on data management and analysis. We currently deliver courses for ecology/organismal biology and are in the process of developing material on genomics and geospatial data. The later in collaboration with awesome training group at NEON.

The support from the Moore Foundation will help us expand our efforts to cover new scientific domains, run far more workshops than we could have otherwise, and develop strategies for delivering this material in online workshops. I will also be leading the development of a semester long Data Carpentry course designed to make it easy to integrate these crucial skills into university classrooms. Check out the full proposal for more details.

I look forward to continuing my work with Data Carpentry and am excited about the opportunity for us to continue to enable data-intensive science by providing scientists the computational and data-oriented training they need to work with the large quantities of data we now have access to.

External challenges of chronic illness (in graduate school)

This is a guest post by Elita Baldridge (@elitabaldridge). She is a recently finished PhD in our group who has been navigating the development of a chronic illness during graduate school and beyond.

This is the second in a series of posts about my experiences completing a PhD with a chronic illness (Part 1, see also these two earlier, posts).

As I mentioned in the first post, having a chronic illness means that there can a lot of problems hiding behind the cheerful facade of graduate students you know. Here I describe some of the external challenges faced by people with chronic illness and disabilities that can be a much greater stumbling block than the condition itself.

Because I had such strong support from my family and my lab, I didn’t have to worry about  the majority of these challenges. I didn’t have to worry about finances, losing health insurance, not being able to afford medical care, having to be on campus, able to work a regular schedule.  Thus, I just had to deal with the physical challenges, and I could invest most of my energy into my research.

Finances/health insurance: Being able to afford medical care as well as paying for all of the regular expenses of being a PhD students can be extremely difficult. Although many PhD programs offer subsidized insurance, the long process of diagnosis, combined with extensive medical tests, plus the expense of treatment can leave massive amounts of medical debt. These issues can be magnified if a leave of absence is necessary, which typically eliminates both salary and insurance. Additionally, during a leave of absence, any student loans come out of deferment, making a leave of absence extremely challenging to take, and a chronic illness is unlikely to improve with a leave of absence with the added financial stress. I have a husband, who has health insurance for the both of us, and we were able to move in with my parents, and this was a situation that has been actually really great for everyone. This completely removed financial/health insurance worries from the picture, and there is always someone around for me just in case.

Advisor support:  A good advisor is important for all graduate students, but it becomes especially important when dealing with a chronic illness.  There are many stories of graduate students who have  struggled way more than neccessary due to unsupportive advisors, or unsupportive university structures. (e.g., 1, 2, 3). The Weecology lab is awesome, and just made accommodations happen. In a later post, I’ll talk about some of the technological tools that made completing my PhD possible, but my favorite was the Kubi (https://revolverobotics.com/), which let me turn my own “head” when I was remoting in. Ethan also was my advocate at the university level, to make things happen so that I could work remotely to finish up.

Working: Chronic illness can result in many factors that make getting work done challenging, from the difficulty of getting to work, due to an inability to drive, chronic fatigue, medication effects, cognitive dysfunction, or being worn down by pain and discomfort. Even the physical environment of work can be challenging; for example, fluorescent lighting that triggers migraines. Getting to campus was taxing enough that by the time I got there, I was too worn out to get much done, and would have to leave early before I broke down.  I also had to surrender my driver’s license, so I relied on public transportation (which caused a significant amount of “discomfort”) or I needed to get a ride from my husband (which caused slightly less “discomfort”).  In addition, I have flares, so I’m good to work some days, but then am unable to function either physically or mentally some days. University campuses also tend to have some major accessibility problems. I was fortunate in that my office and the lab that I taught in were on the first floor, but when I returned for my defense I had to get to the second floor of a building with a broken elevator. Working remotely allowed me to set up my workspace so that I could work longer, and didn’t have to use up all my available energy going to campus to work.

Medical: Many chronic conditions require frequent medical care, through monitoring or trying out treatment options. My condition is chronic, but not progressive, and so I didn’t actually need a lot of medical care after I got my diagnosis. We’d figured out a treatment that worked before then, and I had a great doctor (USU Student Health Center is awesome, particularly Dr. Price). Thus, I didn’t need to spend a lot of time at doctor’s appointments, which for me would end up writing off an entire day, and then generally take the next day to recover from the appointment. Before my diagnosis, I was spending a lot of time at the doctor, experimenting with new medications, to see if they would have an effect, being poked more times than a pincushion, and waiting to see specialists.

Medication:  Medications for chronic illness can have a lot of unpleasant side effects, including nausea, headaches, appetite loss, insomnia, fatigue, immune suppression, etc.  Some of us end of taking medication to treat the side effects of the medication we are on to treat our condition. Most of the treatment options for fibromyalgia work by altering levels of neurotransmitters, and thus tend to be difficult to adjust to, have a long adjustment period, and make working while adjusting impossible. In addition, the beneficial effects generally wear off over time, leaving just the negatives, which happened with the first medication I was on. Because I didn’t have months to spend trying to find another medication that might work, and I was getting work done without medication, I saved that until after I was done with my PhD. I’ve spent the last two months dealing with adjusting to a fibromyalgia treatment I haven’t tried before, am minimally functional, and it seems as if this treatment option is worse than the fibromyalgia on its own. Because fibromyalgia is variable, I still have another two months to go to make sure that the medication is not effective, or whether I could be in a flare instead.

Starting an Inclusive Ecology section in the Ecological Society of America

This is a guest post by Elita Baldridge.

I am working on organizing an Inclusive Ecology Section within the Ecological Society of America. This section will provide resources and support for all ecologists, regardless of race, sex, physical or mental ability or difference, gender identity or expression, sexual orientation, ethnicity, socio-economic status, culture or subculture, national origin,  parental status, politics,  religion, or age.   The Inclusive Ecology section will be a space for ecologists to learn about issues and problems facing ecologists from under-represented groups, share support and solutions  about problems facing ecologists from under-represented groups, work to make ecology a safe and inclusive space for all ecologists, and recognize those who have made significant contributions toward working to make ecology more inclusive to all ecologists.

The first stage in creating a new section is to collect 50 ESA member signatures on petition. If you’d like to support the idea of this section please sign the petition here:

https://docs.google.com/a/weecology.org/forms/d/1z6ALXl1g788mf8hQxUhBmCN_YUQqKMtOOj24kpjUrIQ/viewform?c=0&w=1

Additional information

Mission Statement

To provide resources and support for all ecologists, regardless of race, sex, physical or mental ability or difference, gender identity or expression, sexual orientation, ethnicity, socio-economic status, culture or subculture, national origin,  parental status, politics, religion, or age.

Proposed bylaws:

https://docs.google.com/document/d/1NLj12HJuFLX3zBCeoEy0fgK8rA2CHzuuo2zBQYNeN1s/edit?usp=sharing

Share ideas on the development of proposed ESA Inclusive Ecology section here:

https://docs.google.com/document/d/1IqyquPwOfle-EztvTuwljap00Aa3WblnzFg2iHE7cuQ/edit?usp=sharing

Successfully completing a dissertation with a chronic illness

This is a guest post by Elita Baldridge

Most people aren’t familiar with the challenges of working on a PhD with a disability or chronic illness, and yet there’s a good chance that someone you know is in this situation and isn’t talking about it. This is the first in a series of posts about my experiences completing a PhD with a chronic illness, and about the things that we can do to support our colleagues and students so that they can have the greatest chance at success. Even with the best support in the world, it’s not always possible, but it’s a lot less likely without support.

Introduction to the social model of disability

To give you a little background, I developed a chronic illness during graduate school, eventually being diagnosed with fibromyalgia. Developing a chronic illness gave me a crash course in the social model of disability; here’s the general drift.

A lack of support and accommodations is a major factor in people being unable to function effectively.

The biggest thing that keeps me from functioning effectively at this point (with accommodations) is my chronic illness itself. However, that’s the most disabling factor for me because I had the accommodations available to reduce the impact of my condition as much as possible.

For more information, check out the folks at PhD(isabled), @PhDisabled.

Personal challenges

This is the sanitized, short version of some of my symptoms. I don’t particularly like to share these sorts of things because I want to be seen as a good ecologist, not as an inspiring story of an ecologist that has triumphed over terrible odds. An ecologist is a person, a story is a thing. However, I think that it’s important to understand that while your colleague may be cheerful and smiling and upbeat, they may also be hiding a lot, and being kind and providing accommodations is a small thing that can mean the world under the circumstances.

Cognitive dysfunction: This manifests itself in many ways, but when this is severe, I can’t actually think well enough to read anything more complicated than fairy tales, let alone think well enough to do research.

“Discomfort”: Pain is supposed to be a meaningful signal that something is wrong with the part of the body that hurts. However, with fibromyalgia, things hurt without damage occurring. Pain from fibromyalgia tends to be unresponsive to a wide variety of medications, and one of the best ways of managing the pain is through an exercise regime and visualization. I tend to use the word “discomfort” rather than “pain”, because pain is supposed to be a useful signal of damage, fibromyalgia pain is not useful, and calling it discomfort helps me to try ignore it more effectively.

The clothing thing: The majority of clothing does not work for me any more, because of the feeling of wearing an upset anthill.

The stinging nettle thing: Doing computer work while hands felt like I had been crushing stinging nettles with them.

The bucket thing: Keeping a bucket by my desk while I working, because I was throwing up because of discomfort. Also, avoiding eating before meetings on bad days, so I could make it through the meeting without throwing up into the bucket.

Mobility impairment: This depends on the day. Not a problem at a computer though, so that’s fine, unless on site and the mobility impairment access is garbage (i.e., mostly everywhere).

With sufficient support and accommodations, it is possible to do good science and get a PhD while living with a disability or chronic illness. Dealing with the illness itself is difficult enough, without also having to address barriers that are put in place by people and institutions not working to make things accessible. It’s not that difficult to make things more accessible, and making things more accessible for folks with chronic illness or disabilities also tends to be just good design that makes things more accessible to people without chronic illness or disabilities. Over the next couple of posts I’ll talk about things my lab and university have done to make things more accessible and therefore facilitate my PhD. I’ll also talk about practices that I’m now putting in place for things like seminars I give to make sure that they can reach as many scientists as possible, not just the able-bodied ones.

We’re recruiting a good Scientific Software Engineer

A few months ago Mick Watson wrote an awesome post about How to recruit a good bioinformatician. We’re in the process of hiring a scientific software engineer so I thought I’d use Mick’s post to illustrate why you should come work with us doing scientific software development and data-intensive research, and hopefully provide a concrete demonstration of the sort of things Mick suggests for appealing to talented computational folks.

Here are Mick’s original suggestions and why I think our position satisfies them.

1.  Make sure they have something interesting to do

This is vital. Do you have a really cool research project? Do you have ideas, testable hypotheses, potential new discoveries? Is bioinformatics key to this process and do you recognise that only by integrating bioinformatics into your group will it be possible to realise your scientific vision, to answer those amazing questions?

Software and computational data analysis are core to everything our group does. Just check out our GitHub organization. We’re currently tackling challenging problems in: 1) automatically acquiring and combining heterogenous data; 2) combining large numbers of datasets into single research projects; 3) using machine learning and other computationally intensive modeling approaches to make predictions for ecological systems; and 4) trying to help improve computational and predictive approaches in science more broadly.

2.  Make sure they have a good environment to work in

Bioinformatics is unique, I think, in that you can start the day not knowing how to do something, and by the end of the day, be able to do that thing competently. Most bioinformaticians are collaborative and open and willing to help one another. This is fantastic. So a new bioinformatician will want to know: what other bioinformatics groups are around? Is there a journal club? Is there a monthly regional bioinformatics meeting? Are there peers I can talk to, to gain and give help and support?

Or will I be alone in the basement with the servers?

Many members of my group have strong computational and/or machine learning backgrounds (at least for a bunch of scientists). We are also part of the new Informatics Institute at the University of Florida, which is being funded in part through UF’s “Big Data” preeminence initiative. The Informatics Institute brings together faculty, students, and postdocs from across campus with interests in computational science, and the preeminence initiative is recruiting mid-career folks in this area to move to UF (including myself). I’m also a Moore Investigator in Data Driven Discovery and actively involved in the Software Carpentry and Data Carpentry communities, providing strong connections to researchers and developers in all three of these groups. In short, the challenge won’t be finding people to interact with, it will be finding time to interact with all of the different folks you want to talk to.

Speaking of servers, the other type of environment bioinformaticians need is access to good compute resources. Does your institution have HPC? Is there a cluster with enough grunt to get most tasks done? Is there a sys/admin who understands Linux?

Or were you hoping to give them the laptop your student just handed back after having used it during their 4 year PhD? The one with WIndows 2000 on it?

The University of Florida has a brand new high performance computing platform – the HiPerGator. My lab has priority access to a large number of cores on this system. We also have experience working with, and resources to support, AWS and other cloud providers to address our resource needs.

4.  Give them a development path

Bioinformaticians love opportunities to learn, both new technical skills and new scientific skills. They work best when they are embedded fully in the research process, are able to have input into study design, are involved throughout data generation and (of course) the data analysis. They want to be allowed to make the discoveries and write the papers. Is this going to be possible? Could you imagine, in your group, a bioinformatician writing a first author paper?

Technical and scientific development is strongly encouraged and supported for all members of our research group. You’ll have time and encouragement to learn new skills, support for travel to training/hackathons/conferences, and active engagement in both the scientific and software development aspects of the lab. Taking the lead on projects and writing first authored papers would be enthusiastically supported for anyone interested in doing so.

5.  Pay them what they’re worth

This is perhaps the most controversial, but the laws of supply and demand are at play here. Whenever something is in short supply, the cost of that something goes up. Pay it. If you don’t, someone else will.

We’re doing our best. The position has a top starting salary of $70,000. Thanks to the low cost of living in Gainesville that’s equivalent to about $120,000 in Silicon Valley. We can’t compete with starting salaries for industry, but at least we’re on par with starting salaries for faculty.

6.  Drop your standards

Especially true in academia. Does the job description/pay grade demand a PhD? You know what? I don’t have a PhD, and I’m doing OK (group leader for 11 years, over 60 publications, several million in grants won). Take a chance. A PhD isn’t everything

I don’t consider not requiring a PhD to be dropping my standards. We’re looking for the best person whether they have a PhD or not. If you’re good at computers and interested in science, I don’t know what more I could want.

7.  Promote them

Got funds for an RA? Try and push it up to post-doc level and emphasize the possibility of being involved in research. Got funds for a post-doc? Try and push it up to a fellowship and offer semi-independence and a small research budget. Got money for a fellowship? Try and push it up to group leader level, and co-supervise a PhD student with them.

This position could have easily been budgeted as a postdoc, but I really wanted to promote the idea of more permanent software developer/engineer positions in academic science. This position is currently funded for 5 years as part of a Moore Foundation Investigator in Data Driven Discovery award. My goal is to make this a permanent position in my group by maintaining long-term funding beyond the 5 years. If I find someone good who wants to stick around I want the salary and responsibility to grow over time (and annual increases in salary are budgeted for the next 5 years).

So, hopefully I’ve done a decent job of satisfying Mick’s requirements. If any of this sounds interesting to you, feel free to leave a comment on this post, drop me an email, chat with me on Twitter, or just go ahead and apply.