Author Archives: Ethan White
Academic publishing is in a dynamic state these days with large numbers of new journals popping up on a regular basis. Some of these new journals are actively experimenting with changing traditional approaches to publication and peer review in potentially important ways. So, I thought I’d provide a quick introduction to some of the new kids on the block that I think have the potential to change our approach to academic publishing.
PeerJ is in some ways a fairly standard PLOS One style open access journal. Like PLOS One they only publish primary research (no reviews or opinion pieces) and that research is evaluated only on the quality of the science not on its potential impact. However, what makes PeerJ different (and the reason that I’m volunteering my time as an associate editor for them) is their philosophy that in the era of the modern web it should it should be both cheap and easy to publish scientific papers:
We aim to drive the costs of publishing down, while improving the overall publishing experience, and providing authors with a publication venue suitable for the 21st Century.
The pricing model is really interesting. Instead of a flat fee per paper PeerJ uses a lifetime author memberships. For $99 (total for life) you can publish 1 paper/year. For $199 you can publish 2 papers/year and for $299 you can publish unlimited papers for life. Every author has to have a membership so for a group of 5 authors publishing in PeerJ for the first time it would cost $495, but that’s still about 1/3 of what you’d pay at PLOS One and 1/6 of what you’d pay to make a paper open access at a Wiley journal. And that same group of authors can publish again next year for free. How can they publish for so much less than anyone else (and whether it is sustainable) is a bit of open question, but they have clearly spent a lot of time (and serious publishing experience) thinking about how to automate and scale publication in an affordable manner both technically and in terms things like typesetting (since single column text no attempt to wrap text around tables and figures is presumably much easier to typeset). If you “follow the money” as Brian McGill suggests then the path may well lead you to PeerJ.
Other cool things about PeerJ:
- Optional open review (authors decide whether reviews are posted with accepted manuscripts, reviewers decide whether to sign reviews)
- Ability to comment on manuscripts with points being given for good comments.
- A focus on making life easy for authors, reviewers, and editors, including a website that is an absolute joy compared to interact with and a lack of rigid formatting guidelines that have to be satisfied for a paper to be reviewed.
We want authors spending their time doing science, not formatting. We include reference formatting as a guide to make it easier for editors, reviewers, and PrePrint readers, but will not strictly enforce the specific formatting rules as long as the full citation is clear. Styles will be normalized by us if your manuscript is accepted.
Now there’s a definable piece of added value.
Faculty of 1000 Research
Faculty of 1000 Research‘s novelty comes from a focus on post-publication peer review. Like PLOS One & PeerJ it reviews based on quality rather than potential impact, and it has a standard per paper pricing model. However, when you submit a paper to F1000 it is immediately posted publicly online, as a preprint of sorts. They then contact reviewers to review the manuscript. Reviews are posted publicly with the reviewers names. Each review includes a status designation of “Approved” (similar to Accept or Minor Revisions), “Approved with Reservations” (similar to Major Revisions), and “Not Approved” (similar to Reject). Authors can upload new versions of the paper to satisfy reviewers comments (along with a summary/explanation of the changes made), and reviewers can provide new reviews and new ratings. If an article receives two “Approved” ratings or one “Approved” and two “Approved with Reservations” ratings then it is considered accepted. It is then identified on the site as having passed peer review, and is indexed in standard journal databases. The peer review process is also open to anyone, so if you want to write a review of a paper you can, no invite required.
It’s important to note that the individuals who are invited to review the paper are recommended by the authors. They are checked to make sure that they don’t have conflicts of interest and are reasonably qualified before being invited, but there isn’t a significant editorial hand in selecting reviewers. This could be seen as resulting in biased reviews, since one is likely to select reviewers that may be biased towards liking you work. However, this is tempered by the fact that the reviewers name and review are publicly attached to the paper, and therefore they are putting their scientific reputation on the line when they support a paper (as argued more extensively by Aarssen & Lortie 2011).
In effect, F1000 is modeling a system of exclusively post-publication peer review, with a slight twist of not considering something “published/accepted” until a minimum number of positive reviews are received. This is a bold move since many scientists are not comfortable with this model of peer review, but it has the potential to vastly speed up the rate of scientific communication in the same way that preprints do. So, I for one think this is an experiment worth conducting, which is why I recently reviewed a paper there.
Oh, and ecologists can currently publish there for free (until the end of the year).
Frontiers in X
I have the least personal experience with the Frontiers’ journals (including the soon to launch Frontiers in Ecology & Evolution). Like F1000Research the ground breaking nature of Frontiers is in peer review, but instead of moving towards a focus on post-publication peer review they are attempting to change how pre-publication review works. They are trying to make review a more collaborative effort between reviewers and authors to improve the quality of the paper.
As with PeerJ and F1000Research, Frontiers is open access and has a review process that focuses on “the accuracy and validity of articles, not on evaluating their significance”. What makes Frontiers different is their two step review process. The first step appears to be a fairly standard pre-publication peer review, where “review editors” provide independent assessments of the paper. The second step (the “Interactive Review phase”) is where the collaboration comes in. Using an “Interactive Review Forum” the authors and all of the reviewers (and if desirable the associate editor and even the editor in chief for the subdiscipline) work collaboratively to improve the paper to the point that the reviewers support its publication. If disagreements arise the associate editor is tasked with acting as a mediator in the conversation. If a paper is eventually accepted then the reviewers names are included with the paper and taken as indicating that they sign off on the quality of the paper (see Aarssen & Lortie 2011 for more discussion of this idea; reviewers can withdraw from the process at any point in which case their names are not included).
I think this is an interesting approach because it attempts to make the review process a friendlier and more interactive process that focuses on quickly converging through conversation on acceptable solutions rather than slow long-form exchanges through multiple rounds of conventional peer review that can often end up focusing as much on judging as improving. While I don’t have any personal experiences with this system I’ve seen a number of associate editors talk very positively about the process at Frontiers.
This post isn’t intended to advocate for any of these particular journals or approaches. These are definitely experimental and we may find that some of them have serious limitations. What I do advocate for is that we conduct these kinds of experiments with academic publishing and support the folks who are taking the lead by developing and test driving these systems to see how they work. To do anything else strikes me as accepting that current academic publishing practices are at their global optimum. That seems fairly unlikely to me, which makes the scientist in me want to explore different approaches so that we can find out how to best evaluate and improve scientific research.
UPDATE: Fixed link to the Faculty of 1000 Research paper that I reviewed. Thanks Jeremy!
UPDATE 2: Added a missing link to Faculty of 1000 Research’s main site.
UPDATE 3: Fixed the missing link to Frontiers in Ecology & Evolution. Apparently I was seriously linking challenged this morning.
EcoBloggers is a relatively new blog aggregator started by the awesome International Network of Next-Generation Ecologists (INNGE). Blog aggregators pull together posts from a number of related blogs to provide a one stop shop for folks interested in that topic. The most famous example of a blog aggregator in science is probably Research Blogging. I’m a big fan of EcoBloggers for three related reasons.
- It provides easy access to the conversations going on in the ecology blogosphere for folks who don’t have a well organized system for keeping up with blogs. If your only approach to keeping up with blogs is to check them yourself via your browser when you have a few spare minutes (or when you’re procrastinating on writing that next paper or grant) it really helps if you don’t have to remember to check a dozen or more sites, especially since some of those sites won’t post particularly frequently. Just checking EcoBloggers can quickly let you see what everyone’s been talking about over the last few days or weeks. Of course, I would really recommend using a feed reader both for tracking blogs and journal tables of contents, but lots of folks aren’t going to do that and blog aggregators are the next best thing.
- EcoBloggers helps new blogs, blogs with smaller audiences, and those that don’t post frequently, reach the broader community of ecologists. This is important for building a strong ecological blogging community by keeping lots of bloggers engaged and participating in the conversation.
- It helps expose readers to the breadth of conversations happening across ecology. This helps us remember that not everyone thinks like us or is interested in exactly the same things.
The site is also nicely implemented so that it respects the original sources of the content
- It’s opt-in
- Each post lists the name of the originating blog and the original author
- All links take you to the original source
- It aggregates using RSS feeds you can set your site so that only partial articles show up on EcoBloggers (of course this requires you to ignore my advice on providing full feeds)
Are there any downsides to having your blog on EcoBloggers? I don’t think so. The one issue that might be raised is that if someone reads your article on EcoBloggers, then they may not actually visit your site and your stats could end up being lower than they would have otherwise. If any of the ecology blogs were making a lot of money off of advertising I could see this being an issue, but they aren’t. We’re presumably all here to engage in scientific dialogue and to communicate our ideas as brobably as possible. This is only aided by participating in an aggregator because your writing will reach more people than it would otherwise.
So, checkout EcoBloggers, use it to keep up with what’s going on in the ecology blogosphere, and sign up your blog today.
UPDATE: According to a short chat on Twitter, EcoBloggers will soon be automatically shortening the posts on their site even if your blog is providing full feeds. This means that if you didn’t buy my arguments above and were worried about loosing page views, there’s nothing to worry about. If the first paragraph or so of your post is interesting enough to get people hooked they’ll have to come over to your blog to read the rest.
Dear Ecology Letters and the British Ecological Society ,
I am writing to ask that you support the scientific good by allowing the submission of papers that have been posted as preprints. I or my colleagues have reached out to you before without success, but I have heard through various grapevines that both of you are discussing this possibility and I want to encourage you to move forward with allowing this important practice.
The benefits of preprints to science are substantial. They include:
- More rapid communication and discussion of important scientific results
- Improved quality of published research by allowing for more extensive pre-publication peer review
- A fair mechanism for establishing precedence that is not contingent the idiosyncrasies of formal peer review
- A way for early-career scientists to demonstrate productivity and impact on a time scale that matches their need to apply for postdoctoral fellowships and jobs
I am writing to you specifically because your journals represent the major stumbling block for those of us interested in improving science by posting preprints. Your journals either explicitly do not allow the submission of papers that have preprints posted online or lack explicit statements that it is OK to do so. This means that if there is any possibility of eventually submitting a paper to one of these journals then researchers must avoid posting preprints.
The standard justification that journals give for not allowing preprints is that they constitute “prior publication”. However, this is not an issue for two reasons. First, preprints are not peer reviewed. They are the equivalent of a long established practice in biology of sending manuscripts to colleagues for friendly review and to make them aware of cutting edge work. They simply take advantage of the internet to scale this to larger numbers of colleagues. Second, the vast majority of publication outlets do not believe that preprints represent prior publication, and therefore the publication ethics of the broader field of academic publishing clearly allows this. In particular Science, Nature, PNAS, the Ecological Society of America, the Royal Society, Springer, and Elsevier all generally allow the posting of preprints. Nature even wrote about this policy nearly a decade ago stating that:
Nature never wishes to stand in the way of communication between researchers. We seek rather to add value for authors and the community at large in our peer review, selection and editing… Communication between researchers includes not only conferences but also preprint servers… As first stated in an editorial in 1997, and since then in our Guide to Authors, if scientists wish to display drafts of their research papers on an established preprint server before or during submission to Nature or any Nature journal, that’s fine by us.
If you’d like to learn more about the value of preprints, and see explanations of why some of the other common concerns about preprints are unjustified, some colleagues and I have published a paper on The Case for Open Preprints in Biology.
So, I am asking that for the good of science, and to bring your journals in line with widely accepted publication practices, that you please move quickly to explicitly allow the submission of papers that have been posted as preprints.
A friend of mine once joked that doing ecological informatics meant working with data that was big enough that you couldn’t open it in an Excel spreadsheet. At the time (~6 years ago) that meant a little over 64,000 rows in a table). Times have changed a bit since then, We now talk about “big data” instead of “informatics”, Excel can open a table with a little over 1,000,000 rows of data, and most importantly there is an ever increasing amount of publicly available ecological, evolutionary, and environmental data that we can use for tackling ecological questions.
I’ve been into using relatively big data since I entered graduate school in the late 1990s. My dissertation combined analyses of the Breeding Bird Survey of North America (several thousand sites) and assembling hundreds of other databases to understand how patterns varied across ecosystems and taxonomic groups.
One of the reasons that I like using large amounts of data is that has the potential to gives us general answers to ecological questions quickly. The typical development of an ecological idea over the last few decades can generally be characterized as:
- Come up with an idea
- Test it with one or a few populations, communities, etc.
- Publish (a few years ago this would often come even before Step 2)
- In a year or two test it again with a few more populations, communities, etc.
- Either find agreement with the original study or find a difference
- Debate generality vs. specificity
- Lather, rinse, repeat
After a few rounds of this, taking roughly a decade, we gradually started to have a rough idea of whether the initial result was general and if not how it varied among ecosystems, taxonomic groups, regions, etc.
This is fine, and in cases where new data must be generated to address the question this is pretty much what we have to do, but wouldn’t it be better if we could ask and answer the question more definitely with the first paper. This would allow us to make more rapid progress as a science because instead of repeatedly testing and reevaluating the original analysis we would be moving forward and building on the known results. And even if it still takes time to get to this stage, as with meta-analyses that build on decades of individual tests, using all of the available data still provides us with a general answer that is clearer and more (or at least differently) informative than simply reading the results of dozens of similar papers.
So, to put it simply, one of the benefits of using “big data” is to get the most general answer possible to the question of interest.
Now, it’s clear that this idea doesn’t sit well with some folks. Common responses to the use of large datasets (or compilations of small ones) include concerns about the quality of large datasets or the ability of individuals who haven’t collected the data to fully understand it. My impression is that these concerns stem from a tendancy to associate “best” with “most precise”. My personal take is that being precise is only half of the problem. If I collect the best dataset imaginable for characterizing pattern/process X, but it only provides me with information on a single taxonomic group at a single site, then, while I can have a lot of confidence in my results, I have no idea whether or not my results apply beyond my particular system. So, precision is great, but so is getting genearlizable results, and these two things trade off against one another.
Which leads me to what I increasingly consider to be the ideal scenario for areas of ecological research where some large datasets (either inherently large or assembled from lots of small datasets) can be applied to the question of interest. I think the ideal scenario is a combination of “high quality” and “big” data. By analyzing these two sets of data separately, and determining if the results are consistent we can have the maximum confidence in our understanding of the pattern/process. This is of course not trivial to do. First it requires a clear idea of what is high quality for a particular question and what isn’t. In my experience folks rarely agree on this (which is why I built the Ecological Data Wiki). Second, it further increases the amount of time, effort, and knowledge that goes into the ideal study, and finding the resources to identify and combine these two kinds of data will not be easy. But, if we can do this (and I think I remember seeing it done well in some recent ecological meta-analyses that I can’t seem to find at the moment) then we will have the best possible answer to an ecological question.
- Big data and the future of ecology
- The new bioinformatics: integrating ecological data from the gene to the biosphere
- Statistical machismo (for more on the tradeoffs inherent in being more precise)
As a budding macroecologist, I have thought a lot about what skills I need to acquire during my Ph.D. This is my model of the four basic attributes for a macroecologist, although I think it is more generally applicable to many ecologists as well:
- Knowledge of SQL
- Dealing with proper database format and structure
- Finding data
- Appropriate treatments of data
- Understanding what good data are
- Monte Carlo methods
- Maximum likelihood methods
- Power analysis
- Higher level calculus
- Should be able to derive analytical solutions for problems
- Should be able to write programs for analysis, not just simple statistics and simple graphs.
- Able to use version control
- Once you can program in one language, you should be able to program in other languages without much effort, but should be fluent in at least one language.
Achieve expertise in at least 2 out of the 4 basic areas, but be able to communicate with people who have skills in the other areas. However, if you are good at collaboration and come up with really good questions, you can make up for skill deficiencies by collaborating with others who possess those skills. Start with smaller collaborations with the people in your lab, then expand outside your lab or increase the number of collaborators as your collaboration skills improve.
Achieving proficiency in an area is best done by using it for a project that you are interested in. The more you struggle with something, the better you understand it eventually, so working on a project is a better way to learn than trying to learn by completing exercises.
The attribute should be generalizable to other problems: For example, if you need to learn maximum likelihood for your project, you should understand how to apply it to other questions. If you need to run an SQL query to get data from one database, you should understand how to write an SQL query to get data from a different database.
In graduate school:
Someone who wants to compile their own data or work with existing data sets needs to develop a good intuitive feel for data; even if they cannot write SQL code, they need to understand what good and bad databases look like and develop a good sense for questionable data, and how known issues with data could affect the appropriateness of data for a given question. The data skill is also useful if a student is collecting field data, because a little bit of thought before data collection goes a long way toward preventing problems later on.
A student who is getting a terminal master’s and is planning on using pre-existing data should probably be focusing on the data skill (because data is a highly marketable skill, and understanding data prevents major mistakes). If the data are not coming from a central database, like the BBS, where the quality of the data is known, additional time will have to be added for time to compile data, time to clean the data, and time to figure out if the data can be used responsibly, and time to fill holes in the data.
Master’s students who want to go on for a Ph.D. should decide what questions they are interested in and should try to pick a project that focuses on learning a good skill that will give them a headstart- more empirical (programming or stats), more theoretical (math), more applied (math (e.g., for developing models), stats(e.g., applying pre-existing models and evaluating models, etc.), or programming (e.g. making tools for people to use)).
Ph.D. students need to figure out what types of questions they are interested in, and learn those skills that will allow them to answer those questions. Don’t learn a skill because it is trendy or you think it will help you get a job later if you don’t actually want to use that skill. Conversely, don’t shy away from learning a skill if it is essential for you to pursue the questions you are interested in.
Right now, as a Ph.D. student, I am specializing in data and programming. I speak enough math and stats that I can communicate with other scientists and learn the specific analytical techniques I need for a given project. For my interests (testing questions with large datasets), I think that by the time I am done with my Ph.D., I will have the skills I need to be fairly independent with my research.
We had a great time at ESA this year and enjoyed getting to interact with lots of both old and new friends and colleagues. Since we’re pretty into open science here at Weecology, it’s probably not surprising that we have a lot of slides (and even scripts) from our many and varied talks and posters posted online, and we thought it might be helpful to aggregate them all in one place. Enjoy.
- Morgan Ernest‘s Ignite talk on Why Constraint Based Approaches to Ecology (with script)
- Morgan Ernest‘s talk on Biotic Responses to shifting Ecological Drivers in a Desert Community
- Ethan White‘s Ignite talk on Big Data in Ecology (with script)
- Ethan White‘s talk on Evaluating a General Theory of Macroecology
- Dan McGlinn‘s Ignite talk on Constraint Based Species-Area Relationships
- Dan McGlinn‘s talk on Modeling Geographic Patterns in the Species Abundance Distribution
Ken Locey‘s Ignite talk on The Feasible Set: Putting Pattern Into Perspective
Thanks to Dan McGlinn for help to assembling the links.
Slides and script from Ethan White’s Ignite talk on Big Data in Ecology from Sandra Chung and Jacquelyn Gill‘s excellent ESA 2013 session on Sharing Makes Science Better. Slides are also archived on figshare.
1. I’m here to talk to you about the use of big data in ecology and to help motivate a lot of the great tools and approaches that other folks will talk about later in the session.
2. The definition of big is of course relative, and so when we talk about big data in ecology we typically mean big relative to our standard approaches based on observations and experiments conducted by single investigators or small teams.
3. And for those of you who prefer a more precise definition, my friend Michael Weiser defines big data and ecoinformatics as involving anything that can’t be successfully opened in Microsoft Excel.
4. Data can be of unusually large size in two ways. It can be inherently large, like citizen science efforts such as Breeding Bird Survey, where large amounts of data are collected in a consistent manner.
5. Or it can be large because it’s composed of a large number of small datasets that are compiled from sources like Dryad, figshare, and Ecological Archives to form useful compilation datasets for analysis.
6. We have increasing amounts of both kinds of data in ecology as a result of both major data collection efforts and an increased emphasis on sharing data.
7-8. But what does this kind of data buy us. First, big data allows us to work at scales beyond those at which traditional approaches are typically feasible. This is critical because many of the most pressing issues in ecology including climate change, biodiversity, and invasive species operate at broad spatial and long temporal scales.
9-10. Second, big data allows us to answer questions in general ways, so that we get the answer today instead of waiting a decade to gradually compile enough results to reach concensus. We can do this by testing theories using large amounts of data from across ecosystems and taxonomic groups, so that we know that our results are general, and not specific to a single system (e.g., White et al. 2012).
11. This is the promise of big data in ecology, but realizing this potential is difficult because working with either truly big data or data compilations is inherently challenging, and we still lack sufficient data to answer many important questions.
12. This means that if we are going to take full advantage of big data in ecology we need 3 things. Training in computational methods for ecologists, tools to make it easier to work with existing data, and more data.
13. We need to train ecologists in the computational tools needed for working with big data, and there are an increasing number of efforts to do this including Software Carpentry (which I’m actively involved in) as well as training initiatives at many of the data and synthesis centers.
14. We need systems for storing, distributing, and searching data like DataONE, Dryad, NEON‘s data portal, as well as the standardized metadata and associated tools that make finding data to answer a particular research question easier.
15. We need crowd-sourced systems like the Ecological Data Wiki to allow us to work together on improving insufficient metadata and understanding what kinds of analyses are appropriate for different datasets and how to conduct them rigorously.
16. We need tools for quickly and easily accessing data like rOpenSci and the EcoData Retriever so that we can spend our time thinking and analyzing data rather than figuring out how to access it and restructure it.
17. We also need systems that help turn small data into big data compilations, whether it be through centralized standardized databases like GBIF or tools that pull data together from disparate sources like Map of Life.
18. And finally we we need to continue to share more and more data and share it in useful ways. With the good formats, standardized metadata, and open licenses that make it easy to work with.
19. And so, what I would like to leave you with is that we live in an exciting time in ecology thanks to the generation of large amounts of data by citizen science projects, exciting federal efforts like NEON, and a shift in scientific culture towards sharing data openly.
20. If we can train ecologists to work with and combine existing tools in interesting ways, it will let us combine datasets spanning the surface of the globe and diversity of life to make meaningful predictions about ecological systems.
I’m a big fan of preprints, the posting of papers in public archives prior to peer review. Preprints speed up the scientific dialogue by letting everyone see research as it happens, not 6 months to 2 years later following the sometimes extensive peer review process. They also allow more extensive pre-publication peer review because input can be solicited from the entire community of scientists, not just two or three individuals. You can read more about the value of preprints in our preprint about preprints (yes, really) posted on figshare.
In the spirit of using preprints to facilitate broad pre-publication peer review a group of weecologists have just posted a preprint on how to make it easier to reuse data that is shared publicly. Since PeerJ‘s commenting system isn’t live yet we would like to encourage your to provide feedback about the paper here in the comments. It’s for a special section of Ideas in Ecology and Evolution on data sharing (something else I’m a big fan of) that is being organized by Karthik Ram (someone I’m a big fan of).
Our nine recommendations are:
- Share your data
- Provide metadata
- Provide an unprocessed form of the data
- Use standard data formats (including file formats, table structures, and cell contents)
- Use good null values
- Make it easy to combine your data with other datasets
- Perform basic quality control
- Use an established repository
- Use an established and liberal license
Most of this territory has been covered before by a number of folks in the data sharing world, but if you look at the state of most ecological and evolutionary data it clearly bears repeating. In addition, I think that our unique contribution is three fold: 1) We’ve tried hard to stick to relatively simple things that don’t require a huge time commitment to get right; 2) We’ve tried to minimize the jargon and really communicate with the awesome folks who are collecting great data but don’t have much formal background in the best practices of structuring and sharing data; and 3) We contribute the perspective of folks who spend a lot of time working with other people’s data and have therefore encountered many of the most common issues that crop up in ecological and evolutionary data.
So, if you have the time, energy, and inclination, please read the preprint and let us know what you think and what we can do to improve the paper in the comments section.
UPDATE 2: PeerJ has now enabled commenting on preprints, so comments are welcome directly on our preprint as well (https://peerj.com/preprints/7/).
Over at Dynamic Ecology this morning Jeremy Fox has a post giving advice on how to decide where to submit a paper. It’s the same basic advice that I received when I started grad school almost 15 years ago and as a result I don’t think it considers some rather significant changes that have happened in academic publishing over the last decade and a half. So, I thought it would be constructive for folks to see an alternative viewpoint. Since this is really a response to Jeremy’s post, not a description of my process, I’m going to use his categories in the same order as the original post and offer my more… youthful… perspective.
- Aim as high as you reasonably can. The crux of Jeremy’s point is “if you’d prefer for more people to read and think highly of your paper, you should aim to publish it in a selective, internationally-leading journal.” From a practical perspective journal reputation used to be quite important. In the days before easy electronic access, good search algorithms, and social networking, most folks found papers by reading the table of contents of individual journals. In addition, before there was easy access to paper level citation data, and alt-metrics, if you needed to make a quick judgment on the quality of someones science the journal name was a decent starting point. But none of those things are true anymore. I use searches, filtered RSS feeds, Google Scholar’s recommendations, and social media to identify papers I want to read. I do still subscribe to tables of contents via RSS, but I watch PLOS ONE and PeerJ just as closely as Science and Nature. If I’m evaluating a CV as a member of a search committee or a tenure committee I’m interested in the response to your work, not where it is published, so in addition to looking at some of your papers I use citation data and alt-metrics related to your paper. To be sure, there are lots of folks like Jeremy that focus on where you publish to find papers and evaluate CVs, but it’s certainly not all of us.
- Don’t just go by journal prestige; consider “fit”. Again, this used to mater more before there were better ways to find papers of interest.
- How much will it cost? Definitely a valid concern, though my experience has been that waivers are typically easy to obtain. This is certainly true for PLOS ONE.
- How likely is the journal to send your paper out for external review? This is a strong tradeoff against Jeremy’s point about aiming high since “high impact” journals also typically have high pre-review rejection rates. I agree with Jeremy that wasting time in the review process is something to be avoided, but I’ll go into more detail on that below.
- Is the journal open access? I won’t get into the arguments for open access here, but it’s worth noting that increasing numbers of us value open access and think that it is important for science. We value open access publications so if you want us to “think highly of your paper” then putting it where it is OA helps. Open access can also be important if you “prefer for more people to read… your paper” because it makes it easier to actually do so. In contrast to Jeremy, I am more likely to read your paper if it is open access than if it is published in a “top” journal, and here’s why: I can do it easily. Yes, my university has access to all of the top journals in my field, but I often don’t read papers while I’m at work. I typically read papers in little bits of spare time while I’m at home in the morning or evenings, or on my phone or tablet while traveling or waiting for a meeting to start. If I click on a link to your paper and I hit a paywall then I have to decide whether it’s worth the extra effort to go to my library’s website, log in, and then find the paper again through that system. At this point unless the paper is obviously really important to my research the activation energy typically becomes too great (or I simply don’t have that extra couple of minutes) and I stop. This is one reason that my group publishes a lot using Reports in Ecology. It’s a nice compromise between being open access and still being in a well regarded journal.
- Does the journal evaluate papers only on technical soundness? The reason that many of us think this approach has some value is simple, it reduces the amount of time and energy spent trying to get perfectly good research published in the most highly ranked journal possible. This can actually be really important for younger researchers in terms of how many papers they produce at certain critical points in the career process. For example, I would estimate that the average amount of time that my group spends getting a paper into a high profile journal is over a year. This is a combination of submitting to multiple, often equivalent caliber, journals until you get the right roll of the dice on reviewers, and the typically extended rounds of review that are necessary to satisfy the reviewers about not only what you’ve done, but satisfying requests for additional analyses that often aren’t critical, and changing how one has described things so that it sits better with reviewers. If you are finishing your PhD then having two or three papers published in a PLOS ONE style journal vs. in review at a journal that filters on “importance” can make a big difference in the prospect of obtaining a postdoc. Having these same papers out for an extra year accumulating citations can make a big difference when applying for faculty positions or going up for tenure if folks who value paper level metrics over journal name are involved in evaluating your packet.
- Is the journal part of a review cascade? I don’t actually know a lot of journals that do this, but I think it’s a good compromise between aiming high and not wasting a lot of time in review. This is why we think that ESA should have a review cascade to Ecosphere.
- Is it a society journal? I agree that this has value and it’s one of the reasons we continue to support American Naturalist and Ecology even though they aren’t quite as open as I would personally prefer.
- Have you had good experiences with the journal in the past? Sure.
- Is there anyone on the editorial board who’d be a good person to handle your paper? Having a sympathetic editor can certainly increase your chances of acceptance, so if you’re aiming high then having a well matched editor or two to recommend is definitely a benefit.
To be clear, there are still plenty of folks out there who approach the literature in exactly the way Jeremy does and I’m not suggesting that you ignore his advice. In fact, when advising my own students about these things I often actively consider and present Jeremy’s perspective. However, there are also an increasing number of folks who think like I do and who have a very different set of perspectives on these sorts of things. That makes life more difficult when strategizing over where to submit, but the truth is that the most important thing is to do the best science possible and publish it somewhere for the world to see. So, go forth, do interesting things, and don’t worry so much about the details.
UPDATE: If you’re looking for the information for 2014, checkout the DEBrief post for links.
It’s that time of year again when we’re all busy working on preproposals for the National Science Foundation, and just like last year it’s more difficult than you would think to track down the official guidelines using Google. So, for all of you who don’t have a minute to spare, here they are:
Also, remember that Biographical Sketches are different than for full proposals:
Biographical Sketches (2-page limit for each) should be included for each person listed on the Personnel page. It should include the individual’s expertise as related to the proposed research, professional preparation, professional appointments, five relevant publications, five additional publications, and up to five synergistic activities. Advisors, advisees, and collaborators should not be listed on this document, but in a separate table (see below).
And that there is a big stack of things that should not be included at this stage in the process:
Budget, Budget Justification, Facilities, Equipment and Other Resources, Current and Pending Support, Letters of Collaboration, Data Management Plan, Postdoctoral Mentoring Plan, RUI Impact Statement, Certification of RUI Eligibility, or any other Supplementary Documents.
UPDATE: Included separate links for DEB and IOS.