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.
Communicating research more broadly is not only important for outreach to the public, but with the rapidly expanding literature, we think it’ll also be important for communicating to other scientists. Back in 2012 we started a post type called [Research Summary] which is based on the idea that people might not have time to read a multi-page paper but might be willing to read a <1000 word post conveying the ideas in the paper in a more casual format. Ethan did one of these for an Ecology paper his group published last year. Below, one of our graduate students, Sarah Supp, has taken up the challenge to communicate about her first-authored Ecology paper that just came out and has written the guest post below.
This is a research summary of: S. R. Supp, X. Xiao, S. K. M. Ernest, and E. P. White. 2012. An experimental test of the response of macroecological patterns to altered species interactions. Ecology 93: 2505-2511. doi:10.1890/12-0370.1
While many ecologists focus on why individuals, species, or ecological habitats differ, macroecologists are often fascinated by similarities among different groups or ecosystems. This focus on similarities emerges because macroecologists treat individuals, populations and species as ecological particles, and identify patterns in the structure of these particles to understand ecological systems and organization.
Despite a long history of documenting macroecological patterns, an understanding of what determines pattern behavior, why patterns are so easily predicted by so many different models, and how we should go about addressing real ecological problems using a macroecological approach has still not been reached.
Three common macroecological patterns, and the focus for our study, include: the species abundance distribution (SAD; distribution of abundance across species), the species-area relationship (SAR; accumulation of species across spatial scales), and the species-time relationship (STR; accumulation of species through time). Since these patterns exhibit regular behavior across taxonomic groups and ecological habitats, they are increasingly being used to make inferences about local-scale ecological processes and to inform management decisions.
Recently, discussion on how to do ecology has sometimes presented a dichotomy between two groups: “species identity matters” vs. “species identity is unimportant”. A more useful way of discussing the problem likely lies in asking more nuanced questions such as: How important are species identities for my specific question? When does species identity impact ecological organization? At what spatial/temporal scales? When are species identities necessary for prediction? For example, recent models suggest that the identity of species within a community or ecosystem is unimportant for predicting the shape of macroecological patterns. Instead, these models suggest that some macroecological patterns may only be sensitive to changes in the species richness or total abundance of the ecosystem being examined. This idea is pretty radical (in our opinion), but untested.
We took an approach that we felt could address a few problems simultaneously: 1) Does species identity play a role in determining the form of macroecological patterns, or are patterns only sensitive to changes in species richness or total abundance? 2) Can we effectively synthesize our detailed knowledge of a system with a macroscopic approach in order to link pattern with process?
We used 15 years of data from the Portal Project, our lab’s long-term research site in southeastern Arizona, to evaluate the response of the summer and winter annual plant communities to selective removal of rodent seed predators. It is not known if altered species identity alone (changes in species composition caused by manipulating an important interaction, seed predation [above]) can lead to shifts in the form of these patterns, in the absence of other changes (such as species richness and total abundance). At the Portal site, interactions within and among rodent and plant communities are well studied and we felt that this made our site an ideal experimental venue for this project. Among experimental treatments (control, kangaroo rat removal, and removal of all rodents), we compared changes in plant species composition, species richness (S), total abundance (N), and the form of each of the macroecological patterns (SAD, SAR, and STR). Below are examples of the data for each pattern from plot 22, a control plot, and a photo of a plant sampling quadrat. In the photo, California poppy (Escholtzia mexicana) and Stork’s bill dominate (Erodium cicutarium).
We found that plant species composition was always influenced by the removal of kangaroo rats and by removal of all rodent seed predators. Interestingly, we also found that removing kangaroo rats (keystone species) did not influence plant species richness or total abundance. This suggests that compensatory dynamics are at work. Finally, when we compared the macroecological patterns among the experimental treatments, we found differences in the macro-patterns only occurred when plant species richness or total abundance was altered, and not by compositional changes alone. (Below: Where the parameters do not cross the dotted line, there were significant differences among the paired manipulations [R-C: total rodent removals vs. controls; K-C: kangaroo rat removals vs. controls]. Note that the only place this occurs is in the winter annual community when species richness [S] and total abundance [N] are affected by the removal of all rodent seed predators. The rodent pictured is Merriam’s Kangaroo Rat, Dipodomys merriami.)
So what does this mean? Let’s revisit our initial questions:
1) Does species identity plan a role in determining the form of macroecological patterns, or are patterns only sensitive to changes in species richness or total abundance? Our research suggests that the key to interpreting and predicting patterns lies in our ability to make realistic predictions of community level variables, such as species richness or total abundance. Although changes in species richness or total abundance were most the important predictors for change in our macroecological patterns, we are not suggesting that changes in species identity are inconsequential. In fact, we believe that our findings suggest an important, but indirect role, for the role of species interactions in determining macroecological patterns. Species interactions may facilitate or hinder compensation dynamics, which in turn may lead to shifts in the number of species or the total number of individuals in response to manipulation. In a recent paper, Brian McGill suggests that what drives S and N is a central unanswered question in ecology.
2) Can we effectively synthesize our detailed knowledge of a system with a macroscopic approach in order to link pattern with process? We feel that our approach of using small-scale experimental field data with macroecology (which has largely relied on large-scale observational data) provides a potentially powerful framework for improving our understanding of the linkages between pattern and process. Studies using a similar approach may help bridge important gaps between pattern and process, between local and regional scale ecology and between basic and applied science.
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.
Over the past year, you can’t get two scientists together who submit to the BIO Directorate at NSF without the conversation drifting to the radical changes in the proposal process. If you have no idea what I’m talking about, I’ve added some links at the bottom of the post for you to check out. For everyone else, suffice it to say that there has been immense speculation about the possible impacts of these changes on the scientific process. Well, over the winter break, DEB released its data to date (IOS did this a little earlier and comparisons between IOS and DEB are discussed here). So let’s see what happened!
Table 1. Basic Stats on Funding Rates
|Preproposal Invites for Full Submission||380|
|Full proposals recommended for funding||259|
|*^Number of proposals to be funded||83.6|
|Preproposal Invitation Rate||23.4%|
|New Investigator Preproposal Invitation Rate||20.4%|
|Full Proposal Panel Recommendation Rate||68%|
|Early Career Investigator Full Proposal Panel Recommendation Rate||35%|
|*Anticipated Overall Fund Rate on Full Proposal Panel||22%|
|*^Overall fund rate from preproposal pool||5.1%|
^ numbers I’ve estimated given the statistics provided by NSF *value complicated by uncertain fund rate
You’ll notice some of the items in the table are starred. That’s because things get a little…complicated in the full proposal funding data. When DEB released the data, funding decisions weren’t finalized, so they only had an estimate of funding rates. Also some full proposals didn’t need to submit preproposals to DEB (e.g. CAREER, OPUS, RCN, co-reviewed proposals from other divisions), so the starred items have two possible sources of fuzziness: non-preproposal proposals and uncertain fund rates. The NSF info doesn’t make some of this transparent. For example, NSF reports a full proposal ‘success rate’ of 35% of the 82 full proposals submitted by early career investigators through the pre proposal process. However, the accompanying table (see below) on success rates over the past 5 years shows the 2012 data as 16% out of 181 proposals. I assume the numbers don’t match due to the proposals submitted outside the preproposal process (i.e. CAREERs). It’s also unclear to me whether ‘success rate’ is ‘recommended for funding’ or actual funding.
Table 2. Statistics for Early Career Investigators over past 5 years:
|Fiscal Year||Success Rate||# proposals||% total submissions|
Interesting Stats to Chew on:
1) Preproposal Funding rates: Let’s assume funding rates for full proposals did not differ between CAREERS, RCNs, or invited preproposals (an assumption that is probably wrong). If that’s the case, then in table 1 I estimated the funding rate of the preproposals at 5.1% (i.e. 5.1% of preproposals eventually got funded as a full proposal). It’s important to note that 5.1% is probably wrong, but how wrong is unclear as it hinges on how different the fund rate is for CAREERS, etc. My guess is that the preproposal fund rate is a little higher because things like CAREERs have a lower fund rate and thus bring down the overall average. However, I’d be surprised if the difference takes preproposals above 10%.
2) Quality of Full Proposals: 68% of proposals made a funding category (i.e. not allocated to the ‘Do Not Fund’ category). I’d be interested in seeing the data from previous years, but 68% seems high from my limited experience.
3) Early Career vs overall funding rates: Focusing on the preproposal process data only (i.e. not Table 2 data), my interpretation is that the young people fared well through the preproposal process but took a serious hit in the full proposal process (35% of young investigators recommended for funding vs. an overall recommendation rate of 68%). As a disclaimer, the preproposal data is post-portfolio balancing while the full proposal data seems to be pre-portfolio balancing, so it’s possible that the preproposal panels were equally hard on the youngsters but that the Program Directors corrected for it.
4) Early Career funding rates: I’ve been studiously ignoring Table 2 (as you might have noticed). The truth is even if funding rates were equal between established and early career scientists, 5.1% success rates (or even 10%) mean that anyone who needs a grant to have a job (whether to get tenure or because they are on soft money) or to keep research going (labs that needs techs or uninterrupted data collection) is in a tough spot right now. Additional biases in funding rates clearly exasperate the situation for our young scientists and this is something we should all be aware of when our young colleagues ask for our help or come up for tenure.
There’s enough nebulous stuff here that I’m going to hold off on any grandiose statements until NSF releases its full report in early 2013. But the following are things that the data made me start thinking about:
1) There’s nothing in the NSF data thus far that changes my opinion about the preproposal revolution: until NSF has more money to fund science, 5.1% funding rates are the real enemy of science. What NSF is doing is more akin to shuffling the deck chairs than to blowing a hole in the hull.
2) The preproposals per se don’t seem to be filtering out the young people. It’s the full proposal process that seems to be the big hurdle to funding. I suspect that is not a novel result of the new system, but has been true all along. The interesting insight that the preproposal data might suggest is that the lower funding rates have nothing to do with the ideas of the young scientists but more to do about either the methodologies or with how those methodologies are being communicated.
3) There’s clearly two things that will help our younger scientists: a) increasing funding rates overall (not a solution in NSF’s power) and b) figuring out why the bias in the full proposal rates exists and figuring out how to fix it (something we can all try to work on). Assuming that the lower recommendation rate for full proposals of young career scientists is due to their proposal and not a bias against young scientists (i.e. lower name recognition), then this might be a legitimate argument for how the new system could hurt young people: young people may need more submissions of full proposals (and more panel feedback) before managing to get a proposal recommended for funding.
Additional Links about the Changes at NSF:
Contemplative Mammoth: Inside NSF-DEB’s New Pre-Proposals: A Panelist’s Perspective
Jabberwocky Ecology: Changes in NSF process for submissions to DEB and IOS*, NSF Proposal Changes – Follow-up
We’re looking for a new student to join our interdisciplinary research group. The opening is in Ethan’s lab, but the faculty, students, and postdocs in Weecology interact seamlessly among groups. If you’re interested in macroecology, community ecology, or just about anything with a computational/quantitative component to it, we’d love to hear from you. The formal ad is included below (and yes, we did include links to our blog, twitter, and our GitHub repositories in the ad). Please forward this to any students who you think might be a good fit, and let us know if you have any questions.
GRADUATE STUDENT OPENING
The White Lab at Utah State University has an opening for a graduate student with interests in Macroecology, Community Ecology, or Ecological Theory/Modeling. Active areas of research in the White lab include broad scale patterns related to biodiversity, abundance and body size, ecological dynamics, and the use of sensor networks for studying ecological systems. We use computational, mathematical, and advanced statistical methods in much of our work, so students with an interest in these kinds of methods are encouraged to apply. Background in these quantitative techniques is not necessary, only an interest in learning and applying them. While students interested in one of the general areas listed above are preferred, students are encouraged to develop their own research projects related to their interests. The White Lab is part of an interdisciplinary ecology research group (http://weecology.org) whose goal is to facilitate the broad training of ecologists in areas from field work to quantitative methods. Students with broad interests are jointly trained in an interdisciplinary setting. We are looking for students who want a supportive environment in which to pursue their own ideas. Graduate students are funded through a combination of research assistantships, teaching assistantships, and fellowships. Students interested in pursuing a PhD are preferred. Utah State University has an excellent graduate program in ecology with over 50 faculty and 80+ graduate students across campus affiliated with the USU Ecology Center (http://www.usu.edu/ecology/).
Additional information about the position and Utah State University is available at:
Interested students can find more information about our group by checking out:
Our websites: http://whitelab.weecology.org, http://weecology.org
Our code repositories: http://github.com/weecology
Our blog: http://jabberwocky.weecology.org
And Twitter: http://twitter.com/ethanwhite
Interested students should contact Dr. Ethan White (email@example.com) by December 1st, 2012 with their CV, GPA, GRE scores (if available), and a brief statement of research interests.
ESA has just announced that it has changed its policy on preprints and will now allow articles that have been posted on major preprint servers, like arXiv, to be considered for publication in its journals.
All ESA journals accept submissions of ms that have previously been posted to a preprint service such as arXiv! A great way to get feedback!—
Scott L Collins (@ESA_Prez2013) September 05, 2012
I am very excited about this change for two reasons. First, as nicely laid out in INNGE blog post by Philippe Desjardins-Proulx*, there are many positive benefits to science of the preprint culture. They make science more accessible, allow researchers to get feedback from the community prior to peer review, and speed up the scientific process by making ideas available to others as quickly as possible. We should take this opportunity as a community to start developing the kind of vibrant preprint culture that has benefited so many other disciplines. Second, I am encouraged by the rapid response of ESA to the concerns expressed by myself and other members of the community, and take it as a sign that my favorite society is open to making the kinds of changes that are necessary to best facilitate science in the modern era. More work is clearly necessary, but this is a very encouraging start.
UPDATE: Carl Boettiger has posted his very nice letter to Don Strong that played an critical roll in taking this discussion from a bunch of folks talking over social media to something that effected meaningful change.
We have all bemoaned the increasing difficulty of keeping up with the growing body of literature. Many of us, me included, have been relying increasingly on following only a subset of journals, but with the growing popularity of the large open-access journals I know I for one am increasingly likely to miss papers. The purpose of this post isn’t to give you the panacea to your problems (sadly I don’t think there is a panacea to this issue, though I have hopes that someone will come up with something viable in the future). The purpose of this post is to let you know about an interesting addition or alternative (for the brave) to the frantic scanning of the table of contents or RSS feeds: Google Scholar.
Almost everyone at this point knows you can go to Google Scholar and search for key words and it’ll produce a list of papers. Did you also know that you can set up a google profile with your published articles and that Google can use that to find articles that might be of interest to you. How does it do that? I’ll have to quote Google’s Blog because it’s a little like voodoo to me (obviously this is Morgan writing this post, not Ethan): “We determine relevance using a statistical model that incorporates what your work is about, the citation graph between articles, the fact that interests can change over time, and the authors you work with and cite. “ When you go to Google Scholar’s homepage (and you’re logged in as you) it’ll notify you if there are new articles on your suggested list. I actually have been pleasantly surprised by the articles it has identified for me, including some book chapters I would never have seen. For example here’s several things that sound really interesting to me, but I would never have seen:
MC Emmerson – Marine Biodiversity and Ecosystem Functioning: …, 2012 – books.google.com
A Potochnik, B McGill – Philosophy of Science, 2012 – JSTOR
D West, J BRUCE – International Journal of Modern Physics B, 2012 – World Scientific
It doesn’t just search published journal articles. For example there are preprints from arXiv and government reports on my list. I don’t know if this would work as well for the young graduate students/postdocs since it uses the citations in your existing papers and our junior colleagues might have less data for Google to work with. However, once you have a profile, you can also follow other people who have profiles, which means you get an email every time scholarly work gets added to their profile. Are you a huge Simon Levin groupie? You can follow him and every time a paper gets added to his profile, you can get an email alerting you about the new paper. I also use this to follow a bunch of interesting younger people because they often publish less frequently or in journals I don’t happen to follow and this way I don’t miss their stuff when my Google Reader hits 1000+ articles to be perused! You can also sign up for alerts when someone you follow has their work cited. (And you can set up alerts for when your own work gets cited as well).
As I said before, I don’t think Google Scholar is a one-stop literature monitoring stop (yet), but I find it useful for getting me out of my high impact factor monitoring rut. The only thing you need to do is set up your Google Scholar profile and the only reason not to do that is if you’re worried it’ll give Google the edge when it finally becomes self-aware and renames itself Skynet (ha ha ha ha….hmmm).
Recently a bunch of folks in the biological sciences have started sharing their grant proposals openly. Their reasons for doing so are varied (see the links next to their names below), but part of the common justification is a general interest in opening up science so that all stages of the process can benefit from better interaction and communication, and part of it is to provide examples for younger scientists writing grants. To help accomplish both of these goals I’m going to do what Titus Brown suggested and compile a list of all of the available open proposals in the biological sciences (if you’re looking for math proposals they have a list too). Given the limited number of proposals available at the moment I’m just going to maintain the list here, sorted alphabetically by PI. Another way to find proposals is to look at the ‘grant’ and ‘proposal’ tags on figshare, where several of us have been posting proposals. If you know of more proposals, decide to post some yourself, or have corrections to proposal in the list, just let me know in the comments and I’ll keep the list updated. Enjoy!
- 2008 / New Investigator Grant Application (NERC) *funded
- 2008 / EMBO Young Investigator Programme Application (EMBO)
- 2007 / Responsive Mode Grant Application (BBSRC)
- 2012 / NSF Office of Cyberinfrastructure proposal, Materials and Workshops for Cyberinfrastructure Education in Biology supplement to BEACON. *funded
- 2012 / NSF CAREER proposal, Assembling Extremely Large Metagenomes
- 2012 / NSF BIGDATA proposal, Low-memory Streaming Prefilters for Biological Sequencing Data
- 2012 / Moore Foundation proposal on marine metagenomics
- 2011 / NSF CAREER proposal: “Scaling and Improving de Bruijn graph assembly”
- 2010 / Next-gen course (NIH R25) *funded
- 2009 / Web tools for next-gen sequence analysis (USDA) *funded
- 2007 / Cartwheel
- Kathryn Fuller Doctoral Fellowship application (WWF)
- 2010 / Prairie Biotic Research proposal *funded
- 2009 / Ecological and evolutionary impacts of pollinator sharing between cultivated and wild sunflowers (Norman Hackerman Advanced Research Program)
- 2009 / Lewis and Clark grant proposal (American Philosophical Society)
- Doctoral Dissertation Improvement Grant proposal (NSF)
- Forest Shreeve Award proposal
- Ariel Appleton Research Fellowship Proposal – Ecological Networks
- How do crop-mediated changes in mutualist and antagonist communities affect selection on floral and defense traits?
- 2011 / “Automated and community synthesis of the tree of life” (NSF AVATOL) *funded
- 2010 / “Towards a comprehensive, community-owned and sustainable repository of reusable phylogenetic knowledge” w/Hilmar Lapp (NSF ABI)
- 2009 / “A network for enabling community-driven standards to link evolution into the global web of data (EvoIO)” w/Hilmar Lapp (NSF INTEROP)
- 2012 / Understanding range shift model error: The inﬂuence of generation time and rate of adaptation on species distribution model predictions. w/Scott Chamberlain (NCEAS proposal).
- 2008 / Evolution under simulated climate change in response to trophic shifts. (NSF DDIG) *funded
- 2010 / Protein Design Using Quantum Mechanics (Danish Center for Supercomputing) *funded
- 2008 / Computational Design of Stable Enzymes (Danish National Science Foundation, DSF-NABIIT) *funded
- 2006 / Modeling pH-Dependence in Drug Design (EU Marie Curie Program) *funded
- 2006 / Computational Prediction and Validation of Protein Structure and Function in Protein Engineering and Rational Drug Design (Danish National Science Foundation, FNU) *funded
- 2006 / Prediction and Interpretation of Protein pKa’s Using QM/MM (US National Science Foundation – MCB; rescinded when I moved to Denmark) *funded
- 2002 / The Prediction and Interpretation of Protein pKa’s Using QM/MM (US National Science Foundation – MCB) *funded
- 2010 / Ontology-enabled reasoning across phenotypes from evolution and model organisms w/Todd Vision (NSF) *funded
Heather Piwowar (@researchremix) & Jason Priem (@jasonpriem) (read their thoughts on sharing proposals)
- Uptake proposal (CIHR)
- 2007 / Sxy proposal (CIHR) *funded
- 2001 / CIHR proposal *funded
- 1999 / NIH proposal *funded
- 2012 / Data Management and Computational Skills Training for LTER Scientists w/Ethan White & Greg Wilson (LTER Training Working Groups Proposal)
- 2011 / Fuelwood, Savannas, and Climate Change: Integrating Modeling, Field Experimentation, and Optical and Radar Remote Sensing (NASA Predoctoral Graduate Fellowship) *funded
- 2012 / Genomic tools to study coral reef resilience (University of Melbourne)
- 2012 / Plastid endosymbiosis: a detailed study of genome dynamics (Australian Research Council)
- 2012 / Evolutionary dynamics of the algae: Understanding adaptive potential under environmental change (Australian Research Council) *funded
- Probing key innovations with next generation sequencing
- 2009 / Macroevolutionary dynamics of marine algae
- 2012 / Sustainable and Scalable Infrastructure for the Publication of Data (NSF) *funded
- 2008 / A Digital Repository for Preservation and Sharing of Data Underlying Published Works in Evolutionary Biology (NSF) *funded
- 2010 / CAREER: Advancing Macroecology Using Informatics and Entropy Maximization (NSF CAREER Award) *funded
- 2005 / Broad-scale patterns of the distribution of body sizes of individuals in ecological communities (NSF Postdoc Fellowship) *funded
- 2008 / Understanding multimodality in animal size distributions (NSF Research Starter Grant) *funded
As some may be aware, ESA has launched a new journal: Ecosphere. ESA describes Ecosphere as “… the newest addition to the ESA family of journals, is an online-only, open-access alternative with a scope as broad as the science of ecology itself. “
The description is vague - is it a new incarnation of Ecology? Or is it an ecologically focused equivalent of PLoS One? I’m not the only one who is confused, as illustrated by a comment by Jeremy Fox from Dynamic Ecology. I recently had an interesting experience with Ecosphere that both clarified Ecosphere for me, and also what I think its potential is. I’ve been meaning to post on this for a while, but seeing that Jeremy and I are having similar thoughts finally encouraged me to get off my butt and write the post.
What is Ecosphere? If you’ve ever reviewed a paper, you know that part of your decision is based on the paper and part is based on the journal itself. For journals like Ecology, Am Nat, and Ecology Letters, you are judging both the rigor of the science and its potential impact. For PLoS One, the potential impact is not supposed to be part of the review decision, just whether the science is sound. I recently reviewed a paper for Ecosphere that was sound but not broadly interesting. What to recommend? The editor made it clear to me that Ecosphere is an ecological PLoS One and that what mattered was the scientific soundness. Often I hear these components of the review process conflated – but interesting and rigorous are not actually the same thing. So when Ecosphere talks about maintaining the same ‘rigorous peer-review standards’ as Ecology, it means that it is focusing on the soundness, not the interest component.
Future of Ecosphere? I have no insights into whether Ecosphere is performing as ESA had hoped but I think Jeremy’s view on Ecosphere is probably common. I suspect public relations outreach to clarify the role of Ecosphere in the journal pantheon would help. I also suspect that it could greatly benefit from an incentive to publish there. But what is an easy incentive that doesn’t undercut the economic benefits of Ecosphere for ESA? Jeremy nails it in his comment and it’s the same thought I had after I reviewed for them: make it easy to have solid but rejected papers from Ecology be rapidly accepted in Ecosphere. You see, the paper I reviewed for Ecosphere was also a paper I had just reviewed for Ecology and recommended rejecting based not on any issues with the science, but based on its importance. How much easier would it be if there was a button on the Ecology reviewer form that says “Is this paper suitable for Ecosphere? If yes, is it acceptable as is, with minor revision, with major revision?” Essentially, reviewers can review for both journals at the same time. Then if a paper is rejected from Ecology but recommended for acceptance at Ecosphere, the author can get a letter saying – so sorry about your Ecology rejection, but (if you would like) congratulations on your acceptance to Ecosphere!
I think this is a good idea for Ecosphere because it provides a mechanism whereby really good papers can still end up inthat journal, thus helping improve its impact (used generically, though I suppose it might also help its impact factor). Let’s be honest, when only 3 people are judging whether a paper will be ‘interesting’ to the broader field, bad things can happen to good papers. The direct tie between Ecology and Ecosphere increases the probability of getting those papers into Ecosphere because a guaranteed acceptance can be hard to turn down. If a paper has been ‘making the circuit’, it can be tempting to just take that acceptance, even if it’s not the “quality” of journal you might have been hoping for.
I also think this is a good thing for science. Perhaps you’re review process experience is always smooth sailing, but many of us are spending a lot of time revising and resubmitting papers that are technically sound but that reviewers dislike because they don’t like the topic or are uncomfortable with the take home message, or (my favorite) this isn’t the paper that they would have written themselves. Science slows down when sound science is rejected based on ‘interest’ and not on technical reasons, because papers may take an additional year or more to be published as they are repeatedly submitted to multiple journals. The big journals have the right to judge on interest, and there is some value to this in that they can help serve as filters for the deluge of new papers, but I think having quick avenues for publication of sound science is good for us all. Tying the big ESA journals to Ecosphere provides the benefits of both – the time cost of taking a shot at Ecology is minimized because if judged sound it would still get an acceptance into Ecosphere, even if rejected from Ecology because it ‘wasn’t interesting enough’.
Finally, it’s good for ESA to have Ecosphere capture more of the ecological literature through its open access model. Right now, if rejected from Ecology, the next step for most papers is probably not Ecosphere but some Elsevier or Wiley-Blackwell journal (or maybe PLoS One). Each scientifically sound paper that does not end up at Ecosphere is $1,250 that ESA doesn’t get (based on page charge cost for members). The more papers that end up at Ecosphere, the more $$ goes to ESA, which can then use that money to do all the great things it does both for its members and for ecology in general.