Tag Archives: collaboration

Writing Yourself Into A Corner

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I’ve been fascinated with the idea of investment, and how it can color your thoughts, feelings, and opinions about something. Not the monetary sense of the word (though probably that too) but the emotional and intellectual sense of the word. If you’ve ever been in a bad relationship you might have fallen prey to this reasoning- “I’m in this relationship and I’m not getting out because reasons so admitting that’s it’s absolutely terrible for me is unthinkable so I’m going to pretend like it’s not and I’m going to believe that it’s not and I’m going to tell everyone that I’m doing great”. I really believe this can be a motivating factor for a big chunk of human behavior.

And it’s certainly a problem in science. When you become too invested in an idea or an approach or a tool- that is, you’ve spent a considerable amount of time researching or promoting it- it can be very difficult to distance yourself from that thing and admit that you might have it wrong. That would be unthinkable.

Sometimes this investment pitfall is contagious. If you’re on a project working together with others for common goals the problem of investment can become more complicated. That is, if I’ve said something, and some amount of group effort has been put into this idea, but it turns out I was wrong about it, it can be difficult to raise that to the rest of the group. Though, I note, that it is really imperative that it is raised. This can become more difficult if the ideas or preliminary results you’ve put forward become part of the project- through presentations made by others or through further investment of project resources to follow up on these leads.

I think this sometimes happens when you’re writing an early draft of a document- though the effect can be more subtle here. If you write words down and put out ideas that are generally sound and on-point it can be hard for you, or others who may edit the paper after you, to erase these. More importantly a first draft, no matter how preliminary or draft-y, can establish an organization that can be hard to break. Clearly if there are parts that really don’t work, or don’t fit, or aren’t true, they can be removed fairly easily. The bigger problems lie in those parts that are *pretty good*. I’ve looked back at my own preliminary drafts and realized (after a whole lot of work trying to get things to fit) that the initial overall organization was somehow wrong- and that I really need to rip it all apart and start over, at least in terms of the organization. I’ve also seen this in other people’s work, where something just doesn’t seem right about a paper, but I really can’t place my finger on what- at least not without a bunch of effort.

Does this mean that you should very carefully plan out your preliminary drafts? Not at all. That’s essentially the route to complete gridlock and non-productivity. Rather, you should be aware of this problem and be willing to be flexible. Realize that what you put down on the paper for the first draft (or early versions of analysis) is subject to change- and make others you are working with aware of this explicitly (simply labeling something as “preliminary analysis” or “rough draft” isn’t explicit enough). And don’t be afraid to back away from it if it’s not working out. It’s much better if that happens earlier in the process than later- that is, it’s better to completely tear down a final draft of a paper than to have reviewers completely miss the point of what you’re trying to say after you’ve submitted it.

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Big Data Showdown

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One of the toughest parts of collaborative science is communication across disciplines. I’ve had many (generally initial) conversations with bench biologists, clinicians, and sometimes others that go approximately like:

“So, tell me what you can do with my data.”

“OK- tell me what questions you’re asking.”

“Um,.. that kinda depends on what you can do with it.”

“Well, that kinda depends on what you’re interested in…”

And this continues.

But the great part- the part about it that I really love- is that given two interested parties you’ll sometimes work to a point of mutual understanding, figuring out the borders and potential of each other’s skills and knowledge. And you generally work out a way of communicating that suits both sides and (mostly) works to get the job done. This is really when you start to hit the point of synergistic collaboration- and also, sadly, usually about the time you run out of funding to do the research.
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Gender bias in scientific publishing

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The short version: This is a good paper about an important topic, gender bias in publication. The authors try to address two main points: What is the relationship between gender and research output?; and What is the relationship between author gender and paper impact? The study shows a bias in number of papers published by gender, but apparently fails to control for the relative number of researchers of each gender found in each field. This means that the first point of the paper, that women publish less than men, can’t be separated from the well-known gender bias in most of these fields- i.e. there are more men than women. This seems like a strange oversight, and it’s only briefly mentioned in the paper. The second point, which is made well and clearly, is that papers authored by women are cited less than those authored by men. This is the only real take home of the paper, though it is a very important and alarming one.
What the paper does say: that papers authored by women are cited less than those authored by men.
What the paper does NOT say: that women are less productive than men, on average, in terms of publishing papers.
The slightly longer version
This study on gender bias in scientific publishing is a really comprehensive look at gender and publishing world-wide (though it is biased toward the US). The authors do a good job of laying out previous work in this area and then indicate that they are interested in looking at scientific productivity with respect to differences in gender. The first stated goal is to provide an analysis of: “the relationship between gender and research output (for which our proxy was authorship on published papers).”
The study is not in any way incorrect (that I can see in my fairly cursory read-through) but it does present the data in a way that is a bit misleading. Most of the paper describes gathering pretty comprehensive data on gender in published papers relative to author position, geographic location, and several other variables. This is then used to ‘show’ that women are less productive than men in scientific publication but it omits a terribly important step- they never seem to normalize for the ratio of women to men in positions that might be publishing at all. That is, their results very clearly reiterate that there is a gender bias in the positions themselves- but doesn’t say anything (that I can see) about the productivity of individuals (how many papers were published by each author, for example).
They do mention this issue in their final discussion:
UNESCO data show10 that in 17% of countries an equal number of men and women are scientists. Yet we found a grimmer picture: fewer than 6% of countries represented in the Web of Science come close to achieving gender parity in terms of papers published.
And, though this is true, it seems like a less-than-satisfying analysis of the data.
On the other hand, the result that they show at the last- the number of times a paper is cited when a male or female name is included in various locations- is pretty compelling and is really their novel finding. This is actually pretty sobering analysis and the authors provide some ideas on how to address this issue, which seems to be part of the larger problem of providing equal opportunities and advantages to women in science.

Goodbye to two good friends

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(Note: this post isn’t nearly as sad as it might seem from the title or the introduction below)

Yesterday I lost two close friends. We had been friends for five years, though our relationships had extended a tumultuous 10 or so months before that. Given that we still have unfinished business I expect our friendships to straggle on a little longer. But really, it’s over. My friends have helped me grow in a number of important ways- become more mature, deal with different personalities, forced me to communicate more clearly and to take criticism in a constructive light. The friendships both challenged me in different ways and supported me through a fragile time in my life. I will miss both of these friends for some different reasons- and some of the same reasons.

Like many friendships they have ended because of what other people thought about them. A small number of people had comments on our friendship- some of the comments, upon reflection, were probably well-placed, others certainly were not. But that outside influence is what really broke us apart. I hope that we can become friends again in the future- but we both will have changed so much in the intervening time that we may well be unrecognizable to each other. Still it would be nice to continue this friendship.

Bye Bye

Farewell Systems Biology of Enteropathogens and Systems Virology Centers – you will be missed but not forgotten.

Here are a few mementos of our time together….

  1. Ansong C, Schrimpe-Rutledge AC, MitchellH, Chauhan S,Jones MB, Kim Y-M, McAteerK, Deatherage B, Dubois JL, Brewer HM, Frank BC, McDermottJE, Metz TO, Peterson SN, Motin VL, Adkins JN. A multi-omic systems approach to elucidating Yersinia virulence mechanisms.Molecular Biosystems 2012. In press.
  2. McDermott JE, Corley C, Rasmussen AL, Diamond DL, Katze MG, Waters KM: Using network analysis to identify therapeutic targets from global proteomics data. BMC systems biology 2012, 6:28.
  3. Yoon H, Ansong C, McDermott JE, Gritsenko M, Smith RD, Heffron F, Adkins JN: Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella. BMC systems biology 2011, 5:100.
  4. Niemann GS, Brown RN, Gustin JK, Stufkens A, Shaikh-Kidwai AS, Li J, McDermott JE, Brewer HM, Schepmoes A, Smith RD et al: Discovery of novel secreted virulence factors from Salmonella enterica serovar Typhimurium by proteomic analysis of culture supernatants. Infect Immun 2011, 79(1):33-43.
  5. McDermott JE, Yoon H, Nakayasu ES, Metz TO, Hyduke DR, Kidwai AS, Palsson BO, Adkins JN, Heffron F: Technologies and approaches to elucidate and model the virulence program of salmonella. Front Microbiol 2011, 2:121.
  6. McDermott JE, Shankaran H, Eisfeld AJ, Belisle SE, Neumann G, Li C, McWeeney SK, Sabourin CL, Kawaoka Y, Katze MG et al: Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems. BMC systems biology 2011, 5(1):190.
  7. McDermott JE, Corrigan A, Peterson E, Oehmen C, Niemann G, Cambronne ED, Sharp D, Adkins JN, Samudrala R, Heffron F: Computational prediction of type III and IV secreted effectors in gram-negative bacteria. Infect Immun 2011, 79(1):23-32.
  8. McDermott JE, Archuleta M, Thrall BD, Adkins JN, Waters KM: Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation. PLoS ONE 2011, 6(2):e14673.
  9. Aderem A, Adkins JN, Ansong C, Galagan J, Kaiser S, Korth MJ, Law GL, McDermott JG, Proll SC, Rosenberger C et al: A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm. MBio 2011, 2(1):e00325-00310.
  10. Buchko GW, Niemann G, Baker ES, Belov ME, Heffron F, Adkins JN, McDermott JE (2011). A multi-pronged search for a common structural motif in the secretion signal of Salmonella enterica serovar Typhimurium type III effector proteins. Molecular Biosystems. 6(12):2448-58.
  11. Lawrence PK, Kittichotirat W, Bumgarner RE, McDermott JE, Herndon DR, Knowles DP, Srikumaran S: Genome sequences of Mannheimia haemolytica serotype A2: ovine and bovine isolates. J Bacteriol 2010, 192(4):1167-1168
  12. Yoon H, McDermott JE, Porwollik S, McClelland M, Heffron F: Coordinated regulation of virulence during systemic infection of Salmonella enterica serovar Typhimurium. PLoS Pathog 2009, 5(2):e1000306.
  13. *Taylor RC, Singhal M, Weller J, Khoshnevis S, Shi L, McDermott J: A network inference workflow applied to virulence-related processes in Salmonella typhimurium. Annals of the New York Academy of Sciences 2009, 1158:143-158.
  14. *Shi L, Chowdhury SM, Smallwood HS, Yoon H, Mottaz-Brewer HM, Norbeck AD, McDermott JE, Clauss TRW, Heffron F, Smith RD, and Adkins JN. Proteomic Investigation of the Time Course Responses of RAW 264.7 Macrophages to Salmonella Infection. Infection and Immunity 2009, 77(8):3227-33.
  15. *Shi L, Ansong C, Smallwood H, Rommereim L, McDermott JE, Brewer HM, Norbeck AD, Taylor RC, Gustin JK, Heffron F, Smith RD, Adkins JN. Proteome of Salmonella Enterica Serotype Typhimurium Grown in a Low Mg/pH Medium. J Proteomics Bioinform. 2009; 2:388-397.
  16. *Samudrala R, Heffron F, McDermott JE: Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems. PLoS Pathog 2009, 5(4):e1000375.
  17. *McDermott JE, Taylor RC, Yoon H, Heffron F: Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. J Comput Biol 2009, 16(2):169-180.
  18. *Ansong C, Yoon H, Norbeck AD, Gustin JK, McDermott JE, Mottaz HM, Rue J, Adkins JN, Heffron F, Smith RD: Proteomics Analysis of the Causative Agent of Typhoid Fever. J Proteome Res 2008.

*these were really from slightly before our time- but I’ll count them there anyway

Leading a collaborative scientific paper: My tips on cat herding

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Large collaborative research projects, centers, or consortia have a single goal: to be funded for another round. That’s completely cynical, but it is not so far off the truth. The point of these projects is to advance science by bringing together many different experts in many different areas to do more than what could be done in a single R01-size endeavor. If there are no project-wide collaborative papers that come out of this effort going to high-profile journals there will be nothing- or very little- to make the claim that the project was successful. Why not just fund 3-8 R01-sized project that can work in isolation and accomplish the same thing or more? So publications are important.

The second thing to understand is that there’s no such thing as a ‘group-written’ paper, in my experience. Not truly. Someone always needs to step forward and take ownership of the paper to drive things forward otherwise it’s dead in the water. Maybe it can be two people, maybe it can be more- I’ve never seen it happen. So someone needs to step forward and be chief cat herder. This is a thankless job, but if it results in a solid, collaborative manuscript it can be very satisfying. Not to mention the fact that you will (or very much SHOULD) have your name first in the author order.

Here’s my metaphor for spearheading such a monster, errrr… paper.

Imagine that you’ve gathered a painter, a sculptor who works in clay, a sculptor who works with metal, and a DJ in a room- actually in many cases they’re not even in the same room, they’re distributed around the country in their own studios. Around the room (or in their studios) you have a canvas and paint, a block of clay, a pile of metal, and a box of vinyl. Your job is to assemble a work of art that incorporates all those elements together, blends them where appropriate, and is clear about how the pieces all fit together. You have a limited time to accomplish this. Art critics will be visiting after you’re finished to evaluate your work. Go.

Here are my list of thoughts on how to approach this kind of problem.

  1. Don’t think of this as a collaborative paper. In all likelihood the actual driving of the paper will be done by one person, and that’s you. If you wait around for everyone to chime in, contribute, take ownership for their sections, you will never get anything done. If you aren’t the leader of the paper, but the leader isn’t leading it MAY be possible to just start the process and take leadership. This can be politically dangerous and really depends on the specifics of the project and collaborations, but it’s something to keep in mind. You could be a hero.
  2. Think of this as a collaborative paper. This is a collaborative effort. I realize that this is directly contradictory to my first point. However, it is very important that you don’t lose sight of the fact that you are not the expert in many areas of the paper that you have to put together. Make use of others’ expertise but try to put this in direct requests for input of well-defined portions.
  3. Have a basic understanding of each component. This is really important. Everyone has different expertise and you will not become an expert in a new area by writing a paper. Don’t try. But if there are things that you really are not familiar with that need to go into the paper brush up on them by reading (actually reading from start to finish) previous papers from the group or current review articles in the area. This will allow you to understand at least where the collaborator is coming from and what they can offer.
  4. Don’t overload collaborators with many outlines and drafts. This will only make your collaborators stop paying attention. Instead try to put out one or two outlines, with discussion (teleconference or in person) between. Also with the draft, work with individuals to get portions completed instead of doing everything in multiple rounds of drafts that are commented on by everyone.
  5. Choose a way of collaborating on writing and communicate it with contributors. If you use MS Word for drafts make sure everyone uses the “Tracking Changes” option turned on. Otherwise it’s a nightmare to figure out what parts have been changed. Part of your job will be to manually merge all these changes into a single document. This is a tremendous pain in the ass, but it allows you to evaluate all contributions and make decisions about what to include or how things should be worded. Google Docs seems to work well for producing drafts collaboratively, but at some point the draft should be moved to a single document for finalization.
  6. At the early stages include, don’t exclude. Welcome everyone’s input and suggestions. At some point it may be necessary to make hard decisions about directions of the paper and that may make people unhappy. That’s something you have to live with- but try to listen to the group about these decisions. If there are people with suggestions on more work to do (either experiments/analysis or writing) and their suggestions seem reasonable, make it clear that it’s up to them to carry through with the actual work and try to get a timeline from them for completion. If their piece is essential to the project make sure that you have a plan for extracting this from them- there’s probably a nicer way to put this, but that’s the idea.
  7. At the later stages don’t let newcomers (or others) distract from the plan. If they have really great suggestions, listen to them. If their suggestions seem to distract from the story you are telling fall back on the, “well that’s a great idea, why don’t you investigate that and we can include it if the reviewers request it”- that is, after submission and review.
  8. Have a strategy to create the story you’re going to tell. It can be very difficult to start on a paper cold, when there’s only been discussion about what should be done. A reasonable approach is to do some preliminary analysis yourself then take this to the larger group for input. Make it clear that this is only one possible path and that you’re just trying to promote discussion. Make sure you’re telling a story- this is actually what a scientific paper is about. Be flexible about what the story is. It has to be consistent with the data available- but you may choose to incorporate portions of the results and leave out others that do not help the story along. See also my post on how to write a scientific paper.
  9. Try to avoid redundant effort. Generally this isn’t an issue because everyone is an expert in different areas so the actual work shouldn’t be redundant. Sometimes data analysis needs to be defined to avoid redundancy. If there are large sections to be written (such as an Introduction) it’s better to break it into smaller bits for different people to work on and call this out in the outline or draft so people are clear on who’s doing what. Everyone can revise/comment on all sections toward the end and that’s easier to merge than two disparate documents that are trying to talk about the same thing.
  10. Navigate author order and authorship carefully. This is tremendously important for most people on the project. The critical positions to identify are first author and last author (for biology papers anyway). If you are leading the paper you should be first author, but always remember that for many journals you can specify two or even three ‘first’ authors. For this kind of paper that might be necessary. Don’t try to limit authorship too much. These kinds of papers will have lots of authors. But try to be consistent; if you accept suggestions from everyone’s groups wholesale, it can cause conflicts. Consider that one group might consider technicians who performed the work to be worthy of authorship. If you say OK to this the other groups may chime in with all their technicians, etc. Follow the rules of authorship that you feel comfortable with and believe are ethically consistent, but remember that many, many people may have made significant contributions to the paper. This can be one of the most politically treacherous portions of the paper- have fun!
  11. Find a champion. Identify a senior author who you can communicate with and who you believe will support your positions, or at least will listen to your positions. There may arise situations that require having someone with authority agreeing with you to get others to fall in line.

Finally, here’s an example of a large collaborative research paper that I’ve recently published. It didn’t turn out quite as grand as I’d hoped (what paper does?) but it’s still a nice example of integrating the input of many different groups. I am currently working on (leading) at least three more such papers that are in various stages of being completed.

McDermottJE, ShankaranH, EisfeldAJ, BelisleSE, NeumanG, LiC, McWeeneyS, SabourinC, KawaokaY, Katze MG, Waters KM. (2011). Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems. BMC Systems Biology. 5(1):190.