Dr. Sora Kim gave an excellent seminar on professional development in academia today and you can view the PDF on her website at the following link - http://geofaculty.uwyo.edu/skim11/Updates/Entries/2012/10/11_Building_a_Professional_Network.html.
I'll write a more extensive post about this soon!
Friday, October 12, 2012
Friday, October 5, 2012
Graduate School Part I
I have had a few inquiries from some undergraduate friends about applying for graduate school. I thought I would publish some advice here but also open up the comments to anyone who has further advice or competing views.
Many undergraduates are concerned about the fact that they don't have straight A's. Well, it's no big secret, but neither did I (at least for my entire program)! I did "okay" in first year but just scraped by in my second (by the skin of my teeth! Okay, teeth don't have skin, but you know what I mean). The good news is that biology programs normally only consider your last two years (or equivalent number of courses). You should always check with the program you're interested in, but many seem comparable. So if you're entering your third year and a little disappointed with your first two, you'll be just fine.
I also don't think straight A's necessarily make a good researcher. Does the ability to memorize thousands of facts make you a critical thinker? It might get you good grades but it won't help you design a relevant and interesting research project. This is not to say that all people with straight A's merely memorize facts (please, no angry comments!). All I am saying is that the person with the B+ average and the person with the A+ average may not be so different when it comes time for grad school. It all depends on personal characteristics that are at least partially independent of grades. Some important qualities that successful students possess are self motivation, competitiveness (healthy competitiveness!), and passion (not like a romance novel!).
Firstly, self motivation is important because no one is going to be there prodding you to finish your experiment. When your yearly committee meeting rolls around and you haven't made any progress, there will be no one to blame but yourself. You have to be good at setting goals and meeting them. I use two levels of goal setting. The first are long-term goals. For example, "I will submit paper A by January." The second are short-term goals. For example, "Today, I will write three paragraphs of paper A." Because I am self motivated I usually meet my goals (or at least come close).
Secondly, I want to be clear about what I mean by competitiveness. I define this as the desire to excel when your goals for excellence are set by the people around you. This means feeling motivated by how well others are doing and working hard to match their successes (or maybe to do a bit better!). I am only talking about hard work and not about immoral means of competing with others (purposely scooping their research, "bad mouthing" them, or sabotaging experiments). These are all unacceptable and place you into the category of ruthless rather than competitive.
I suspect there is a near linear relationship of healthy competitiveness with number of publications and number of scholarships. Publications and scholarships (in addition to experience and interesting research) are good determinants of future success (jobs, more scholarships) assuming they are of good quality. Of course, there may be exceptions to this "rule."
Finally, you have to be passionate about your research. If you are passionate you are most likely also self motivated and competitive. I think very positively about my research and am always generating new ideas. I wake up in the morning and immediately start thinking about science. But I don't want to be unrealistic. Every day will not be positive! Experiments fail and equipment breaks, but if it doesn't stop you from wanting to pursue research, then you're on the right track!
Part II will cover choosing a supervisor and a university.
Many undergraduates are concerned about the fact that they don't have straight A's. Well, it's no big secret, but neither did I (at least for my entire program)! I did "okay" in first year but just scraped by in my second (by the skin of my teeth! Okay, teeth don't have skin, but you know what I mean). The good news is that biology programs normally only consider your last two years (or equivalent number of courses). You should always check with the program you're interested in, but many seem comparable. So if you're entering your third year and a little disappointed with your first two, you'll be just fine.
I also don't think straight A's necessarily make a good researcher. Does the ability to memorize thousands of facts make you a critical thinker? It might get you good grades but it won't help you design a relevant and interesting research project. This is not to say that all people with straight A's merely memorize facts (please, no angry comments!). All I am saying is that the person with the B+ average and the person with the A+ average may not be so different when it comes time for grad school. It all depends on personal characteristics that are at least partially independent of grades. Some important qualities that successful students possess are self motivation, competitiveness (healthy competitiveness!), and passion (not like a romance novel!).
Firstly, self motivation is important because no one is going to be there prodding you to finish your experiment. When your yearly committee meeting rolls around and you haven't made any progress, there will be no one to blame but yourself. You have to be good at setting goals and meeting them. I use two levels of goal setting. The first are long-term goals. For example, "I will submit paper A by January." The second are short-term goals. For example, "Today, I will write three paragraphs of paper A." Because I am self motivated I usually meet my goals (or at least come close).
Secondly, I want to be clear about what I mean by competitiveness. I define this as the desire to excel when your goals for excellence are set by the people around you. This means feeling motivated by how well others are doing and working hard to match their successes (or maybe to do a bit better!). I am only talking about hard work and not about immoral means of competing with others (purposely scooping their research, "bad mouthing" them, or sabotaging experiments). These are all unacceptable and place you into the category of ruthless rather than competitive.
I suspect there is a near linear relationship of healthy competitiveness with number of publications and number of scholarships. Publications and scholarships (in addition to experience and interesting research) are good determinants of future success (jobs, more scholarships) assuming they are of good quality. Of course, there may be exceptions to this "rule."
Finally, you have to be passionate about your research. If you are passionate you are most likely also self motivated and competitive. I think very positively about my research and am always generating new ideas. I wake up in the morning and immediately start thinking about science. But I don't want to be unrealistic. Every day will not be positive! Experiments fail and equipment breaks, but if it doesn't stop you from wanting to pursue research, then you're on the right track!
Part II will cover choosing a supervisor and a university.
Monday, October 1, 2012
A Brief Introduction to Testing for Phylogenetic Signal in Comparative Data
Phylogenetic comparative methods (PCMs) were the subject of my last post. You can read it here (http://evolbiology.blogspot.ca/2012/09/phylogenetic-comparative-methods-some.html). It was a VERY brief description of two commonly employed PCMs (Phylogenetically Independent Contrasts and Phylogenetic Generalized Least Squares Regression). However, it is important to note that PCMs should not be applied unless their use is justified. It's true that the availability of phylogenies and the array of methods for reconstructing phylogenies has skyrocketed. As a result, reviewers are suggesting PCMs more and more. But it is important to consider whether PCMs are necessary and whether they add to your analysis or aid in the interpretation of your data. But how can you determine if your study needs PCMs?
One of the questions to ask is whether your data (rather the residuals; Revell 2010) are phylogenetically structured. In other words, do your data show phylogenetic signal? Two common methods are the K statistics of Blomberg et al. (2003) and Pagel's lambda (Pagel, 1999). The K statistic compares the observed and expected variance for calculated independent contrasts (Blomberg et al. 2009; Glor, 2009). Pagel's lambda is a multiplier of the off diagonal elements of the covariance matrix that varies between 0 and 1. Lambda transforms the phylogenetic tree with the purpose of comparing a complete lack of phylogenetic structure (lambda = 0; star phylogeny) to the untransformed topology and branch lengths of your original tree (lambda = 1) (Pagel, 1999; Gor, 2009). In other words, Pagel's lambda determines which situation, a star or structured phylogeny, fits your data best.
Here is some basic R code for using Blomberg et al.'s K:
require(picante)
One of the questions to ask is whether your data (rather the residuals; Revell 2010) are phylogenetically structured. In other words, do your data show phylogenetic signal? Two common methods are the K statistics of Blomberg et al. (2003) and Pagel's lambda (Pagel, 1999). The K statistic compares the observed and expected variance for calculated independent contrasts (Blomberg et al. 2009; Glor, 2009). Pagel's lambda is a multiplier of the off diagonal elements of the covariance matrix that varies between 0 and 1. Lambda transforms the phylogenetic tree with the purpose of comparing a complete lack of phylogenetic structure (lambda = 0; star phylogeny) to the untransformed topology and branch lengths of your original tree (lambda = 1) (Pagel, 1999; Gor, 2009). In other words, Pagel's lambda determines which situation, a star or structured phylogeny, fits your data best.
Here is some basic R code for using Blomberg et al.'s K:
# Help file http://127.0.0.1:17385/library/picante/html/phylosignal.html
require(phytools)
# Help file http://127.0.0.1:17385/library/phytools/html/phylosig.html
lamb<- phylosig(tree,data,method="lambda")
# Your data also require names that match the tip labels on the tree
# This will return a lambda value and log likelihood, values of lambda closer to 1 indicate singificant phylogenetic signal
Using the K statistic and Pagel's lambda, you can justify the use of PCMs or demonstrate that they are not necessary. Although I feel strongly that PCMs are powerful tools in comparative studies, I also feel they should only be used when it is statistically justifiable to do so.
You can follow the instructions of Glor (2009) to further understand Pagel's lambda. There are also other methods for testing for phylogenetic signal that I have not covered here.
References
Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.
Glor. 2009. IV. Testing Phylogenetic Signal in R. Bodega Phylogenetics Wiki.
Pagel, M. 1999. Inferring the historical patterns of biological evolution. Nature, 401, 877–884.
Revell, L. J. 2010. Phylogenetic signal and linear regression on species data. Methods in Ecology and Evolution 1:319-329.
kstat<-phylosignal(data,tree)
# Your data must have matching taxon names or be sorted in the same order as the tip labels of the phylogeny
# This will return the K statistics and p value (as well as the variance of the independent contrasts and the associated z value)
Here is some basic R code for using Pagel's lambda:
require(phytools)
# Help file http://127.0.0.1:17385/library/phytools/html/phylosig.html
lamb<- phylosig(tree,data,method="lambda")
# Your data also require names that match the tip labels on the tree
# This will return a lambda value and log likelihood, values of lambda closer to 1 indicate singificant phylogenetic signal
Using the K statistic and Pagel's lambda, you can justify the use of PCMs or demonstrate that they are not necessary. Although I feel strongly that PCMs are powerful tools in comparative studies, I also feel they should only be used when it is statistically justifiable to do so.
You can follow the instructions of Glor (2009) to further understand Pagel's lambda. There are also other methods for testing for phylogenetic signal that I have not covered here.
References
Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.
Glor. 2009. IV. Testing Phylogenetic Signal in R. Bodega Phylogenetics Wiki.
Pagel, M. 1999. Inferring the historical patterns of biological evolution. Nature, 401, 877–884.
Revell, L. J. 2010. Phylogenetic signal and linear regression on species data. Methods in Ecology and Evolution 1:319-329.
Friday, September 7, 2012
Phylogenetic Comparative Methods: Some Generalities
The comparative method, in one form or another, has been of paramount importance in biology since its inception. It is the basis for understanding how and why organisms differ. Prior to the 1980's the comparative method most often involved simple statistics (regression, correlation). There are several problems with this approach when comparing different taxa (species, genera etc.). Firstly, taxa do not have completely independent evolutionary histories. This is not a new concept. Even Linnaeus' hierarchical classification system indirectly represents the non-independence of species. Secondly, simple statistics such as correlation assume complete independence and the evolutionary process inherently violates this assumption. But comparative biologists need not despair!
There are a few methods for dealing with the issue of phylogenetic non-independence. These include (but are not limited to) Phylogenetically Independent Contrasts (PIC) (Felsenstein 1985) and Phylogenetic Generalized Least Squares Regression (PGLS) (Grafen 1989). The most commonly used method has been PIC.
PIC involves the calculation of contrasts (branch length calibrated differences) between sister taxa. Regressions are then carried out on the contrasts (through the origin) as opposed to the raw data, which effectively removes the influence of relatedness (Felsenstein 1985). PIC can be used for the comparison of one continuous with one categorical trait and for two continuous traits.
R code:
require(ape) # Paradis (2006)
tree <- read.nexus("tree.nex") # A tree with branch lengths
data <- read.csv("data.csv", header=T,row.names=1) # Data with row names as taxon names and two traits
pic1 <-pic(trait1,tree) # Calculate contrasts for each trait
pic2 <-pic(trait2,tree)
piclm <- lm(pic1~pic2 -1) # Regress contrasts through the origin
On the other hand, PGLS is very similar to statistics employed by ecologists concerned about spatial autocorrelation (localities closer to each other are likely to be more similar). PGLS constructs a correlation matrix based on the distance of taxa on the tree. The matrix is then incorporated into a generalized linear model (Grafen, 1989). One advantage of PGLS over PIC is that it can accomodate several models of evolution (e.g. changes in evolutionary rate, stabilizing selection). In contrast, PIC assumes a Brownian Motion (stochastic; BM) process of trait evolution (Felsenstein, 1985). Although PIC and PGLS may be equivalent under a BM model (Blomberg et al. 2012), the comparison cannot be made under other evolutionary models (evolutionary models will be the subject of a coming post). PGLS can be used to compare two continuous traits (for the comparison of a continuous and categorical traits, Phylogenetic Generalized Estimating Equations (Paradis and Claude (2002)) function similarly to PGLS).
R code:
# PGLS assuming BM
Blomberg, S.P., J.G. Lefevre, J.A. Wells, and M. Waterhouse. 2012. Independent contrasts and PGLS estimators are equivalent. Systematic Biology 61: 1-61.
Felsenstein, J. 1985. Phylogenies and the Comparative Method. The American Naturalist 125:1-15.
Grafen, A. 1989. The Phylogenetic Regression. Philosophical Transactions of the Royal Society of London B 326:119-157.
Paradis, E. and J. Claude. 2002. Analysis of Comparative Data Using Generalized Estimating Equations. Journal of theoretical biology 218:175–185.
Paradis, E. 2006. Analysis of Phylogenetics and Evolution with R. Springer Science+Business Media, LLC, New York.
There are a few methods for dealing with the issue of phylogenetic non-independence. These include (but are not limited to) Phylogenetically Independent Contrasts (PIC) (Felsenstein 1985) and Phylogenetic Generalized Least Squares Regression (PGLS) (Grafen 1989). The most commonly used method has been PIC.
PIC involves the calculation of contrasts (branch length calibrated differences) between sister taxa. Regressions are then carried out on the contrasts (through the origin) as opposed to the raw data, which effectively removes the influence of relatedness (Felsenstein 1985). PIC can be used for the comparison of one continuous with one categorical trait and for two continuous traits.
R code:
require(ape) # Paradis (2006)
tree <- read.nexus("tree.nex") # A tree with branch lengths
data <- read.csv("data.csv", header=T,row.names=1) # Data with row names as taxon names and two traits
pic1 <-pic(trait1,tree) # Calculate contrasts for each trait
pic2 <-pic(trait2,tree)
piclm <- lm(pic1~pic2 -1) # Regress contrasts through the origin
On the other hand, PGLS is very similar to statistics employed by ecologists concerned about spatial autocorrelation (localities closer to each other are likely to be more similar). PGLS constructs a correlation matrix based on the distance of taxa on the tree. The matrix is then incorporated into a generalized linear model (Grafen, 1989). One advantage of PGLS over PIC is that it can accomodate several models of evolution (e.g. changes in evolutionary rate, stabilizing selection). In contrast, PIC assumes a Brownian Motion (stochastic; BM) process of trait evolution (Felsenstein, 1985). Although PIC and PGLS may be equivalent under a BM model (Blomberg et al. 2012), the comparison cannot be made under other evolutionary models (evolutionary models will be the subject of a coming post). PGLS can be used to compare two continuous traits (for the comparison of a continuous and categorical traits, Phylogenetic Generalized Estimating Equations (Paradis and Claude (2002)) function similarly to PGLS).
R code:
# PGLS assuming BM
require(ape)
tree <- read.nexus("tree.nex")
tree <- read.nexus("tree.nex")
data <- read.csv("data.csv", header=T,row.names=1)
gls1<- gls(trait1~trait2,data=data,correlation=corBrownian(phy=tree),method="ML")
# Using the maximum likelihood method enables the comparison of evolutionary models using AIC
This post has been rather brief but I intend to continue posts on this topic.
References
# Using the maximum likelihood method enables the comparison of evolutionary models using AIC
This post has been rather brief but I intend to continue posts on this topic.
References
Blomberg, S.P., J.G. Lefevre, J.A. Wells, and M. Waterhouse. 2012. Independent contrasts and PGLS estimators are equivalent. Systematic Biology 61: 1-61.
Felsenstein, J. 1985. Phylogenies and the Comparative Method. The American Naturalist 125:1-15.
Grafen, A. 1989. The Phylogenetic Regression. Philosophical Transactions of the Royal Society of London B 326:119-157.
Paradis, E. and J. Claude. 2002. Analysis of Comparative Data Using Generalized Estimating Equations. Journal of theoretical biology 218:175–185.
Paradis, E. 2006. Analysis of Phylogenetics and Evolution with R. Springer Science+Business Media, LLC, New York.
Thursday, August 23, 2012
Scientific Politics Part II
Scientific progress is built on testing hypotheses and refining our ideas about the world. We test and re-test hypotheses until we are satisfied that they are supported (or not). An inner belief or intuition is not enough to convince any scientist that a particular hypothesis is true. In other words, the words "I believe in evolution" are meaningless. It's the bountiful evidence for evolution that has convinced every biologist that it is a fact. The greatest thing about the majority of scientists is also that they're willing to discard even their most beloved hypotheses in the wake of new evidence.
However, I think it is sometimes easy to get involved in personal rivalry over competing hypotheses. I have seen people at conferences very nearly yelling at each other and even heard stories about death threats! This is not appropriate conduct for anyone, let alone an educated scientist. In my opinion, you should never take a disagreement with your hypothesis (no matter how awesome!) personally. It's not an insult. It is on disagreement that scientific progress is built! Every time a reviewer or fellow conference attendee disagrees with me, I take time to remind myself that they are enabling science to move forward. After all, if each of us stuck with our respective hypotheses and worked only to find support for them, we would not know that the earth revolves around the sun or that the big bang really happened!
But how should we disagree with each other in a productive way? If someone disagrees with you, it is their responsibility to demonstrate convincingly that your hypothesis is not supported and not to call you names (the reverse is also true). Probably the best outlet for debate is in the scientific literature. Firstly, publication avoids name calling and death threats (usually!). It is important to avoid personal slurs in print. This does not reflect on the person you are refuting, only on you. It doesn't do anyone any good to gain a reputation as a whiner. But if you are scientific and careful in presenting your evidence, you will gain a reputation as a respectable scientist. Secondly, publication involves other scientists in the debate. Broadening your audience will always bring unexpected insight.
Most importantly, if it is demonstrated that your beloved hypothesis is not supported, you should discard it. There is no merit in clinging to debunked ideas. Of course, I think the VAST majority of us scientists find new hypotheses exhilarating and are therefore unlikely to marry any particular one. That's what I love about science!!!
However, I think it is sometimes easy to get involved in personal rivalry over competing hypotheses. I have seen people at conferences very nearly yelling at each other and even heard stories about death threats! This is not appropriate conduct for anyone, let alone an educated scientist. In my opinion, you should never take a disagreement with your hypothesis (no matter how awesome!) personally. It's not an insult. It is on disagreement that scientific progress is built! Every time a reviewer or fellow conference attendee disagrees with me, I take time to remind myself that they are enabling science to move forward. After all, if each of us stuck with our respective hypotheses and worked only to find support for them, we would not know that the earth revolves around the sun or that the big bang really happened!
But how should we disagree with each other in a productive way? If someone disagrees with you, it is their responsibility to demonstrate convincingly that your hypothesis is not supported and not to call you names (the reverse is also true). Probably the best outlet for debate is in the scientific literature. Firstly, publication avoids name calling and death threats (usually!). It is important to avoid personal slurs in print. This does not reflect on the person you are refuting, only on you. It doesn't do anyone any good to gain a reputation as a whiner. But if you are scientific and careful in presenting your evidence, you will gain a reputation as a respectable scientist. Secondly, publication involves other scientists in the debate. Broadening your audience will always bring unexpected insight.
Most importantly, if it is demonstrated that your beloved hypothesis is not supported, you should discard it. There is no merit in clinging to debunked ideas. Of course, I think the VAST majority of us scientists find new hypotheses exhilarating and are therefore unlikely to marry any particular one. That's what I love about science!!!
Sunday, August 19, 2012
Scientific Politics Part I
I thought I would write about a very touchy subject tonight! That topic is politics. What I include in the term politics are things like authorship, how to deal with disagreements, and what to publish and when (especially when someone disagrees with you or works on a similar or even the same topic). Of course, being a PhD candidate and not a seasoned professor, I am far from an expert but I have some opinions that may or may not be the same ones that others hold.
I'll start with the subject of authorship. Who should be included on your papers, in what order, and why? These are difficult questions, especially when you're a budding scientist. I definitely want to collaborate and share authorship with people that I admire. For me, it has always been a relatively easy process but I usually have a frank discussion with collaborators about who should be included and in what order. It might seem like a subject that is uncomfortable to talk about, especially if you're worried about upsetting someone. But I guarantee that it is less uncomfortable than having a disagreement when it comes time to submit a publication or even after the paper has been published.
It is important to determine what the contributions of individuals will be to the paper. In my opinion, all authors must make an intellectual contribution. Acceptable contributions usually include analyses, writing, or data collection (or all of the above). There are some cases, however, when it is unclear if someone should be included on the paper. For example, if an individual has given you advice or ideas, do you include them on the paper? I think this depends on the gravity of the advice. Did they simply suggest you use a particular analysis? I would not include this person as an author. Did they come up with the idea? I would include this person. Whenever you feel an individual's contribution might merit inclusion on the paper, you should always proceed by asking them if they wish to be. If they decline, then you've done your political duty and made sure that there will be no hurt feelings. If they say yes, the next step is to determine the order of authorship.
For me, author order has always been obvious. The person who writes the paper and does most of the analyses is first author, the person who did some of the analyses or contributed some data is second author and so on. But I have never been involved in a project with more than three or four authors. Once the number of authors starts to grow, I think an explicit agreement should be made prior to any paper submission. Don't leave the discussion until it is too late or until someone is put off. Ask them outright! "Shall I include you as second/third/fourth/fifth author?"
The situation is a bit different when it comes to your supervisor. Different supervisors have different policies when it comes to authorship. Some prefer to be included on all papers and others make this judgement on a case by case basis. Sometimes supervisors prefer to be included as the last author (this is sometimes reserved for the PI) while others are open to any position depending on their contribution. Regardless, when you move on to a new supervisor you should always ask! That way no one will have any excuse to be angry. You'll also avoid pissing off the person that controls your funding!
I have only heard of people having major problems with authorship. I can imagine there have been situations when someone originally agreed to contribute but then failed to uphold their obligation. This can be politically difficult if this person is an office or lab mate but especially if they are a seasoned professor or famous researcher. I can't offer solid advice on this front but if you have made every effort to elicit a contribution, I think you would be justified in excluding them. A well worded email or phone call is likely to smooth things over.
Unfortunately, graduate students are often afraid to have these conversations or exclude authors that haven't contributed. All I can say is that, you're not at the bottom of the ladder. If you're going to put the effort into a project then you should be satisfied with everyone's contribution.
I'll start with the subject of authorship. Who should be included on your papers, in what order, and why? These are difficult questions, especially when you're a budding scientist. I definitely want to collaborate and share authorship with people that I admire. For me, it has always been a relatively easy process but I usually have a frank discussion with collaborators about who should be included and in what order. It might seem like a subject that is uncomfortable to talk about, especially if you're worried about upsetting someone. But I guarantee that it is less uncomfortable than having a disagreement when it comes time to submit a publication or even after the paper has been published.
It is important to determine what the contributions of individuals will be to the paper. In my opinion, all authors must make an intellectual contribution. Acceptable contributions usually include analyses, writing, or data collection (or all of the above). There are some cases, however, when it is unclear if someone should be included on the paper. For example, if an individual has given you advice or ideas, do you include them on the paper? I think this depends on the gravity of the advice. Did they simply suggest you use a particular analysis? I would not include this person as an author. Did they come up with the idea? I would include this person. Whenever you feel an individual's contribution might merit inclusion on the paper, you should always proceed by asking them if they wish to be. If they decline, then you've done your political duty and made sure that there will be no hurt feelings. If they say yes, the next step is to determine the order of authorship.
For me, author order has always been obvious. The person who writes the paper and does most of the analyses is first author, the person who did some of the analyses or contributed some data is second author and so on. But I have never been involved in a project with more than three or four authors. Once the number of authors starts to grow, I think an explicit agreement should be made prior to any paper submission. Don't leave the discussion until it is too late or until someone is put off. Ask them outright! "Shall I include you as second/third/fourth/fifth author?"
The situation is a bit different when it comes to your supervisor. Different supervisors have different policies when it comes to authorship. Some prefer to be included on all papers and others make this judgement on a case by case basis. Sometimes supervisors prefer to be included as the last author (this is sometimes reserved for the PI) while others are open to any position depending on their contribution. Regardless, when you move on to a new supervisor you should always ask! That way no one will have any excuse to be angry. You'll also avoid pissing off the person that controls your funding!
I have only heard of people having major problems with authorship. I can imagine there have been situations when someone originally agreed to contribute but then failed to uphold their obligation. This can be politically difficult if this person is an office or lab mate but especially if they are a seasoned professor or famous researcher. I can't offer solid advice on this front but if you have made every effort to elicit a contribution, I think you would be justified in excluding them. A well worded email or phone call is likely to smooth things over.
Unfortunately, graduate students are often afraid to have these conversations or exclude authors that haven't contributed. All I can say is that, you're not at the bottom of the ladder. If you're going to put the effort into a project then you should be satisfied with everyone's contribution.
Friday, August 17, 2012
Leaving Ottawa in Ten Days!
I'm officially leaving Ottawa in ten days to study at the University of Wyoming for 9 months! I recently received a Fulbright Scholarship (http://www.fulbright.ca/ if you're interested in applying) to study under Dr. Mark Clementz. I will be investigating isotopic signatures in the hard tissues of pronghorn (Antilocapra americana) from the USA and Canada. I have to say that I am excited.
I applied to Dr. Clementz' and Dr. Rybczynski's labs in 2010 to start a PhD program. It was a difficult decision but I decided to pursue my PhD at Carleton and the Canadian Museum of Nature with Dr. Rybczynski. Now that I have received the Fulbright scholarship it feels like I am getting the best of both worlds. So in a couple of weeks I will be saying a temporary goodbye to Ottawa and will be heading to Laramie, Wyoming.
Laramie is about 2 hours northwest of Denver, Colorado. According to Wikipedia, Laramie is home to only 31,000 people and the University of Wyoming has approximately 14,000 students. At the University of Wyoming I will have access to a stable isotope laboratory, which is something that Carleton does not have. I hope that the experience will broaden my skill set and, let's face it, get me some more publications!
Aside from research, Laramie is located near several national parks and there is ample opportunity for enjoying the great outdoors. There is even a nearby ski hill (although I can't speak for it's quality, yet). I have been missing snowboarding since moving to Ontario. Skiing in the west is significantly better than in the east and I refuse to lower my standards.
It will be a challenge to be away from Canada for so long. It is somewhat difficult to travel into or out of Laramie as the nearest international airport is in Denver. But I am up for the challenge and look forward to interacting with the students in Laramie!
I applied to Dr. Clementz' and Dr. Rybczynski's labs in 2010 to start a PhD program. It was a difficult decision but I decided to pursue my PhD at Carleton and the Canadian Museum of Nature with Dr. Rybczynski. Now that I have received the Fulbright scholarship it feels like I am getting the best of both worlds. So in a couple of weeks I will be saying a temporary goodbye to Ottawa and will be heading to Laramie, Wyoming.
Laramie is about 2 hours northwest of Denver, Colorado. According to Wikipedia, Laramie is home to only 31,000 people and the University of Wyoming has approximately 14,000 students. At the University of Wyoming I will have access to a stable isotope laboratory, which is something that Carleton does not have. I hope that the experience will broaden my skill set and, let's face it, get me some more publications!
Aside from research, Laramie is located near several national parks and there is ample opportunity for enjoying the great outdoors. There is even a nearby ski hill (although I can't speak for it's quality, yet). I have been missing snowboarding since moving to Ontario. Skiing in the west is significantly better than in the east and I refuse to lower my standards.
It will be a challenge to be away from Canada for so long. It is somewhat difficult to travel into or out of Laramie as the nearest international airport is in Denver. But I am up for the challenge and look forward to interacting with the students in Laramie!
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