united states - Why are most of the top universities American?

In any ranking system, at least 50% of top universities (top 100, for example) are American. What is special about American higher education?

etiquette - What should be included in a departmental email policy?

I receive about 10k emails per year and send about 2-3k. In my department (the sphere I can influence) there are those who almost never respond and those who respond as soon as I have hit the send button. There are also those who send mails on weekends, in the middle of the night, seemingly expecting answers fairly immediately. The record was probably the mail that was send shortly after midnight on a Sunday night about stuff that needed to be sorted out by Monday morning. In short, different person have different views on how and for what email should be used. I should add that in my system, the university email is strictly not to be used for private emailing.

I am therefore interested in hearing about if and if so how one can establish an email policy which provides guidelines for reasonable emailing practices within a department.

I have heard about guidelines against sending mails from off duty hours, about avoiding disputes over email, and about reasonable (expected) response times, etc. but have so far failed to locate any good sources for such practices and guidelines.

Answer

After searching and collating ideas from numerous internet sources (a simple search provides plenty of sources of variable quality and usefulness) I have come up with the list below. In my own case there are over-arching rules about usage of university e-mail, which for example makes it clear that the university mail should not be used for private purposes and that all e-mails are public documents. Since each organization probably have such central rules, I have omitted such points and concentrated on good practises in the professional email correspondence.

• 
  
  

  Be courteous. Beware that written communication is more likely to be misunderstood than personal communication. Include courteous greetings and closings to prevent your e-mail seem demanding or terse. Don't hesitate to say thank you, how are you, or appreciate your help! Sign your name and include contact details in the footer of the mail.

  
  


• 
  

  Be concise and clear. Keep e-mails brief and to the point. Make sure your point(s) is (are) clear from the beginning. Be sure to fill out the Subject: field and that it accurately reflects the content of your email. It is sometimes better to write several mails than to fill one mail full of questions on different topics, alternatively number them. Save long conversations for a telephone/Skype/personal meeting.

  
  


• 
  

  Proofread. Read your email out loud to ensure the tone is that which you desire. Avoid spelling and grammatical errors. Try to avoid relying on formatting for emphasis; rather choose the words that reflect your meaning instead. A few additions of the words "please" and "thank you" go a long way!

  
  


• 
  

  Avoid emotions. Do not attempt to solve emotional problems or issues over e-mail. Instead, suggest a personal meeting. Always wait at least a day before attempting to send or answering emotional e-mails. Keep copies of all such correspondence and seek advice from colleagues to prevent issues to build.

  
  
  


• 
  

  Received e-mails. Always try to answer an e-mail within a workday or two. Always acknowledge the receipt of a mail as soon as you can if you are not able to provide a comprehensive reply within a day or two.

  
  


• 
  

  Sending E-mails. Never use an old email to hit reply and start typing about an entirely new topic. Do not send e-mails during weekends or off hours since this may give off the wrong signals or excuse yourself if you do. Do not expect immediate answers to your e-mails. A couple of days is a reasonable delay. Use a phone or visit the colleague if something is urgent.

  
  


• 
  

  Using Cc:, Bcc: and Return Receipts. Include addresses in the To: field for those who you would like a response from. Include addresses in the Cc: field for those who you are just informing. Remove the addresses of those who your reply does not apply to when replying to an email with multiple recipients noted in the To: or Cc: fields. Use Return Receipt sparingly since it can be viewed as intrusive and annoying; save it for when you really need to know.

  
  

These points can be summarized by: Mail others as you would have them mail you!

publications - I am an editor for a lousy paper and I found a better algorithm than theirs. Must I share it with them?

I am handling a paper as an associate editor that proposed an algorithm that I find to be weak. In fact, I was able to show that a very simple, brute-force approach actually has a better running time than their algorithm. Therefore, I will recommend rejecting this paper. Do I have an obligation to share my proof that the brute-force running time is better? I want the higher-level editors to have confidence in the rejection, but it also occurs to me that I might be able to improve my own result and publish it independently. Is this a violation of ethics?

UPDATE: Well this certainly took off! I would like to add the following:

• 
  

  The overwhelming consensus is that it would be unethical for me to "scoop" the other authors, so I will not do that. The advice from all is greatly appreciated.

  
  


• 
  

  The journal is the top in its field, so we have to be extremely selective. The problem proposed is fairly interesting, but overall the paper does not meet our threshold.

  
  


• 
  

  When I say that brute force is better than their method, I mean it in a provable, big O sense.

  
  
  


• 
  

  The authors' proposed scheme is not only inefficient, it is written in a very confusing way. In fact, I asked them to compare their approach to brute-force as a way of helping them clarify their argument, and they did a bad job of it, which is what led me to look into it in the first place.

  
  


• 
  

  The fact that brute force performs better than their scheme is not totally trivial because it relies on a combinatorial argument that is not amazing, but not completely obvious either.

  
  


• 
  

  I will share my proof with the Editor in chief, but I have decided not to give it to the authors; I will consider publishing independently in the future if their work ever appears elsewhere.

  
  



• 
  

  Their paper is not on arXiv or any other website.

  
  

publications - Studies over how noisy is it to accept/reject submissions

This year, the NIPS 2014 conference did an interesting experiment: conference chairs duplicated 10% of the submissions (170 papers) and sent them to two different groups of reviewers. The result: 25.9% of disagreement.

This indicates that for almost every one out of four papers, the paper is accepted by one group of experts while rejected by the other group. This just shows how noisy the reviewing process is. I was wondering if there were other similar experiments for other fields and what the disagreement percentage was in each (regardless of the venue: journal or conference).

Answer

There have been many studies on this. Here is a recent meta-analysis of 48(!) of them:

Bornmann, Lutz, Rüdiger Mutz, and Hans-Dieter Daniel. "A reliability-generalization study of journal peer reviews: a multilevel meta-analysis of inter-rater reliability and its determinants." PLOS ONE 5.12 (2010): e14331.

Here's the abstract:

Background

This paper presents the first meta-analysis for the inter-rater reliability (IRR) of journal peer reviews. IRR is defined as the extent to which two or more independent reviews of the same scientific document agree.

Methodology/Principal Findings

Altogether, 70 reliability coefficients (Cohen's Kappa, intra-class correlation [ICC], and Pearson product-moment correlation [r]) from 48 studies were taken into account in the meta-analysis. The studies were based on a total of 19,443 manuscripts; on average, each study had a sample size of 311 manuscripts (minimum: 28, maximum: 1983). The results of the meta-analysis confirmed the findings of the narrative literature reviews published to date: The level of IRR (mean ICC/r² = .34, mean Cohen's Kappa = .17) was low. To explain the study-to-study variation of the IRR coefficients, meta-regression analyses were calculated using seven covariates. Two covariates that emerged in the meta-regression analyses as statistically significant to gain an approximate homogeneity of the intra-class correlations indicated that, firstly, the more manuscripts that a study is based on, the smaller the reported IRR coefficients are. Secondly, if the information of the rating system for reviewers was reported in a study, then this was associated with a smaller IRR coefficient than if the information was not conveyed.

Conclusions/Significance

Studies that report a high level of IRR are to be considered less credible than those with a low level of IRR. According to our meta-analysis the IRR of peer assessments is quite limited and needs improvement (e.g., reader system).

This meta-analysis includes studies of peer review agreement in economics/law, natural sciences, medical sciences, and social sciences.

Here is another paper, which includes a section on the reliability of peer review (i.e. agreement between reviewers) in which a number of other studies are summarized:

Bornmann, Lutz. "Scientific peer review." Annual Review of Information Science and Technology 45.1 (2011): 197-245.

Specifically in computer science, there's this:

Ragone, Azzurra, et al. "On peer review in computer science: analysis of its effectiveness and suggestions for improvement." Scientometrics 97.2 (2013): 317-356.

They measured inter-reviewer agreement in

a large reviews data set from ten different conferences in computer science for a total of ca. 9,000 reviews on ca. 2,800 submitted contributions.

and found

in our case we have six conferences with ICC > 0.6, i.e. with significant correlation, 3 conferences with a fair correlation (0.4 < ICC < 0.59) and one conference with poor correlation among raters (ICC < 0.4).

They also found that agreement on "strong reject" papers was much higher than agreement on other papers. More precisely,

A more detailed analysis shows that if somebody gives a mark from the "strong reject" band, this increases the probability of giving marks not only from strong and weak reject bands (by 14 and 63% correspondingly) but also from borderline band (by 11%). In the "strong accept" set the probability of others giving a "weak accept" mark is 20% higher than the overall probability, but the probability of giving marks from other bands are almost the same as the overall probabilities.

Therefore, we can say that we have marks skewed towards the "weak accept" and reviewers still agree on very bad contributions while disagree on very good.

conflict of interest - Recommendation letter by significant other if you worked with them professionally?

My significant other (SO) and I are in five years of relationship. We met working in a lab and she already earned her PhD at that time. I was a graduate student. She was the main person I went after my adviser if I had logical questions about my project and helped me the process throughout. We now have two publications together. We worked professionally together for almost three years. She knows my potential both personally and professionally.

I am wondering if it is okay to use a letter of recommendation from her to apply for a PhD program. Updated: I edited the word "supervisor" as suggested by a commenter. She wasn't an official supervisor but, provided tremendous help with analytical issues. We also stay collaborating on other research projects until today even after we both were out of the lab.

Any species returning to the land twice throughout their evolution?

Here's a question from my son I've found interesting enough to ask here. There are plenty examples of species returning back to the water environment, like dolphins, sea lions, walruses, some snakes, crocodiles etc.

The question is - are there any evidences that in evolution of certain species there was return to the land twice, that is, they've came from sea, then evolve as land species, then, again, evolved to somewhat marine or fresh-water and, finally, turn into land species again?

UPD Chordate are the most interesting but any example, even plants, would be nice.

vision - Why can cones detect color but rods can't?

I don't know if this question applies to only humans but why can cones see much greater detail than rods? Is it possible to have a rod that can detect light intensity and color?

Answer

The spectral sensitivity of photoreceptors expressed is the key to color vision. See figure below for the sensitivity of three-types of cone cells (S, M, L) and rod cell (R, dashed line).

From this figure, one can say rod cells provide information about the "blue-greenness" of vision, however, despite their spectral sensitivity, it seems that in human vision rod cells do not contribute to color vision, because they are highly sensitive to intensity, and thus they are mostly saturated in their response (does not induce firing of downstream bipolar cells) during normal daylight conditions. Rod cells specialize for night vision (scotopic conditions) which is crucial for survival, and under this condition the cone cells are pretty much useless.

professors - How do universities deal with loss in productivity post tenure?

A tenure from a good university is generally considered to be the pinnacle of academic achievements. It is packed with so many benefits that it is easy to lose direction in one's research career post tenure.

• In the event that such a thing happens, i.e., if a professor loses interest in research after obtaining a tenure (due to health, family or whatever), what steps do universities take?



• Is there any procedure built into the functioning of universities that helps them minimise productivity loss post tenure?


• Are there incentives which universities (could) offer struggling professors?

Answer

The cynical answer is "nothing". But in truth there are other ways to monitor progress and dole out rewards/lack of reward.

• If your productivity drops off a cliff after tenure, you're unlikely to get promoted to full professor (US-specific), and get the associated salary increases etc. You may be comfortable with this (less service is a good thing!)


• Some universities do 5-yearly post-tenure review. Doing poorly on such reviews can lead to loss of raises, reduced access to new space and facilities, increased service load (if you're not pulling in funding or teaching well for example), and so on.

but ultimately, the final incentive is your own desire to perform. It's very hard to fire faculty. But administrators can try to kill entire departments.

botany - Where can I find historical data on world-wide ecology parameters?

I hope this is the right StackExchange forum for such a question.

I'm looking for large data sets on world-wide ecology parameters, such as annual temperatures by latitude, annual rainfall, CO₂ and O₂ concentration in the atmosphere and in the oceans, data about the insect population, plants, algae, and so on.

Is there any research institute or NGO collecting such data and making it available to the public?

Answer

Adding some additional database sources:

-- Climate --

Prism

The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns.

WorldClim

WorldClim is a set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer. The data can be used for mapping and spatial modeling in a GIS or with other computer programs.

NOAA's NCEI

NOAA's National Centers for Environmental Information (NCEI) are responsible for hosting and providing public access to one of the most significant archives for environmental data on Earth with over 20 petabytes of comprehensive atmospheric, coastal, oceanic, and geophysical data. See NCDC (climate), NODC (Oceans), NGDC (Geophysics), and NCDDC (Coasts).

• NCEI has a HUGE repository of datasets. Sometimes you have to dig (e.g., here).

TROPICAL STORM TRACKS DATABASE

The storm tracks database is derived from the storm data published by the National Hurricane Center (NHC). This web page provides a convenient user interface for casually browsing storm information, including location, category, and wind speed.

-- Traits --

TRY

TRY is a Plant Trait Database. More formally, it's a network of vegetation scientists headed by DIVERSITAS/IGBP, and the Max Planck Institute for Biogeochemistry, providing a global archive of curated plant traits.

BioTraits

This is an online resource for empirical data on how biological traits respond to environmental drivers such as temperature, light, and salinity.

-- Species --

GPDD

The CPB / NCEAS Global Population Dynamics Database largest collection of animal and plant population data in the world, and brings together nearly five thousand time series in one databa

Global Invasive Species Database

The GISD is a free, online searchable source of information about species that negatively impact biodiversity. It focuses on invasive alien species that threaten native biodiversity and covers all taxonomic groups from micro-organisms to animals and plants.

Movebank

Movebank is a free, online database of animal tracking data hosted by the Max Planck Institute for Ornithology. They help animal tracking researchers to manage, share, protect, analyze, and archive their data.

Global Plants

Global Plants is the world’s largest database of digitized plant specimens and a locus for international scientific research and collaboration.

COPEPOD

The Coastal & Oceanic Plankton Ecology, Production & Observation Database (COPEPOD) is an online database of plankton abundance, biomass, and composition data. COPEPOD's online zooplankton and phytoplankton data content ranges from long term ecosystem monitoring surveys to detailed process studies

COMPADRE & COMADRE

COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database site. Contain matrix population models of hundreds of plant and animal species

Catalogue of Life

One of the most comprehensive and authoritative global index of species currently available. It consists of a single integrated species checklist and taxonomic hierarchy. The Catalogue holds essential information on the names, relationships and distributions of over 1.6 million species.

-- Forests --

FAO Forestry Database

The FAO Forestry Department maintains an array of online databases where information covering various aspects of forestry is stored for analysis and further dissemination.

FIA

U.S. forest Service's Forest Inventory and Analysis database contains continuous forest census data for the United States.

European Forest Institute Database

EFI is running a number of online databases with data and information on different aspects of European forests, forestry and forest research.

CTFS-ForestGEO

The Center for Tropical Forest Science - Forest Global Earth Observatory (CTFS-ForestGEO) is a global network of forest research plots and scientists dedicated to the study of tropical and temperate forest function and diversity

-- Other --

NutNet

The Nutrient Network (NutNet) is a grassroots research effort to address pervasive human impacts on ecosystems (via alteration of global nutrient budgets and changes in the abundance and identity of consumers) within a coordinated research network comprised of more than 40 grassland sites worldwide.

---

...Also see the Ecological Databases page on INNGE Wiki for more resources!!

• Ex: Ecological Data Wiki: The site is a source for finding ecological datasets and quickly figuring out the best ways to use them. Just think of it as the Wikipedia of ecology data.

funding - Are there post-doc grants available for non-US citizens in the US?

I was looking for postdoctoral grants/fellowships for a potential post-doc opportunity in the US. It appears as the entire grant/fellowship system is geared towards US citizens, or permanent residents of the US. It is understandable with respect to use of federal resources, but slightly discouraging nevertheless.

Are there any funding opportunities available for non-US citizen? In terms of field I'm interested specifically in Biomedical and STEM fieds.

Answer

There is relatively little funding of the sort you are looking for--funding that non-U.S. residents can apply for directly to fund post-doctoral work inside the U.S. However, that is not really that important, since that is not how most post-doc positions are funded anyway.

Most post-docs do not apply for their own funding. The funding comes from the institution that hires the post-doc (Very often--but not always--the money ultimately comes from a research grant from an outside agency, but the post-doc would not be involved in writing or administering the grant.) If an institution has funds for a post-doc, they will advertise a job opening and hire somebody. Generally, the search is global; they would be willing to hire somebody from anywhere in the world, provided they are a qualified. (There are some technical caveats about how this works, but they are of little import in practice; and--again--they are things that the hiring institution mostly needs to worry about, not the person they choose to hire.)

evolution - What is the difference between orthologs, paralogs and homologs?

These three terms are often misused in the literature. Many researchers seem to treat them as synonyms. So, what is the definition of each of these terms and how do they differ from one another?

Answer

First, a note on spelling. Both "ortholog" and "orthologue" are correct, one is the American and the other the British spelling. The same is true for homolog and paralog.

On to the biology. Homology is the blanket term, both ortho- and paralogs are homologs. So, when in doubt use "homologs". However:

• 
  

  Orthologs are homologous genes that are the result of a speciation event.

  
  


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  Paralogs are homologous genes that are the result of a duplication event.

  
  

The following image, adapted (slightly) from [1], illustrates the differences:

Part (a) of the diagram above shows a hypothetical evolutionary history of a gene. The ancestral genome had two copies of this gene (A and B) which were paralogs. At some point, the ancestral species split into two daughter species, each of whose genome contains two copies of the ancestral duplicated gene (A1,A2 and B1,B2).

These genes are all homologous to one another but are they paralogs or orthologs? Since the duplication event that created genes A and B occurred before the speciation event that created species 1 and 2, A genes will be paralogs of B genes and 1 genes will be orthologs of 2 genes:

• A1 and B1 are paralogs


• A1 and B2 are paralogs.


• A2 and B1 are paralogs.


• 
  

  A2 and B2 are paralogs.

  
  
  


• 
  

  A1 and A2 are orthologs.

  
  


• B1 and B2 are orthologs

This however, is a very simple case. What happens when a duplication occurs after a speciation event? In part (b) of the above diagram, the ancestral gene was duplicated only in species 2's lineage. Therefore, in (b):

• A2 and B2 are orthologs of A1.


• A2 and B2 are paralogs of each other.

A common misconception is that paralogous genes are those homologous genes that are in the same genome while orthologous genes are those that are in different genomes. As you can see in the example above, this is absolutely not true. While it can happen that way, ortho- vs paralogy depends exclusively on the evolutionary history of the genes involved. If you do not know whether a particular homology relationship is the result of a gene duplication or a speciation event, then you cannot know if it is a case of paralogy or orthology.

References

1. R.A. Jensen, Orthologs and paralogs - we need to get it right, Genome Biology, 2(8), 2001

Suggested reading:

I highly recommend the Jensen article referenced above. I read it when I was first starting to work on comparative genomics and evolution and it is a wonderfully clear and succinct explanation of the terms. Some of the articles referenced therein are also worth a read:

• Koonin EV: An apology for orthologs - or brave new memes. Genome Biol, 2001, 2:comment1005.1-1005.2.



• Petsko GA: Homologuephobia. Genome Biol 2001, 2:comment1002.1-1002.2.


• Fitch WM: Distinguishing homologous from analogous proteins. Syst Zool 1970, 19:99-113. (of historical interest, the terms were first used here)


• Fitch WM: Homology a personal view on some of the problems. Trends Genet 2000, 16:227-31.

community - Is web-presence important for researchers?

How important is web-presence to researchers? How does its importance vary by fields? (My interest is STEM, theory in particular)

I noticed that there is a pretty large variation in amount of web-presence even within a single field (I will use theoretical computer science/related math as an example). There seems to be 3 different levels of web-presence:

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  [High] Very active member of various internet tools (MathOverflow, cstheory, blogs, G+, etc) usually accompanied by a clear homepage with all the [Medium] info.

  
  


• 
  

  [Medium] A clear up-to date website that provides a clean bibliography/CV (usually with links to self-hosted pdfs), a repository of course-notes and teaching information, and list of students.

  
  


• 
  

  [Low] No personal website (or extremely outdated website).

  
  

Increasing your web-presence usually requires effort. Should you invest this effort? Or are you just wasting research time?

If you enjoy being active on the internet (so it is not a cost for you, but maybe a time-sync) is there any danger to having a high web presence?

Answer

The short answer, at least in theoretical computer science, is yes. Especially pre-tenure.

The Coin of the Realm in academia is fame. Hiring and promotion decisions are based primarily on the perceptions of your impact by leaders in the research community. Those intellectual leaders must know who you are, they must know what you do, and they must think that what you do is excellent. This is precisely why it's so important to network, network, network — go to conferences, visit other departments, talk to visitors, ask questions, answer questions, go to lunch, drink beer, play pool/golf/frisbee/Settlers of Catan, race go-karts, exchange business cards, all that stuff. Having a visible online presence is just another form of networking.

Similarly, if you want to attract good students, they have to know who you are, they have to know what you do, and they have to think what you do is interesting.

Similarly, if your work is not freely and easily accessible on the web, it is much less likely to be cited than freely accessible work of comparable quality.

To give some personal examples, I have good reason to believe that these web pages were a significant factor in my academic job search and even my tenure case, and this stuff definitely helped me get promoted. I expect that these pages similarly helped Suresh, and these pages similarly helped David.

evolution - Very introductory online source of information in evolutionary biology

We receive quite a lot of questions from layman in evolutionary biology in this site that are sometimes difficult to answer just because there is way too much to say. Why don't human keep evolving? is a perfect example. I often want to give some pieces of information and link to a good source of information for them to get introductory knowledge in evolutionary biology.

Obviously nobody that ask a question on a Stack website want to read an answer of the kind You should read that book!. It is often not really pleasant to read a book, it takes time and the OP that get this answer will likely just forget about their question.

What are the best online resources that offer a very introductory understanding in evolutionary biology?

Either a written course of videos such as Khan Academy videos would do (I haven't watched the evolutionary biology section of Khan Academy though and can't really judge of how good it is).

Answer

At least online, I think the single best introductory evolution resource is the Evolution 101 tutorial at UC Berkeley's Understanding Evolution project. The site has been designed by some of the top evolutionary biologists and evolution educators in the country, and does a very good job presenting a basic overview of how evolution works.

molecular biology - Why is DNA Shuffling more efficient than Point Mutation?

In a related post on Biology-SE the following insightful comment was made:

The advantage of DNA shuffling over introducing single mutations is that you have to screen fewer mutants and the activity/stability of the protein could be improved several hundred fold more.

Is there a mathematical argument as to why DNA Shuffling should be more efficient than introducing point mutations?

Even supposing each DNA Shuffling / Point Mutation step is equally "expensive" (and in reality DNA shuffling seems much more work) they are both generating one variant per-step, right? So why is the variant in one case grossly better than the other?

Related Post: Directed evolution: Point mutation vs Insertion-Deletion vs Shuffling

Answer

You should read this paper. Here is the gist of what you are interested in:

Because most point mutations are deleterious or neutral, the random point mutation rate must be low and the accumulation of beneficial mutations and the evolution of a desired function is relatively slow in such experiments. For example, the evolution of a fucosidase from a galactosidase required five rounds of shuffling and screening before a >10-fold improvement in activity was detected4. Naturally occurring homologous sequences are pre-enriched for 'functional diversity' because deleterious variants have been selected against over billions of years of evolution...

Although shuffling of a single gene creates a library of genes that differ by only a few point mutations1, 2, 3, 4, 5, 6, the block-exchange nature of family shuffling creates chimaeras that differ in many positions. For example, in previous work a single beta-lactamase gene was shuffled for three cycles, yielding only four amino-acid mutations3, whereas a single cycle of family shuffling of the four cephalosporinases resulted in a mutant enzyme which differs by 102 amino acids from the Citrobacter enzyme, by 142 amino acids from the Enterobacter enzyme, by 181 amino acid from the Klebsiella enzyme and by 196 amino acids from the Yersinia enzyme. The increased sequence diversity of the library members obtained by family shuffling results in a 'sparse sampling' of a much greater portion of sequence space15, the theoretical collection of all possible sequences of equal length, ordered by similarity (Fig. 4). Selection from 'sparse libraries' allows rapid identification of the most promising areas within an extended sequence landscape (a multidimensional graph of sequence space versus function)

plagiarism - How can I determine whether a student has written an excellent paper themselves, or hired someone else to do it?

In class, over the course of a semester, I tend to build up a mental model of individual students' communication abilities. For example, one student might seem more or less eloquent than another when they speak. One might make more cogent points than another.

Occasionally, I will receive an essay which appears exceptionally eloquent and cogent, from students who seemed distinctly average. Of course, I make the cursory checks for plagiarism (searching certain unlikely phrases, etc.), but if nothing comes up, I sometimes still remain unconvinced that the work is the student's own.

Some sites offer to sell the services of trained academics and graduate students for menial essay writing, and often the questionable assignments look more like the quick and under-edited work of a master than the polished work of a novice. Of course, if the essays are paid for, and original, it's naturally very hard to prove it.

Are there any techniques I could use to determine whether an essay that I suspect was written by someone else, but not published elsewhere, is a student's own work? Are there any steps I can take to ensure I don't receive questionable essays in the first place?

Answer

Are there any techniques I could use to determine whether an essay that I suspect was written by someone else, but not published elsewhere, is a student's own work?

Ask the student to come to your office hours or a private meeting.

Then, say: "I was really impressed by your recent paper, it was an excellent piece of work! Let's discuss it some more."

Student who did the work will be able to discuss said work intelligently.

Student who didn't do the work is unlikely to be able to carry on an intelligent conversation about said work.

(This still isn't proof of any kind, and you can't accuse student of cheating without some evidence - but if you're lucky, student will at least be spooked enough to never do it again.)

Are there any steps I can take to ensure I don't receive questionable essays in the first place?

Assign essays that are a few steps beyond, but still closely tied to, what's been discussed in class, such that somebody who wasn't in the class will not be able to produce the kind of essay you're expecting (the effectiveness of this depends very much on the subject matter).

graduate admissions - Will my age affect my chances of finding a funded PhD position?

I just finished my masters studies. My question is, will my age (29) affect my chances of getting a funded PhD position? I finished Bachelor of Engineering when I was 24 (took 6 years to graduate, normally my program was 5 years). I worked as a Lab course assistant (teaching the practical part of the course at a local university) for 2 years before I started my MSc a bit late due to financial reasons. I started working in industry a year ago (for same financial reasons) in a computer science field but irrelevant to the research area. I know that most of PhD scholarship positions officially require the applicant's age to be under 35, but do not they prefer someone who is 25 rather than 34?

I have two conference publications, one in IEEE, and about to send the third for journal publication. My field is computer engineering with a focus on the software side and I am trying to apply in Europe mainly.

Answer

The short answer is: no. Quite the contrary, many universities value the experience (preferably from industry) so it is beneficial. Also, your conference papers will be the most important factors in getting the PhD position and/or funding. I'd also emphasize that you should highlight the fact that you have a journal article (preferably A or A*) in progress.

I have not seen any such requirement which is restricting PhD or any academic degree to a specific age. In fact, doing so is illegal in most (read: all) European countries as it comes under age discrimination.

To narrow it down further, different funding bodies could impose their own restrictions per project, for instance DAAD's grant that you mentioned is "to promote and fund young artists" according to them, that is why it is restricted to a certain age group. A similar example could be feminist studies where it could be restricted to a single gender. Again, this has nothing to do with general conditions of admission.

publications - Using slang in a Scientific Paper

We are writing a paper about an algorithm that takes a group of solutions and sorts them by their likelihood of being equal to the one and only 'good solution'.

Internally, we refer to this 'good solution' as meromero, which is a Mexican street slang for 'the most important' or the precisely for 'the one and only'.

I like the word because it allows naming an important concept with a funky and short sound. Also, it forces you to draw your eyes towards it, creating some kind of highlight about the ideas related to this 'one and only good solution'.

However, I'm not sure of using it, precisely because it is a slang word and I don't remember ever reading a paper using slang words.

Therefore, my question is:

Is using a slang word to name a concept bad taste in a scientific paper?

Notice that this is merely a personal curiosity, we have already choosen not to use the word.

Answer

Your goal in writing scientific papers is for the language to be as intelligible as possible. That is, you want to use language which won't hinder people who are trying to read your paper.

The disadvantage of slang is that it is often not intelligible to many people (especially non-native speakers), and may be idiosyncratic to specific groups. Therefore, if your goal is for the paper to be as intelligible as possible, you would want to avoid using slang.

In addition, scientific writing tends to be relatively formal, which is why slang will often look inappropriate and out of place.

Some papers which mention that you should avoid using slang:

http://www.sciencedirect.com/science/article/pii/S1443950600900817

http://onlinelibrary.wiley.com/doi/10.1002/micr.20960/full

It's possible that conventions will be different in your field. When in doubt, look at other publications and use them to guide you.

What is exactly the role of a phd advisor?

I'm a westerner doing my PhD in an Asian country. While writing this I've just finished my first year, but I'm getting so fed up with my academical environment that I decided to move my PhD.

One of the reasons why I went to Asia is that I'm in a technological field. Now a days with all these Asian countries up-and-coming, developing innovative products, it felt like a good moment to ride along on their train.

In the last year I'm experiencing major difficulties with my advisor and I'm not sure whether it's because of cultural differences or just me. Let me highlight some of the major issues:

• When I did my masters, my advisors were actually people who gave me advice. My current professor is somebody who gives orders instead of advice. The big problem with that is that there's usually no room for persuading him with counter arguments. As stubborn as I'm, it usually ends up in me ignoring what he says.


• There seems to be a big difference in how I approach weekly meetings. I make a selection of what I investigated during the week and decide myself which direction I go into and thus what I eventually present to him. It seems that he wants every direction thoroughly investigated and presented to him so that he can make a decision about the direction eventually.

These and other reasons, I don't think it makes people better. It won't let people think for themselves when they are just following. I got the comment last week that he thinks that my output is too low, but in fact I'm making the most progress, I'm just not presenting as much as everybody else because I make my own decisions upfront. I noticed that I intentionally not share everything with him anymore, because he always manage to turn everything upside down in one hour per week and ends with "just do it." Like he always creates the strangest and most complicated experimental designs (e.g., 3x3x3) with factors that I don't think are related. I just want to perform a simple 2x2 design and deepening it more and more based on the results. It just feels very odd that somebody who only gets involved into a project an hour per week gives orders about the direction.

Well the thing is that I seems to be the only one who thinks this is not normal. Since I always hear those stories that doing a PhD is always tough and sometimes makes you hate your advisor, I'd like to know where the problem is. I don't mind toughness, but it needs to serve a goal. Before I'm accepted to a different PhD in another country, I'd like to know if I'm getting in the same situation. If so, I don't think a PhD is the right thing for me then.

feedback - Who reads teaching evaluations?

At the end of each semester, usually a month before final exams, my school (in the US) distributes teaching evaluations. What people will read these? Do people only see these after the final grades are posted?

Research Methodology Books for beginners in Computer Science research

I am looking for Research Methodology textbooks in the field of Computer Science. Most books I came across are useful to social science, economics etc.

In our part of the world a person who wants to do research should study a course in research methodology. But most books in research methodology are from social science, economics perspective.

Would like to know some from technology/computer science/engineering perspective. Some must read books for general reading also would be good

Answer

Research methodologies are usually independent of your area. Often, the application of some methods and the examples used to describe them differ depending on the area. For example, empirical research is a methodology that can be applied whether you do research in CS, psychology, or physics. This methodology teaches you how to conduct experiments that are sound (by minimizing different threats to validity) and how to analyze the data in a statistical way (e.g., when to use which statistical test to accept a certain research hypothesis). In medicine, you might conduct experiments to analyze the effects of some drugs whereas is CS, you would do some performance experiments to analyze the effects of some optimizations. The methods used in both areas are the same, the application might differ largely though.

Hence, it would not harm to study a text book from psychology / statistics, because the tools you learn there are also valuable for CS (e.g., knowing the difference between qualitative and quantitative research or descriptive and analytic). Furthermore, there is no CS-methodology (at least I am not aware of).

evolution - Drake's Law. What is the genome-wide mutation rate and what are the estimates?

Drake's rule

Drake's rule states that the genome-wide mutation rate is more or less constant across all species — from E.coli to the house sparrow.

Data

From what I think being Drake's original paper (table 1, page 4) on the subject (see here) is at the order of $3\times 10^{-3}$. When I look at this paper, I see that the genome-wide mutation rate is roughly around 30 for human. When I look at this paper, they cite some other papers suggesting a genome-wide mutation rate in the order of 0.1 to 1 in multicellular eukaryotes and typically at the order of 1 for vertebrates. Finally, when I look at this speech (at the 60th minute), it seems however that the genome-wide mutation rate in human is 2.2.

What is going wrong?

Do I mix-up different concepts or are there some very contradicting estimates depending on the article we look at? Isn't the genome-wide mutation rate, $U$, which is just the number of de novo mutations transmitted to one offspring on average? What is a correct estimate of $U$ for human for example (1, 2.2 or 30)?

Answer

There are so many things that are implied in this paper, not explicitly said.

The mutation rate here detected seems to be the emergence of chain-terminating (CT) mutations, which truncate protein coding genes, usually just one gene in a bacterium or phage, which would be possible to observe from inspecting a plate to see which colonies die or survive.

This is only a specific kind of mutation, but Drake assumes that its frequency is related to the overall mutation rate. Which is probably fine. Mutations we infer from this work arise spontaneously from a similar mechanism in all organisms. This is just ionizing radiation for the most part. So at a first glance we still believe this. That there are no specific mechanisms for mutation. Since this is usually ionizing radiation we would expect that the rate would go up when there's more radiation around and it most certainly does.

There are lots of reasons that animals and humans would have a smaller rate. Drake is including in the paper the involvement of DNA repair mechanisms in the experiment as they are intrinsic to the survival of the yeast and bacterial cases and the phage may also enjoy the benefit thereof.

In some organisms there is a lot more DNA repair possible. So that would mitigate the mutation rate in some cases like Deinococcus radiodurans.

Metazoans and diploid organisms which undergo meiosis for sexual reproduction have other methods for reducing the number of mutations they pass on. Meiosis and recombination will allow the removal of many mutations by competition in gametes. Since there are two copies of each chromosome, mutations are constantly being competed against their unmutated versions as gametes. Then eukaryotes have their own repair enzymes and conditions. Then lastly recombining into diploids, they also show mutations less often.

For these reasons and others, the mutation rate being even across the genome does not mean that that the mutations accumulate evenly. Mutations still tend to accumulate in regions where positive selection is operational. Here is an excerpt from a recent genomic comparison of five strains of rice:

Despite strong purifying selective pressures on most Oryza genes, we documented a large number of positively selected genes, especially those genes involved in flower development, reproduction, and resistance-related processes. These diversifying genes are expected to have played key roles in adaptations to their ecological niches in Asia, South America, Africa and Australia.

phd - How should one choose a supervisor if they haven't yet narrowed down their research focus?

From what I know, every Phd student needs to choose a supervisor in the first year. For someone who is entering a Phd with a broad interest in a field, this might be difficult since they don't immediately know what they want to work on. How do they go about choosing a supervisor in that case?

publishability - Is combinatorial novelty without insight useful? Who cares if we're the first to use tool T on problem P?

I'm a bioinformatician and former student of applied math. I want help to see if I should change my view).

Many academics, including all of my PI's on my major projects, justify their work by saying "We're the first ones to apply fashionable technique T on problem P". This is in situations where P is often well-studied and T was developed and established by other groups. I call it "combinatorial novelty" in the title because the novelty is not in new tools, nor new insights, but rather in new combinations. The justification is essentially "We're early adopters."

This would be fine if the studies produced valuable new insight about P. P is important and I'd be proud to make progress on it whether or not I'm using fancy new techniques like T. But usually, our progress on P is weak despite using T, so we need to turn to T's fanciness to justify our work. I see people using this "combinatorial novelty" to make their work seem like a big deal.

This seems flimsy, but if every PI I've worked under is doing it, then either it impresses grant reviewers, or it actually is valuable to science and I just don't understand why. Or both. Is this valuable to science? If so, why?

Answer

Is this valuable to science? If so, why?

Because it leads us to understand if tool T works on problem B. How big the "insight" that we gain from this is depends a lot on how different T is to other tools that have already been used on B, or, conversely, how different B is from other problems that T has been applied to.

The range here goes from "it is mind-blowing that T could work on B" all the way to "meh, everybody knew that T would work because we use it all the time for B' anyway" - although I will grant that most works following this schema in practice end up more on the rather incremental side of things.

publications - Why is it bad to judge a paper by citation count?

I hear a lot from the experts that citation count is a bad idea as a measure of judging a paper. This seems simply counter-intuitive to me. I would like to know if any study has been done in this direction?

academic life - Is it common for people in academia to experience prolonged lack of sleep and how to deal with it?

Stuck in the lab again, for the third night in a row. I have never done three in a row I think I might just break a record. I cannot sleep, because the lab is super crammed and uncomfortable and I am afraid that someone will come in and take my belongings, I am afraid that I will fall sleep for so long that I will miss something so important that will ruin this semester, especially at a time when I beginning to see the light at the end of a long dark tunnel that has been both physically, emotionally and mentally draining.

Why not go home? Because home is an hour drive away and I am stuck working on multiple projects that lasts from 8 in the morning to 3 in the morning. Have to get up to school again at 9 am. Not much time left in my time zone. Should I go to sleep? Might just wake up in a lab full of people and look embarrassed. Should I stay awake? I will just wind up falling asleep in the lecture which is ten times worse!

But that's not the point I am asking this question. I am asking this question because I am the only one left in the lab. Eyes wide open from dusk till dawn, headed for a big crash. Can't form a full sentence, my mind is going blank. I am starting to wonder if it is just me. What is it about me that forces me to wonder the halls at 4 in the morning to go to the bathroom to brush my teeth? What forces me to drink energy drink at 11 pm at night and wonder how long before I will have my first heart attack? Can you call an ambulance over the internet, my phone is dead.

How do academicians view the subject of sleep when things left undone, problem left unsolved swirls around your mind? How to be productive, meet deadlines, and get adequate sleep?

Answer

There are short-term and long-term approaches. Sometimes, a lack of sleep is necessary because of, eg, unexpected and valuable time on expensive valuable equipment X. Or, because the grant really is due today and you didn't budget time correctly. Or because process X has to be babysat and really does take 72 hours. For those short-term upsets, coffee and lots of water works, followed by about twice as much rest as the time you missed sleeping. Never pretend you can make time up on your long-term schedule by missing sleep. For processes, that part just sucks, either try to work in shifts with another student or set up a nearby sleeping area. If you're gadget-friendly, simple setups can often improve efficiency by allowing uninterrupted sleep.*

Long-term, missing sleep does not work for most people. Planning can help a lot. So, start by figuring out everything you want to do, estimate how long it should take, and then prioritize. Start by filling in an 8 hour workday and try to get everything on your list done. Listing tasks such as 'try X' works better than 'solve Y'. For most people, it won't come close to happening in 8 hours. Next, measure the ratio of estimated to actual time and use that to scale your future estimates. Keep reviewing.

Next, find a weekly hour limit that keeps you healthy. Then, optimize for efficiency. (commuting 2 hours?? move. Reading StackOverFlow instead of working? Um... Stressed, not getting much done? - Try the gym.)

Finally, after a few iterations, compare your productivity to your peer group while adjusting for your career goals and choose realistically. If you aren't close to living healthily while being as or more productive as people likely to achieve your career goals, it would be reasonable to rethink them.

*One of my friends moved into his office. There was a lab shower, so it wasn't bad - and saved a ton on housing. Add a lockable door == no problem.

cell biology - Why are hard boiled eggs so homogeneous?

A eukaryotic animal cell is a complicated piece of biological machinery. Some major structures inside of the cell (see the image below) include: the nucleus, mitochondria, Golgi vesicles, and various tubular structures. Why then is the single-celled, unfertilized chicken egg so homogeneous when it is cooked (or before)? The only major structure I can recognize is the cell nucleus.

*Image Credit: "Animal cell structure en" by LadyofHats (Mariana Ruiz) - Own work using Adobe Illustrator. Image renamed from Image:Animal cell structure.svg. Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Animal_cell_structure_en.svg#mediaviewer/File:Animal_cell_structure_en.svg

Answer

Disclaimer: This is my understanding of the egg anatomy as a general biologist. There is most certainly better references and sources out there to explain this (please add better references if you know of any).

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If I understand you correctly, your question is why we do not see cell organelles in a cracked or boiled egg. If so, your question seems to stem from a misunderstanding of what the egg white and egg yolk represents. A chicken egg is not simply an enlarged cell, and the egg yolk is not the cell nucleus.

When an oocyte matures in the chicken ovary, it stores yolk inside the cell and therefore enlarges. The yolk is therefore part of the oocyte cytoplasm. However, as it enlarges, the yolk is separated from the germinal disc, which holds all the other cell organelles (including the nucleus). The germinal disc is seen as a small white area on the egg yolk. Eventually, when the oocyte has accumulated enought yolk, it disattaches from the ovary (ovulation) and goes into the hens oviduct. This process is happening continuously, and oocytes of different stages of maturation are present on the ovary, which can be seen in this image:

(image from Chickscope: Formation of the Egg. University of Illinios)

When the oocyte passes through the oviduct it builds up layers of egg white (albumen) and other supporting structures. The shell is formed last in the shell gland, which is located at the end of the oviduct. Therefore, the egg white and the shell is formed externally of the oocyte and the egg yolk.

So the reason why we do not see more structures in a boiled egg is that the majority of the egg is yolk and egg white, which are both structures to store energy ment for a growing embryo (mainly protein and fat). It should also be noted that oocytes in most (all?) animals contain yolk granules, but the storage of yolk is most extreme in birds (see this webpage on animal development from State university of New York for a short comparison).

It should also be noted that an egg that is fertilized will naturally contain more than one cell, since the embryo will have started growing.

References:

Should a mid-career faculty application include a letter from the former PhD advisor?

I have been in a tenure-track position in 5-6 years and I am applying to positions in other universities, including tenured positions. For the list of references, should I include my former PhD advisor? I have some other good names to include and also I have a good relationship with the former advisor. I was wondering the pros and cons of including/excluding the former advisor in the list of reference.

Answer

This might vary from field to field, and based upon how established and well-regarded you are within your own field, but personally I would not feel that it is mandatory to get a letter from your prior advisor, if you have other letter-writers who you think will be more suitable (know your work better, are better-regarded in the community, will write you a stronger recommendation). At this stage in your career most faculty are now established enough that they are your own brand and can stand on their own, separate from their advisor. In particular, 5-6 years in a tenure-track position is probably far enough along in your career that I don't think the hiring committee will look askance if you don't have a letter from your prior advisor.

In any case, if the hiring committee wants an assessment from your former PhD advisor, they will ask your former PhD advisor. For jobs at this level, it's not unusual for them to ask others for their opinion of you (beyond the letters that you provide), if you are a serious candidate.

That said, usually your former PhD advisor is someone who knows you well, wants you to succeed, appreciates your work and your interests, and is well-informed about your research -- so they are often a good choice of a letter-writer, all else being equal.

biochemistry - Why can't the brain and red blood cells use fuels other than glucose?

The question is rather straight forward: I have always been curious as to why, but cannot find an explanation online.

I can imagine that the mechanism is different for each, but why does brain tissue and red blood cells use specifically and only glucose for energy metabolism?

Do you not belong in a PhD program if you don't live and breathe your field of study?

In my PhD program everyone talks about how passionate they are about their field of study, and how they go to seminars because they're fun. Is it expected that PhD students truly love their subject, or is it enough just to do good work and publish papers?

This thread was prompted by discussion here.

Answer

In my experience, good primary investigators are always rather unbalanced human beings in one way or another. If you aren't intensively passionate about something closely connected to the research process, then you can't last, because so much of leading research involves shaping your own agenda. That said, you can still be doing work in research, even at a Ph.D. level or beyond, without having such independence and drive, but instead being a "super-technician" following somebody else's agenda and leadership.

The scientific ideal holds that every scientist should be of the primary investigator type, and Ph.D. programs are typically designed to select for and cultivate this. That said, in practice it depends a lot on the group that you are in. Some professors expect their students to develop their own research agendas very strongly, others are (whether they admit it or even realize it) more looking for good technicians to execute on their grants, and a Ph.D. is more of a byproduct.

We don't really like to admit this as a community, but with the current market structure of academia, we actually need to have the second type of education and people as well. Look at it from the perspective of simple flux balance analysis: the rate of Ph.D. students entering programs is far higher than the rate at which primary investigators retire or die. If every Ph.D. student either ultimately ends up as a primary investigator or a "failure," then it means most Ph.D. students are failures. But I don't think that is actually the case: people who aren't hyper-passionate to the point where it distorts their lives can still succeed just fine in a Ph.D. program and at research, they just are likely to take one of the other tracks besides being a professor or other form of PI.

That said, even if you don't end up going the harrowing road of PI-ship, research work is very hard, and there are a lot of easier and/or more financially rewarding ways to make a living. To get a Ph.D., you need at least enough passion for the subject to find more value in this difficult and low-paid path than in any of your alternatives.

human biology - Where does the gluteus medius attach to the greater trochanter compared to the gluteus minimus attachment?

Where does the gluteus medius attach to the greater trochanter of the femur compared to the gluteus minimus attachment? Is it above, below, next to it, etc.? Ideally I'd like to know the distance as well and location relative to the greater trochanter.

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What I have found so far: some diagrams place the gluteus medius attachment below the gluteus minimus attachment, some place it above and some place it next to it at the same height.

Example of diagram placing the gluteus medius attachment below the gluteus minimus attachment (image source (mirror)):

Example of diagram placing the gluteus medius attachment next to the gluteus minimus attachment (image source (mirror))

Example of diagram placing the gluteus medius attachment above the gluteus minimus attachment (image source (mirror)):

Answer

{1} greatly answers the question.

Regarding the insertion location (lateral view):

Note that unlike the gluteus minimus insertion, the gluteus medius insertion can also be viewed anterolaterally:

Table 2 contains more detailed insertion location data:

Regarding the insertion area:

• Gluteus maximus: Average Area, mm^2 (95% CI): 473.4 (381.0, 565.8). Thick, muscular insertion along the posterior femur on the linea aspera


• Gluteus medius: Composed of 2 contiguous rectangular footprints, the lateral facet oriented in the sagittal plane and the superomedial facet primarily in the transverse plane:
  
  
  
  
  • Lateral facet: Average Area, mm^2 (95% CI): 141.1 (117.7, 164.5).
  
  
  • Superomedial facet: Average Area, mm^2 (95% CI): 501.5 (442.8, 560.2).
  
  
  
  


• Gluteus minimus: Average Area, mm^2 (95% CI): 280.9 (229.3, 332.5). Two morphologies noted: The majority (11/14) were long, thin, and crescent shaped and ran medial to lateral and concave facing inferiorly, while a few (3/14) were bowtie in shape.

Below are some actual pictures of the gluteus minimus insertion, the gluteus medius insertion to get another sense of their shape and location. Still from {1}, this picture shows the gluteus medius (but not the gluteus minimus):

From {2}, this picture shows both the gluteus medius muscle and the gluteus minimus muscle:

I also find this picture good to give a better sense as to where the greater trochanter is located with respect to the human body (image source (mirror)):

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References:

• {1} Philippon, Marc J., Max P. Michalski, Kevin J. Campbell, Mary T. Goldsmith, Brian M. Devitt, Coen A. Wijdicks, and Robert F. LaPrade. "Surgically relevant bony and soft tissue anatomy of the proximal femur." Orthopaedic journal of sports medicine 2, no. 6 (2014): 2325967114535188. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Surgically+Relevant+Bony+and+Soft+Tissue+Anatomy+of+the+Proximal+Femur&btnG= ; https://www.ncbi.nlm.nih.gov/pubmed/26535335 ; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555602/ ; https://doi.org/10.1177/2325967114535188


• {2} Woodley, Stephanie J., Susan R. Mercer, and Helen D. Nicholson. "Morphology of the bursae associated with the greater trochanter of the femur." JBJS 90, no. 2 (2008): 284-294. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Morphology+of+the+Bursae+Associated+with+the+Greater+Trochanter+of+the+Femur&btnG= ; https://www.ncbi.nlm.nih.gov/pubmed/18245587 ; https://doi.org/10.2106/JBJS.G.00257

career path - How does the experience of working at a "top" university differ from working at an average one?

Being a professor at the top university in your field must be a very different experience from being a professor at a top 20 university, which must be a very different experience from working at a top 100, 500, etc. university.

But how do these jobs differ? What are the qualitative differences between working at universities in different tiers? I'm less interested in pay and benefits -- more interested in the day to day experience.

career path - Does taking an academic job in Asia or Africa make it difficult to get a job in the US or Europe later?

Many in this community know of the struggle of finding a job in tertiary education after completing a PhD. However, there are many options available for people who are willing to work overseas in exotic locations in Asia, Africa, and the other developing areas of the world.

I am wondering if working overseas would be detrimental to someone's career. Is it hard to get back into the States/Europe after spending a few years teaching and writing in the developing world? How do search committees view someone who has been away in order to find employment? Let's say the person asking has a PhD in education.

Answer

If you go to top schools overseas, you should be fine. You can always explain your decision by saying that you needed to travel, help developing countries, explore opportunities. In my field, many Chinese schools have very high-tech requirements in their labs! My old school (not in China) has unlimited funding! So, research wise, it might be a good idea to travel for a year or two to establish a good working relationship with labs and schools.

Many of the professors (engineering) I know will go to the Gulf region and work in Dubai/Kuwait for a sabbatical year or even as a visiting professor (1-3 years) because of the good pay (can get up to 140-160k), benefits (paid housing, car, air tickets, schooling etc), no taxes, less stress (no need to write proposals or get funding).

As long as you keep your contacts in the US/Europe "happy", you should be fine. I know some professors will hold international conferences and put their colleagues on committees! I know some will hold 1-2 days seminars/workshops and invite their previous department chair as a keynote speaker or lecturer for crash-courses (they make easy money out of this).

My PhD advisor is writing most of an article without considering some of my inputs

We are currently writing a first article with my PhD advisor. The research was mainly designed by him; I performed all the experiments and found a model to explain the results.

The article is soon to be finished. I am supposed to be the first author of the paper but at this point, I am more and more considering to ask my advisor to put me as the second author or maybe even ask to have my name removed.

My advisor wrote most of the article, which I already find weird given that I am the first author. This is not due to my lack of initiative, I've been explicitly told not to write some parts. It really feels that I am simply a secretary asked to check for typos, formatting issues, and to make graphs and figures as he sees fit. When I correct some of his writing, he sometimes takes into account what I propose/correct, sometimes he disagrees and explains why, but sometimes completely ignores it (my comment is deleted and not addressed). Some of these ignored comments are not fundamental, but I find some others very important and I am really not comfortable with some parts of the manuscript (he knows this given that I wrote it in the comments).

As an example, my advisor is very excited by a small theoretical model I proposed to him once that could roughly explain the experimental results. Yet there are still gaps and unsupported hypothesis. He knows that I am very reluctant to make this modeling an important part of the paper (which otherwise present both qualitative and quantitative experimental evidence of phenomena not previously reported), but this is now becoming more and more important and is almost the central part of the article. We discussed that once, his answer was simply that I should be proud of myself to have explained something rather than be too critical.

Other examples include him writing something along the line of "when XX was observed, we systematically found that this was due to YY". Something I actually only observed once and I am not confident that it can be so easily generalized.

My question is simply how to deal with that. My relation with my advisor was great but it is becoming more and more strained (especially from my side) as this article evolves. Is this a regular flow between a student and its advisor during the writing process? This is a long, in-depth >30-page article, if this information can be useful. With my Master advisor I had written and published a short-letter (4 pages), and I was completely leading the writing.

publications - During applications, how can I prove I was the first author of the papers that I couldn't become first author for various reasons?

One year ago, I started working on a subject with one of the professors of our department. The idea was mine, and all of the subsequent steps including modellings, simulations, generation of figures, writing the paper and even responding to the reviewers were done by myself completely, and the professor just reviewed the paper and reminded some typos and minor mistakes of this kind, and also added a short paragraph (completely unnecessary in my opinion) to the Introduction section.

But at the end he wrote his name as the first author and submitted the paper. He told me that being second author for him means getting no credit from the department.

Anyway, I'm going to apply for grad school, and he told me he will compensate in the recommendation he will write for me.

As this is my only published paper as an undergrad, and being first author means everything for me in my application, is there any way for me to prove to the admission committees (or the professors; whoever will review my application) that I was the main contributor of the paper?

How do mathematics graduate committees view Mathematics subject GRE scores around the 60th percentile?

I am a little curious as to how mathematics graduate programs in the United States view subject GRE scores. Does a low score in the range of 60th percentile rule one out of top 20 programs?

I am quite slow and I probably got 35-38 (attempted 38) out of 66 questions right in the subject test which will probably put me somewhere between 55th and 65th percentile. As an international student, I have no idea how terrible such a score is and I am considering stopping the application process to US universities since I doubt if I stand a chance.

Answer

Does a low score in the range of 60th percentile rule one out of top 20 programs?

I am already confused by "a low score in the range of the 60th percentile." A score in the 60th percentile is, by definition, high rather than low. A math PhD student should know that. :)

In terms of whether that score would "rule you out": again, every program and even every member of every admissions committee has to decide how to weigh the various factors. But that is why you apply to more than one program. I think that if your application is otherwise magnificent, you are a very likely admit at several top 20 programs.

As an international student, I have no idea how terrible such a score is and I am considering stopping the application process to US universities since I doubt if I stand a chance.

Yes, you stand a chance, so please don't stop your application for this reason. The smart thing to do is to divide the programs of interest to you into tiers and apply to a few schools in each tier. For instance, my program (at UGA) is about the 50th best in the US, and for us a 60th percentile score would in all likelihood not hurt your application at all. So it would be smart to apply to some schools in the UGA tier. (Perhaps even UGA itself...)

journals - Are there any good strategies for conveying the true significance of the results that look obvious post factum?

In nearly any field there is a number of important results which look obvious to experts post factum but somehow are not that easy to come by in the first place (e.g. in mathematics some important definitions look exactly like this). Unfortunately, this apparent post factum simplicity makes conveying the importance of the idea to expert audience (and in particular to the journals' editors and referees) very difficult. To make things worse, sometimes the author is unable to illustrate the application of the idea by sufficiently striking examples.

The question is whether it is possible (and if yes, how) to mitigate this apparent post factum simplicity in the talks and research articles, i.e., what can be done to adequately convey the significance of "post-factum-obvious" results to the audience and, in particular, to get these results to the journals they truly deserve?

I am particularly interested in the advice applicable to mathematics/mathematical physics but the suggestions suitable for other fields are very welcome too, as the situation in question does not seem to be all that field-specific.

Teaching Assistantships and research

I'm working as a TA now, and I've found that I'm spending an inordinate amount of time on my TA-ship. Is this normal? Furthermore, is this expected? I'm worried that my research career will suffer because of my lack of research productivity.

Answer

I suppose it depends on the factors that are causing you to spend more time teaching than you think you should. You should talk to (1) the other TAs and (2) the course leader/director. Find out what is expected and what others are doing.

If you are a relatively new graduate student, then I think it's normal to spend more time teaching and preparing for your teaching. As you start teaching the same courses repeatedly, the time you have to spend in preparation will decrease.

If you think of the time you are teaching as working on a craft that you will use for the rest of your career, then it is time well spent.

graduate admissions - Letter to a potential PhD supervisor (mathematics)

I am finishing my Master's in mathematics in Germany and I'd like to apply for a PhD in Europe, preferably in the UK. Most departments recommend that students get in touch with potential supervisors prior to submitting a formal application. I am a little nervous about it and I would appreciate advice regarding the following:

(1) Some people say it's advantageous to mention interest in specific papers published by a given professor. But I'm not sure how applicable this is to mathematics. To be honest, I haven't read a single paper by most of the people I'd like to apply to. (Reading and understanding a math paper takes a long time, so I think it's rather normal.) Is it OK just to say, for example, "I've seen you have published a lot of papers on non-linear PDEs, which is an area I'd like to do research in", or does this sound too generic?

(2) Is it OK to mention that my Master thesis supervisor or lecturer at my university recommended a given professor to me as a potential PhD advisor (they know each other), or does this sound somewhat awkward/patronising?

(3) How long should my email be? Is about 300 words too long?

(4) What should a first email accomplish? Should I just introduce myself and express interest? Or should I ask some specific questions about a potential research project straightaway?

I will really appreciate your advice, especially from academic mathematicians. I think one of the problems is that I find it a bit hard to see how the situation looks from the perspective of the potential supervisor. Do they get hundreds of such emails every year and just get annoyed when they get another one? Do they want the applicants to be very specific from the start, or is it better to first introduce oneself and see if they are at all interested before asking more specific questions about a research project, etc.?

genetics - How is incomplete dominance different from codominance?

Ok let me start with the definitions of incomplete dominance and codominance.

incomplete dominance - The situation in which the phenotype of heterozygotes is intermediate between the phenotypes of individuals homozygous for either allele.

codominance - The situation in which the phenotypes of both alleles are exhibited in the heterozygote because both alleles affect the phenotype in separate, distinguishable ways.

It is the standard textbook example of incomplete dominance to show a cross of red and white Snapdragons of pure bread. Which yields the phenotypic ratio of 1 red : 2 pink : 1 white. The standard text example of codominance is AB blood type where the A glycoprotein and B glycoprotein together produce a distinguishable phenotype apart from AA or BB.

I really don't understand the distinction between intermediate phenotypes and distinguished phenotypes. How is pink not distinguished from white and red. Consider a hypothetical example of some insect that interprets red, white and pink as distinguishable signals where perhaps red is a safe flower, white is ignored, and pink is dangerous. Say these signal recognitions have evolved based on the insects contrast against the flower pigment and the probability of being eaten by a predator because of increased exposure while getting nectar from the flower. I suppose I have a misunderstanding but wouldn't the red, white, and pink phenotypes be distinguishable rather than intermediates in that case?

So I'm probably just being an idiot but how is incomplete dominance different from codominance?

neuroscience - Why did the Brain develop in the front in most organisms?

I was wondering: why most, well, pretty much all organism with a brain have it right in front of their bodies or at the top.

Does the time spent doing a PhD count towards research experience for job applications?

There's a job application (UK based) which says something about having to have around 2 or 3 years research experience, but not necessarily have obtained a PhD.

What if I have a PhD and 2.5 years research experience postdoc? Does the PhD also count towards the "research years" ? I increasingly view a PhD as "training" rather than "research"...

EDIT

Of course there is a research element to any PhD, but the emphasis is on training to become an effective researcher.

Answer

Excellent question! I put it down as experience anyway, but it seems to be shrugged off more often than not, as it was considered necessary as part of a degree. When I was applying for jobs, I got a call from one where the guy went over my resume over the phone, saying things like "And you don't have ANY industry experience? You didn't even do an intership? What were you doing over your summers?" to which I could only give the flabbergasted reply "Doing...research..."

So, yea, I'd definitely put it down as experience. But don't think that people will necessarily take it too seriously, sad to say.

work life balance - How do I deal with my family which doesn't respect science and math while I am doing a double science and math major?

How do I deal with two parents who don't see the utility of science and mathematics while pursuing an education in science and math (while depending on their income to fund my tuition?). They come from a blue-collar background and don't think learning arcane symbols has anything to do with innovation or make big bucks in today's world.

My parents wish for me to go off to the industry ASAP or do some freelance or make an app that get them rich quick. I want to pursuit further education beyond that of a bachelor degree.

In the summertime I am preparing for some course work for next semester, but they keep on telling me that I should sign up for some fitness class or make money. I appreciate their viewpoint, but I can't bring myself to balance between studying and concentrating on course work while doing things that are a waste of time.

What should I do?

Answer

If the goal of your parents for you really is that you become the next Steve Jobs, Sergey Brin, or Mark Zuckerberg, then letting you go to graduate studies and letting you hang with all the other smart people is certainly more useful than telling you to "start getting rich now". This is akin to wanting to raise an Olympian athlete, and, rather than making him train, shouting at him that he needs to run faster right now. Of course the assumption that any specific person will break through and get super-rich is unrealistic to the point of being ridiculous, but obviously it will be hard to convince your parents of that (at least short-term), so you may need to work with what you got.

While the idea of Nox is generally useful (find statistics and show them), it sounds like your parents may be the type for which statistics are too abstract and would probably not work very well. Rather, you can try convincing them with anecdotes of well-educated people who "made it" (became rich, to use the terminology of the question). Of those there are many - opposite to popular opinion, most startup founders etc. are not random people off the streets who were selling sandwiches before breaking through. Rather, most greatly successful ideas and companies have been developed by people with degrees from top universities in, yes, math and science. There is a reason why Silicon Valley is in the Bay Area, and it's likely not the weather.

Concrete examples include:

• The company Google sprung out of a research project by Sergey Brin and Larry Page, two Stanford graduate students. Sergey is now the 18th-richest person in the world.


• Facebook was not a research project, but (at least so the story goes) the original ideas have been developed by Mark Zuckerberg in a dormitory in Harvard in discussion with other students in breaks between computer science classes.


• Bill Gates never finished, but even the founder and long-time CEO of Microsoft was in Harvard for some time. Incidentally, there he met Steve Ballmer, who became CEO of Microsoft after Gates - another case of a very wealthy and important person who happens to have great education.

job search - Is "Assistant Professor Position (Tenure Track) for a female Researcher" illegal in Austria?

Not so long ago an Austrian institution posted a job ad titled "Assistant Professor Position (Tenure Track) for a female Researcher". The description says further

As part of a special measure towards increasing female employment in scientific positions and promoting young researchers, the Faculty of [censored department] at the [censored institution name] invites applications for an Assistant Professor position (tenure track) for women expected to begin on May 2, 2018.

My understanding is that it is unlawful to ask for a particular gender in a job ad unless the gender is strictly necessary to perform the job duties. For academic tasks, the gender of the researcher is irrelevant. Does the institution run into legal issues with this ad? Could someone legally generate profit from such an ad by suing the institution (or the goverment behind it) for discrimination? Have such attempts been already undertaken in academia? (Outside academia, we see successful lawsuits at least in Germany.)

An aside: Hey, we all agree that in some fields there is a widespread opinion that they would profit from hiring people of the underrepresented gender; we also agree that gender issues exist before hiring, during hiring, and after hiring: they are way too numerous to discuss here. (An example: I see women underrepresented both in my research field and as sewer machine drivers. Why on earth is the politics caring about the first category but not about the second; after all, in an average town, there are way more sewer cars than academic research positions. Another example: my prior boss was an excellent first-class female researcher, but she never ever hired women herself. I could continue with the list ...) So, let us cut short and speak only about the legal and financial part of the particular situation rather than anything else. Let us not discuss whether the job ad and the institution should be condemned or praised or anything of the kind.