text:
Why aren't grant applications systematically published online, regardless of whether they are accepted?
Much work is put into constructing grant applications: sharing them would allow diffusing novel ideas faster, which I would expect it to be seen positively by researchers. Why aren't grant applications systematically published online, regardless of whether they are accepted?

Please could you illustrate what you mean by providing a link to all of your current and past grant applications? Oh, and any draft ones, or indeed any serious ideas for future grant applications, that you've got in preparation? Let's get those ideas of yours diffused!

@EnergyNumbers: It's a reasonable question. No need to be sarcastic.

@Reid while the sarcasm is not particularly helpful, I think the comment makes a good point.

"The main challenge of implementing open proposals is not technical but cultural: researchers currently have no incentive to share research proposals, and research funders have no habit of making their funding decisions public, nor who has applied for what."

@StrongBad: OK, then why not restate the point in a non-rude way and delete the rude comment? These things are separable. Embedding useful content in disrespectful content shouldn't be a get-out-of-jail-free card; this encourages a culture of disrespect. Here we go:

A thought experiment to consider is whether you would feel comfortable providing a link to your own current, past, and draft proposals.

@StrongBad I disagree, he may easily feel that this is unreasonable to do if he is the only one to do it, but reasonable if everyone else does it.

I am broadly in agreement with @Reid's comments

@Superbest I doubt the reasonableness. It will encourage "fellow travellership" even beyond the level we see now and increase the power of fashion avalanches and those who are able to ride on them. Smaller/more original/exotic communities will lose the protection of their "dark horse" status (that keeps them under the radar until ready) and be absorbed by the established ones even more strongly than it already happens now.

I second @Reid and YemonChoi: the underlying topic of EnergyNumbers' comment may be reasonable, but the sarcasm leans towards unnecessary rudeness. Please, let's keep Academia.SE better than the comments section on news sites.

Confidentiality is a real issue.  In an ideal world, ideas could be shared freely, and this would enhance the flow of knowledge and the progress of science and other research endeavors.  However, we live in a world with limited resources.  In particular, there is a shortage of grant funding.  Most grants do not get funded.
Consider the following scenario:  A researcher has some very interesting preliminary data, and they want to get a chance to follow up and do a more full study.  So they apply for a research grant from some agency.  If their proposal is not funded, but the content of the proposal is made available online, other researchers may troll through and pick up on the idea.  If another scientist with access to more resources sees the proposal and decides to do the follow-up work themselves, this robs the person who originally had the interesting idea and started the project of a fair shot at completing it and getting adequate credit for it.
Sometimes, having a good idea for something to study is the hardest part of the research process.  I personally have some ideas for projects that I do not want widely disseminated, because I would like to do them myself at some point, but I don't have the time and resources I need right now.  They do not involve urgent questions that really need to be answered, so there is no great loss if these avenues are not pursued promptly.  Thus I feel justified in keeping some of my best ideas confidential.

"Sometimes, having a good idea for something to study is the hardest part of the research process." This is a common misconception that I thoroughly disagree with. Ideas are a dime a dozen. Implementation and results are king, at least in applied computer science.

@xLeitix As someone interested in theoretical physics and mathematics, I can assure you that that does not generalize to all other disciplines. Hence the "sometimes"...

"another scientist with access to more resources sees the proposal and decides to do the follow-up work themselves" So the work gets done? I'm missing the downside.

Yeah. Wouldn't it still benefit the scientist who had the idea, since they would still have to be credited with the idea?

@PyRulez:  Things being what they are, that's not always the way things work in real world.

@Sam If the rejected proposal was "almost there", the original authors will often want a chance to revise and resubmit. They may not get this chance if scooped.

I strongly object to the implicit zero-sum perspective underlying this comment.  Zero-sum thinking may be baked into the current culture of many experimental sciences, but this is not inherent to science, only to its social construction.

@xLeitix Ideas may be a dime a dozen, but well-developed plans of study are much more costly.

@PyRulez Would you rather have something known as "PyRulez's theory of quantum wobbliness" or "Richerby's theory of quantum wobbliness which, for the next couple of decades, conscientious authors will mention was inspired by ideas of PyRulez and which somebody will make a holodeck documentary about in a hundred years time, saying that you deserved more credit"?

@xLeitix Your claim that ideas are a dime a dozen in no way contradict's Buzz's point about the value of good ideas.

I strongly disagree with @xLeitix. I would say in computer science the hardest problem is coming with up good ideas -- once I have a really good idea, the implementation and paper are easy.

@xLeitix: are you saying that in your field, anybody can take a random idea, code up a great implementation, and produce a revolutionary, ground-breaking paper? Ideas may be a dime a dozen, but I suspect really good ideas aren't.

@KlausDraeger, Thank's but I'm not stupid. Science is not about who does the work, but that the work gets done. So I'm still missing the downside.

I can think of many reasons why researchers are not prone to sharing (especially failed) grant proposals, but fundamentally I think your premise is flawed:

sharing [grant proposals independently of success] would allow diffusing novel ideas faster, which I would expect it to be seen positively by researchers.

This I think is wrong on two axis. First, the trivial one: most researchers are for pragmatic reasons not happy if their ideas are "diffusing" before they had the time and  money to work on them themselves. This is usually called "being scooped," and it's not exactly something that researchers actively try to make happen. Yes, as researchers we love to make people aware of our work, but generally only if we already have results available or at least sufficient head start that getting scooped is unlikely.
Additionally, I am also unconvinced why sharing proposals would even "diffuse novel ideas faster". In many fields, there are nowadays so many finished, peer-reviewed papers to be read that adding more stuff that fundamentally proposes at the time of writing untested ideas and hypotheses to the discussion would likely mostly add noise, and hence get largely ignored. As I wrote in a comment above, at least in my field ideas are a dime a dozen, and unless you have thought of something particularly radical or mindblowing (which most proposals are not, just like most papers are incremental rather than radical), the content of a proposal alone is unlikely to get much attention.

The context of this answer is applied CS in Germany. It may or may not be applicable to other contexts.
I fully agree with

Much work is put into constructing grant applications

However, I think

sharing them would allow diffusing novel ideas faster

is unlikely and misses an important point: Grant applications often do not contain much in terms of actual ideas.
At least with grant applications directed at national ministries and connected organizations, the target audience of the grant applications (at least the part that will decide on whether or not to accept a given application) is not particularly knowledgeable about the field. They cannot possibly evaluate whether a particular approach is promising; they can only check whether a given grant application appears to be aimed at what was asked for (e.g. in a call for proposals), appears to be achievable based upon the applying entity's previous experience, and looks like a solid endeavour that can be completed as planned with the resources (money and time) provided.
At the same time, from the applying researchers' point of view, the goal is not to secure funding for a particular idea, but to secure funding. If possible, without any ties attached, but as that is usually not possible, the art of writing a grant application includes trying to make realistic promises that leave enough ways of interpretation so the actual direction the research takes is still relatively open.
Therefore, such grant applications focus on the following content:

Defining the fields in which the applicant1 can provide their expertise. This builds upon the applicant's experience from other projects and previous publications. It touches upon solutions only on a per-topic basis (fictitious example: "Based upon the previous experiences in automated manufacturing processes, the applicant has the expertise to examine opportunities for using directed microwave radiation in manufacturing."), not with concrete ideas.
Addressing the points/topics requested by the call for proposals, on a rather high level. Like the previous point, this will rather indicate possible combinations of topics with keywords from the call for proposals, but not elaborate a lot on concrete ideas for new developments.
Explaining the general expected outcome from the results (such as scientific publications and practical applicability). The information related to this will often focus on the typical publication rate in a given field, and the current scientific activity level with respect to the topics asked for in the call for proposals, the topics the applicant has experience with, and - most importantly - the combination thereof.
Justifying how it is feasible. This might go more in depth, but not about solutions. Rather than that, there may be information about operational and administrative details of conducting the research (usage of quality assurance tools; planned communication and decision processes in a consortium; ...).

So, while there is plenty of information in such a grant application, it does not necessarily describe any concrete ideas. And in the end, that is probably not too surprising, given that the start of a grant application is usually a somewhat vague idea about "several intriguing topics that should be combined", or "one intriguing topic that we should extend our expertise to and that sounds compatible with what we have been doing so far", while the actual ideas are developed only during the run of the grant.
1: This can be an individual researcher or a whole organization, or anything in between.

sharing them would allow diffusing novel ideas faster, which I would expect it to be seen positively by researchers

The lack of current availability suggests that most researchers would view it negatively.  If you feel publicizing your grant proposals would help spread your ideas, you can easily post them on your web page.  A few people do this for funded grant proposals, but it's rather unusual.
Specifically, in the areas I work in, I'd guess that perhaps 5% of funded proposals are directly posted by the PIs.  Government-funded grants are generally available with some effort through FOIA or other open records laws, while other funding sources may not make them available at all.  Unfunded proposals are never made available by the funding agency, and it's really rare to see them posted online by the PIs.
What this indicates is that almost all PIs either don't want their proposals publicly distributed any more so than they already are, haven't thought of distributing them online or don't know how, or feel it's not worth the very minor effort of adding them to a web page.
I agree with the other answers regarding the reason for this, but I want to emphasize that regardless of the underlying reasoning, you can tell most people aren't enthusiastic about distributing their own proposals.

Where would be the point in sharing grant applications?  The only conceivable outcome (which you actually state so yourself) is that others will likely produce similar grant applications.  It then basically becomes a random matter which of the applications will actually be granted, and that means that a potential grant may end up with someone who is not invested in the matter to a degree where he would have been even able to put together a well-researched grant application.
You don't apply for a grant out of the blue.  You apply when there is a tangible chunk of research work you have whittled down to something that can be resolved by you with reasonable chance for success and/or progress in the scope of the grant.


text:
Why are UK universities "disproportionately successful at winning EU-awarded research grants"?
I read on Wikipedia:

UK universities rely on the EU for around 16% of their total research funding, and are disproportionately successful at winning EU-awarded research grants.

Why are UK universities "disproportionately successful at winning EU-awarded research grants"?

By what measure are they "disproportionately successful" at winning grants?

The Wikipedia article doesn't say. I would guess looking at some ratio [number of grants|grant money]:[population size|number of universities|number of academics].

This question was also raised by a professor at my University, it seems that they have specific technicals departments which are responsible for the guiding in writing a very good research proposals. Moreover, the research grants might be offered based on continuity in research for the institutions.

People speak English in the UK. Most academics are proficient in English. Not having to learn the local language is obviously a factor when deciding between job offers. As a result, UK universities have a competitive advantage over e.g. French or German universities when hiring, and they are able to hire more people with the potential to get EU funding.

I wonder if "UK Universities" means UK universities or just (Oxford + Cambridge). In the latter case, well, that's easy.

This is an interesting question, but extremely complex, debatable, and open-ended; it doesn’t seem like a terribly good fit for the StackExchange format..

@PLL many well-received questions are complex, debatable, and open-ended on this Stack Exchange website.

@JouniSirén You actually don't have to learn the local language, at least in Germany. Instead, we try to do everything in English, which results in massive communication problems, impeding science. Most academics are not very proficient in English, i'm afraid.

@Karl It's possible to work in English almost anywhere in the world. The hard part is living in a country permanently without speaking the language.

@JouniSirén The latter is a matter of personal taste, and how much of a shut-in you are. At my university, i personally know a dozen people who work and live here for years, and can hardly discuss the weather. I'd hate to live that way, but? The real problem is work: A team of five in which only one or two people can really communicate fluently is extremely inefficient. Discussions take forever, they often end before getting to the actual problem, misunderstandings, etc.

And: It doesn't improve over time. You don't learn English from talking to people who are as bad in it as yourself.

Universities in the UK (and universities in Northern Europe in general) are just better on average, with better infrastructure, better English skills, the means to hire better people, attract better students and in the end to write better grants (that might or might not be written by professional grant-writers). These are Framework programme grants for which there is Europe-wide competition.
This isn't entirely fair, that's why universities/research projects in poorer European regions are funded by the EU using more localized grants, as part of the European Structure funds. Usually these grants are not open to Europe-wide competition, and the UK can't win them all.
Another comment on how "disproportionally successful" could be counted. If they just counted #grants-with-UK-participants / #grants the UK would come out ahead because of its large population (the same for Germany). Almost all grants will have UK / German groups involved, as it's hard to get 10 research groups & companies together without taking one from the UK (or Germany).

Do you have a better suggestion? The THE ranking is one of the best-known rankings, but all of them will put the UK ones at the top. So this argument is not that relevant.

This argument is not relevant, as UK universities are at the top of every ranking. Of course there are all sorts of issues with these rankings, but those don't change the results: no matter what you measure, UK universities are at the top. The rankings (and I) are also not saying they're better in every way, just better overall on average. And this partially explains the success of the UK in getting these grants.

The argument is worth dissecting because it appears again and again: a ranking is produced; its validity is basically destroyed by nearly every competent expert studying it, for gross bias or methodology reasons; and yet the ranking is still invoked as demonstrating some intrinsic features of the ranked entities. The whole operation seems to work like what they cal a self-fulfilling prophecy...

What measure would you use to call one university better than the other? I'm almost certain that in most reasonable measurements the UK universities will come out on top.

Fields medalists? (To be explicit, since this seems necessary, I am not advocating to take the number of Fields medalists as a measure of anything.) But the question you might want to ask yourself is not "which measure to decide that one university system is better than another" but what does it mean for a university system to be "better" than another? For example, is a given country the right scale to measure these things or is each country actually hosting several university systems? And so on. Oops, suddenly things are looking more complicated, (not) sorry for that...

Of course it's more complicated, I'm just saying that whatever you're looking at, some universities will just be "better" than others, no matter what you measure. And there are sufficient reasons to believe that the UK universities are on average better than the average EU university. And after that you're just trolling, country scale is EXACTLY right, since we're looking at countries here.

The Royal Society has looked into this. The "disproportionately successful" claim is complicated. The report says

The UK is one of the largest recipients of research funding in the EU and, although national contributions to the EU budget are not itemised, analyses suggest that the UK receives a greater amount of EU research funding than it
  contributes.

According to the report, the UK contributions account for 10.5% of the total EU budget, but the UK then "only" receives 6% of the total budget. The report claims that approximately 7% (5.4 billion out of 77.7 billion) of the UK contribution to the EU went to research funding and that 19% (8.8 billion out of 47.5 billion) of the money the UK received from the EU was for research. The report goes on to say

There are two major routes by which the EU directly funds research in the UK – Framework Programme funding and structural funds. The UK is very successful in attracting Framework Programme funding, particularly that allocated for excellence. The UK recieves relatively little structural funding, which is largely targeted at building capacity in the least economically developed regions of the EU.

Roughly half the EU research budget is for "excellence" (the other half is for "capacity building"). There is a large asymmetry in distribution of the "excellence" funding with Germany, France, and the UK capturing approximately half of the excellence funding and only a quarter of the capacity building funding.
I do not think it is surprising that countries with strong "economies" are better able to capture funding earmarked for "excellence". If the EU shifted funding from excellence to capacity building, then the UK share would likely decrease. This in turn would decrease the UK share of the total EU budget and possibly lead to an earlier vote by the UK to leave the EU.

"There is a large asymmetry in distribution of the "excellence" funding with Germany, France, and the UK capturing approximately half of the excellence funding." -- well, these three countries have a population of 82+67+65=214 million, out of a total of 510 million. You would expect them to get "approximately half" of the funding, even if their universities/researchers were no better than those in the rest of the Union. It is certainly not a "large asymmetry". (But I would suspect that their share is actually much larger than "approximately half".)

@WolfgangBangerth see edit. Germany, France, and the UK only account for 25% of the Capacity Building research funding. The UK only outperforms its contribution on one type of research funding.

Yes. But I still think that the three getting roughly 1/2 of the excellence funds is statistically not a "large asymmetry", simply based on the population numbers.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/

sort: votes


text:
Why would a conference ask for a title page if the submitted paper has been submitted somewhere else?
I am in the process of submitting a paper to a conference. In the submission page I read:

Title Page (Mandatory if multiple submissions, submitted to a
  pre-print archive, or substantial overlap with prior publications)

Why would a conference ask for a title page if the submitted paper has been submitted somewhere else?

Because conference organizers are not coordinating between different conferences

@BasileStarynkevitch why not just stating the name of the venue where the paper was also submitted?

I'm hardly an expert on this topic, but I have coordinated the online content for professional conferences (as opposed to academic conferences), and based on how this requirement is worded...
I believe it implies that they ask for the title page so that any existing instance of the work online can easily be identified as your work specifically, and not any indication of plagiarism. I also would think it protects against an attendee later writing something that plagiarizes a paper which was presented at the conference, without proper attribution.
I'm not sure if there's yet another layer, of how they would cite a paper they first heard about at a conference, in regard to the container within a container format of citations. But it could be yet another reason they would make such a request, just to cover bases.

I agree, this is probably it. Perhaps whatever auto plagiarism checker they use allows them to submit the title page so that any paper it comes up with that has the title page is excluded from being red flagged?

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Why do some publications mention the phone of the corresponding author?
Some publications mention the phone of the corresponding author, e.g. https://doi.org/10.1093/jamia/ocw168
Why do some publications mention the phone of the corresponding author? Do readers sometimes directly call the author?

Email is the number one way to get in touch with an academic. I would assume that this is not the author's personal phone number, but probably a departmental number. At most it might be an office phone.

Likely a hold over from those dark ages before the internet. Actual people, not just telemarketers, used to make phone calls.

Probably the same reason we include page numbers in citations.

So you can call them?
I've been reached out to by readers before via telephone, particularly reporters trying to get a story together, on one of my more media-friendly papers.
It's rare, and phone numbers are hardly stable things (though neither are email addresses), but it's also a pretty close to no-cost addition.

I believe it is an old tradition when phones where relatively fewer and the numbers are fixed. However, nowadays, the phone numbers are changed frequently. I did included my phone number in an elsevier article once. However, the editors asked me to remove it for the previously mentioned reason. So, I suppose it is not a very good practice. The email, fax and P.B. are sufficient.


text:
Printing a poster near a conference location at a cheap price
For travel convenience, I prefer to print posters near the conference location. How can I efficiently look for the best poster printing price near a conference location?
(In my experience, conference organizers sometime point to a printing business, but they typically tend to be not price optimized, far from it)

In my experience it is much more convenient to travel with a poster than to print it there.

This doesn't seem to be a question about academia, to me. How is it different from "How can I efficiently find a good restaurant/shop/bar/museum near my hotel?"

I saw some places printing in fabric. Way easier to carry than paper, you can just fold it and put on the carry on... (just a side note)

First off, avoid the print shop at any conference location, which is quite likely to be both overbooked and expensive.
Second, make sure to send your poster far enough ahead of time to avoid a potential last-minute rush (particularly at large conferences).
Finally, you can use Internet map sites (Google Maps, Apple Maps, etc.) to search for copy shops near the conference location, and call to find out their rates.

First, I'd find the word for "copy shop" in the local language, and only then I'd search ;-)

If you are in the United States, phdposters.com has local pickup in Baltimore, Boston, Brooklyn, Denver, Durham, Richmond and San Francisco, usually in a university library. Their rates are also pretty reasonable.

Open Google Maps, and navigate to the desired location.  Then choose "nearby." You can zoom in and pan around to make more options visible.  You can glance at the review vote average, and click to see review contents.
This way of finding stores and service providers works better, for most things, than the regular google search, which has a primary focus on text.


text:
Graduate school admission: what is the point of having a waitlist of size 50 while intending to eventually offer a position to only a few applicants?
At that time of the year, graduate schools in the United States are notifying applicants that they have been placed on a wait list.
I read this email sent by a top-ranked University's computer science department:

Our Committee on Admissions has asked me to tell you that we have placed your application (along with about 50 others) on a "Wait List" for any openings which may occur. […] in the past several years we were only able to offer admission to only a few students who were on the "Wait List".

As a reference point, this University's department accept around 100 student every year.
What is the point of having a waitlist of size 50 while intending to eventually offer a position to only a few applicants?

Why don't you ask them?

It doesn't require any particular extra work to have a longer waitlist (just more rows in a spreadsheet somewhere), and conversely it is bad if the waitlist is not long enough.  So it's clear that the incentive is to make it plenty long.

@NateEldredge I assume it generates extra work (some wait-listed applicants will update their applications, check their status, ask questions ... some wait listed candidates will pressure the admissions committee to hurry up a decision b4 they make a commitment to another university), but in this case OP's top ranked university successfully passed some of that work on to StackOverflow.  However, if it really did not require any extra work, the logical thing to do would be to accept some applicants and wait list everyone else.

In my experience, most members of admission committees usually agree on who the star applicants are, or which applicants are simply unqualified.  However, there's a lot of disagreement in the middle, and it comes down to essentially arbitrary decisions among a sizable group of applicants who might or might not be admitted.  This is a strong incentive to have a big wait list:

It can function as a polite form of rejection, to minimize hurt feelings among applicants who genuinely might have been admitted under other circumstances.
It plays the same role for admissions committee members.  If someone is strongly in favor of a particular candidate but there isn't a consensus for admission, adding them to the wait list can be a good compromise.  Insisting on rejecting them outright just leads to pointless arguments.

This role as a sort of "honorable mention" is essentially independent of the chances of admission from the wait list.  However, having a large wait list can have important practical benefits even if there's no chance of admitting more than a few people from the list.  The key insight is that the list isn't necessarily rank ordered, with the first few candidates likely to be admitted but the fiftieth having almost no chance.  Instead, who is admitted may depend on the department's needs, based on which of the previous admissions decisions were accepted.  For example, it may take into account the representation of different subfields in the entering class.  From this perspective, a 50-person wait list may effectively amount to 10 different 5-person wait lists, with no one knowing in advance which of the smaller lists will end up being used.

Also, the sizes of the waitlists are probably also reflective of the actual matriculation rate in previous years, ie. they don't fill their waitlists with people they wouldn't admit anyway.

The wait list might simply be a list of all the applicants who were considered reasonable candidates (i.e., could probably succeed in this program if admitted) but were not ranked highly enough to be admitted yet. The fact that only a few wait-listed students were admitted in the last few years doesn't mean that the department can count on that being the case again this year.
Anecdote from about 20 years ago, when I was in charge of graduate admissions for my department: We had applied to the (U.S.) Department of Education for fellowship support for a rather large number of graduate students. We were awarded that grant, so a lot of students who would otherwise be teaching (either recitation sections or their own sections of freshman courses) not only could but must be given fellowship support instead --- "must" because the grant money came with the requirement that it be distributed immediately.  Unfortunately, we were notified of the grant in August, with fall-term classes about to start in September. So we suddenly needed a lot more teachers. I started checking which of our reasonable-but-not-admitted candidates might still be available, while other department administrators started looking for possible visiting faculty. That year, I would have been very pleased to have a big wait list.  (The problem was alleviated somewhat, because we had assumed earlier that we'd get at least a small grant from the Department of Education, so we had admitted a few more students than usual, but of course we couldn't admit a lot of extra students and risk not having funding for them.)

To answer this question statistically we need to know what is the chance that a person on the wait list would accept the position if they were offered it. Your question seem to imply that the chance is large, which may be true if the position were offered fast enough. However, if administrative delays are significant, most candidates from the waiting list are likely to accept offers elsewhere (or die of old age before receiving the offer), and the chances of acceptance are small. From the size of the waiting list, I assume the chances of acceptance are about 2%.
Or it may be just a random number invented from the blue sky by an admission officer who has a lot of other important things to do this day.

Thanks. The chance that a person on the wait list would accept the position if they were offered is >50%.

@FranckDernoncourt how do you know that? I assume that most grad school applicants will apply to many schools and the yield of accepted students is less than 50% - even at top schools.  Why would wait listed students behave differently? ... because wait-listing forces them to make alternative plans which (1) they might either prefer (eg school b is actually better than school a); or (2) which they become committed to (eg sign a hard to get out of lease before being moved off the wait list)

Basically, the only way you’re likely to get somebody off the waitlist is if (1) it was the student’s top choice all along or (2) the student hadn’t finalized plans for the following year when the offer from the waitlist comes.

Adding to the other good answers: there is also a genuine issue of "limited capacity" of mentoring/advising in various specialties within a "subject". I'm thinking of mathematics, for example. As an extreme case, if an applicant has a very strong record, but is ardently interested in a specialty in which absolutely no faculty in our department have interest, would it be good to admit the candidate? More usually, there is a question of having enough faculty in topic X to advise students interested in X. If we're already fully booked, the qualifications of further candidates (expressing ardent interest in X) are perhaps not the primary issue. That is, strong students without (effective) advisors are in trouble, usually, even if they are willing to try to "go it alone".
Also, there is usually a limit of Teaching Assistant funding, and in mathematics the vast majority of grad students are funded by TA-ships. If we have more excellent applicants, that does not mean that we get more TA funding. Limited capacity, again.
EDIT: to be clear, then, a big reason to have a large "waiting list" is that not all (well-qualified) applicants are interchangeable (dues to interests...), and, at the same time, we cannot admit all well-qualified applicants.

if an applicant has a very strong record, but is ardently interested in a specialty in which absolutely no faculty in our department have interest, would it be good to admit the candidate? — NO! This is not an argument for putting someone on a waiting list; this is an argument for rejection.

@JeffE, perhaps, but, on the other hand, although I'd not want to second-guess peoples' avowed interests, to outright "reject" seems rather strong. Not to mention that people have been known to change their minds, upon acquisition of more information.

The wait list is not there for the convenience of the students; it's there for the convenience of the institution. Universities know that some of the students they admit will turn them down. It happens everywhere - Harvard, Princeton, Stanford, West Bumstumble Tech - all of them get turned down. That's why they have a "wait list" - a collection of acceptable-but-not-their-top-choice students who they'll offer those "turned down" slots to. And those students who are wait-listed may be told "You pay full price - still want the slot?", so if they decide to attend they may actually be more profitable to the institution that an "early" or "regular" admit who will be offered more financial aid. An unfilled slot is money they don't get, so at most institutions there's a strong incentive to fill every slot. The wait list will be kept larger than the number of unfilled slots they believe they'll have, because the percentage of rejections from wait-listed students is higher than the rejection percentage from early and regular admits.

There's another purpose and that's the role that professors have in pulling strings to get people pulled off a wait list. If a professor knows one of the waitlisted candidates personally and knows that the candidate would be a good fit, he or she can call the admission office and get that person admitted if space permits, regardless of where that person lies on the list.
The other reasons about subspecialties are true as well.
Bottom line is - if there's 50 people on the waitlist it is surely not a top-to-bottom queue.

In addition to the other answers, I would add that the wait-list sends more of a "try again next year" than an outright rejection does.  These are often candidates who didn't make the "admitted" list in large part due to attributes of the applicant pool outside their control, prediction, or knowledge.  The applicant might make it on to the "admitted" list with an application the following year which is similar (and maybe improved a bit depending on what the applicant does in that year).
Since the school's rating depends in part on acceptance rate, it is beneficial to the schools to encourage more applications, especially from candidates they think would be qualified to come even if those candidates don't make it to the top of the stack for any one specific year.
I have seen candidates who in the first year of applications did not get accepted to anywhere they wanted to go, but did get waitlisted at more than one.  After a year of additional experience and probably a stronger effort in preparing the application materials, in at least one case the applicant got multiple offers of admission and matriculated at a top choice school.


text:
How often academic inventions have already been invented in private companies?
Is there any research/study/survey that looked at how often academic inventions have already been in private companies (or public but secretive organizations, e.g. the RSA cryptosystem was first discovered UK intelligence agency GCHQ)?
I am most interested in the field of computer science.

It's not a study, per say, but you might be interested in "The Idea Factory: Bell Labs and the Great Age of American Innovation"

Related: http://academia.stackexchange.com/q/7360/65

I do not get the (-1)-votes, please explain -- (+1)'ed for me.

In theoretical computer science, if an algorithm is invented in a private company, it almost never has an associated proof or a solid argument that it works properly and/or efficiently (the RSA cryptosystem is a notable exception, since GCHQ probably knew just as much, if not more, about it than Rivest, Shamir, and Adelman). And such a proof is a very important addition to our knowledge about the algorithm.

I expect that such a study cannot, in fact, be effectively done.
The reason is that academic re-invention of a closely held secret will be virtually impossible to reliably document except in particular anecdotal high-profile cases, such as RSA.

Most secret information probably does not become public at all: I suspect that most obsolete trade secrets in companies eventually simply end up in the trash or shredder, because why would anybody bother doing anything else?
For any secret that does become publicly available, there generally will be a long delay before that occurs.  At that point, the relationship between two independently invented decades-old technologies will be difficult to detect by anybody not specifically motivated, since they likely would have very different terminology and specifics.

As such, I would expect that it is impossible to do any general study on the frequency with which academic research is a re-invention of secret work elsewhere.

How about asking the guys in industry? They possibly cannot give you an answer to the "what" in all cases, but most certainly to the " how many".

@Gerhard Speaking as a researcher in at least one flavor of industry, I can say with absolute confidence that I cannot possibly answer that question even for the company that I am part of.  Any company with a significant research division has a lot of different things going on in it, and it's very difficult for people to stay informed about what other people are doing even if there isn't any particular reason to keep things secret.  Collective memory is very lossy as well: heck, the company I work for created GenBank and then forgot about it!

There are plenty of documented instances of excellent computer science research done in private industrial research organizations. For instance, IBM Research is the largest R&D organisation in the world in the general area of computer science .
In particular, some of these are:

Fast Fourier Transform
Fractals
Relational Databases

It happens and has happened often, historically.

I don't think this actually answers the question, which is about how often academics create something that has already been created, but has been kept secret.


text:
To what extent are technology transfers weighted when reviewing a professor's tenure application in a US university?
I wonder to what extent technology transfers are weighted when reviewing a professor's tenure application in a US university. E.g. in the case that a professor collaborated, possibly via one of their PhD students, to a technology transfer performed within some private enterprise, e.g. deplying an algorithm developed/advised by/etc. by the professor and their PhD student into a product/program of this entreprise.

I don't really have any experience here, but I would suspect that this varies a lot between universities and between different departments/subject areas within universities. Is this really a question that has a general answer?

@avid thanks, in this case I'm interested in computer science > natural language processing or computer vision.

Technology transfer seems hard to quantify. Something like a patent is more objective and I know that carries some weight.

To expand on @Buffy's question:
In many ways, each tenure and promotion case is different from all others. Professors are evaluated with regards to many criteria:

Number of papers
Grants received
Patents
Tech transfer
Quality of teaching
Number of graduate students and how they did
Service in the department
Service in the university
Outside service
Conference organization
Mentorship of graduate students, postdocs, and younger colleagues
...

How each of these is weighted depends on the university, department, and the field you're in, but the key point I want to make is this: Most cases are not "obvious" in the sense that the candidate is excellent in all of these areas. Everyone has their own foci and most candidates are good in some areas and maybe not so rounded in others. The challenge in deciding whether there is one or a few areas where a candidate is really good, and good enough in the others.
What that one area (or these few areas) are will be different from candidate to candidate, and is something that as a candidate is worth working out for yourself. In essence, you'll have to make the case why you're worth getting tenure or getting promoted, and that's why (i) talking to senior colleagues, (ii) your research, teaching, and service statements matter.
In other words, tech transfer can be an important piece of the puzzle if it fits into the areas of strength of a candidate, and I could imagine it being a key criterion a department takes into account even in areas where tech transfer is uncommon. On the other hand, if you don't have a lot going on in research, but you're a good teacher, and then there's the one-time $10k fee a company paid to use something you came up with, then the latter is probably not going to carry much weight: It looks like a one-time thing, and people will not consider it as a big deal. A part of the consideration here is that giving someone tenure isn't just a judgment on a candidate's past work, but just as much about the future prospects of that person, and something that looked like a one-time thing just isn't going to give much of a boost.

Like a lot of things in academia, this depends on the field, the university, and the individual professor. It can be very important in, say, medicine or engineering, but less so in others. Some universities (Stanford, for example) built quite a reputation for this and fostered the development of important technology and its spread. This is why "Silicon Valley" exists, actually.
If you want to make it count in your own application, of course, you need to make the case for it. But that might be like anything else.
If you mean applying for actual tenure (rather than for a TT position), then you need to convince your colleagues who will likely have a say in your application, as well as your Dean. This will be more natural some places than others. If you are an outlier, then it probably has less effect than if others do similar things.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Why do top conferences/journals between different research fields sometimes have a large difference in h5-index?
Example: the top conferences/journals for computer vision have an h5-index almost twice higher than the top conferences/journals for natural language processing:
https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_computervisionpatternrecognition  (mirror 1, mirror 2):

Index
Publication
h5-index
h5-median

1.
IEEE/CVF Conference on Computer Vision and Pattern Recognition
299
509

2.
IEEE/CVF International Conference on Computer Vision
176
295

3.
European Conference on Computer Vision
144
286

4.
IEEE Transactions on Pattern Analysis and Machine Intelligence
131
261

5.
IEEE Transactions on Image Processing
113
156

6.
Pattern Recognition
85
126

7.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
73
110

8.
International Journal of Computer Vision
70
150

9.
Medical Image Analysis
67
115

10.
Pattern Recognition Letters
59
80

https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_computationallinguistics  (mirror 1, mirror 2):

#
Publication
h5-index
h5-median

1.
Meeting of the Association for Computational Linguistics (ACL)
135
220

2.
Conference on Empirical Methods in Natural Language Processing (EMNLP)
112
197

3.
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL)
90
148

4.
Transactions of the Association for Computational Linguistics
53
112

5.
International Workshop on Semantic Evaluation
50
93

6.
International Conference on Computational Linguistics (COLING)
49
73

7.
Conference of the European Chapter of the Association for Computational Linguistics (EACL)
45
68

8.
Conference on Computational Natural Language Learning (CoNLL)
39
68

9.
International Conference on Language Resources and Evaluation (LREC)
38
54

10.
Workshop on Machine Translation
35
64

Is it surprising that there would be different citation patterns between these two fields? C.f. the case of journals.

@Anyon great link, thanks!

For fun you should pull up a medical field such as cancer research.

Different fields have different sizes, speed and publication culture. Thus, it is completely expected that h5-indices will vary a lot between fields. This effect is going to be more pronounced when larger values are considered.
Size: To push an h5-index from eg 199 to 200, the additional paper needs to be cited 200 times within 5 years. In a smaller field, this could easily mean getting citations from an absurdly high fraction of the new papers published.
Speed: The h5-index only counts citations in a 5-year window. In a fast moving field, that doesn't matter much. In a slower-moving field, this can easily ignore most of the potential citations a paper could get.
Publication culture: How big or small are the units people publish? How many references does a typical paper have? Do authors always explicitly cite the masters, or only if really needed?


text:
Which journals or conferences have publication accessibility requirements?
When submitting papers, I often see some elaborate requirements for submission formats, but none pertaining to accessibility. Which journals or conferences have publication accessibility requirements? (put aside accessibility conferences)
I am mostly interested in the field of computer science > machine learning, and English-speaking venues.

I would be very surprised if the answer was not "None."

Working from the guidelines you linked: http://www.si.edu/Content/Accessibility/Publication-Guidelines.pdf, I think APA style (and probably many others) meet many of the requirements. Most of the accessibility recommendations relate to the final printing (e.g., matte paper) and not to the electronically submit manuscript. APA style recommends 12 pt Times to be used as the font with double spaced lines, 1 inch margins, and a ragged right margin, which seems to tick all the typesetting rules apart from the use of a non-recommended typeface (but as a translational typeface with high quality printing it might still be accessible).


text:
Is it necessary to add mentions such as "Any opinion expressed in the paper does not necessarily reflect the view of the funder"?
I sometimes read in papers that the opinions, findings, and conclusion or recommendations expressed in the paper are those of the authors and do not necessarily reflect the view of the [funding agency name] in the acknowledgments section. E.g. in this paper:

Stanford University gratefully acknowledges the support of the Defense
  Advanced Research Projects Agency (DARPA) Deep Exploration and
  Filtering of Text (DEFT) Program under Air Force Research Laboratory
  (AFRL) contract no. FA8750-13-2-0040 and the Defense Threat Reduction
  Agency (DTRA) under Air Force Research Laboratory (AFRL) contract no.
  FA8650- 10-C-7020. Any opinions, findings, and conclusion or
  recommendations expressed in this material are those of the authors
  and do not necessarily reflect the view of the DARPA, AFRL, or the US
  government

Is it necessary to mention this? If the authors omit such statements, does this mean that the opinions, findings, and conclusion or recommendations expressed in the paper do reflect the view of the funding agency?

It depends on the grant that you receive to do your work. Most if not all grants require you to acknowledge the granting agency. Some grants give you very specific language that you must use in your acknowledgement (ranging from the request to include the grant number and use the full name of the agency to very pedantic requests for particular wording).
And some grants do require you to specifically disclaim the granting agency from your conclusions and opinions. For example, NSF requires the following:

An acknowledgment of NSF support and a disclaimer must appear in publications (including Web pages) of any material, whether copyrighted or not, based on or developed under NSF-supported projects:

“This material is based upon work supported by the National Science Foundation under Grant No. (grantee must enter NSF grant number).”

NSF support also must be orally acknowledged during all news media interviews, including popular media such as radio, television and news magazines.
Except for articles or papers published in scientific, technical or professional journals, the following disclaimer must be included:

“Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.”

The NSF example is interesting because you don't need the disclaimer for academic journals -- presumably because the readers aren't presumed to be idiots.
In any case, this is all part of the grant letter that you receive when you get a grant -- and part of the contract you sign when you agree to receive their money. There may be other terms inside the grant contract that are not visible (rights of first refusal in licensing technology, etc.) but by their very nature, acknowledgements are the most visible.

Perhaps "aren't presumed to be idiots" could be strengthened to "are presumed not to be idiots."

The current wording seems to imply that readers in other channels are presumed to be idiots. Could be just me.

That was my intent. Disclaimers are infantalizing.

Is it necessary to mention this?

If research is funded by a "DoD component" (as in your example), then typically
any conference or journal publication stemming from the research

must be cleared for public release,
must carry an appropriate distribution statement, and
must carry a disclaimer similar to the one in your example (e.g. "Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of ...").

In practice, it is sometimes sufficient to satisfy items 1 and 3 above.

If the authors omit such statements, does this mean that the opinions, findings, and conclusion or recommendations expressed in the paper do reflect the view of the funding agency?

Again, in the case of articles stemming from DoD component research funds: No.
Manuscripts must first be submitted to the appropriate reviewing "center" for the particular DoD component funding body (such as the Public Release Center in the case of DARPA, etc.) to be reviewed for compliance.
If the article does not contain the appropriate distribution statement and/or the required disclaimer, the reviewing center rejects the manuscript (it is not cleared for submission to a journal or conference), and the author must include the appropriate distribution statement and/or disclaimer before then resubmitting for clearance.
The trivial contradiction to my answer includes those cases where authors are required to obtain clearance prior to publication, but don't do it for some reason (e.g. they forget to submit the paper for clearance). I'm not sure what happens to these authors, and I don't want to find out the hard way.

Edit: While my answer specifically addresses DoD funding per OP's example, as an author, it is important to follow the guidelines put forth by each particular funding body with respect to disclaimers, etc.

Only DoD funded? How about other government and non-government organizations?

@scaaahu: Yes, it's pretty much only DoD. NIH, DOE, and NSF have no such rules, and most of the other agencies that fund research probably don't even allow publication in the first place (or at the very least probably don't allow acknowledgment of their funding!).


text:
How does the author's native language impact the likelihood of having his publication accepted in an English-speaking venue?
I've heard a few authors whose native language wasn't English complain that they are put at a disadvantage with respect to authors who are English native speakers, when it comes to publication acceptance in English-speaking venues.
Is there any research/study/survey that looked at the impact of the author's native language on the likelihood of having his publication accepted in an English-speaking venue?

Not sure if this is a duplicate, but definitely related: http://academia.stackexchange.com/questions/15824/is-there-such-a-thing-called-native-speaker-bias-when-publishing/15851#15851

While this doesn't address academic publishing, you might find this research interesting: http://dx.doi.org/10.1016/j.jesp.2010.05.025 "Why don't we believe non-native speakers? The influence of accent on credibility" I'm no expert, but apparently non-native speakers are at disadvantage in making themselves sound credible. Interestingly, the authors claim that this is due not to stereotypes of prejudice against foreigners but to difficulty in processing non-native speech. I wonder if hard-to-process non-native writing also causes a similar phenomenon...

It's probably more a commentary than an answer. I look forward to others' input on this issue as well.
As an ESL myself, I joined the English academic community knowing that language will be an extra barrier. I wrote (or perhaps still write) many awkward sentences that are grammatically sensible but funny to read from the angle of general English usage. To compensate this shortcoming, I read papers and study their styles, I read books on scientific writing, and I coauthor with native speakers. My English has improved somewhat and now I can even throw a couple trivia about academic writing in front of my native speaker colleagues. But deep down, I know it's a never-ending project, and chance is I will not be as fluent as a native speaker, and should perhaps settle for "good enough."
If I were to talk to your author friends, I'd probably ask if those submissions were anonymous. And if they are anonymous, and in multiple occasions comments like "English needs improvement" were given, I would suggest them to spend some time to work on their English rather than complain about unfairness. Non-native speakers having more difficulty to publish in English speaking channels is logical; to expect a fair ground that would totally ignore linguistic difference is unrealistic.
To formally answer the question, I have not come across with any study similar to what you described. And even there is one, I will study the results very cautiously. Native language cannot be randomized, so it's impossible to have a randomized controlled trial. This means what we will likely to see are observational studies, which are prone to missing important confounding variables: Native language intertwines with too many factors (education system, country of origin, culture, socioeconomic status, writing style and syntax construction, quality of English education, existence of equivalent concepts in English, etc.) that probably cannot be fully adjusted for in the analysis. At the end, we will likely see that non-native speakers are at a disadvantage, but the true cause of such disadvantage may be lurking elsewhere, and the true effect contributed by the linguistic difference alone is next to impossible to be parsed out.
Truth be told, I believe discrimination exists. If a reviewer reads that the data were collected in a non-English-speaking country, he/she may pay more attention to the grammar, or be more likely to suggest comments like "Please consult an editor or a native speaker in order to polish the English." I am fortunate enough to be working in the biomedical field in which most writings tend to follow a framework of being succinct, direct, and optimally simple. Most of the time I could easily parse out the ideas from the writing quality and give comments separately to both. Researchers in other fields may feel differently.

Writing a paper is, of course, more difficult for non-native speakers. They have to learn a second language and master it well enough to convey compplex ideas to their audience. And that is no easy task.
That being said, I don't believe there is much of a bias against non-native speakers. I myself have published several papers (authored with other non-native speakers) and never did we encounter any problems with the language. Bear in mind that English is not always the native language of the referees themselves so they usually have some understanding for your struggles (and don't necessarily see all grammar errors you did anyway).
The main catch for non-native speakers lies, in my opinion, more in effectively and clearly explaining their ideas which is, naturally, more difficult if you struggle with the language. This issue can be solved, at least to some extent, by reading other papers and books and studying their style as Penguin_Knight suggests.

This doesn't answer the question "Is there any research/study/survey..." (Questions tagged with [tag:reference-request] are specifically seeking answers that provide documentation or a supporting citation)

Not being a native speaker of English does of course affect your ability to publish in English speaking journals, but it cuts both ways.
Many journals and conferences these days are dominated by non-English speakers, while the majority have a degree of balance.  The transactions style journals in engineering and computing publish shorter papers with a more mathematical flavour and a conventional format, and often have editors and reviewers with an Asian background. In round numbers less than a quarter of the world's population speak/write English with native level efficiency, the major languages/grouping being English, Spanish, Chinese and Indian - and their dialects and relatives.
As a native speaker of English and a linguist, I have sometimes been told by reviewers that I need to improve my English, include more display equations and more references to their journals, and get my paper checked by a native speaker! I can quite easily see their linguistic background from the idiosyncrasies and errors in their review. Although this may sound laughable, there is a message here for both of us, the native speaker and the non-native speaker of English.
As a native speaker and an academic used to using English in a sophisticated interdisciplinary way, I need to learn to write in a simpler way to be comprehensible to readers who do not share this background - whether native English speakers or not. For the non-native speakers, they need to do exactly the same thing.  In fact, computers also need simpler language, so for natural language processing, optical character recognition, speech recognition and machine translation systems, the same simplifications will also help.
This simplification includes

reducing sentence length and complexity
reducing vocabulary size and complexity
reducing the use of jargon and idioms
reducing the use of informal models

Let's explain this a bit more, and I'll draw on some Computational Linguistics background to help here... Parsers can't handle long sentences and shortly before the turn of the century people started to scrap sentences longer than 40 words before publishing statistics about their systems - because they just couldn't handle the complexity and ambiguity.  English is very rich, and individual words and phrases can have many meanings and alternative apparent synonyms can have very different nuances. This creates a lattice of possibilities.  In English every second word tends to be a simple one, one of the 150 most frequent words, and to have a grammatical function, but often overloaded with different possible meanings (at least two on average). These more grammatical words are called functional closed class words (there is a fixed number), and the others that have more meaning associated with them are called contentive open class words (there are always new words and usages being invented). Many words have a dozen or more meanings (but again assume at least two).
So a 40 word sentence can have a million (2^20) legal grammatical parses and a million (2^20) semantic combinations expressed as a network or lattice of possibilities to choose from. We choose the intended grammar and meaning based on the salience of the concepts in our physical or linguistic context - in the real world, that includes things we know and see and hear in our society/environment, but in a paper it includes the things an expert in the field should know and the ideas expressed in the sentences or paragraphs before.  When speaking, we see when someone isn't following, we hear when something is ambiguous, and we adjust accordingly.  We might stop a sentence half way through and go back to basics.  We might pause and repair with a parenthetic 'that is'.  We might simply say 'pun not intended' because we've recognized that there is another meaning that jumps out.
In text we need to anticipate how different readers will understand things, what the ambiguities are, where the complexity lies.
In relation to length, if a sentence is more than 40 words, or 4 lines, chop it up. I tend to make use of participles (-ed and -ing) words and relational connectives (which/that) to join phrases and clauses together.  I tend to put in lots of parenthetic comments, often shown with commas rather than parentheses, but these add complexity.  So look particularly at the wh-, th-, -ed and -ing words (closed class functional/grammatical words or suffixes that can be used in many different ways). Break these up into separate sentences and that way the non-native reader or writer won't get confused by the subtleties of how they are used. Words like 'to' and 'through' and 'that' can be a problem, and reflect different usages between different brands of English. Sometimes the problem is that a th- word is omitted, particularly 'that' or 'the'. Adding the word in can resolve ungrammatical or ambiguous sentences.
If a paragraph is more than about 10 lines, chop it up. People scan papers quickly with their eyes being drawn to the headings and the first sentence and last few words of each paragraph.  Make sure paragraphing is clear and the formatting compact (modern journals don't require double spacing which is designed to slow the proof reader down, and should not be supplied to people are meant to be understanding the content, and inline use of equations or superscripts should not cause line spacing to open and close like an accordion - select fixed mode and make sure everything fits). The first sentence of a paragraph should introduce the idea explained or developed.  The last should provide a bridge from that idea to the next. A punchy paragraph may have a single line, particularly the conclusion at the end of a section.  A long paragraph may consist of three or four sentences.
Note. Things you have in brackets that make a sentence or paragraph too long can be moved to a note. If you are using (hyperlinked) footnotes or endnotes, yes it can be that sort of a note, but endnotes are archaic and don't really help due to the need to flip back and forth.  The comment can simply be moved to a following paragraph.
In relation to content word usage, make sure technical terms and acronyms are defined at first use in the body of the paper, and acronyms are refreshed at the beginning of each section. Definitions of mathematical variables may also need refreshing, particularly around equations or in table or figure captions.  That is people forget these things, or skipped over them without taking them on board.  They should be able to look at a table or a figure or a section on their own, and make sense of them without (re)reading the preceeding text.  Avoid informal speech forms and idioms (phrases that have special meaning to people of particular nationalities rather than following from the standard meanings of the words used). Language teachers like to teach idiomatic phrases to their students to make them think they sound more natural, but it has the opposite effect because they invariably use them inappropriately. Use the appropriate technical terms, but explain them in simple language if your whole audience can't be expected to be familiar with them. Be sure you are using a word of phrase correctly before you use it.  Don't just copy things other people have said, particularly authors who are clearly not native English speakers.
In relation to functional word usage, be careful of words like 'this/that', 'the/a'. English normally requires an article or a conjunction or relative pronoun, and while the native speaker knows when to omit them, it is normally grammatical to include one.  Also be careful of words with negative connotations, the word 'not' and hedges like 'hardly' or 'quite' - it is easy to miss such a word when scanning quickly, and get the wrong impression, and non-native speakers tend to use the hedges incorrectly. This again is something computers are bad at, and information retrieval, recommender and ranking systems can't currently deal with such words correctly.
It is also important to talk about equations, particular from papers by non-native authors, or in papers sent to non-native editors/reviewers.  Don't just copy equations from paper to paper.  Always go back to the original source, the originator of the idea, to understand equations, models and assumptions. Always explain the equations and the insights that lie behind them. Many authors/journals use models and equations in a way that obscures their meaning and makes it hard to understand. The non-native speaker is particularly tempted to avoid trying to explain something in English by expressing it in Mathematics. Many students (and all too many academics who should know better) copy equations into their papers that they don't understand (which are thus often wrong, incorrectly applied, or simply inappropriate to the paper). Mathematics should be used to clarify not to obscure. Explanations should always be provided. Don't describe what going on in operation-by-operation words, but explain the insights and the effect of the equations.


text:
Where do people leaving academia go?
Is there any extensive research/study/survey that looked at where people that leave academia go?
I mostly interested in the computer science field (machine learning) in the US, but curious about other fields and locations as well.
People leaving academia can be PhD students or after (tenure-track, tenured, soft-money research positions, national lab researchers, etc.).

Big Rock Candy Mountain?

@NateEldredge or is it the Lake of Fire?

Copied from this question per Franck's request: the NIH recently examined where people with a biomedical Ph.D. ended up. The results of their survey are discussed here.


text:
How to interview undergraduate candidates for research assistants?
I am at a public high-ranked Canadian university where undergraduates are heavily encouraged to participate in research, and currently recruiting an assistant for myself (and my supervisor). While at this university both as an undergrad, undergraduate researcher, visiting lecturer, and graduate researcher I have seen a very large variance in undergraduate performance:

A good number of students have great research potential, motivation, and creativity; these students typically contribute well to undergraduate research and go on to top graduate schools in their specialties.
A good number of students are here just to prepare for a professional school (typically Med School, but sometimes Law). Typically these students are very hard working, but don't have a passion for research. They apply for research positions just to flesh out their CVs. They typically don't display great creativity and tend to be overworked and over-committed.
The majority of students are hard to motivate, and seldom display great research potential or creativity. It is hard to get results from them on tasks that are not route/mechanical in nature.

Are there any tips and tricks to how to attract students of type 1. More importantly, how can I structure interviews to better recognize students of type 1? Is it impossible to attract type 1 students with high probability and I should just hope for the best, but plan for the worst? Is the division I observed artificial and it is my job as the supervisor to turn every student into type 1?

You want to find a strong B-student.

@bobthejoe why?

My advisor, bless his heart, entrusted in me the responsibility of interviewing and selecting undergraduate students to work in our research lab while still being a graduate student myself.  Sometimes it's difficult to determine what a person is really like just from a few minutes speaking with them.  I found a few particular questions to be particularly helpful:

Ask them what their goals are for the future.  Most undergraduates give very general answers because they are not sure what they want.  A highly motivated student knows what they want, and are likely to link their goals as part of the reason why they want this particular research job opportunity.
Ask them to describe in detail a difficult problem that they had to overcome, and explain how they overcame it.  This is a question they typically anticipate, but it's the manner in which they answer that matters...  Observe how well they put together their answer, how long it takes them, and how well they articulate it and its relevance to the research job that they're applying for.
Ask them about their greatest strengths, and greatest weaknesses.  In particular, examine how they explain how their weaknesses may affect their job and how they intend to overcome these weaknesses.  A strong candidate will not only be self aware and honest, but will seek ways to overcome these deficiencies.


text:
Advice and tools for hosting an online journal club
I have some experience with hosting reading groups/journals clubs in an off-line setting. Usually we meet every week in a room and discuss the paper for that week. However, recently some colleagues and I (~7 people) are trying to catch up on a specific topic quickly, and we want to hold a short reading group/journal club. Unfortunately, some of my colleagues are at other universities, and cannot physically attend meetings.
Do you have any advice or suggestions for tools to hold an online reading group?
My first instinct was G+ Hangout (especially with the new beta features they are making available right now). Skype is also an option, but last I checked required a fee for group video chat.
The preferred features are:

Talk to (and preferably see) each other
Have access to some sort of shared whiteboard
Easy to use and set up

Google+ seems to work okay, at least for seminars.

Some of the answers to my question on MathOverflow can be applicable. In particular, there are several links to online whiteboards, real time collaborative document editors, and/or mathematics enabled chat.

@WillieWong I was at IQC when those started, but unfortunately never came out to the conference room to see a Q+ Hangout (I think they projected it so that many people could participate from one location). My bad on passing up a great opportunity. I am just worried that the more interactive atmosphere of a reading group might require different tools.

True, the one possible downside to G+ hangout is that if multiple users started talking at once, the "autofocus" of the camera to the speaker gets confused. For seminars this is much less an issue; it will be okay also if you strictly enforce a rule of one person talking at one time. But this detracts a bit from the reading group environment.

There's an online Astronomy Journal Club hosted on Wordpress.
Here's my main advice: make the process as frictionless as possible. Grad students tend to have very busy lives, and journal clubs will inevitably have high dropout rates unless they have some motivation (even through guilt) to stay in and to continue participating. Keep them updated through some service that they'll constantly check even if they aren't doing science (emails end up annoying people, but Facebook, Reddit, and Google Plus might work).
It's also probably easier to convince people to set an online journal club when the journal club is about some specialized subject that only a small number of people at any particular university know about (and if they have a strong urge to talk about the subject with people from other universities).
Finally, if there's an academic conference for grad students (for my area, for example, it would be AbGradCon), discuss it with people there! Journal clubs are an excellent way to keep in touch with grad students at other universities.

I've tried the wordpress route for our in-person reading group but it is almost impossible to get people to contribute. I definitely agree that you need to keep things frictionless and somehow appealing.

Yeah - what about a Facebook group? Or Facebook/Wordpress integration if that is still possible?

Google+ comes with great communication tools called Hangout. Shared documents and even presentations can be achieved with. There is no real setup process involved but getting an account at Google.
Another option could be Moodle which has more features on relevant for teaching courses online and maybe less emphasis on direct communication.

You can find many softwares or online services for that purpose : blackboard IM, tinychat, etc.

Shepherding this is a lot of work. Most such efforts die on the wine because the initial enthusiasts get bored/overwhelmed. Find a solid group of people willing to work together on this, keep the organization flat (not "I started this, so I'll decide what software to use" or so). Keep it simple, no fancy, complex interfaces. No big hassle to set up, use a simple package running (hopefully) under your control on department servers.
You might start by (mis)using the pages used for classes to hand out material, assigments, and class discussions. Everybody involved should already know how to use this. See where this leads, if it outgrows your initial system, you'll know in more detail what you require, and have something to show to convince the department to chip in by e.g.funding a couple of students to help with the day-to-day management (two at least, with overlapping longish tenures, you do not want to train the helpers for a term just to get new ones when they are starting to get the hang).


text:
Internships for PhD students (e.g. in data science or programming)
I still have 2-2.5 years of my PhD program, but it want to try my skills in programing, especially in the taste of data science (needless to say, I do it overnight for some hobby projects and I like it a lot).
Moreover, as working in companies may seem as one of the options "what to do after getting PhD", I would be nice to test how does it work for me in practice, and to learn skills, network, etc.
Is there such a thing as internships (e.g. in data science or programming) for PhD students?
If so, where to search for them?
(If it changes anything, I'm doing PhD in EU; but US would be a nice target.)

Yes this happens all the time.  Often you can find nice research internships by talking to your advisor (who probably knows someone)

@BenBitdiddle (I am after PhD; and I had a very nice internship in Bay Area.) Well, if my advisor had known then I wouldn't have asked it here. :)

Yes. Almost every PhD student in my (US computer science) department has at least one summer internship between admission and graduation.  In fact, most PhD students go through multiple internships.
Off the top of my head, I know CS PhD students who have interned at Google (Mountain View, Manhattan, and Shanghai), Facebook, IBM, AT&T, Yahoo, Motorola, Microsoft Research (Redmond, New England, and Bangalore), Disney, Zynga, Los Alamos, Sandia, Argonne, and Livermore.  Most of these places regularly advertise for interns on the web.

I heard it from my friends doing PhDs in US (and also googled some stuff for them). However, I hardly see anyone doing the same in Europe. But them I have no idea if the companies are not interested (e.g. extra cost, no established contacts), no such culture in Europe (not that unlikely) or they are discouraged/not allowed (I'm not even sure if at my institute I can, but they are pretty open, so I will try).

@PiotrMigdal: At least in some European countries (and possibly only in some fields), that may be related to the circumstance that such internships are usually done at some point between starting one's studies and getting the Master's degree (or equivalent). Some majors even require such an internship of at least a few months as a mandatory precondition for graduating. Once you have that degree, you enter your work life, either in the industry, or in academia while being a doctoral candidate, i.e. you are kind of past the point where you do internships rather than getting a permanent job.

@O.R.Mapper Actually, most of the internships in my list were held by PhD students who were well past the master's/qualifying exam hurdle.  Late PhD students are more likely to get research internships, which can help immensely in getting a research job (either industry or academia) post-PhD.

@JeffE: Granted, I have seen doctoral candidates do proglonged business trips for "exchanges" or "stays at other organisations" to see another research-oriented workplace beside their regular one. Usually, these are also meant to strengthen ties and further collaboration with the organisation one stays at, e.g. to promote the research of one's regular department, to serve as a liaison in a mutual project, or to draft a project proposal together with the other organisation for an upcoming mutual project. I presume those are such research internships and different terminologies are used.

@O.R.Mapper Nope. They're internships, hired through the company's internship programs. Research in many computing companies looks a lot like academic research in computer science departments, except that industry researchers aren't surrounded by graduate students, who are an enormous source of energy and ideas. So they use internships to attract graduate students over the summer, and those interns become research collaborators, just as they are in their home departments.

(Perspective from Germany)

I've been programming for an IT start up as undergrad student (got in contact when one of the founders gave a presentation in a series of business start-up talks) and later done data analysis a few hours a week as PhD student for another company. It is done, also in Europe.
Big companies offer positions on their web pages, e.g. Merck
However, if you receive funding for your PhD project (scholarship), that often forbids further work for hire.
The PhD may technically be your "private fun", depending on the field/university/country. If that is the case (here), you often get a teaching assistant part-time job assigned to earn some money. The institues have a real problem of getting the teaching done (to the point that I heard rumours that some departments want to make "did so many hours of teaching" a prerequisite for getting the PhD). These job offers are often not offers the student can take or leave. Refusing to teach may create a lot of bad blood (with the prof, who anyways doesn't have enough people to cope with the teaching workload, and with the colleagues who get a slice more of this work). But it will usually go right to the limit which you need to obey in order not to loose the student status. So in fact, you cannot go for a paid industry job, neither. More importantly, the vacations are the only time when you can continuously work on your research.
These rules, however, do not forbid to volounteer in e.g. in open source projects.
You may consider Google Summer of Code and similar programs (e.g. Season of KDE).
It may be easier to convince your funder/employer that participating in such a program has much more importance than just earning some money, and that your contract with them could go dormant for the months in question.
Disclaimer: I've been mentoring at GSoC before and hopefully will again. :-)

It's true that you're typically forbidden from doing other work for hire while you're employed by a university. However, for internships people take a leave of absence from graduate work for the duration of the intership (and therefore aren't forbidden from other work as they're not students for that time period).  That's the case in most places in the US, at least, and I've been told it's the same in the UK, though I know nothing about the rest of Europe.

As Joe mentioned, I expect to take a leave for the internship period. My main point of getting internship is not to get extra money, but valuable experience (both technical and of working in a company, which I never did). (Anyway, no teaching assistant part-time job at my place.) When it comes to GSoC - you brought an interesting option. However, I doubt that I would help much for persuading (except maybe for max 1 months instead of vacations; especially as my field is theoretical physics). Plus, daily f2f contact with other programmers are crucial for me (I code on my own, anyway.).

@PiotrMigdal: I don't understand your sentence "I doubt that I would help much for persuading" (the "for persuading"). Can you explain this a bit more, please?

@cbeleites Sure. My point was that I doing open source programming projects still does not count as doing theoretical physics, so doing open source internship (in general, I'm very for open stuff, see http://offtopicarium.wikidot.com/v1:open-science-2-0) most like would not help enough, when it comes to convincing advisor or institute.

@JoeKington: Typically here, you have a working contract for teaching of 19.5 h/week (and teach much more). The vacations are then the only time when you actually can concentrate on your research. So you don't want to go working then also. The possibility not to do teaching at your institute is often only theoretical. The institutes have far too few employees to do the teaching, and it has to be done. If you refuse this, your institute will just be mad at you. I heard rumours some departments are considering to make teaching a prerequisite for the PhD degree...

The 19.5 h/week derive from the fact that if you work for hire more than 20 h/week, you become a normal half-time employee, cannot get a student ID and loose all the benefits that come with student ID. There are also things like health insurance offering very low student rates (though I'm not sure whether/how much that applies for PhD students for sure it apples for undergrads, and it applies also for PhD students with scholarship).

@cbeleites - It's essentially identical in the US (except for the 19.5 hour contract).  You just take a leave of absence for a semester when you do an internship.  You're not refusing a TA, you're just not enrolled for a semester.

@PiotrMigdal: so you are looking for a theoretical physics internship in industry? I don't think the "open source" is the convincing part of programs like GSoC. The convincing part is that being accepted for a project puts you into a top 800 worldwide category of students. And successfully finishing your project means that you have proven a very high level proficiency not only in programming but also in the soft skills necessary for finishing a project, integrating in a team and so on. There's also a wage, but IMHO having "successful participation in GSoC" on your CV is worth much more.

@cbeleites (Better priv or chat, as comments here are not intended for a discussion) No; my main point is to get real experience in data science or programming. I believe that GSoC counts well on CV. But first, it's not easy to get it. Second, I want finally a project when I'm in office doing programming with others, who are in physical proximity (one of reason is also to see an alternative of almost always lonely work in TP, with feedback once a few months).

@cbeleites When it comes to teaching, my institute is research-only. But in European univs its mostly this is a mandatory requirement, not an extra job.

@cbeleites: "you become a normal half-time employee, cannot get a student ID and loose all the benefits" - that must be university-specific. I know various universities in Germany where you (at least in CS) are employed as a full-time employee and can optionally keep your student ID to get many of the benefits (such as a significant discount on public transportation).

@O.R.Mapper: there are significant benefits of the student status that you loose for sure with a full time job, namely the student status for social insurance (much lower fees e.g. for health insurance). You may not have noticed that as those fees are automaticically subtracted before the wage is payed out. Further it is AFAIK up to the university to decide whether you have student status or not. The universities I know use employee status for full-time employees: you then have employee benefits. Your student status would then be more like the student status of a Gasthörer (guest student).

@cbeleites: True, I was rather thinking of benefits within the university (or organisations in direct contact with it, such as public transportation, which can save well up to 1000€ a semester). The universities I know leave it up to doctoral candidates whether or not they want to register as students (who are then regular students, not guest students) in addition to being employed, in which case at any opportunity where student and employee benefits may apply, one can freely choose to use either the former or the latter. I think there is quite some variation there across states and places.


text:
Conflict declaration in reviews
Consider the following scenario:

A is working on a problem X and has unpublished new material. A is
  then asked to review a paper: upon reading the abstract, A realizes
  that the authors are also working on problem X and appear to have
  duplicated some of A's unpublished material (the results - not clear
  from the abstract if the approach is also duplicated).

A now has four possibilities:

decline to review the paper, or review it
stop working on X, or continue to work on it

The safest combination might be to (decline to review, and stop working), but would it also be legitimate to (decline to review, and continue working) ?

The only problematic situtation seems to me to agree to review the paper, give it a deliberately bad review so it is rejected and then publish the own results. I personally would not review the paper in question at all because it might turn out that it is legitimate to reject it, and then it would be difficult to continue working independently.
Whether you should stop working on the problem is a more difficult question. In general I would say no unless it's something very specific with limited scope for further work. If you are reasonably sure that your work would go beyond what the other authors have done, there's no reason not to do it. You could always wait to see if the paper is published and if it is contact the authors and propose to work together.


text:
Data sets of academic conference budgets
I am looking for data sets of academic conference budgets. I am mostly interested in the expenditure/revenue breakdown. Ideally, other information such as as number of attendees and location would be useful as well.
I am principally interested in the field of computer science (natural language processing and machine learning), and English-speaking venues.

It might be worth your while to just email someone on the ACM Executive Board (possibly the Treasurer here?) and ask for the info.

If you're affiliated with a university, I'd also ask a the computer science/data services/equivalent librarian for help a dataset(s) that fits your needs.

Based on my experience as a conference organizer, there are only two sets of organizations likely to have this information:

Professional societies, such as IEEE and ACM, which require their sponsored conferences to make budget reports as a condition of sponsorship.
Funding agencies, such as the US National Science Foundation (NSF), which have explicit programs for sponsoring meetings, and which therefore require reporting on how those funds are expended.

No other organization is in a position to compel systematic reporting (not even universities, unless they are managing the conference accounts), and thus conference organizers will not in general report it anywhere else.
Neither funding agencies nor professional societies are likely to give this financial information out willingly, since it is financial---though you never know what you might get if you ask the right people nicely enough.  Government agencies like the NSF, however, have public reporting requirements and may be susceptible to data gathering via requests for records release, under FOIA in the US or its equivalents in certain other countries.


text:
Why do some conferences ask authors whether a paper they are submitting to the conference was published in arXiv?
Why do some conferences ask authors whether a paper they are submitting to the conference was published in arXiv?
Example:

Submitted elsewhere or arxiv? Is the paper being submitted, or has it been submitted to another conference/workshop? If so, put the name of the other conference/workshop in the box below. If your paper is on arxiv.org or similar place please mention this here.

Presumably some reviewers check for plagiarism, so it is useful for them to know that there exists a preprint out there that is expected to significantly overlap with the submission they are reviewing.
From the CFP for the conference you gave as an example:

Reviewers will be told: “The author(s) have notified us that there exists a non-archival previous version of this paper with significantly overlapping text. We have approved submission under these circumstances, but to preserve the spirit of blind review, the current submission does not reference the non-archival version.” Reviewers are free to do what they like with this information.


text:
Introducing abbreviations in an abstract for a repeated term
I use a long term (e.g. "artificial neural network") more than once in the abstract of a paper. Is it best to introduce the abbreviation (e.g. "ANN") after the first occurrence, or repeat the non-abbreviated term each time?

It is almost certainly not "best" to introduce acronyms/abbreviate terms like "artificial neural network" in the abstract. I can think of two reasons to use acronyms/abbreviations in a manuscript. The first is to save words/space and the second is to improve readability. While using abbreviations is a quick way to save a few words, generally you will be better off spending more time (assuming the deadline is not pressing) thinking about why you have hit the word limit. Cutting words is often better done by saying things more concisely or leaving out unneeded details.
In regards to readability, the abstract is different than the body of the manuscript. While in both you need to consider readability by both experts and non-experts, the percentage of non-experts reading the abstract is higher than the percentage reading the body. Therefore, the readability of the abstract should be geared towards a non-expert. What this means is that while acronyms like ANN may help the expert reader, they will not help the non-expert. On the other hand, acronyms like NASA are more readable to the non-expert than National Aeronautics and Space Administration.
Abstract are tricky. For example, APA 5 style used to say that abstracts had to be self contained. That meant you had to introduce acronyms in the abstract and then again in the body. In APA 6, this has been dropped (cf. this blog post). I still go with introduce the acronym on first use in the abstract and then again on first use in the body. I also introduce acronyms on first use in figure captions (not sure that is APA style or Strongbad style).

Thanks, my question wasn't clear indeed: I didn't mean introducing abbreviations in the abstract would exclude re-introducing them latter in the body. But that was actually another question I had in mind (and my answer was to introduce them in the body). Interesting link!

@FranckDernoncourt at some point I want to look into what the "rules" are regarding reintroduction.