text:
Just got tenured and promoted, but need to move for family reasons
I just got tenured and promoted to an associate professor, the contract for which will start from Fall this year. However, my partner, who is also in academia just accepted an position in another city starting from this Fall. Our plan is to give up our current tenured positions and move the entire family to the new city (for a better environment and better education for our children). The question is: when would be a good timing for me to move?
We have small children (toddler and infant), so it seems to make most sense if we all move together this summer. I am not currently on the job market, but I am open to giving up my tenured position (to trade with a better location for the family) while trying to find something in the new city (I am ready to apply for adjunct teaching positions, contract based faculty positions, or tenure-track positions in the new city, whatever becomes available, or to find some creative ways to engage in work meaningful and inspiring to me while trying to meet our financial needs).
Now, should I at all consider the possibility of staying behind and teaching as an associate professor for a year before leaving my current job?
In other words, in thinking about future career trajectory, would having one actual year of experience as a tenured associate professor make a big difference on the prospect of future job applications? Or, would showing evidence of having achieved tenure/promotion in a previous institute be equivalently useful?
What are some other possible alternatives?
Any advice on this would be much appreciated!

PhD's work very hard to get tenure, yet you're willing to give it up and adjunct. This is very bizarre to me. How long have you been tenured? How can you explain your willingness to abandon it?

@AaronHall the reason is written explicitly in the question. What is it you find 'bizarre'?

I find bizarre your willingness to possibly derail your career. I'm confident in your ability to work as an adjunct. I'm not confident in your chances of getting tenure at a convenient or equally prestigious location when you move. I hope I'm wrong. Perhaps you're publishing and getting cited in the 99th percentile in your (uncompetitive) field, which I hope you'll say you are. Or, perhaps you're at the 50th percentile for your graduating cohorts in a very competitive field, and you do not fully comprehend your good fortune in getting tenured.

If you stay behind and work for a year as an associate professor and go on the job market the next academic year, you'll be in a better situation to search for a new tenure track position than if you leave your current position and move immediately.  It's always better to look for a job from a tenured/tenure track position than as an unemployed academic with no current affiliation.  Furthermore, if you leave at this point it may appear as though you didn't receive tenure in your current position.
When/if you do go on the job market you can explain in your cover letter that you're moving to the new city because your partner is there in a tenure track position.
Another avenue to follow up on is seeing if your partner's new institution might be willing to employ you (either now or perhaps in a year from now.)   Many institutions have specific "spousal hiring" policies that might be helpful.

Doesn't read like it's tenure track, reads like it's tenure.

Another addition to Brian's answer: you can see if you can go on leave without pay from your current position for a year.  This is a normal thing to do at decent-sized universities, though I don't know about smaller schools.  This gives you the flexibility to go back if the city doesn't work out, and you'll still have a position that you can put on your CV when you apply for jobs.

In addition to Brian Borchers' good answer, your partner may also be able to defer their start for a year, particularly if their contract is for a tenure-track or other long-term position.  This possibility might also motivate your partner's institution to think more seriously about hiring you as well.

Echoing a point made by others: perhaps you've been lucky with the whole tenure-track/tenure thing, and don't realize that even for very good people there is a huge element of chance involved in getting a tenure-track job, much less tenure. I get the impression that this might be the case from your (to me seemingly casual) remark about "adjuncting until you find a tenure-track job"... in a specific city. Unless (and even if) you are more-or-less a superstar, that day might never come, I fear.
Unless I'm completely missing the mark, I'd recommend that you not give up your current tenured spot until you see what the possibilities are. A year's leave of absence ought to be ok with your current institution. If you discover that tenure-track jobs in the new city are not so easy to come by, you might want to pursue some long-distance commuting arrangements, as @Flexo mentions. The difference in salary between tenured and adjunct might make it more-than-worthwhile.

@BenCrowell, well, maybe, but what if the answer is "never"? When a question has self-fatal premises that we don't truly know how carefully the questioner has thought through, I feel it'd be remiss to not raise those background questions. After all, some people who ask questions here are naive or ill-informed about The Game of Academe, etc.

Though Paul's valuable and sensible answer does not address the question, at least not directly, it is often the issues or the questions that people don't ask that are the most important. I concur that one should be very cautious before leaving a tenured job, but is it really so hard to get even a tenured track job these days? Paul writes: "even for very good people there is a huge element of chance involved in getting a tenure-track job, much less tenure" and also "Unless (and even if) you are more-or-less a superstar". (Upvoted, of course.)

Depending on the subject, flexibility of your institution and the distances involved it may be possible to do a compromise deal of some sort, for example:

do all your teaching work in one semester and work from home on research the rest
do all your teaching on one or two days of the week and fly weekly
do all your teaching each semester intensively over a two week period and run long coursework stints.

Of course that may not be acceptable but it does no harm to ask even if it's only a slim chance. They may be less inclined towards flexibility if your intention is to quit though.


text:
Doing PhD in math/physics after obtaining a bachelor degree in an unrelated field
I want to do a PhD in mathematics (Specializing in algebraic topology or algebraic geometry ) or mathematical physics (String theory). I have a bachelor degree in a totally different field (medicine).
Here's my situation :
I have independently studied mathematics and physics. I have spent a lot of time with hatcher's textbook in algebraic topology and lang's complex analysis among with other topics in math and physics. These are proof-heavy textbooks and I usually do the exercises.
Now , after having studied analysis ,differential topology ,geometry, abstract algebra , quantum mechanics and QFT .
Is this a good preparation for a PhD in math/physics ?
Please note that I have studied from standard textbooks and solved a lot of exercises.
How do I convince PhD committee to accept my application ?
If I apply now , Apart from formal qualifications,my application will be missing one crucial component which is the letters of recommendations. I'm not sure where do I get them ? I don't know any physics or math professors who can evaluate my knowledge.

"I have studied from standard textbooks and solved a lot of exercises" Did you ever ask someone to look at/review your solutions/proofs to those exercises? If not, how do you know you are qualified to do a PhD in mathematics? If yes, that person can write recommendation letter for you.

No , I usually solve the exercises on my own. If I get stuck , I google. I  try to get the correct answer or prove the required statements , If I can prove them and the same strategy I use work correctly for other kinds of problems. , I think the steps of my calculations/proof are very likely correct.

That "very likely correct" is your problem. If you take your proofs/solution to someone (preferably a Math prof)) to ask him to check, then not only you would have more confidence, but also, you would have someone to recommend you. I'll urge you to do so, now!

An alternative would be to do it on our sister site Math SE. There are plenty of people would like to check the correctness of exercise solutions. Then at least, you can have some evidence to say in your cover letter that you do have math knowledge. Whether this is enough is up to your luck. Also, you can take GRE Math Subject test to prove your Math capability. You need to take it anyway.

scaaahu , Thanks ,This is a good suggestion, I never thought about it before.I use math.stackexchange too.

You probably need a master's degree in math or in physics. In some countries (e.g. in some European countries) you might be able to follow a master's in math or in physics with a bachelor's in medicine. This does not mean that you would succeed.

Do you know some specific schools ? I checked some schools and they all require related undergraduate coursework at a university. Also , I'm not in Europe but I would like to go there to do a master's degree

AFAIK (but things has changed last few years, so I might be wrong), in France one can in theory do a master in math at some University after a "License" (approximately bachelor's) in medicine. The issue is to convince someone that you are able to follow a master. Be careful, you might greatly overestimate your math skills.

I'm pretty sure you could get into a UK applied maths masters program at a respectable (but not top) institution with an undergraduate degree in medicine; e.g. Heriot-Watt University's MSc asks only for "Preferably, but not exclusively a First or Second Class Honours degree in a suitable numerate science or engineering subject." (http://www.postgraduate.hw.ac.uk/prog/msc-applied-mathematical-sciences/#requirements)

If you are looking for a masters degree in France (should work for any country though), contact the director of the master studies. A friend of mine did that, had a professional chat with the professor and was accepted (and this is at one of the best universities for mathematics).

You should contact potential advisors who work in the areas in which you are interested.  Introduce yourself, explain any work you've done, and ask if they have specific feedback for moving on in your career.  You should contact several people.  If you live near a university, arranging a time to meet with some current faculty members is also a good way to go about this.

Start by taking a class at a university as a non-matriculated student.  You can audit if you need to save money.  Choose the class carefully.  Don't forget to look at ratemyprofessor.com.  This class is a way for you to get your feet wet, and find out if you enjoy institutional learning as much as you've enjoyed your autodidactic learning.
The instructor of that class is going to be your prime target for looking for a recommendation letter.

source: Stack Exchange

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

sort: votes


text:
The use of phrase "per se" in academic writing
Using expressions from Latin is a common theme in academia. Expressions like in/ex vivo, in vitro or in situ are very common in biomedical articles, since they are very concise way of expressing some concept. There are however other expressions which have pretty much direct translations in English, and in some cases not even much longer in number of words/characters.
I have heard conflicting opinions on the use of the phrase per se and wanted to get more thoughts and ideally a generally applicable guideline for authors?
Edit: just wanted to mention that this is not necessarily for my own writing, the inspiration for the question came from a manuscript written by collaborators.

Of course, before using per se, be sure you know what it means and how it is normally used.  Don't just put in arbitrary phrases ex post facto in order to sound de rigueur.

@GEdgar I know what the phrase means, and that's why I suggest that there are perfectly fine words in the English language to use instead

Using any phrase  as relevant ie QED is useful in maths / engineering

Following from @GEdgar, correct usage of per se is fine, but I find usage to be indirect, irrelevant, suggestive, ... For instance, an example from https://en.wiktionary.org/wiki/per_se states As your substitute teacher, my issue is not about your respect for me per se, but to see that you are not causing disruptions for other students or giving me a bad impression of yourself, which leaves me wondering what the teacher is really thinking. I'll reiterate: Correct usage is fine. But, I'm left wondering whether avoiding per se is good advice.

Given that English is, more and more, not the mother tongue of many of those reading you article, using Latin phrases is likely to confuse more than help that portion of your audience. Clear, concise language is likely better. Yes, it goes against all those years I studied Latin. If in doubt, ask around of your colleagues who are not native English (or American) speakers. As a further test, I suggest that many younger people have not encountered the Latin phrases nearly as often as some of us older folk did.

I guess my advice would be to write in the way that seems most natural to you and gets your ideas across efficiently rather than worrying about the "word police."
If a reviewer objects then you can make a change, or not, as you see fit.
Clearly objectionable terms would be an exception, of course, but reviewers will point those out pretty regularly.
I think it is a fair statement to say that language rules evolved from language usage and not the other way around.

As with all writing, it is critical that the audience for the work be considered. A very challenging situation that is somewhat unusual to academia is that in many areas, the field in question is fundamentally international and does not necessarily read/speak English at an extremely advanced level. At the same time, the audience is expected to otherwise be highly educated in some specific area of expertise.
If this were something like journalism, the advice would along the lines of writing such that someone who reads at an 8th grade English reading level would be able to understand close to everything in the article. This sort of advice is insufficient to an international audience. For example, I used the term "8th grade" as if everyone outside the US would or should have any idea that I mean the level of formal education expected of 13-14 year olds who have been raised as primarily native speakers of English.
The result is that the best advice I've ever heard is that terms of art which should be familiar to people in the field you are writing to are OK generally, but should be rethought if you experience problems being understood among your peers. This means that terms like "in vivo" and "in vitro" in biological science is fine, as would "a priori" and "a posteriori" in fields where logic and statistics are the norm.
On flip side, as a writer this means that if you have experience in multiple areas it can feel like you need to write with one hand tied behind your back. You might want to say "ipse dixit", but unless your field is philosophy you are probably going to lose people. You might want to passingly refer to "sublimation", but unless you are writing in psychology you need to at least define the term if you can't avoid it, especially if you are writing to a field where there are both physicists and psychologists because they both use the same term to refer to completely different concepts.
The other thing to be aware of is that historically Latin, Greek, and French terms were used heavily, especially in Europe up through at least a good portion of the 1900s, because classically educated people were expected to have been taught these languages as part of their upper-class - often aristocratic - upbringing. To not know these languages was to be rather woefully ignorant, and conveniently also an indicator of being from the inferior lower classes who generally weren't seen as fit to be scholars anyway. It happened to be also during this time that much of what we now know as academic fields were delineated, and so whatever terms and culture was common at the time ended up being adopted into the permanent history of that field, for better or worse.
Now academia is not strictly a European affair, and many of us are no longer particularly enamored of the strict class systems of snobbishness and birth-right aristocracy that was previously accepted as the natural order of things. Since academic disciplines have spread throughout the world English has become the "lingua franca", but learning English, Latin, Greek, and French is now the rarity among academics worldwide outside of a very small set of fields.
So if you want to be well understood, this can mean having to force yourself to write - or at least rewrite - in a way that isn't actually very natural or even pleasing to you as the writer. I have found no solution to this, save to remind myself that it is more important that readers focus their energies on understanding the important technical aspect of my work - even if it means I do not get the visceral pleasure derived from crafting an eloquent turn of phrase that deliciously references both Rick and Morty and the cult of Pythagoras all at once. Kill your darlings, even if they are Latin masterpieces.

This is perfect advice. Still, I would have thought "per se" is a rather common expression, not particularly field dependent.

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


text:
How to ask my professor to write recommendation letter for me?
Now I'm in a community college and I am trying to gathering recommendation letter to apply to graduate school.(I have already got my bachelor degree). I performed well in calculus and got an A in that course. After i got my grade, I wrote a thankful email to my professor and he replied me that I am the top student in his course. I thought he is nice. Therefore I asked him whether he can write me a recommendation letter to me by e-mail but there is no-reply. I considered if he didn't see my email, so I went to his office and wanted to see him in his office hour. His office hour was posted on the wall and on the schedule he said if anyone want to see him please notice him ahead of time. So I e-mail him again to ask for an appointment. Here is the content:

Dear Prof XXX:
It's XXX, a student who finished your calculus 1 last semester. I went to you office yesterday and record your office hour on the wall. Would it be possible to meet with you next Monday 11 am (office hour) about whether you can help me write an recommendation letter?
Regards

He didn't reply me. I almost lost my confidence. I don't know whether I need to go to his office directly. I am afraid it's impolite or disrespect. And I don't know  if he don't want to do it, why he praise me in the first thankful note?
Hope anyone can help me.

At least in the culture of Europe and the US, dropping by and asking politely will not be seen as rude, and the worst you can get is "Sorry, I am busy now, come back tomorrow".

Probably just show up at that time.  He might not consider it necessary to reply, since he does have an office hour then.

Your e-mail looks okay as an appointment mail. There are a few possibilities. The professor may be busy with some important things he has to do. He may be on vacation, not available. Please don't lose your confidence yet. You probably should wait a few more days for him to reply.
It is also possible that he didn't think an A in Calculus I is good enough for him to write a recommendation letter for you. He may want to wait until you get another A for Calculus II and then write a good recommendation letter.
However, depending on which field you are going to study in graduate school, the professor who taught you Calculus I may not be a good candidate to write you recommendation letters.
You already have Bachelors degree. I think the recommendation letters written by the professors in the university where you graduated from, who had more working experience with you would be more helpful for your graduate school application.

Honestly, it seems unlikely to me that any level of performance in calculus could be relevant to any graduate school admission process or prediction of success/failure. For mathematics or engineering or science, it's too elementary, by far. For non-mathematical things, it's simply irrelevant. In either case, the intellectual context of "calculus" is almost always too rudimentary, uncritical (whether or not there are epsilons and deltas). That is, at most, its intellectual context is that for students just beginning college, most likely disinterested in the subject, and most likely not really needing the content, either. And large numbers of students take such a course.
Thus, even a top grade in first-year calculus is not really a distinction in the first place, and is certainly not a good basis for letters of recommendation for grad school.
In fact, to have such a letter might be viewed as an indicator that you do not understand what the grad programs involve.

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


text:
Publishing policies - copyright transfer
I submitted a paper to a journal and now it is under review. Since I would like to publish it on the Research Gate I was reading the journal rules to be sure this is possible. I also had a look on the SHERPA/RoMEO website and this is what I found:

Author's Pre-print: author can archive pre-print (ie pre-refereeing)
Author's Post-print: author can archive post-print (ie final draft post-refereeing)
Publisher's Version/PDF:  author cannot archive publisher's version/PDF
General Conditions:
Author's pre-print on pre-print servers such as arXiv.org
Author's post-print on author's personal website immediately
Author's post-print on any open access repository after 12 months after publication
Publisher's version/PDF cannot be used
Published source must be acknowledged
Must link to publisher version
Set phrase to accompany link to published version (see policy)
Articles in some journals can be made Open Access on payment of additional charge

So, from the SHERPA/RoMEO it seems I can put a pre-print version on-line on other websites.
Then I had a look at the "Instructions for authors" section in the journal website and there it says that, after acceptance, I have to sign a Copyright transfer statement.
What does this mean?
Also, if my paper get refused from the journal, can I still publish it on the Research Gate?

You can publish the preprint online, e.g. on arXiv, RG, or your university preprint server.
If your paper is accepted in the journal, you probably will be asked to transfer the copyright to the journal. The copyright applies to the accepted version of the paper, i.e. after reviewers' suggestions are implemented, the paper is proofread, copy-edited and typeset in journal style. According to SHERPA/RoMEO, you can upload a post-print version to your own website immediately, and any other website 12 months after the acceptance.
If your paper is not accepted by the journal, you are free to make it available anywhere on the web.

Just one more question: since I wrote my paper using LaTex, I used the journal formatting which was available for download on the journal website. If I put the paper on RG, do I have to change the formatting to a more standard one? (For example, the journal asks for a 2 columns text, should I use a one column formatting for the RG paper?)

The journal should have a policy on how the style file(s) are used, usually provided on the website or in comments inside the style files. The license makes it clear whether or not one can use journal style for personal preprints, etc. I personally think it is best to use more neutral style for preprints to avoid confusion.


text:
Is there any research direction which is closely related with map reading?
I like maps very much, any common maps, geographic, historic, statistic, etc., or I would say reading maps is my favorite hobby. I can sit at table and read maps for several hours without any stop.
I was studying courses about math and physics in university. I am planning to pursue a PhD degree in the future. It would be great if I can convert or leverage the my map hobby as my career.
So my question is, considering my background, Is there any research direction which is closely related with map reading? If difficult to find such position in academia, is there any jobs in industry can greatly fulfill my idea?

This may be useful http://en.wikipedia.org/wiki/Geographic_information_system

A lot of newer map-related studies use density related to territory. This website maps geography but resizes the volume of the location using values.

It sounds as if Geographic Information Systems (GIS) in Geography would be a possibility.

There is work even on maps for blind people, such as "The MGIS: A Minimal Geographic Information System Accessible to Users Who Are Blind" By Megen Brittell, Michal Young and Amy Lobben

When it comes to pursuing dreams in academia, I've always found Will Shortz's educational background to be a source of inspiration.

Since I'm not really sure what is available in the GIS field for research I won't try to answer that part, but when I was in university I did some consultant work for a GPS company called baseline geo.
They had me in to help with some database stuff, but much of what they did seemed to be making custom gps/gis maps for various clients. For example they might have a client who is a fisherman that needs a map of all his fishing areas, or where his traps are. Another example could be a wildlife preserve where they need to keep track of the animals with gps trackers in realtime. Either way it would fit both your skillsets. This is just a industry example and you seem to me more looking for a research position or ideas.

I'm sure you are aware of OpenStreetMap: "a collaborative project to create a free editable map of the world" (Wikipedia article).
Last year, a friend of mine was hired by a research center , Fondazione Bruno Kessler, in Trento (Italy) to develop software related to OpenStreetMap and Wikipedia (among other things)[1].
This is not actaully "map reading", but surely the OpenStreetMap is a thriving project which could benefit from your competences and passion. Many data companies are interested in it.
[1]: For what is worth, I think his position is still vacant, but I don't know where you live.


text:
What more should universities do to limit student exposure to dangerous speech?
It's 2017.  Universities should be a safe space for students of all cultures, creeds, gender identities, sexualities, nationalities, economic backgrounds, or other intersectional marginalized categorizations.  This is not the case.
What more should Universities, and especially the faculty, do to preserve Universities as safe spaces and as open and inclusive environments?

I voted to close this because it is clearly not a serious question asked in good faith, but just a way for you to express your opinion about higher education. See the [help/dont-ask].

Your fundamental premise is flawed. Universities are not a place to shield people from things that exist in the world. They are a place to learn what is in the world and how to face those things successfully. You do that through exposure, understanding and discussion. People at college age are still learning what they believe, and that process is significantly retarded if their beliefs aren't challenged.
Safe spaces are themselves the bad idea. They prevent young people from learning how to take responsibility for their own well-being at a critical time in their transition to adulthood. Universities should do less to infantilize their students, not more.

Perhaps it should be clearer what OP intends "safe spaces" to signify?

I can not even right now.  On a stack exchange such as this are you seriously condoning violent speech?  Many Universities such a Cambridge and others provide counseling and other resources for exactly the type of rhetoric you're attempting to defend.  Cambridge is also renowned for banning speakers from campus when they condone potentially hateful rhetoric against intersectional oppression.  They even allow students to retake tests when they are triggered by violent speech.  Violence in a University setting is unacceptable.  Are you implying you are a better educator than those at Cambridge?

The philosophy that has grown out of intersectionality is divisive and conflicts directly with the self-responsibility core to adulthood.
The idea that speech is not violence is the premise underlying the difference between civilized society and barbarism. It allows us to talk through our differences rather than going straight to killing each other like animals.
The very few people who are actually "triggered" by speech either due to mental illness or unhealed mental trauma should absolutely seek counseling where they will be exposed to amounts they can handle so they can recover.

Cambridge and many other universities are providing safe spaces because of activist and legal pressure from culturally Marxist postmodernists--descendants of the Frankfurt School, Derrida and others--not because it's the right thing to do for quality education. Outside their own circles they are infamous, not "renowned" for banning speakers from campus, as doing so implies they are afraid their ideas won't hold up under scrutiny.

You can't expect students to effectively oppose proven bad ideas such as Nazi Fascism or Stalinist Communism unless they face them. They have to learn what those things are, and through discussion and argument, learn to express why they disagree without physically hurting people. If you shield them completely from the ideas, they won't know their ideological enemy and will have no defenses when those same ideas come back with a different name tag.

@TheAtomicOption why do you think it's gotten like this?

"Why" is a question that could be answered at many different levels and most are too long and involved for a comment thread. For the philosophical history level and some political level explanations I'd recommend the book "Explaining Postmodernism" by Stephen Hicks as a good place to start.

Another proximate cause is that the social science departments driving these ideas are ridiculously politically biased (I've seen ratios from 40:1 to over 267:1) and have in practice rejected the Enlightenment philosophy foundational to science. The goals have shifted ideas from inquiry to a positive feedback loop of activists creating activists to push their ideas and stomp out disagreement by any means. Yet another proximate cause is the cumulative effect of the fact that politics and temperament have strong correlations, so many balancing personality types have become wholly excluded.

-1 These arguments, of course, with the unwritten assumption that such speech does not literally make life more dangerous for students at that university. Not everybody gets the luxury to just sit back and let the population decide who's right in a fair and even discourse.

To be very clear, it's obvious to most people, that the principle of free speech does not defend anyone who says "All of you go outside and punch every ____ you see right now!" Avoiding physical violence, by moving all fights into words instead of actions, is the point of free speech, so directly calling for physical violence conflicts with that. However, speech that merely offends or upsets--however strongly--but falls short of a call for physical violence must not be inhibited. To do so is to remove student's chance to learn to grapple with an idea.

You sound like you're against fair and even discourse. You want uneven, unfair discourse?? What then when your ideological opponents came into control of the discourse (as often happens)? If you don't believe your ideas will win when the discourse is fair and even, why should anyone think those ideas have merit? Letting the population decide is what's supposed to happen. It's one of the goals. It's how individuals agree to create society rather than fight. And it's not "a luxury" for anyone--convincing even a few takes a lot of effort.

@TheAtomicOption It's never that obvious. There's a long, grey, very transient line between what counts as hate speech and what counts as education, and many people have made a very lucrative living convincing people that certain populations shouldn't be allowed to live or possess rights without making it as obvious as you did. We're still having a national debate on whether literal eugenics proponents deserve an academic platform. But this is giving the question more credit than it deserves, I'll say no more.

The idea of "hate speech" impossibly requires listeners to first accurately ascertain and then cooperatively agree on the speaker's primary internal motivation. It's an amorphous standard with no basis in publicly available fact. That's why it's a bad measure leaving tons of grey area, and why hate speech codes should be opposed. As long as it doesn't violate the call-to-violence standard, highly controversial speech, including that motivated by hate, is exactly the sort of thing that students need to have the chance to confront and intellectually defeat in order to mature.


text:
Release software before the accompanying publication gets reviewed or accepted?
Is common practice to release an academic software before the publication of the accompanying paper in a scientific journal?
What are the pros and cons of a prior release?
Edit: I am not concerned about patents, software will have an open source license.

My experience in computer science has been that papers are rarely about software per se, but rather about the science that is either enabled by or instantiated in the software.  Computer science also typically seems to have a fairly open culture where people don't worry much about being scooped.  As such, research software is often available on open repositories like GitHub or Bitbucket long before it is published---it's just that nobody notices or pays attention to it before there is a significant paper to motivate them to do so.
So, to finish answering the question:

Pros: demonstrate commitment to openness, people might use it before publication
Cons: you can't do this with something you want to patent

This may be field-specific, but within neuroscience, the typical path seems to be as follows:

Publish papers detailing new signal processing/statistical analysis/etc techniques
Write software that makes use of these techniques
Continuously add techniques, refine the code, and improve the UI (well, sometimes)
Write a paper on the software itself

The main benefit of this approach is that the algorithms that power the program are vetted via peer review and the general community even before you attempt to write the first line of software code. The only novelty to the software is that it makes the techniques available to a larger audience, specifically, those who can't write their own analysis code. Even better, as you write and publish new algorithms and analysis techniques, you can eventually add those to the software.
Some software that follows this approach is SPM and MNE, among many others.

I can't believe that people do #1 in your list without having written "the first line of software code". In my business (CFD, finite-elements, high-performance computing in general). The biggest open source codes are in continuous release, more or less, and the authors publish papers about the science or new software improvements as they happen. This usually means the papers are delayed 6 months to a year after the code is out there due to publishing cycles.

@BillBarth - I guess in my mind there's a distinction between writing the algorithm in MATLAB to test whether it works and re-coding the algorithm in c/java/whatever the language will be used to write the actual software package. I'm referring to the second, not the first.

Even in that respect, I don't think your method is common except for people who are just starting out. I don't think most people start over for every new approach. They develop a baseline framework or infrastructure and add new methods to that. That being said, my experience is that software engineering sophistication varies widely across disciplines (not just across science disciplines).

@BillBarth - Fair enough. FWIW, in neuroscience, people who are just starting out virtually never make software like this; the software is almost all from very well respected labs, developed by people in the field for years.

This varies very widely by field, but also by PI preferences; in my experience, younger faculty (and newer software packages) are more likely to be open early. My experience in Chemistry has been similar to what @eykanal describes (especially when patentable methods or results are involved)...
BUT: software often contains bugs, which may change the results you get in ways that are not obvious from a paragraph description of the algorithm. In some cases, this has lead to huge, sweeping errors (like a retraction of papers cited 729 times), and some journals or funding agencies are adopting policies that encourage release of code alongside the first paper that uses it. If it's not already part of your peer review process, releasing the code early might become a requirement in the future.  (It is difficult to properly review findings when they depend on subtle, unstated decisions in potentially buggy code)
So, just be sure to separate what you are expected to do from what best accomplishes your goals. Norms involving software are still evolving, and it's a good idea to evaluate your strategy based on time, resources, and expected audience.

Note: In the US, the time window for submitting patents starts when the invention is first disclosed "publicly" (which can include publicly demonstrating it even if you don't discuss how it works) and ends a year from that date. Depending on your plans for this work, you may or may not want to release earlier than you must.

Two important cautions: 1) The US system is currently in flux, and 2) this strategy still precludes obtaining patent protection in any other country, even with a valid US patent.

I wasn't so much suggesting a strategy as pointing out that failing to consider this could rob you of options.


text:
Is it alright for the main body of my paper to not contain any references?
I have been working on a few set of ideas, and while I can give references on the introduction, about the general idea, and the problems that my ideas can possibly solve in other fields, but unfortunately for me, the work is a little out of the box, and the main body cannot contain references.
I wanted to know if this is against norms or even considered unusual?
Also, some of the work is very old, for example, Descartes, and Tarski.
I was wondering how to get references for that work, and how it could work out, since I am not sure if they ever published papers or how discussion around very old topics tends to go.
The situation is a bit unusual for me, and I want to find the best way to be read and accepted in the community, following all the proper norms, rules and regulations regarding writing a proper academic paper.
The work is all application of informal logic to various other fields. Some of it is philosophical, some of it relates to maths and physics as well. I am just trying to get it read at the moment. I will try to submit it to a journal, but at this point, I am having a hard time finding such a journal.

What field are you in? What are you planning to do with this paper - make it part of your thesis, submit it to a journal, something else? We need more information to answer your question - see Academia varies.

The papers are all applications of informal logic to various other fields. Some of it is philosophical, some of it relates to maths and physics as well. I am just trying to get it read at the moment. I will try to submit it to a journal, but at this point, I am having a hard time finding such a journal.

When referring to Descartes and the likes - you probably didn't listen to Descartes, but instead, got the information from some other source. Cite that source instead.

https://plato.stanford.edu/entries/descartes-epistemology/#2 , will a source like this be good enough. It isn't a research paper, but stanford's directory on philosophy. Thank you so much!

Why can't the body of the text contain references?

@novice Yes, that will do.

"Also, some of the work is very old, for example, Descartes, and Tarski." Hmmm. I am reminded of a passage from Orwell's Down and out in London and Paris: <>..."[S]ince I am not sure if they ever published papers" You're not sure if the leading mathematical logician of the mid 20th century ever published a paper?!?  (He did.)

I was wondering how to get references for that work, -- I recommend the library. (I have cited both Descartes and Tarski.)

@JeffE Thank you! The main problem is that the work is way out of the box.

Not having references in the main body of a paper isn't necessarily a problem, but it can be a major symptom of other factors that will cause you to have difficulty publishing a paper. You may be having trouble finding references because you haven't narrowed down what you are doing enough to find out which other people will be interested in it.
"Some of it is philosophical, some of it relates to maths and physics as well."
This sounds like you are writing about the philosophy of science, which is a field with a pretty active literature. Perhaps you can read more in that field, and see if any of it sounds like what you are doing?
"the problems that my ideas can possibly solve in other fields": if you think your work can solve important problems in other fields, then there are probably useful references where someone else has attempted to solve the same problem. Explaining how your work differs from these attempts, and where it can improve on them, would be a standard place to include references in the main body. If you cannot find this, you may not have that well-defined a problem yet.
Even if your work has wide applications, it is often useful to choose one specific problem and address that first - this forces you to be specific, address the questions of one field, and make sure that you deal with what other people have done before.

The thing is this is very hard to do. The idea is just way out of the box.


text:
How can I tell a professor that I don't want to join his group? (science major)
I am a incoming first year grad student this fall. I have contacted prof A during when I was applying and talked to him during the visit. He strongly encouraged me to join his group.
After the visit, I emailed students in Prof A's group and other current grad students. I started feeling that his group atmosphere is not the best for me, and I don't want to join him anymore.
He is really nice and recently offered me an educational opportunity that is only shared by current students. I am grateful and I really want to take that course.
I feel guilty right now b/c he is so nice to me. But I don't want to spend my next 5 years in a group I don't like.
Is there any way that I can let the prof know I am not interested in his group anymore? Do I have to give up the course he offered? I am not assigned to prof A so I have the freedom to choose.

It sounds like you have not made any sort of commitment, so if that is the case then there should be absolutely no hard feelings by simply writing a polite email saying that you have decided not to do research with him.
If you wish to elucidate the reason, just say you don't feel that you'd fit in well with the group. He'll understand. You should not feel obligated to come up with an elaborate explanation.
It's hard to tell without more information on the course you speak of. If it's an ad-hoc, informal course taught by the professor himself then I can see how the investment of time may only be worthwhile for his own students. If it's a proper course that you pay fees for and earn credits, then perhaps it is reasonable to ask if you can still do it.

Thank you! It's a training course offered and paid by the grad program. A professor from another institution will teach it. Prof A forwarded me the email  and encouraged me to take it. And only current students and faculties were on the mail list. I just think it is really nice for him to do it.

Be professional about it, you don't want to cause any hard feelings as it won't really help you while you are at university.
If I was you, I would do the following things:

Quickly note down a list of logical reasons why you think you would benefit more from not joining the group.
Go find some other groups that you do like. Write down a list of the advantages of joining those groups.
Now keep these points in mind and when you next see your professor just explain these points to him/her in a calm and professional manner.

Remember this is your degree we are talking about, its up to you what you want to do. Be bold, don't be shy to follow your interests, otherwise you'll end up board and frustrated that you didn't make the best choice earlier on.
Hope this helps.


text:
What does a managing editor do in scientific journal editorial board?
I need to contact the editorial board of a journal I recently submitted an article to about an issue, and I am having some thoughts as to who would be the appropriate person to contact.
I know that the Editor-in-chief is pretty much in charge of the show, and the associate editors contribute based on their personal areas of expertise.
I have no idea what a managing editor does however. Intuitively I would assume that this job title would encompass more of the administrative and less scientific aspects of the publication process, but I am not certain.
So what does a managing editor do, and what type of questions should be directed to the managing editor?

@Stefan in the case of the linked question, yes. But I wanted to ask this question generally since it would be relevant for more people that way.

Yes, the managing editor is more administrative.  For example, when a review is overdue, he may send a reminder to the reviewer.  After a paper is accepted, he may send to the author the paperwork to complete.
I would say: do not communicate with him directly, except in response to something he sent to you.

There's no way to tell without more info. The word 'editor' has a wide range of meanings. I have personally worked with journals whose managing editors were effectively the co-editors-in-chief. I have also seen journals whose managing editors were employees of the publisher with no formal training in the journal's subject.
As a general guideline for who to write to: if the issue involves something that only an academic can understand or fix (e.g., the content of the paper, something to do with the peer review process, etc) then write to the editors of the journal. If it involves something mundane (and I would classify the issue you describe in the linked question as such), write to the publisher. You can also just write to the publisher regardless of the issue. A responsible publisher will direct questions they can't handle to the editorial board.

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


text:
How to deal with GPA-related questions in standardized job application forms, if you have no GPA for graduate studies
I recently finished my PhD and am currently applying for several positions both within academia and in industry. Some companies I'm looking at have standardized job portal where you can leave your info and do a "general application" (e.g. IBM).
In the education section they ask for GPA for each entry, including PhD which appears to be a mandatory field. Here's the problem none of our grad courses are graded, you pass or fail. Thus no GPA. I tried to write N/A, but that wasn't accepted by the system.
Anyone with experience on how to deal with this situation?

Did they say GPA for what degree? PhD or Bachelors?

If it is just GPA, you can write your Bachelors\Masters GPA. I doubt you will get any GPA for courses taken during PhD courses.

I would contact the employer and ask what they want. I would not try to guess, or take advice from anyone not with the company. If you guess wrong, they might think you lied on the application, which could cost you the job.

As long as you include an explanation somewhere in your application, it's okay to fudge your answer to that question, to accommodate the rigid web form.

As noted in the comments, the first thing to do is to contact the company HR department.
If you don't get a rapid response, however, then I would recommend filling in the form with as obvious a nonsense number as it will let you (e.g., -1, 9000), or if it won't let you do that, then fill in whatever it thinks is a perfect GPA (you passed all your classes, right?).  Then explain the form problem and your true situation in text.  The best place to do this is, in order of preference:

A free-text "Comments" or "Other Information" field on the application form, since that will typically be seen at the same time a person is seeing the GPA.
Your cover letter
Your resume

Note, however, that you are not very likely to get a positive response in any case from this type of completely generic application form, since it is likely getting filled to the brim with pseudo-spam from a vast number of recent graduates worldwide.  HR departments often put such a "catch-all" form out in order to satisfy formal requirements, rather than because they expect to actually do much hiring in this manner.


text:
Are there any new(er) models of publishing/reviewing and if so how will they address the problems that we have today?
I was reading this article on The Guardian about scientific publishing and peer-review process. The author raises a fair number of concerns with the existing model, most of which I agree with. The article ends with the following statement:

... but the need for reform is profound.

I ended up thinking about this idea, a reform in the way we do science. By doing science I mean the whole process of writing, submitting, reviewing, publishing and measuring the results of our work (and not the actual work itself).
While I agree with the author about the current system being faulty, sick, or corrupt (depending on the shade of glasses you look at it), I cannot really come up with a system that would magically solve the existing problems just like that.
The Arxiv idea is named in the article and from what I understand people in physics and math quite enjoy the benefits of that site (whatever it is) however with the amount of money and prestige involved, I cannot imagine how it would be implemented in fields like biology, chemistry and medicine.
To sum things up, my question is whether or not there are/have been alternative models to the existing one: blind, peer-review, hundred of journals, and metrics of "quality" flying left and right. If so which shortcomings of the existing model were amended?

Arxiv is not peer-review.

@GEdgar that is not a helpful comment. If my misunderstanding of a platform that I've never used is important enough for you to comment on, you could have at least corrected my mistake.

That is a helpful comment. Its purpose is precisely correcting your mistake. What should he have done instead? You would have reacted worse to an uncalled-for edit to remove that statement. And please don't get offended because of the correction: when people correct you, that doesn't mean they think you're an idiot. Everyone is wrong sometimes. :)

@FedericoPoloni It's not that I am offended, and I have no illusions thinking that i'd be right all the time :)
I just think that it's a besserwisser comment, it points out an mistake and makes no effort in improving it. I would have been thankful if the comments was something like "Arxiv is not peer-review, but rather ..."

I think this is unclear, far too broad, and answers will probably be opinion-based. You haven't even stated what the "problems" are, how they relate to publishing models, and each separate problem may or may not be solved by differerent new publishing models.

As I see it, the issue is not actually how publishing and reviewing work.  There are lots of alternative models out there, including modifications of blinding (e.g., Frontiers names reviewers when a paper is published), alternative approaches to peer review (e.g., IETF RFCs, math and physics use of arXiv), and decreased need for additional journals by removal of artificial "throttling" of publication rate (e.g., PLOS ONE).
Instead, my belief is that the problems we face lie more in the way in which quantification of publications is used as a proxy for evaluating significance and impact of work.  Peer review is a reasonable system, and many of the tweaks on it look like fine systems as well.  Making peer review high stakes, however, turns it into a system that people need to try to game and distort because it directly impacts their ability to remain employed and to fund their research.
Because of this, I think that any attempt to "fix publishing" will not actually fix the problem: at best, it will simply shift the focus of high-stakes distortion to a different area.  This is something that we see already, with the emergence of this great diversity of different "quality metrics," each of which is gameable in various different ways.
At the end of the day, however, all decisions about scientific funding boil down to some mixture of personal judgement and some sort of metrics.  The more that we have metrics (publication or otherwise), the more that the system can be gamed; the more that we rely on personal judgement, the more subject the system is to bias, prejudice, and cronyism.  I don't have any clear notions on how best to go about improving the balance in order to improve things, but I think it's important to have a clear identification of the actual locus of the problem that needs to be addressed.

Are you saying that the problem is not with the publishing system but with the academic hiring/promotion system?

@BrenBarn ... and funding too.

In that case, +1! :-)

(Genuine question) What, in your opinion, would make for good 'quality metric'? Some abstract notion of how much 'impact' the person's work has?

@DTR I think that the notion of a single quality metric is fundamentally mistaken, since there is not a single type of scientist or scientific success.  I think that we would do much better with a spectrum of quality metrics that can help identify which paths to scientific impact a person has taken, and how much impact they have had in those ways.  These are not commensurate, and blending into "one big metric" is just as incorrect when evaluating a mid-career scientist as it is when evaluating an application for university or grad school.


text:
Is it normal to be suspicious of a really interesting finding but in an obscure journal and/or by authors from unknown universities
EDIT Since there are some misunderstandings regarding my question I'll try to rephrase, and also avoid posting questions late at night when I am really tired :)

A typical part of my work as a bioinformatician/data analyst is to check the relevance of my results, when I am drafting my articles, by doing a series of literature searches to see how my results relate to the field in general. This typically takes me beyond my own competencies, and into fields like histology, oncology, tumor biology etc..
Quite often I run into very interesting results presented in articles that were published in obscure journals with pretty low IF (say for example 1.0-1.5). Sometimes the authors turn out to be from a rather unknown university from a unexpected country. I realize that this is a bit controversial, and I really don't mean to be looking down on anyone's creativity or work ethics but biomedical research is usually expensive, with all the lab consumables, customised reagents/antibodies/proteins/peptides etc..
One such example was an article I found yesterday where the authors claim prognostic potential for a particular protein which also happens to be significantly regulated in my dataset. Naturally I was excited at first but then two questions arose:

Could these people have really done what they claim they have done?
If the work is legit, then why it ended up going to a journal that's so obscure, considering that the findings might be very relevant for patient care. Cancer and biomarkers are two "hot" fields and there are literally lots of well known avenues for publication, before you come to think of this one, or this one. If the results are as interesting as I think they are then they should have surely been published in a venue where they'd get more attention.

Am I being too harsh to be suspicious?

Regarding unexpected places: I know that Pakistan is making an effort to boost research by forming PhD students abroad and bringing them as faculty. I personally know a bunch of bioinformaticians from there in Stockholm that will bring back a well connected research network.

I am not from the biomedical field, but are any plausibility checks, other than re-performing everything, really impossible? I don't think they would be possible, otherwise how would journals publish anything at all? They don't sit down and reproduce everything you say, and neither are they simply taking your word for it!

Take into account that for some fields an IF of 1.5 is not quite low: the IF depends also on how many people works in that specific field. For example in my field all the top journals have IFs between 1.5 and 2.

@MassimoOrtolano That is definitely true.. Good call to point it out! however in this case, 1.5 is definitely subpar. Nothing I have done so far could be considered high impact and I'll be the first one to admit it, but I'm typically going after something at the 4-5 range for a typical study.

It is not only normal but necessary to be suspicious of every really interesting finding, no matter where it is published or by whom.

Let's take an example from the other end: "vaccines cause autism" was published in The Lancet with IF 45. It's never good to focus too much on the journal reputation but not enough on the research itself.

Are you suggesting that Spain and Japan don't have money for scientific research? That these are somehow "backwards nations"? WTF?

@LightnessRacesinOrbit That was not at all what I was suggesting, the two links you are referring to are two examples of the low-IF venues I was talking about.

Yes, I got that...

@LightnessRacesinOrbit in that case your comment above is rather unnecessary, don't you think?

@posdef: No. Your odd and offensive ethnocentric views are.

@LightnessRacesinOrbit you either misunderstand what I wrote, or choose to interpret it in an odd and offensive way. Since you seem to show no interest in figuring out what I was trying to say or my motivation with the question, despite my efforts in trying to explain my thoughts, I say it doesn't matter either way. Have a nice day :)

I am concerned by your choice of words:

"Obscure": one person's obscure may be another person's core community.
"Low IF": IF is very dependent on both community and "trendiness."  A lot of very good work is published in low IF venues because they are the appropriate place to publish the result.
"Unknown ... and unexpected": again, not unusual at all for something interesting to come out of an unexpected place.

All of these words, to me, point to lazy "popularity-contest" thinking in evaluating the context of a work.  Instead, I think that you should use more scientifically meaningful heuristics, such as:

Does the investigator have a history of solid work, into which this result fits?
Does the venue have a clear, well-defined focus and community, and does it have reputable management?
Is the work well-suited for the venue?

If a work doesn't pass these filters, then I think you should be highly suspicious and not bother digging more deeply.  If it does, however, then you should be just as skeptical as you should be for work coming out of a high-prestige laboratory in a leading institution---and no more.

Yeah, I agree entirely with this answer.  I had been reading the OP as talking about "shady journals" or ones that, from his knowledge of the area, he knows are not the sort that breakthrough results get published in.  Just going by the impact factor: that's for administrators who can't possibly acquire a clue in the needed amount of time.  Be a professional and acquire a professional opinion.

I fully understand your concern, as I said I'm aware that it might give the wrong impression. Anyways I tried to clarify some of the misunderstanding with some additional text to the question body. RE: your answer - the issue stems from the fact that I am beyond my own competency here, in other words I cannot judge how solid the immunohistochemistry is, or if there's anything fishy with their choice of antibodies. Looking at the source and venue of the publication, I became suspicious since I cannot easily say if they pass the test, with what your call "heuristics"

The impact factor, in biology, is not a bad proxy for journals' acceptance standard.

@CapeCode Two major problems with that: 1) "Low IF" is in the eye of the beholder.  I know people who snoot at a 5.0 IF because they're always gunning for Nature/Science/Cell.  2) Audience size is not the same as acceptance standard.  Consider, for example, the SJR rankings for ecology journals: a lot of quite good journals have low IF simply because they are specialized.

@jakebeal wrt specialized - according to the OP, the results could be very relevant to patient care.  They should be broadly communicated to everyone interested in patient care and not restricted to a highly specialized audience.

@emory We can't know without actually reading the paper ourselves---see the nice answer by anon.

I don't think it's too harsh to be suspicious.  I think that suspicion should be the default state of academics appraising each other's work.  Nor do I think that you need to be equally suspicious of everyone: if certain indicators make you more suspicious, go for it.  Having "a nose for the truth" is part of being a successful academic, and I don't think it's worth apologizing for.
The question is what to do with your suspicion.  In my field -- mathematics -- if you get suspicious of something, the remedy is in principle easy: you read it and check it.  In reality this can take a lot of time and energy, but the more important the result is the more necessary and valuable that time spent will be.  Note also that it may have the effect that you come out having completely understood the work, and then you can vouch for their work, so your suspicion will be a net positive for the authors.  (And if their work turns out to be flawed and invalid, they should still thank you for your efforts.  Even if they don't, your conscience is clear.)
I'm afraid I don't really know what you should do in a biomedical field for which reproducing the projects would be an undertaking involving more capital than just your own time.  I would guess that a sufficiently important biomedical study would get rechecked and refined by others.  But if you really just think "I highly doubt that if I did that study correctly, I would get the result that I read about, and if I didn't, then it will have been a waste of time" then depending upon how skeptical you are it seems reasonable to contact the authors and ask for more information or simply ignore it altogether.  The last sounds harsh, but it seems to to be necessary: what else are you supposed to do, go slavishly down a path that you firmly believe is wrong?
Added: @Kimball asks whether by "ignoring" these papers I mean not to cite them.  Good question.  In my opinion, just because paper A is published in field X and you are writing a paper in field X does not force you to cite A.  If your work contradicts A then you still are not necessarily required to cite it: e.g. when you write a paper about the transcendence of Pi you will probably not cite papers purporting to show that Pi is algebraic or rational.  You need to cite paper A if you use or rely on it in any way.  You probably should cite a paper A if you think that others in your field will be aware of it, take it seriously and that a lack of citation would create confusion in the field.  I would say that except in the precise circumstances enumerated above, you should not cite work that you believe but do not know to be false.
Further Added: I think that @The Dark Side made a crucial point in a comment above.  In every technical field, papers get refereed, and serious/breakthrough papers get seriously refereed.  This process however does not involve nontrivial expenditure of capital or systematic, large-scale reproduction on the part of the referee.  But the referee must do something, and I would hope that the authors' institutional affiliation plays a small role in the evaluation process (ideally: no role at all).  So the generalization of the answer above from mathematics to other academic fields seems to be: if you're suspicious of a potentially paper, you should re-referee it.  In this particular case the OP seems to lack the expertise to fully do that.  Okay, that's what friends and colleagues are for.

Besides  ignoring it completely or reproducing the experiment,  there's also the option of saying "that's interesting, if so" and waiting for someone else to make the effort to reproduce the results. Which is the same thing we do -- or should do -- with claims outside our own field of expertise.

I think the OP may be concerned about how/if to cite some of these works in their own paper.  Are you saying to simply ignore it in this context also, or just that you shouldn't rely on it a basis for your research?

@keshlam: I agree.  I only advocated "ignoring completely" results that you feel couldn't possibly be right. Whether there are any such results in the OP's field, I am not sure. But for instance about once a week a paper gets uploaded to the math part of the arxiv for which the only reasonable reaction is to completely ignore it: such papers are surely false (in some cases they directly contradict other work) but nailing down their falsity is more trouble than it's worth (e.g. because the exposition is so poor). Unfortunately many such papers are now being published in "obscure journals"...

An obscure result in an obscure publication probably deserves its obscurity. There are certainly exceptions, but ...

A while back, I found a very "nice" paper about integral transform theory in a very low impact journal. It's work that is tangentially related to mine. I've read it several times and I'm still not very convinced about the material therein. Both authors respected mathematicians but they cut a lot of corners in the paper (stated things without proof). I'm even fairly suspicious of the journal. It hasn't made the list for predatory journals but it doesn't seem to be very rigorous. Stuff like this happens sometimes unfortunately.

@Cameron: In truth I think there's a continuum here.  I know of at least one journal that publishes a lot of papers of the following kind: the results they contain are exciting breakthroughs in the field; the exposition is not careful enough to make it easy to tell whether the proofs are correct; in fact the proofs are manifestly wrong or incomplete in some superficial ways; nevertheless the paper, flaws and all, is clearly of value.  Caveat lector, to be sure.

concerning your remark:

Quite often I run into very interesting results presented in articles that were published in obscure journals with pretty low IF (say for example 1.0-1.5). Sometimes the authors turn out to be from a rather unknown university from a unexpected country.

I feel that it may be difficult for 'unexpected countries' or 'rather unknown universities' or 'underexposed research groups' to actually publish in a highly ranking journal just due to this suspicion, which also bugs the reviewers and journal staff.
Also it happens in reverse. Well known groups can sometimes just a little bit too easily publish a sloppy experiment in a high ranking journal.
In that sense, I think this suspicion is overrepresented in literature, and we should be careful to apply it too much...

It's a good point definitely, it's a vicious cycle if the lesser known groups are discriminated against and all we have left is a "rich get richer" scheme.. I feel it would have been so much easier if there an easily accessible track record for a publication, where one could see if a the work has bounced from several highly reputable journals and settled on a less known journal, or if it directly went into that journal because the authors feel it's the "right forum" for that paper.

This danger probably exists. But since academia works like a guild, I feel it's truly hard to learn to write good papers / do good science, unless you learn from a successful scientist. (Affiliation is a mediocre proxy for this, since scientists move).
Last time I heard such a complaint (from a researcher of little fame who had an actual cool finding), the complaining researcher added "reviewers made me waste tons of time because they forced me to make my paper self-contained". With such ignorance of how to write a paper, no wonder publishing it took so long.

I think that the results are simply not as interesting as you think they are.  A result from a new (while perhaps theoretically sound) statistical technique or data set that has not been subjected to rigorous cleaning (because if it were then there would be nothing left of it).
As far as having the equipment goes, I'm not sure exactly what papers you're looking at, but from the standpoint of someone doing some biostatistics research at the moment, we often make use of data that has been made public or that which is obtained through collaborators as we have no wet lab.  We have to wade through cleaning that data properly (which is extra difficult because it is hard to gather reliable biological data in a good form even when the biologist knows what is needed, many do not).  We are often trying very new techniques, between potentially bad data and techniques that have not yet gone through the proving grounds... no matter how rigorous we are we will have some doubts about the result.
If we are the first with such a result we would not be willing to trumpet it to too high IF journals.  The results are not interesting to us unless:

it also reproduces good findings from others besides the new thing it finds or
the results are experimentally confirmed (which would usually be quite a while after publication, though alternatively a more biology based collaborator with a wet lab may do so).

Your work is the reason their work is interesting, it's not interesting on its own.  Balance this with some healthy skepticism in general of course though, statistics has a special case of irreproducible results, unreasonably so.
Unlike some other posters, I believe that the magnitude of their results may not be very apparent at all to them in the case where they actually are major.  This is because, like yourself, they may not have a deep knowledge of the field that the math is being applied to, they run their code on some data they got and oh, it turns out gene xyzl_at is significant, that holds no meaning for them in the way it would for a biologist working on the disease.

This typically takes me beyond my own competencies, and into fields like histology, oncology, tumor biology etc..

When I want to evaluate the plausibility of a result from outside my area of expertise, I typically look for a friend or colleague who has the necessary expertise, or who might be able to point me to someone who does.
I think this is much more likely to yield meaningful results, and much better for science as a whole, than evaluating an article I'm not competent to read based on the impact factor of its journal or the authors' institutions and countries of origin.
To put it more harshly: if you're not competent to do something, don't do it. Instead, collaborate with someone who has the skills you need.

I would normally agree on the notion in your last sentence, and that is how I normally approach this problem, however based on how often this situation occurs, it's practically not feasible to ask to a colleague every time I run into something I cannot evaluate. It might be how things are done at my lab...

I think you are right to be suspicious - but as long as the journal is included in those abstracted by pubmed etc then I you should still include it in your review. Just put a "flag" by it (if you really don't like it put it in an appendix). Including the paper shows;
a) your search was extensive
b) you have some criteria for judging value (you've flagged it)
There are "social" reasons why a good paper might appear in a low IF journal, eg: the author needed a publication quickly 'cause they were going for promotion, the author had a "spat" with one of the "silver backs" in the field, the author lacks confidence in their own ability or has been undermined by "colleagues", the institution they work for might be "behind the curve" in the obsession with IF etc etc.

The only two valid metrics you should use to assess individual papers, as a rule of thumb, are recency and number of citations. When it comes to recency, the results might become disputed in the future, but it is possible that the paper reflects the latest developments. When it comes to number of citations, this is often a clear indicator of the impact of the paper. Web of Knowledge or Google scholar can provide you with a citation metric. For rapidly moving fields, such as the natural sciences, 3 years is an acceptable cutoff for recency, but may vary from field to field. Generally, whether a paper is either "classical" or "fresh" is used as a criterion for inclusion as a potentially citeable source (e.g. for a literature review) and not for assessment of quality.
Due to the change toward open access the IF and related metrics are starting to become less valid. In addition, they are controversial. They can be useful for authors planning to publish, but without the background knowledge required to evaluate the validity of the paper, what others think becomes more important. Thus, the measure of the individual impact of a paper should be consulted. You could potentially use IF indirectly, by checking what the impact factor of the journals citing the paper amount to. However, citation does not imply that the paper is a "good" paper. For example, the paper by Stanley Milgram "Behavioural Study of Obedience" has been cited 3847 times according to Google Scholar, but it is a text book example of when research ethics goes wrong.

We all know that IF is a very problematic way to judge work quality, and most of what happens in science is not published in the "glamour" journals. We can also agree that every paper should be judged by its content and not by the journal it is published in. Finally, the notion of importance can be very subjective.
However:
I completely understand your concern and I think it is legitimate. I don't ever recall meeting a biologist that had an important result and would want to have it published in an extremely obscure journal. And yes, in biology even within specific subfields there will be several journals with IF higher than 1.0-1.5 (in this case the IF is not an indicator of quality but often is an indicator of what other people read). To be even more blunt, I don't remember ever seeing important results in a journal with this kind of IF. To me, this suggests that if the work ended up in such a journal, it is very likely the authors couldn't get it accepted in a "better" journal, so at least some people thought there was something wrong with it.
The bottom line:
It is technically possible that the results are very important and the author didn't care where they were published or the importance of the results was not realized. However, this is extremely unlikely in my experience.


text:
Is this considered citation stuffing?
I've had experiences where some reviewers comment something along the line with:

Why didn't you cite [x]. They solved a similar (but not strictly
  applicable) variation of your problem.

Now most of the times, I have read [x]'s abstract and conclusion + skimmed it, but I chose not to include it in my citations as it wasn't particularly relevant to my problem.
My question is, if I have read [x]'s abstract, conclusion, and skimmed its content, can I write a sentence such as:

A considerable amount of work [a,b,c,x] addressing this problem, as
  well as its variations, has emerged in the recent years.

Is this considered citation stuffing?

This question is opinion-based because "citation stuffing" does not have a generally accepted meaning because it is an extremely obscure term.  I found zero results in Google Ngrams, five results in Google Scholar, and regular Google brought me back to this question.

I think Anonymous Physicist is quite right, and I also think the Question fails on the simple point that "A considerable amount of work […] addressing this problem, as well as its variations, has emerged in the recent years" appears to be the main point but in fact has no bearing on any obvious problem.
If you hoped [a,b,c,x] would be seen as some kind of abbrevation for works not in fact cited, can you explain that?

I think you're asking the wrong question here.  Instead of asking whether this is "citation stuffing", I would suggest you simply ask yourself whether this makes the paper better or worse (or no different) for your reader.  That is always the ultimate question that should inform all other choices.
Given that you are only proposing to add a single sentence to the paper, citing some purportedly related works, it is not onerous to read that additional sentence.  If the papers are indeed related enough to be of interest to some readers, then it is probably not going to make your paper any worse to mention them.  Bear in mind that when you read an academic paper about a particular problem, you are often interested in related problems, even if they are variations that are different to the immediate problem.  On the other hand, if the papers you propose to cite are not really related to the topic at hand, or are too far removed to be of interest, it will be annoying if you lead the reader to them on the basis of a false promise.
As to the fact that you have only "skim-read" the papers you propose to cite, that is not necessarily disqualifying, but you need to be careful.  If you're going to say that these works are "addressing this problem, as well as its variations", then you have to make sure that you read enough to be absolutely sure that this claim is true.  It is also important to have read enough of them to be satisfied that these are good papers ---i.e., you want to direct your reader to papers that are valuable for them to read.  You don't necessarily need to read an entire paper to know that it relates to a variation of the problem you are working on, but you have to make sure you read enough that you are not just guessing or taking someone else's word for it.

I would instead advise you to add your thought and decision process to your paper.

This problem is also addressed in [x]. While they achieved Y, Z remains open.

Then you present in your paper how to solve (parts of) Z.


text:
Should I review papers according to my conviction about correct methodology or according to the usual methodology applied in published papers?
If you disagree with most of the research community about some methodological questions**, how should you act as a reviewer of a paper?
Should you review as most reviewers would? Or should you follow what you think is scientifically correct?
More details on the disagreement. The typical paper in my field is something like this: An algorithm is described (for solving an optimization problem), the algorithm is tested on one of the four famous data sets, the results are reported and discussed (in the sense that: "if there are more than 4 required hubs and the number of sources and sinks is over 100, then my algorithm is 50% faster, otherwise not"). So I have concerns that the algorithms are overtuned to the famous data set, and that they might be less strong if one applied them to data sets of different structure.

Comments are not for extended discussion; this conversation has been moved to chat.

In real world applications, over-tuning algorithms to datasets is a growing problem that has seen a lot of criticism in recent years. Whatever your academic community's current stance is on this topic, you should bring it up in this particular case since the kinds of people who may be using this research for practical applications are likely to question such methods, even if the academic community does not.

Your wording, using "beliefs", rather than reference to things that might be "objective" or at least "falsifiable/verifiable" certainly weakens the rhetorical sense of the question... After all, you have tangible, explainable reasons for your "belief". Reword it?

@paulgarrett These are not my words, the word "belief" came in through the edits of other people.

Hm. Not good edits. Oh, well, nevermind, ...

@JFabianMeier The original title was very short; it is not surprising that it was edited. You probably know this already, but edits must always respect the author’s wishes (this is emphasised on the side of the edit screen); if they failed here, you should maked your own edit to restore your wishes.

I would approach the issue with an open outcome: Don’t demand that the authors do X, but ask them why they don’t do X and request at least a justification for not doing X (in the manuscript). For an issue like in-sample optimisation, you can possibly refer to some suitable paper, that while not from your field is sufficiently general to apply to your field.
You will probably not achieve that the authors actually do X, but you will likely achieve that the authors write a sentence (about why they don’t do X) that is not very convincing or comfortable and reduces the value of the manuscript. This also raises awareness of the issue for the readers and maybe for their next work, the authors will consider improving on this very aspect. And of course there is the possibility that the authors can bring a good reason for not doing X, in which you will learn something (and they should likely bring the argument in the manuscript).

As a peer reviewer, you should offer your own assessment of the manuscript at hand. This way, you contribute to and possibly advance the field. If you just followed what others did common mistakes would just propagate, and this would be undesirable, of course.
However, do not expect that others must follow your advice. It is also conceivable that you are wrong, always consider this as a possibility. Editors and other reviewers might come to a different conclusion than you. (Leaving your example aside)

If you are willing to put your differences/reasoning in writing to an editor then there is no reason not to judge the paper according to the principles that you think are important and correct.
I question, however, whether it is a dominant view that the training and test data can be the same. Separate random selections from a large enough set would be a different matter.
But some "views" diverge into crankery so you need to be willing to justify your own.

To answer your question: In hub location papers (an area in discrete optimization), the results of the optimization algorithms are always reported for two or three data sets from the literature. Algorithms are considered strong if they perform well on those data sets, containing less than a hundred test cases each. I have never seen a paper mentioning training data.

@JFabianMeier Do you mean they do not split these datasets just because they are small?

@Lodinn I mean that the aspect of training vs. test data is never discussed. The parameters for the algorithms are not chosen by numerical tests on a training data set, they are chosen by theoretical arguments or by preliminary tests on the test data set. I have never seen anybody separating the data set into training vs. test data or even discussing this aspect at all.

"I have never seen anybody separating the data set into training vs. test data or even discussing this aspect at all. "
There you go, a low-hanging fruit ready to be picked up by you, write that paper and discuss the issue!

@JFabianMeier "they are chosen by theoretical arguments or by preliminary tests on the test data set", these are two very different situations. In the former case, why do you think using the whole data for testing is an issue?

@JFabianMeier this is a nice point. So while it is obvious for everybody that for ML/NN you need to do that, one should probably do it for hand tuned algorithms as well. But does it really give an advantage? Lets say 100 groups research this, and optimize on a training set. Before publication they run it on the 'data set' and 95 of them are so bad that they arent published. Doesnt habe the published the same problem? (it is not individuals then tuning to the data set, but using the community). Not sure if I could make that point clear.

"by preliminary tests on the test data set" you are right to be concerned.  This sort of problem is rife in machine learning, and there seemingly benign practices can have very substantial effects on the outcome. This is my effort (with Mrs Marsupial) to explain why this is a serious problem https://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf (that is being taken more seriously these days than it was a decade ago).

"they are chosen by theoretical arguments" in this specific case, then they're "training" it on a separate data: the theoretical framework, and in this case there is no need for further separation of data, since they don't train anything. On the other one "preliminary tests on the test data", this might warrant more details. But given your comment so far, perhaps try to search for the justification before claiming that there is something fishy on-going in the whole community (it's possible, but you shouldn't rule out that you misunderstood the field as well)

If many groups are using the same datasets for evaluation, this is likely to lead to over-tuning algorithms for those specific datasets, this has also been a subtle over-fitting issue in machine learning.

@LucaCiti By "theoretical arguments", I do not mean that there is a mathematical proof, rather that it is discussed (without experiment) that some parameter settings are more plausible than others and therefore chosen.

@justhalf I would not say "something fishy is going on", I rather think that because most of the researchers are applied mathematicians, they lack knowledge about the risks of overtuning. I do not doubt that many of the algorithms are good, I just think that the method of experimentation is not as statistically sound as it should be (and this is probably true in other areas as well).

Sounds good :) Then like EarlGrey mentioned, go write something about that :D

"it is discussed (without experiment) that some parameter settings are more plausible than others" This is a big red flag to me - not uncommon for people to do the experimentation and then write up the parameter choice finding some supporting arguments. Of course, that is fundamentally incompatible with the scientific method but there are researchers who do not even see an issue with that...

@lalala another new paper - run the 5 algorithms on a different random set and show they are not as good as claimed!

Separating test and training data is necessary to confirm that the algorithm can predict unseen data. If the claim is made that the algorithm, presented with the parameters they used, can predict unseen data, then they need to split the dataset. If that claim is not being made, the case can still be made for it, but it's more nuanced than 'YOU MUST DO THIS FOR EVERY ALGORITHM'.
In my opinion, it's all in what claims are being made of the algorithm. If you feel that the claims being made are not supported by the methods, it's your choice to recommend they either:

dial down their claims so that they reflect the evidence in the paper
perform more analysis to support their claims

It's fine to give the authors the option

This would require that the papers state a hypothesis and try to find evidence for and against it. Papers are usually written in a way that they define an algorithm, make numerical tests and then discuss the tables.

And it depends on what is discussed as to whether the separation is important. If the discussion says 'the algorithm fits the data' that's fine and is factually accurate (but maybe a note should be added in limitations). If the discussion says 'the algorithm will predict unseen data' then they have not demonstrated that and that claim will need to be proven or removed.

Should you review as most reviewers would? Or should you follow what you think is scientifically correct?

I'm not convinced this is a binary choice as you describe it. A good review probably isn't either of these.
Your review is sought, to check the papers adequacy and caliber. As part of that, if they wanted "most" reviewers views, the editors are more than able to ask other more typical reviewers, or a wider range of reviewers, to review it. Therefore you should assume they actually want your view, not a mere echo of what you perceive to be a popular view.
But giving your view need not be idiosyncratic or fringe-y either.

If the field's methodology is weak or flawed, there are, presumably, reputable papers that say this. You can allude to those papers, and state that you have concerns that the paper is prone to/may have suffered from the weaknesses X, Y and Z, as described in [list of cites], because [reasons].  You ask the authors to address these concerns as usual.

If there are no such papers, or they are not seen as significant in the field (or overlooked), then describe that you see a possible concern that the algorithm may be so tuned to the specific data, that it is unclear if it has general interest, because the authors do not appear to show its performance against appropriate general data. (And if needed: the data they do test against cannot in your view be considered appropriate as a test with general data, because (reasons), notwithstanding that it is a widely used dataset). That too, is a sensible, professional statement.

Really, your review is to state what, in your view, needs to be addressed, in order that the paper become acceptable, adequate, and professionally worthwhile publishing (if not already so). So you are not advocating a view, so much as identifying possible inadequacies (as you personally yourself feel they may exist), that you convey to the authors so they can address them - subject to the editors overriding judgement on the need for this and willingness to publish.
Because you are recounting possible issues to check, rather than advocating a position, you should find that there is a wording that allows you to state you have a concern, without adopting a fringe-y position in doing so.

Re: "if there are no such papers", you might consider writing one.

Not relevant for this, wrong timescale.  That's a different thing for future.

Obviously it is a different thing for the future, but it is still something OP should consider doing.

So you are saying, what to do with papers which follow best practices of your field, but you think (with good reason) that these best practices are not very good or insufficient. I think trying to change the community by rejecting papers/forcing authors to follow your ideas is not the way to do it; especially since it is quite random which papers reach your desk. So you should:

Do the peer review following best practices of your field without pushing your personal agenda; you might mention your concern, but not enforce it; maybe the seed will grow in the researcher

If you think you cannot do this, then do not accept peer review requests

If you want to change the community: publish papers on that subject, show what has been overlooked, show how crappy some of the celebrated algorithms are on other data, hammer the message home at every conference. Also offer a viable path for research in your field continue, i.e. positive messages, not 'dont do...' but 'do a, b, c, d)

Edit: To clarify: It is not the right way to change a scientific community by imposing standards on publication when you are the only one seeing a problem there. The right approach is to change it through scientific discussion. Peer review is not (well it is a little) scientific discussion, worst it is not public and there is a power imbalance.
Apart from that not being the way, it is also inefficient.

"papers which follow best practises of your field" - you mean worst practises? ;-)

You can state in your review that you are taking a contrarian position to most in your field and explain why you think the authors should change their manuscript, and let the editor decide. It is always up to the editor anyways.


text:
Has there ever been a pure mathematics thesis longer than 909 pages?
When I heard that a friend of mine finished his PhD thesis with 909 pages, I wondered if there has ever been a longer thesis in (pure) mathematics?

See  http://academia.stackexchange.com/questions/46050/very-short-phd-thesis-by-famous-people for why questions like this are a bad fit.

@StrongBad What would you do if you wanted to answer this question?

Now that has the potential to be a good question for this site: soemthing like how to find statistics about theses in Math. But for an "exact" answer maybe maybe ask on a Purr Math email list server.

The answer to the question as asked is yes. For instance, in MathSciNet you find MR2685954 Thesis. Hugger, Jens. Computational aspects and adaptive solution methods in the finite element method for non-linear, parametrized problems. Thesis (Ph.D.)–University of Maryland, College Park. 1990. 1040 pp. ProQuest LLC.

MathSciNet indexes data from thesis, as provided by ProQuest LLC. It does not yet catalogue theses from outside the States. I do not know of any reasonable way of looking for global statistics or "records".

@AndrésE.Caicedo Thank you! How did you query MathSciNet for this information?

@AndrésE.Caicedo The reason is: Although I am impressed that found this so quickly, I still want to solve the question for pure math.

Your question title and the content do not match-up.  First says, what is the longest paper in maths, the second says, is there anything longer than this one?

@Deleuze As it is probably not possible to give a conclusive argument for what "the longest" thesis is, I tried to make a bounty with a reasonable condition. I am still interested in solving the original question of the title.

I'm not sure whether this is an issue (not familiar with the specific rules) but the question and the description of the bounty don't seem to match up. The question is polar (either there has been or there hasn't been). The description of the bounty allows for a third response (effectively, "I don't know, but this is why I'd consider it unlikely to be yes").

I am not sure that this is a good question, because the length of a thesis is obviously highly dependent on factors such as line spacing.
Having said that, it is possible to give a definitive answer to the question as written, even in the restrictive form asking about pure mathematics:

Yes.

From MathSciNet:

Lan, Kai-Wen Arithmetic compactifications of PEL-type Shimura varieties. Thesis (Ph.D.)–Harvard University. 2008. 1077 pp.

Here it is.

Thank you! You're of course right that page numbers are a very rough indicators of length (actually, the thesis you mentioned has large margins), but it seems to be the only measure for it is reasonable to find information.