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What are our main concerns regarding academic dishonesty in Massive Online Open Courses?
As illustrated by the recent NY Times article, Massive Online Open Courses (MOOCs) have recently gained a lot of attention because of their new model for educating students.
What are the main concerns regarding academic dishonesty in Massive Online Open Courses?  It appears to me that academic dishonesty may be difficult to police for an online course.  Is this correct?

This question is a bit tendentious, and probably would be a better question if you just deleted the entire opening paragraph and stuck with the one-sentence question.  The opening paragraph contains several premises that are debateable yet not really relevant to the real question.

Right. The opening paragraph here was deliberately designed to ruffle feathers. That's probably not necessary in this forum.

@JonBannon - I agree the opening paragraph is somewhat inflammatory, but I think it should be edited rather than deleted outright. It does provide some needed context to the question. Why don't you try to edit it to tone it down instead of just removing it?

I've just approved DW's edit, but the core question is unconstructive, so I'm voting to close.

I think that the question is somewhat misleading. Ethics only factor into a very small percentage of students in Massively Open Online Courses (MOOC's). Most students who participate in MOOC's are only taking the course to learn something. Academic integrity is more of an issue when the students are taking the course for some external reason.
Some possible external reasons are:

Enrolled in the course through the university, and receiving credit for the course
Required to take the course by an employer
Taking the course as a pre-requisite for another course or program

In each of these situations, the role of verifying learning seems like it would be outside of the course itself. If students choose to cheat, it does not necessarily reflect poorly on the course or the program, but rather reflects poorly on the student.
TL;DR - Ethical concerns only matter for students who are required to take the course. The people requiring them to take the course are responsible for ensuring their academic integrity.

This is an interesting point of view. Currently, the burden is placed on the provider of the course to ensure academic integrity.

@benNorris - You are absolutely correct! While the onus of proof has typically resided with the institution offering the course, MOOC's now allow anyone anywhere to take the course. This is one reason providers like the idea; they hope (I believe) that people will like the course and enroll in a program.

An obvious contrast to the peer grading efforts in Dr. Chuck’s Class
  title ‘Internet History,Technology and Security’ and ‘Science Fiction:
  The Human Mind, Our Modern World’ exists.
1) ‘Internet History, Technology and Security’ has a far more lenient
  rubric to follow in comparison to the ‘Science Fiction’ class.
2) I’ve observed that the essays in ‘Internet History,Technology and
  Science Fiction’ are far more thoughtfully written and at-least follow
  the question for the peer response instead of a brief recap of work
  covered in the reading.
I’ve had to evaluate responses which describe in a way such as ‘I read
  Alice in Wonderland was my  favorite story when I was small…….’, for a
  question which clearly states that you need to form a thesis(or a
  perspective view) for the reading material/novel/assignment for the
  week.
3) A major reason for this might be the obvious. ‘Science Fiction’
  class releases videos of possible interpretations after the peer
  response whereas the ‘Internet History,Technology and Science class’
  releases videos on the topic, therefore equipping its students to
  tackle the peer responses.
Also a funny thing occurred on the Coursera forums where ‘I was
  accused of cheating(plagiarism) from my own blog when I merely
  submitted my Peer Response anonymously while posting my copy on my
  Personal blog. The other students did not have an idea of who I was
  ‘since the peer grading’ process is anonymous.
However, there were students smart enough to recognize that it was
  perhaps the blog of the ‘person who submitted ‘ and the issue was
  clarified.
Thanks to [person] for bringing this to my notice and arguing the
  case in my favor. Also a note: Coursera’s plagiarism check systems
  should probably account for these when it does come into place.

The above block-quote is from my blog. I find it to be relevant to this question.
This accusation of plagiarism happened with me as well. I was luckily contacted  by one of the other students taking the course and I could talk the other student out of down-grading me.
This could be a problem even with OpenAccess accounts as some people may like to post work which they did in classes elsewhere.
I also took part in the programming-based classes such as Machine Learning and Natural Language Processing. NLP was tough, with real life problems to be solved. However, despite being forbidden to share their programs online, a lot of students did so, because it seemed to be the only way to make something out of the course(the certificate would not count).
The reasoning being, that if I learn some concepts from these courses, I could use my github account to appeal to an employer about my skills.
'By sharing programs online, I mean people started public repositories from which any student could cheat and get a working program for submission.' Not even the best auto-grader could possibly prevent this from happening. I don't think this can be stopped in universities as well. Sure you have a honor code, but people can copy parts of code from all over the web. Make it a little original perhaps.
There's also the 'theory' that 'don't re-invent the wheel'.
For example:

'Most students would not bother with programming a separate module for
  Fourier Transform calculations for part of a academic program. or
  doing this they may use a library from elsewhere to directly import a
  function.' Would this be considered as copying or plagiarism? Probably
  not?
However, some universities(which include mine) think that programming
  Fourier Transforms is an important part in understanding them. So my
  university gives us programming assignments in a computer lab without
  access to the internet. All we are allowed to rely on is the already
  available libraries on the system. The systems are wiped clean every
  time.
This becomes frustrating when we need to move from AM to FM to delta
  modulation and so on. We need to repeatedly write the same piece of
  code for fourier tranforms and this drastically reduces our
  efficiency.

So getting an optimal solution between preventing plagiarism and 'not re-inventing the wheel' is pretty important if Coursera or any online program would need to get. Right now, there is too much focus on plagiarism and not enough effort to realize the problem of redundancy in the system.

While I tend to agree with @jelkimantis, I would go a step further. I think the issue of academic integrity is a bit misguided. This is true not just in MOOC's but in more traditional institutions as well.
Some people want to think that when they see a candidate has a degree from XYZ University that they no longer need to put in any effort into the interview (or whatever) process. Most humans are naturally lazy and if they can skip digging, they are happy to do so. The problem comes in that those people are making some very big assumptions which likely should not be made.
I would argue that the hiring process is one of the most important processes in any company. A bad hire can haunt you for a very long time and a good hire can save you in so many ways. Still, people want to skip as much of the hiring process as they can (on both sides) so if someone has the right degree they are assumed to have the knowledge which goes with that degree. The problems is, they might not have that knowledge.
The knowledge might not be there for several reasons:

They might have learned and then forgotten due to workload
They might have had someone else take an exam for them
They might have cheated during the exams and never learned in the first place

Regardless of the reason, if the knowledge is not there, it is not there. So, why make any assumptions? The only reason I can see is for those who do not understand to manage those who do understand. However, even in that case, if you don't understand and your subordinate does, then you better give that subordinate a pretty free hand...because you can't check anyway.
I know, it's a long answer but I think the entire question about academic integrity is not a huge issue. As a teacher, I care a great deal if students are cheating. That said, with the number of students I teach, there is no way I can effectively monitor them all. Even if I could, it doesn't solve the underlying problem. If someone is going to hire someone (or promote someone) based on their taking a course or gaining a degree, then they should be willing to do the work to ensure that what was taught was retained.

One of the biggest challenges is to perform assessment.  To scale, the most natural approach to assessment  is to use purely automated methods, i.e., automated grading.  However, building good automated graders is difficult.
The path of least resistance is to use multiple-choice quizzes for assessment.  However, multiple-choice quizzes with a fixed question set are inherently vulnerable to cheating: it is easy for people to pool their answers or copy off each other, and difficult to detect such cheating.
One can think of ways to defend against this, but in general, I expect that providing high-quality (yet not gameable) assessment may be one of the non-trivial challenges facing MOOCs.


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Why are CS researchers reluctant to share code and what techniques can I use to encourage sharing?
While researching a topic area I have come across a number of papers that claim to improve on the state of the art and have been published at respected outlets (e.g. CVPR, ICIP). These papers are often written in a way that obscures some of the details and their methods can be lacking in detail. Upon contacting these authors for more information and asking if they would kindly make their source code available they stop replying or decline the offer.
Why are computer science researchers reluctant to share their code?
I would have expected that disseminating your source code would have positive effects for the author, e.g., greater recognition and visibility within the community and more citations.  What am I missing?
For the future, what are some better ways to approach fellow researchers that will result in greater success at getting a copy of their source code?

An important issue, but you you split it in two questions? (On SE sites there should be one question per one... well, question.) That is, could you make another post out of the second question?

I considered separating the questions but thought that the 2nd would not stand on it's own.

You may want to take a look at Collective Mind initiative: http://www.hipeac.net/system/files/grigori.pdf and corresponding publication model http://ctuning.org/cm-journal

I could post an answer, but it would something like - how could we change things so that more people will publish their source code? Would that be acceptable, or does it belong to a different question?

@FaheemMitha, that sounds like a different question to me -- but a good one.  Why don't you post a different question asking how to change things so that more CS researchers will share their source code?

The fix for this problem is simple, make CS research (and research in general) better funded.  The problem is there is no support for the cost of getting research code to a level of quality where maintenance costs are supportable.

@DikranMarsupial I'm not convinced that throwing more money at researchers will improve the situation as they have limited time. I don't think that distributing high quality code is the issue, it's just an excuse people use. Releasing bad code is better than releasing none.

additional funds will solve the problem as it means that either they can hire somebody to maintain the code or they can be bought out of other duties to release the time to do it themselves.  I like releasing code, and have done so in the past, the reason I don't release more is that I don't have the time for maintenance, that I know from experience is necessary.  I disagree that bad code is better than none, I wasted several weeks earlier this year trying to get someone else's research code to work, to no avail.  Of course the authors were of limited help as they have the same problems I do.

I should add, my wife is quite active in providing code and is driven to distraction by the volume of user requests for help, bug fixes, extensions etc.  In my case there is a limited audience for my software, if you are in a field where there is a large audience, the cost of maintenance is far from trivial.

Why researchers might be reluctant to share their code: In my experience, there are two common reasons why some/many researchers do not share their code.
First, the code may give the researchers an important advantage for follow-on work.  It may help them get a step ahead of other researchers and publish follow-on research faster.  If the researchers have plans to do follow-on research, keeping their code secret gives them a competitive advantage and helps them avoid getting scooped by someone else.  (This may be good, or it may be bad; I'm not taking a position on that.)
Second, a lot of research code is, well, research-quality.  The researchers probably thought it was good enough to test the paper's hypotheses, but that's all.  It may have many known problems; it may not have any documentation; it might be tricky to use; it might compile on only one platform; and so forth.  All of these may make it hard for someone else to use.  Or, it may take a bunch of work to explain how to someone else how to use the code.  Also, the code might be a prototype, but not production-quality.  It's not unusual to take shortcuts while coding: shortcuts that don't affect the research results and are fine in the context of a research paper, but that would be unacceptable for deployed production-quality code.  Some people are perfectionists, and don't like the idea of sharing code with known weaknesses or where they took shortcuts; they don't want to be embarrassed when others see the code.
The second reason is probably the more important one; it is very common.
How to approach researchers: My suggestion is to re-focus your interactions with those researchers.  What are your real goals?  Your real goals are to understand their algorithms better.  So, start from that perspective, and act accordingly.  If there are some parts in the paper that are hard to follow or ambiguous, start by reading and re-reading their paper, to see if there are some details you might have missed.  Think hard about how to fill in any missing gaps.  Make a serious effort on your own, first.
If you are at a research level, and you've put in a serious effort to understand, and you still don't understand ... email the authors and ask them for clarification on the specific point(s) that you think are unclear.  Don't bother authors unnecessarily -- but if you show interest in their work and have a good question, many authors are happy to respond. They're just grateful that someone is reading their papers and interested enough in their work to study their work carefully and ask insightful questions.
But do make sure you are asking good questions.  Don't be lazy and ask the authors to clear up something that you could have figured out on your own with more thought.  Authors can sense that, and will write you off as a pest, not a valued colleague.
Very important: Please understand that my answer explaining why researchers might not share their code is intended as a descriptive answer, not a prescriptive answer.  I am emphatically not making any judgements about whether their reasons are good ones, or whether researchers are right (or wrong) to think this way.  I'm not taking a position on whether researchers should share their code or not; I'm just describing how some researchers do behave.  What they ought to do is an entirely different ball of wax.
The original poster asked for help understanding why many researchers do not share their code, and that's what I'm responding to.  Arguments about whether these reasons are good ones are subjective and off-topic for this question; if you want to have that debate, post a separate question.
And please, I urge you to use some empathy here.  Regardless of whether you think researchers are in right or wrong not to share their code in these circumstances, please understand that many researchers do have reasons that feel valid and appropriate to them.  Try to understand their mindset before reflexively criticizing them.  I'm not trying to say that their reasons are necessarily right and good for the field.  I'm just saying that, if you want to persuade people to change their practices, it's important to first understand the motivations and structural forces that have influenced their current actions, before you launch into trying to browbeat them into acting differently.

Appendix: I definitely second Jan Gorzny's recommendation to read the article in SIAM News that he cites.  It is informative.

+1 Most of the code is written to a deadline, be it a PhD student trying to finish up or a post doc getting a deliverable done in time. I know many people (myself firmly included) who would be embarrassed to be judged on their code quality rather than the actual research it supports.

Stephen, I have just the same experience as you do, and my explanation is that the benefit/cost ratio is too low.
Packing a piece of software, so that it can be usable by another person, is difficult - often even more difficult than writing it in the first place. It requires, among others:

writing documentation and installation instructions,
making sure the code is runnable on a variety of computers and operating systems (I code on Ubuntu, but you may code on Windows, so I have to get a Windows virtual machine to make sure it works there too),
answering maintenance questions of the form "why do I get this and that compilation error when I compile your program on the new version of Ubuntu" (go figure. Maybe the new version of Ubuntu dropped some library required by the code? who knows).
taking care of 3rd-party dependencies (my code may work fine, but it depends on some 3rd-party jar file whose author decided to remove from the web).

Additionally, I should be available to answer questions and fix bugs, several years after I graduate, when I already work full-time in another place, and have small kids.
And all this, without getting any special payment or academic credit for all that effort.
One possible solution I recently thought of is, to create a new journal, Journal of Reproducible Computer Science, that will accept only publications whose experiments can be repeated easily.  Here are some of my thoughts about such a journal:
Submitted papers must have a detailed reproduction section, with (at least) the following sub-sections:
 - pre-requisites - what systems, 3rd-party software, etc., are required to repeat the experiment;
 - instructions - detailed instructions on how to repeat the experiment.
 - licenses - either open-source or closed-source license, but must allow free usage for research purposes.
The review process requires each of 3 different reviewers, from different backgrounds, to go through this section, using different computers and operating systems.
After the review process, if the paper is accepted for publication, there will be another pre-publication step, which will last for a year. During this step, the paper will be available to all the readers, and they will have the option to repeat the experiment and also contact the author in case there are any problems. Only after this year, the paper will be finally published.
This journal will enable researchers to get credit for the difficult and important work of making their code usable to others.
EDIT: I now see that someone already thought about this! https://www.scienceexchange.com/reproducibility
"Science Exchange, PLOS ONE, figshare, and Mendeley have launched the Reproducibility Initiative to address this problem. It’s time to start rewarding the people who take the extra time to do the most careful and reproducible work. Current academic incentives place an emphasis on novelty, which comes at the expense of rigor. Studies submitted to the Initiative join a pool of research, which will be selectively replicated as funding becomes available. The Initiative operates on an opt-in basis because we believe that the scientific consensus on the most robust, as opposed to simply the most cited, work is a valuable signal to help identify high quality reproducible findings that can be reliably built upon to advance scientific understanding."

I don't think that's a valid answer. You can just document your setup or even just distribute the code. Even if I can't run, I can learn lots by just inspecting it.

I also thought this way, but over time I learned that when you release your code to the public, you inevitable have some responsibility over it. If the code isn't well-documented, if it doesn't compile, if it doesn't run - people will hold you responsible, and it might be bad for your reputation.

I agree that if your code doesn't compile or doesn't run it might be bad for your reputation but I think it's reasonable to turn down requests to change/fix your code if you are simply publishing it. If you're managing an open source project that's different but why would publishing source code require more than simply answering questions (as would any publication)?

Submitted papers must have a detailed reproduction section -- a similar idea has been bandied about in the computer architecture community for a few years, but it involves providing a working virtual machine with the code installed and ready. You boot the virtual machine and away you go.

@ErelSegalHalevi It's much worse for the reputation to hide the implementation details. IMHO, it basically means "believe me". That's not how science works. Hiding the code violates the most important law of making science: falseability. You can't invalidate a work if you don't have access to it. The author can hide behind this black curtain denying whatever attempt to reproduce/invalidate his paper by saying it's not identical to his method.

@Spidey, Erel's answer is completely accurate.  He does describe a mindset that many researchers do have.  That mindset might be a good one, or might be bad for the field -- but regardless, what matter is that many researchers do share that mindset, and act accordingly.  The original poster asked for an explanation of why many researchers have decided not to share their code; Erel has given an accurate description of why some/many researchers have decided to do so.  You can agree or disagree with whether they've made the best choice, but that's not the question here.

@Spidey, I agree that it would be much better for science to have all code published. That's why I suggested a way to encourage authors to publish their code.

+1 for "Journal of Reproducible Computer Science", which makes for interesting googling, by the way. But as a developer I think your requirements for packing the code are much too strong. Anyone who knows the phrase "bit rot" will know that it's unreasonable to expect someone else to maintain code which was simply created to demonstrate a point.

There's a lot of research in this area made by G.Fursin : he tries to make the CS reproducible. See presentation at http://www.hipeac.net/system/files/grigori.pdf for example.
He also tries to push a new publication model http://ctuning.org/cm-journal

@D.W. You are 100% right. I meant to say it's not a valid reason not to share.

@ErelSegalHalevi: Your dream of having reproducible papers is very nice. But think about publication time. Currently it takes about 6-18 month to publish a paper. Publishing in that journal may take 2-3 years if non-paid reviewers (usually professors) are needed to read all your documentation, learn used language, install tools, troubleshoot the code, and run to check if everything is okay. It sound more practical to use a cloud instance, load the code, and enable reviewers/readers to access your loaded code rather than doing all by themselves. Doing so, increase trust too.

@Espanta: One step at a time. First, share the code. Then we go thinking about peer reviewing the code.

@Spidey: "It's much worse (...)" - when only looking at science, yes, but (at least in my place) many PhD candidates have the ultimate goal of leaving academia after their PhD to either take a job in the industry, or start their own company. Once they are there, they will be judged by the quality of any code of theirs that is publicly available, and "experimental prototype style code" is going to reflect extremely badly on their reputation. Hence, I can fully understand if they share code only on request rather than make it downloadable somewhere. (They should react to requests, though.)

This article in SIAM News sheds some light on the first question, so it might be worth a look.  It argues, for a mathematical audience, why researchers ought to publish their source code, and lists many of the reasons you might hear why researchers do not share their source code.  It does so by a clever analogy, one that compares the sharing of mathematical proofs to the sharing of  source code.  Take a look; it has quite an extensive list of reasons why researchers might prefer not to share their source code (as well as some responses arguing that those reasons are not good ones).
Here's a citation:
Top Ten Reasons To Not Share Your Code (and why you should anyway). Randall J. LeVeque.  SIAM News, April 1, 2013.

I suggest that you give more information about the article you link to.  For example, state the title and perhaps a sentence describing the main idea.  Note that links expire, and your answer would be more useful if it provided the information even if the link fails.

In sharing code there are several issues:

The first issue is the copyright matters, since some of CS researches/projects are funded by certain industrialists/funding organizations that discourage sharing sensitive information such as algorithms, code, or software while publishing in public periodicals.
Indeed, there are papers based on certain data (collected from code execution) that unfortunately are manually modified by the authors. If they share the code, catching their mistake/error/modifications becomes very easy leading to failure in either their MS/PhD or research project which is undesirable.
In CS research and especially publication, developing code, particularly a lengthy, complex code is a non-trivial task and in most of the cases is considered money-making and paper-generating asset. By sharing the code to the public, they are unveiling facts in very much detail which may degrade their contribution in future researches. Also they may not be the only one who can regenerate article and make credit of that particular research and code. In most of the cases, master students pick an algorithm or method, slightly change it and submit a thesis and paper based on it, that may contradict with the findings and claims of the first author. Remember Thoma Herdon a graduate students who criticized findings of two eminent economist of Harvard university(here is the link ). If the codes in CS are revealed the consequences are likely catastrophic (it might not be too many cases, but if happens it will be catastrophic).
Codes are vital property to most of the researchers to conduct experiment and research. If you have a code, you can simply play with it and modify it to generate new set of findings that might be more valuable than the initial findings. Without having authorship of the initial author, there is no credit to them.

However, Elsevier recently introduced a new feature using COLLAGE called Executable Papers that is currently available for Computers & Graphics journal by which codes and data are available and researchers can modify the code and input values to play with.
Hope it helps.

If the codes in CS are revealed the consequences are likely catastrophic. — So you're accusing an entire intellectual discipline of fraud?  Really?

@JeffE I wouldn't be so harsh to call it all a fraud, but it would definitely improve the overall quality of research papers.

Sounds like an accusation of fraud to me, or least criminal incompetence.  The only reason publishing data/code would be "catastrophic" is if that data/code did not support the published conclusions about that data/code, as they didn't in the Reinhart-Rogoff paper referenced one sentence earlier.

I think your 2nd point is really the most important one.  If others can replicate state of the art research and tweak it slightly to produce new publishable results, then you lose the ability to capitalize on all your hard work developing the code to begin with.

you lose the ability to capitalize on all your hard work [citation needed]

I don't get the second point and the latter half of the third point. Since when does CS stand for dishonest junk pseudo-science where authors manipulate data and hide the details because otherwise what they call "results" would be falsified? If it results in a catastrophe for authors to be all honest and make things verifiable, your field should have collapsed already and gone forever. Like JeffE said, you're accusing CS if you're suggesting these are valid answers to OP's question. You must present evidence. Oh, you collected evidence by your code and manipulated it? That's how CS works, huh?

"copyright [...] large number of CS researches are funded by certain organization that does not allow people to share their codes" - Which organization are you referring to?  I don't know of a single one that prohibits CS researchers to share their software, or any reason that copyright prevents sharing of software.  I think this statement is just plain wrong.

These are all good hypotheses, and the problem isn't confined to CS.  Anyone know of a study to determine which is actually at play?  (killer nonresponse bias, probably... =)

@Erel Segal Halevi: I edit the last paragraph and provided link to the feature.

@JeffE: I have no intention of accusation, it is my own opinion from what I have already observed and that is why I used "likely" to protect good guys and researchers, like you, who do not falsify findings.I further add one more sentence to that. However, if the point hurts you and others, I really apologize. I had no intention for that. Sorry dude :)

@D.W.: To me, it's logical that industrialists funding projects keep the right for the algorithm and code. Do you think papers from big companies of CS and communication can be replicated easily? and are available to the public? They may publish the significance of their research, but I think they try not to share valuable data such as code to the public. Don't forget competitors are looking at each other. A common evidence is the statement on published paper like "Approved for External Publication". This [http://www.hpl.hp.com/techreports/2011/HPL-2011-55R1.pdf] is one of those. Don't agree?

@Espanta, it's a huge leap from "you think it would be logical if funders prevented researchers from sharing code" to what you actually wrote.  Just because you think something would be logical, doesn't mean it is actually so.  What you actually wrote in the answer is almost certainly wrong.  If you care about accuracy, you will edit your answer to fix what you wrote and remove the claim that "large number of CS researches are funded by certain organization that does not allow people to share their codes".

@D.W.: Thanks friend. I did further change to make it more accurate. Hope it does not have much problem now.

My point of view on this is that if you can't share the code and data, you can't publish. Simple as that. You can still research and sell your workforce to anyone, but what good does it make to tell the science community of your achievement if no one is able to reproduce it?

Yo must share data, but not necessarily code. In journals, reviewers asked to evaluate if the authors provide sufficient info to replicate the work. Code is not the only thing for research replication. There are many others that impact on results which varies from domain to domain. e.g., for cloud-based apps running on mobile devices, there are very large number of metrics like device and cloud type, prog.language, mobile-cloud distance, data collection time, network quality and many more. So replication of work is not easy in all domains; authors art is to establish trust in his paper.

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How do I put industry research in an academic context?
I am an industry based researcher. I research and write policies mostly relating to workplace issues such as codes and manuals. I have just completed my PhD in a related field.
On my CV, I can include about 10 years of industry research (plus my qualification as above). As far as I know, industry research is not considered in the same light as peer-reviewed articles.
Question: How do I make industry research a selling point in my CV (in the academic world)?
Note: I have no peer-reviewed articles to my credit and there is no possibility of publishing my industry based research outcomes in the wider sense.

Would your Ph.D lead to peer-reviewed publications ?

Yes, it certainly can but I am struggling as I am not based in academia.

Patents count as journal articles for some promotion and tenure committees.

I have just completed my PhD....I have no peer-reviewed articles — Wait.  What?  Is that normal in your field?

I have been wondering about this too. From what I am reading on this site, most students have at least one peer-reviewed paper before admission to PhD and several afterwards (resulting from their ongoing research). I am based in Australia and was not required to adhere to this 'rule'. My PhD is from a major university. Have I missed something in my 6 years of part-time study!

most students have at least one peer-reviewed paper before admission to PhD – This is definitely not true; some do, but not most, even in the strongest departments.

In non-science disciplines, it's not unusual to construct a Ph.D first, and then peel off chapters for journal articles.

You didn't answer @JeffE's question which I think is important to answer this question. In Computer Science, I have seen many seminal papers come from the industry.

I guess whatever is normal in the Social Sciences would apply in my case too. Perhaps someone with experience can elaborate....

@JaveerBaker yes, it is hard to publish research articles in this field. IMO, you need to convince the committee that your works had research aspect. I mean you've used research methods to analyze the system to write policies.

@D.W. - That question relates to what qualifies, this question relates to constructing a CV. The two questions seem fairly different to me.

Emphasize strengths that mirror what would be expected for a 10-year Academic career:

No peer-reviewed publications? List instead all the internal white papers you've written.
Mention any academic collaborations when describing roles. (No collaborations? List internal cross-departmental collaborations.)
List trainees/interns you've mentored.
List any important talks/presentations you've given related to your research. It won't be the same, as the level of discourse when talking to other researchers is different than when talking to senior managers, but it does demonstrate presentation experience.
Get very strong letters of recommendation attesting to your strength as a researcher, communicator, and mentor.

This makes so much sense and is good practical advice.

The relevant issue is how to make your research meaningful to your application. Since this would typically not get much attention in a CV, the best place to do this will be either in your cover letter or in the introduction to your research statement.
If your industrial research has informed your choice of problems to study as an academic, or has expanded your skill set, this is information you should relate to the committee. However, if the work is completely unrelated, you may have a hard time convincing a committee that it's worth considering as related experience (beyond the traditional justification of industrial experience in and of itself).

I think there might be some confusion.  If you write policies, codes, and manuals, that most likely does not qualify as (scientific) research as the term is generally understood by the research community (or the academic community).  In our context, (scientific) research generally refers to systematic investigation that leads to new knowledge.  The end result of (scientific) research is some new knowledge that was not previously known before.
I know that in other contexts, people sometimes use the word "research" in a different way.  For instance, they might talk about "researching an issue", by which they mean, go find newspaper articles, scientific papers, policy briefs, etc. on the topic and read them to get up to speed on the topic as quickly as possible.  That's a fine meaning of the term "research", but it's not research as the academic or research community mean it.  That kind of activity generally is not a replacement for what academics call (scientific) research.
(Scientific) research is also usually published in a peer-reviewed conference or journal.  Academics may give credit only to published work.  There are good reasons for this.  For one thing, academics value discovering new knowledge and making it available to humankind.  If you haven't published, you haven't advanced that agenda.  For that reason, if researcher X discovers something new but doesn't publish, and then a year later researcher Y discovers the new thing and publishes, we usually award credit ("priority") to researcher Y, because researcher Y published.  Also, peer-reviewed publications are one of the ways that we evaluate the quality of work.  Typically, folks on a search committee are not expert in your area and may find it difficult to directly evaluate the quality of your work.  If it has been published in a peer-reviewed conference/journal, that speaks to its quality; and the more selective the conference/journal, the more of a testament to quality it is.  If your work hasn't been published in a peer-reviewed forum, it's harder to know whether it's any good (and there may even be a suspicion that it wasn't published in a peer-reviewed forum because it wasn't good enough or would not have been able to survive peer review).  So, publications matter for hiring.  I'm not saying that unpublished work is never taken into account, but it's a much higher hurdle if none of the work has been published, and you need to be honest with yourself about the situation.
I noticed that you asked a similar question about a month and a half ago (Does my work in industry carry any weight in academia?).  You got similar answers, and some very good advice, at the time.  Perhaps it'd be worth starting by reviewing the answers you go to the earlier question, and then editing your question to provide more context and detail, taking into account what you've read there.
Also, you haven't given us much to work with: for instance, you haven't told us what field you are working in; you haven't told us why your work wasn't published and cannot be published in a peer-reviewed forum; you haven't told us what aspects of the industry research you think might be relevant to your application or what options for how to include it in your CV you have considered; you haven't told us what was the work you did in industry, or what the novel scientific contributions were, or what its impact on industry was.  The less information we have, the less likely it is that we can provide useful advice.

Industry research experience, like almost all research experience, only counts if there is a tangible outcome (e.g., grant income, patents, or peer-review publications). In the absence of a tangible outcome, the research experience (industry or otherwise) and contact (industry or otherwise) are nice, but not worth very much. If you want to make you past experience a selling point, you need to create some tangible outcomes. If the experience is valuable in an academic setting, then 10 years of experience should allow you go generate a tangible outcome quickly. Maybe one of your industry contacts will fund a study or provide you with unique data that could be used in a peer-reviewed publication.

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


text:
After publishing many articles in the last few years, why does my work have few citations?
During last 3 years I have published 17 articles, but I have only 15 citations (excluding self citation), which I find disappointing.
My articles have 100s of downloads almost every month.  They seem to be in  high quality journals: 2 articles are in Q1 journals (i.e., a journal that is among the top 25% based on avg JIF percentiles), 5 in Q2 journals (i.e., 25-50th percentile), and the rest in Q3 journals (i.e., 50-75th percentile).  I share my work on LinkedIn and upload them on arXiv as well, so I don't think  reach is the problem.  I work in the field of networks and security.
Does this mean that my work is not worthy enough?  Or I am missing something in attracting the research community to cite my work?

I think you have two choices.  Do the work you like, regardless of how few citations it garners.  Or: switch to a hot topic, and do work with the citations in mind, ignoring how much you like or don't like that area.

Perhaps the problem is because you only publish in journals?  As I understand it, for the field 'networks and security', conferences are more important.

@MBK  What is the typical citation rate of other articles in the same publications you are publishing in?  It strikes me that a raw X citations per Y publications is meaningless without a benchmark of publication and time.  You may already be doing significantly better than average.

@Myles The IF of journals in 'network and security' area are not high. One of my article in IEEE Transactions on Information Forensics and Security has 2.4 IF (It is one of the best journal in my area), I have  another article published in IEEE System Journal (IF 2.1) and rest are below. The average IF of all 17 articles is 1.3.

Bear in mind that some/many authors that get a lot of citations, exceeding a journal's impact factor (and therefore boosting it), get them by default just for who they are. I have seen many cases where groups of people just cite each other, regardless of quality, over and over. It's almost like an informal citing coalition (i.e., one that has emerged rather than being explicitly agreed). Likewise, the most interesting papers I read sometimes get cited few times. If you co-author with the right people, or you become better known, things may change.

You might want to calibrate your expectations.
What predicts how many citations you should expect?

Journal impact factor
Time since publication
Number of publications
Database used for counting citations

In rough terms, the journal impact factor can give you an idea of how many citations you can expect to receive per year for a given article. Note that this is based on the relevant database. Google Scholar citation counts tend to be two or three times larger from my experience. Note also that the distribution of article citation counts are highly skewed. So many will get fewer citations, but hopefully as you aggregate over a body of work, central limit theorem will kick in. So it's a reasonable guide to a benchmark  how many citations you should expect over the body of your work.
Note also, the time it takes people to build on your work. So if you publish something late in 2014, it will often take at least a year for people to notice and start incorporating into journal submissions and for those articles to be published.
The basic consequence of this is that if all your work has come out in the last 3 years, and therefore much of your work has come out in the last year, then you may have to wait a few years to see the citations accrue.
How to calculate expected citations
So let's do some basic math.

Mean impact factor: Based on your stated quartile impact factors, I'm going to guess that your average impact factor per article is around 0.8 (e.g., top quartile is around 2 or 3+; second quartile is around 1.5; third quartile is around 0.5, based on discipline it could be different).
Mean time since publication: I'll guess 1.5 years, i.e., half-way between 0 and 3 years.
Discounting self-citations: Impact factor does not discount self-citations, but you have. So let's say 20% of citations are self-citations, especially early on in an article's life.
Number of articles: you say 17

So a very rough estimate of expected number of citations is:
expected citations = mean IF * mean time since publication *
                       self-citation discount * number of articles
 = 0.8 * 1.5 * 0.8 * 17
 = 16.32 (i.e., 16 citations)

A few caveats:

If you are using Google Scholar as your metric, then you should multiply expectations by a factor 2 or 3 (so that gives you an expected citation count somewhere around 32 and 39).
You could  get a more refined estimate if you calculated expectations for each article separately and then summed the expectations.
There is quite a bit of uncertainty about the timing issue. For example, with impact factor, the first few months often don't count and sometimes differences between accepted versus online access versus published with page numbers might make a difference.

So actually, that does not look that different from what you have.
Also, note that in theory, you could stop doing research today, and the formula would predict that 15 years, you could expect about 160 citations. As with all things, this depends on various assumptions. But if you are comparing your citation count to senior researchers who have been publishing for 20 years, then you need to really understand the fundamental role of time passing in generating citations.
More generally, citation counts per article and even on aggregate can be quite noisy and the underlying distribution can be heavily skewed. So the difference between being above or below expectations may be whether you have one or two articles that have really taken off in terms of citations.
Take home messages

If you are a PhD student or early career researcher where most of your work has been published in the last two or three years, citations will generally look small. Citations just take time to accrue. And the number of citations an article accrues per year is quite noisy.
In the first year after a publication comes out, the impact factor is often a better predictor of how many citations it is likely to accrue per year.
Based on the assumptions above, publishing in an impact factor 3 journal will on average generate 3 times as many citations as an impact factor 1 article, and 6 times as many citations as impact factor 0.5 article. While this is all an on-average thing, the point is that it is quite possible that your  two Q1 publications are equivalent or more important than your 10 Q3 publications, from a citation generation perspective. So if you view things from an impact/citation perspective, then this can inform your understanding of the quality versus quantity trade-off when publishing your work.

Generating more citations
That said, if your goal is to generate citations from others, there are all sorts of things that you could be doing.

Try to do great work and publish in high impact outlets. When evaluating a quantity versus quality trade-off, give a lot of weight to quality/impact.
Advertise your work at conferences and online.
Attend conferences and network so that people get to know who you are and what you are doing.

Other options that  may or may not go into grey territory:

Self-cite where appropriate. Some citations will come from others seeing how your work is being cited. By self-citing, you are providing a template, and further secondary exposure to your work.
Collaborate and co-author papers with leading figures in your field. When they publish without you, they may cite your co-authored work.
Think about what topics receive more citations and do work on that. Important review articles, meta-analyses, methodological papers with a clear recommendation, etc.

"If you are using Google Scholar as your metric, then you should multiply expectations by a factor 2 or 3". Does GS usually miss half of the actual citations? Is there another reason for this?

@Andrea I think they are saying the opposite, google scholar counts some citations double. This is true in my experience, though not as extreme as the answer here suggests.

The distribution of citations among papers seems to be a power law (see also gaborous's answer), so I imagine that most papers are going to fewer citations than the expected value.

Do you go to conferences? Giving a good talk is a great opportunity to advertise your work and get it known by a larger circle of researchers.
Also, I don't know where those download numbers come from, but don't take them too seriously. Typically these digits are inflated by search engine crawlers and other automated downloads.

One could contextualize the download numbers by comparing them to the download numbers of highly-cited papers. Not clear that's worth the effort, though.

This is probably normal.
Citation analysis has created a small storm in the late 2000's by showing that up to 90% of academic papers are not cited by anyone. This figure however only holds true for social sciences, since in other fields the figure goes down to 50% for most or even lower such as 18% for medical papers.
To go further, you can look at the distribution of the number of citations for a particular journal, and the median number of citations, instead of the mean, to get an idea of the citation repartition.
Citations distributions per journal:

Median citations per journal

Figures from Quantixed article: The Great Curve II: Citation distributions and reverse engineering the JIF.
These figures were generated on 2014 for articles published in 2012 and 2013 in the same journal.
If you look at high impact factor journals, such as the venerable Nature, you can observe that the distribution is highly skewed: most articles only get 0, 1 or 2 citations, not more (at least over the timeframe of 2 years since publication). Thus, publishing in high impact factor journals does not guarantee citations, it just increases the likelihood.
This also shows that high impact factor does not equate high number of citations: a high impact factor is a measure of popularity, in other terms a measure of how many people have read it. You might potentially get more citations by publishing in journals that are more focused on your topic, even though the userbase is smaller (smaller impact factor). This is something to keep in balance (journal scope vs impact factor).
Why are citations propagation so slow? Contrary to journalistic, or social media, posts that get propagated a lot faster, here the slow pace of citation increase can be explained by various factors (that are still being researched), but without doubt two factors are proeminent:

Academic articles are composed of long and dense information to read. It's not just something you glance a few seconds/minutes and can decide to share, reading academic articles sufficiently to understand them takes much longer.
Citing is not sharing (it's more): citations are an estimation of the number of "derived works", whereas sharing is just copying or creating a link. Thus, citing is much more involving than just sharing, since to cite you need to write your own paper and get it published. This is why some research tools offer alternative metrics such as Mendeley reader counts.

There is no clear solution to the "citation issue" right now, so that's why in my opinion the most important is to enjoy research and to be proud of the work you accomplished with your publications.


text:
Does my work in industry carry any weight in academia?
I have no academic (peer-reviewed) publications to my credit but close to 6 years of industry-based experience. I have just completed a PhD in sociology.
Does my work in industry carry any weight in academia?
My work in industry included activities such as developing codes and operational manuals in a particular field to be used by operators in that field (e.g., a health and safety manual).

Weight in academia is carried mostly by peer-reviewed research publications, published in academically reputable venues such as research journals, conferences, books with reputable publishers, reputable preprint servers, ... If you get these publications from industry research, you get academic weight. A famous example for prominent research in electrical engineering done in industry are the Bell laboratories.
Codes and operational manuals are not academic publications, and typically carry little weight. In order to get that, you have to publish academic papers about the codes and manuals, or publish papers about research results obtained with them.

It's not academic publications, it is peer-reviewed publications.  In my field, industry researchers publish almost as much as academics, in the same journals/conferences.

@D.W. true, I added peer-reviewed.

Thanks for your note!  I guess I would still take issue with the statement that "Weight in academia is carried mostly by peer-reviewed academic publications."   I believe a more accurate statement is "Weight in academia is carried mostly by peer-reviewed publications."  Whether the publication is from an academic or not is irrelevant.  (The fact that a paper appears in a peer-reviewed journal/conference does not make it an academic publication; I know plenty of industry researchers who publish great research in the same places that academics publish.)  Sorry if this sounds overly nitpicky!

P.S. Another way to think about it: try defining what you mean by an "academic publication".  (I suspect you'll find that the modifier "academic" is either redundant or overly restrictive, depending upon what you mean by it.)

@D.W. In my understanding, the adjective "academic" refers to the form of the publication, not the institution of the author. Instead of defining what an academic publication is, let me give some example of non-academic publications: works of fiction, newsletter articles, blog entries on the internet, my answer to this question, ... Some of these may still be peer-reviewed, but that doesn't make them "academic". Does this clarify your issue?

@D.W. For a real definition of an "academic publication", we probably should put up another question...

@Silvado: I think the phrase you're looking for is research publications.  I'd also add "publicly-available" to exclude company-internal and classified research reports, which may be peer-reviewed but are not available to the outside world.

@JeffE I thought research publications and academic publications were synonyms, but seem to be wrong. I agree with the public availability. In fact the publication venue will be a very important factor for the academic weight of the publication.

Does industry research carry any weight in academia? It varies. It varies by subject, by institution, by the individuals doing the hiring.
Are there subjects, places, people where industry research can get you a post even with no journal papers? Yes. Are such appointments common? Not anywhere I know of.
A good employer (those are the ones you want, right?) will be able to look past the traditional indicators of a good researcher (PhD, journal papers, career in academia), and look at other, non-traditional ones. But that takes time and effort, and hiring can be a drain on those as it is, so you'd need to give a potential employer some really good reasons up front why they should put the effort in to establishing your abilities.
A personal recommendation from a senior academic, or from a retired senior academic - one who's already trusted and respected by the employer you're targetting - is the sort of thing that can help open doors for you.

The problem here is with your use of the phrase "industry research". Writing codes and manuals isn't actually research.
Research is research: if it is good research, it counts and definitely carries weight in academia.  Research is evaluated by its merit (e.g., intellectual depth, correctness, importance) and its impact (how has it changed practice?  how has it changed the course of research done by other researchers?).  The names or affiliations of who did the research is irrelevant.  It doesn't matter whether the work was done in a university or in a company.  It doesn't matter whether the affiliation on the title is a university or a company.  What matters is the content of the paper.
If it's good research, it carries weight with academia.  It also carries weight with industry research labs.
However, writing health and safety manuals is not research.  You shouldn't call it "industry research".  You should call it "writing health and safety manuals".  And if it's not research, it doesn't count towards your research record and doesn't carry weight.
A good indicator of research is that it is (a) novel, and (b) published in a highly regarded, (c) peer-reviewed forum.
(The question gets more interesting if we are talking about people who do novel, scientific research that could have been published in a peer-reviewed forum, but isn't, because the company wants to keep the results secret.  This kind of research is harder for academics to evaluate, and thus might not carry as much weight with academia, because it isn't published.  In that case, it's not that the research was performed in industry so much as that the research was never published.  In any case, it sounds like that's relevant to you, based upon what you've told us here.  Writing health and safety manuals is most likely not something that could have been published in a peer-reviewed conference or journal.)

Unsure why industry research is not research. It involves the same skills but at a different, perhaps more practical level.

@JaveerBaker, I can understand why you find this puzzling.  The problem is that by using the phrase "industry research" to describe the activity of "writing health and safety manuals", you begged the question: you made an implicit assumption that this activity is research, when actually it probably isn't really research.  To help you avoid this self-inflicted confusion, I suggest that you try removing the phrase "industry research" from your lexicon and re-ask your question, elaborating if necessary on the activities you were doing and whether they carry weight as research experience.

@D.W. - This answer is misleading. There is a very important distinction between industry and academic research; academic research is all public, lots of industry research is private. Because of this, it can be very hard to gauge a researcher's skill when working in industry, as he may have no collaborations, no publications, no public speaking engagements, and no name recognition.

@eykanal, thanks, those are good points.  I've revised my answer based upon your comments.  Here's why I think my answer is not misleading.  Based upon the description in the question, what the OP is doing isn't research in the first place, so we don't even reach the issue you raise.  While I agree there is a lot of non-published industry research that is real research, I don't think that's the core issue here.  I think the core issue is that writing health and safety manuals probably isn't research in the first place.

@D.W. - I maintain my disagreement. Industry research is a tremendous force in the advancement of science in many applied fields, without which there would be little advancement. Calling it "writing codes and manuals" is insulting, and definitely an inaccurate portrayal of the field.

@eykanal woah, what!?  I think you are totally misinterpreting me.  I did not call industry research "writing codes and manuals".  I never said that!  What I'm saying is that "writing codes and manuals" is not industry research.  I'm a big fan of research from industry labs; in my field, there's outstanding research coming out of research labs.  But go re-read the original question: the OP says he was "developing codes and operational manuals".  That's not research!

@D.W. - Ah, I had didn't see that you were quoting his statement. My bad. (That's what I get for trying to do this while doing other stuff.)


text:
Is it ethical to get some help from Stack Exchange for my research as a PhD student?
As a PhD student, I am expected to do research on my own, possibly assisted by my supervisor or colleagues. Like any researcher, I sometimes spend days looking for a small piece of information, sometimes just a single sentence, that answers a question whose lack of an answer was preventing me from moving forward. Sometimes, it was enough to stumble upon the right article after several days to get the answer directly, so it didn't require any real research work, except for browsing the internet.
What is absolutely wonderful about Stack Exchange is the ability to simply ask such a question and get the answer, without doing anything. Without doing anything ... That's not really true. It's not even true at all! Asking a question on a specific subject requires quite a lot of work, which in my opinion can be summarised in a few steps:

Research to see if the question has a direct answer to be found (a few hours of research via various articles, or even a few days)
Establish a developed context so that anyone with basic knowledge can access the question
Formulate the question in the most understandable and educational way possible (this step is not so obvious: putting words to a question is not easy and requires serious knowledge of the subject!)
Pay attention to the comments and answers to either edit the question and improve it, or to judge whether the feedback offers us a real answer to our question or not. In the latter case, it is then necessary to state how the answer does not answer the question.
Once a tangible answer has been received, do some research to verify it and then accept it as the official answer to our question.

All this work is, in my opinion, absolutely necessary, both out of respect for the people who are going to read the question and try to answer it and for oneself in order to obtain the much-desired answer to our question.  But despite the work that I believe a serious question should represent, I can't avoid feeling uncomfortable when I ask a question on Stack Exchange and the answer unblocks my research.
Is it ethical to use Stack Exchange for research, following these steps carefully?
I would like to make it clear that I have no intention of making the people who answer my questions work for me. When I ask a question on the site, my only goal is to get an answer that helps me get unstuck, not to expect someone to do a scientific research job for me. I see Stack Exchange more like an interactive service that tells you what has already been done.

Part of research was always to walk around and talk with folks about where you were stuck (and talk to them about where they were stuck). Just nowadays you can 'talk' with many more people at once, and increase the likelihood of getting an on-target answer to a good on-target question.

You might be interested in this Q&A: I have stack-exchanged through my undergrad math program. Am I likely to succeed in mathematics PhD programs?

(Ab)using SE for you own purposes is what it's for. "I am expected to do research on my own [and not pay someone to do it so that the piece of paper I'm paying them for might actually mean something]." By the time you graduate that paper had better at least mean you know how to use the internet.... That it's SE doesn't matter. Did they say you can't use the internet, like how they warned me that I might not have a calculator in my pocket 24h a day? What if the internet is down? Then life as we know it is over and that paper will be good for starting fires.

Follow up question:  "Hi guys, here's my dissertation problem. Please write out several chapters including history of the field and suggest several problems for future research.  And double space it.  ASAP, please!"

I always see questions like this and suspect they are just reputation farming. Is there any controversy on this matter whatsoever? Does anybody say using SE during your PhD is unethical? If such a person exists, would they be found on this website?

@ZachMcDargh I don't care about reputation farming, I care about ethical. Nobody said it was unethical, I just asked a question.

"Is it ethical" on this site always goes straight to, is it plagiarism, which leads to, is it properly attributed, the answer to which gives you the answer to the first question.

Here's a question I'd like to throw back at you: Would you use information found on StackExchange? And if you would, does it really matter who asked the initial question?

Yes, it is ethical to seek, accept, and use help in a dissertation. The only difficulty in accepting help from others, and especially on sites like this, is giving acknowledgement to those who provide critical help (insight) into your work, since many of us are anonymous here.
But students have, since time immemorial, sat around a table (mead, tea, coffee, ...) and chatted about issues in research.
The general issue is one of plagiarism, if you attribute something to yourself that came from another. In mathematics a conversation of a few minutes  can be enough to earn an authorship position. The same is likely true in other fields.
But "research" doesn't need to be just archaic texts retrieved from dusty library shelves or the 75th page of a Google search.
Of course your personal contributions to a dissertation need to be sufficient to convince advisors and reviewers of your advance of the art and science of your field.
And, asking the right question can be an important element in research.

I would consider it acceptable to cite or acknowledge a anonymous or pseudonymous source (such as an answer on a StackExchange site) by the identifying information presented - so it would be okay to acknowledge "The answer given by user 'Buffy' on the StackExchange question Blah Blah Blah helped by ..."

I found in the old days that I rarely got the answer from chatting, but instead got nudged on to a different path or made to realize some alternative approach. Sometimes that was because I had to explain the problem to someone else, and that act of explaining it (rubber ducking) clarified my issues. I still think talking is better than posting a question on SE (which I generally don't do for technical stuff anyways).

@AlexanderWoo Don't you think it might create a lack of credibility to quote a question asked on stackexchange? It doesn't seem very professional at first sight. Unless you are saying this with regard to the acknowledgement section?

@tomweelen, as in many other situations, a quick explanation that suggests verifiable truths, or "more official" sources, can be very, very useful. Some writing-style purists are opposed to mentioning "intermediate" sources that were actually necessary to find the critical resource, but I think that is a very unfortunate and dishonest pretense! It's ethical to use MSE, and it is important to acknowledge (not just vaguely, but specifically) how it was useful... so other people know how you succeeded! :)

@tomweelen I have seen a number of papers refer to (cite) a mathoverflow post.  It's not much different from citing a webpage, which is often done for data, computations, etc.  It's certainly not quite the same as citing a peer-reviewed journal paper, but it's not particularly unprofessional.

Ok I see, thanks for your opinion!

I was acknowledged by a paper as "Dikran Marsupial", and was perfectly happy with that.  IIRC the authors did ask for my real name (which isn't a secret) and I gave it, but was happy for my pseudonym to be used instead.  I did once consider publishing a paper under that name, as it wasn't on my area of genuine expertise and I didn't want my academic credentials to imply expertise that I only had by proxy.  For me the primary issue is not to claim credit for other peoples work, which doesn't require the real name be given.

@AlexanderWoo: Even better, save the question (and its answers) in the Wayback Machine and include a link to the archived version of the question as well as a direct one to the question itself.

@tomweelen there have been journal papers (in good journals) that stemmed directly from discussions on stack exchange sites (such as the one I mentioned above).

There's a big difference between citing and acknowledging. You acknowledge people who were helpful without necessarily saying how they helped (the help might have been purely personal); you cite documents that you are using to support the argument presented in your thesis. A casual internet post will often be helpful, but it will rarely by citable, because it's unlikely to be authoritative enough.

"Of course your personal contributions to a dissertation need to be sufficient to convince advisors and reviewers of your advance of the art and science of your field."  This is a very crucial point.  It's unethical to hide from your advisor which contributions are coming from SE, and if you SE your way through too much of your project your advisor may have legitimate concerns about whether you've satisfied the requirements of a PhD and/or whether they can write you a strong recommendation.  So it's perfectly fine to ask SE questions occasionally, but it can't be your only approach!

The other issue, of course, is that since SE is not any kind of academic or other authoritative source, everything you get from it should be confirmed, either through your own research and/or actual authoritative sources.

I would say, yea it’s ethical with acknowledgment. When I was in grad school, we had a large, rolling dry erase board in our “lab” (cubicle area) that I would write equations on about my dissertation subject and sometimes a better mathematician/engineer than me would wander over and ask what was going on. So, I’d have to erase the board and start over and explain.  Usually, about halfway though, the lightbulb in my head would turn on and I’d have the solution. I’d continue the explanation until I got past the part where I’d been stuck and applied my new idea. If the watcher agreed, I’d keep going until we got to an end. Then I could go off and start implementing this in code. Sometimes we’d find an error and I’d have to backup and start some part over. The positive moments often led to acknowledgements or coauthorships in a resulting paper. Others in the lab would do the same.
You can think of SE like that whiteboard, but maybe more critical. You can put things here (in the form of a question) and see if it’s a reasonable question and maybe get some guidance on it.

I think the more general issue is citing a source as supporting evidence when it hasn’t gone through peer review. While the stack overflow process suggests a type of peer review it is not required and referees may have no formal affiliation. So, YMMV.

Well, yeah, unverified/unverifiable remarks by anonymous people on the internet are not good sources. :) But remarks on Math Stack Exchange and other sites often give very solid citations, in addition to "showing how to do it", so that the thing is reproducible/verifiable.

Is "cutting" supposed to be "citing"? The question says "I would like to make it clear that I have no intention of making the people who answer my questions work for me... I see StackExchange more like an interactive service that tells you what has already been done." So they aren't asking for original research, they're asking for references to existing research.

Citation is not to just provide supporting evidence, but to acknowledge that the idea came from someone else and that the author is not claiming credit for it.  So if it was the only source, it would have to be cited anyway.  "personal communication" (i.e. "they told me") citations have been in the literature for a very long time and they obviously are not peer reviewed.

The idea that a source is reliable if it is peer reviewed is fundamentally wrong headed IMHO, it is a very low hurdle and I would venture that the majority of peer reviewed papers are wrong to a non-negligible degree.  You have to read the source to know if the citation is reliable.  The citation is there to allow you to investigate the reliability of the source if you wish to do so, it is not itself an indication of reliability (other than that the author presumably thought it was reliable)

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


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Is it in the best interest of supervisors that his/her students publish their research results in top journals?
I am a math PhD student in a foreign country. When finishing my papers, my advisor (who is a big name in the field) always proposes me to submit them to a journal. The problem is that so far none of these have been top-tier journals. On the other hand, in my mind, there is no doubt that the results are good enough to be published in really good ones, and I have also confirmed this fact after giving talks at conferences. In addition, in one of these papers, he/she was signed as a corresponding author, even when I did 99% of the job. Why would someone do this to his/her student?

The first question is "Of course, yes!"; The second one is impossible to answer objectively without further details and would be too narrow for Academia.se. Did you ask to be corresponding author? Did you tell her that you would like to send the paper to a better journal? If not, you should start communicating with your supervisor and let your expectations clear. In general supervisors are open to discussion, but if you don't communicate you can't expect them to magic guess what you want.

I didn't ask to be the corresponding author: I didn't know this existed at the time. The second time I actually spoke and said that I wanted to submit this to a better journal. Although I agree that I should communicate more, what strikes me is the fact that the default option that I am being offered is not a good one. @The Doctor

I don't have a huge experience in this, but I think that her being "corresponding author" only mean that she's the one submitting the article on the journal website or to the editor. It doesn't indicate any difference in contribution to the article. See also this question on the topic.

So, when you asked "why not submit it to journal B, rather than journal A", how did your supervisor respond?

it's not clear what you mean when you say "I have confirmed this fact". How have you confirmed it exactly?

@T_M I have confirmed this because, after the talks,  some other professors have approached me with really good feedback on the work and even asking me to send them a draft of the paper. I don't see this particular interest in other talks, unless they have done something good.

This is not the 'confirmation' you think it is. Like @Dan Romik says, it is not likely that your sense of these things is a good as your advisors. A big name giving informal, verbal praise is nowhere near the same as a referee recommending acceptance for a top journal and the editors agreeing. When I was a PhD student, I didn't have a good sense of these things and was told I was not thinking ambitiously enough... but come on, you are close to sounding like you are convinced your work is amazing and others can't see it... that's a little crackpot, why not just listen to those who know better.

This sounds more like a rant and not really like a question.

@T_M I really understand what you are saying, but I don't think this is the case here. At least in my field, I have seen lower quality papers in better journals. In addition, if my advisor does not read the paper, then how could he/she know better? I don't want to sound like a crackpot, or like I am giving a rant, as user2705196 suggests. I am just concerned about my career.

First of all, journal quality is not an objective thing (measures like acceptance rate and impact factor and so on really do not mean that much, and can be gamed).  There isn't necessarily universal agreement about which journals are "top-tier".  So consider the possibility that your supervisor simply has a different opinion about the reputation of these journals than you do.
The advisor does have a responsibility to give advice that they believe will be beneficial to students, including advice about where to publish.  (Of course, the student also has a responsibility to educate themselves about publication options as best they can, ask questions to try to understand the advisor's suggestions, and speak up if they disagree.)  I'm not sure what you mean about whether it is in the advisor's "best interest".  It is not like they will get paid more if you publish in Acta, but it does generally reflect well on an advisor if their students are successful.
But there is a serious trade-off of submitting to top journals: time to acceptance.  The peer review process in math is much slower than in many other fields, and top journals are very selective.  Even if your paper is "good enough" for a top journal, that doesn't mean that it will be accepted by the first one you submit to, and it may be under review for several months before being rejected.  Then you have to start over.  As a PhD student, you really want to have papers accepted before you begin to apply for postdocs or other jobs.  A paper that hasn't been accepted anywhere, even if it's an awesome paper, doesn't help your job prospects much, because there's no independent confirmation that it's awesome.
So at this particular stage of your career, it could be very sensible to submit your work to a less-than-top journal, even if you think it has a chance to be accepted somewhere better, because it's more important to get it accepted fast, without having to go through several submit/review/reject cycles with different journals.  This is something your advisor ought to take into account when suggesting journals.  But it's a balancing act between the considerations of "publish in the best possible place" and "get accepted on the first try", and you should of course speak up if you have a different opinion about how to balance those.
(If your advisor is a co-author, this same balancing act could apply to them as well.  For instance, they might have a promotion decision coming up, and they want to have the paper accepted somewhere before that happens.  Sometimes different authors may have conflicting needs in this regard, and they have to reach agreement somehow.  Personally I think that the needs of more junior authors, such as PhD students, ought to be weighted more heavily, but that is just me.)
I also want to comment on the "corresponding author" question.  People around the world seem to have different opinions about what it means to be corresponding author.  In my part of the world (US), it's my impression that being corresponding author only means what it says: this is the author who corresponded with the journal (sent in the submission, filled out the forms, etc), and who is the best person for a reader to reach if they have questions about the paper.  Around here, it doesn't carry any particular prestige, and it isn't meant to imply that "this person did most of the work".  So on that basis, I wouldn't say that there was anything wrong with your advisor being corresponding author.  (For one thing, your advisor probably has a more stable email address, since you are going to graduate before too long.)  There may be some benefit to being corresponding author as a student, just to get practice with the journal submission process, but it's not really a reputation benefit.
(If "corresponding author" has a different connotation among people in your part of the world, or more importantly, the part of the world where you intend to apply for postdocs, then the previous paragraph may not apply to you.)

I have the same experience of what "corresponding author" means, in the UK in theoretical CS.

Congrats on passing 100k!

Agreeing with Nate and David - In the US, in biology, "corresponding author" is merely a technical point that carries no prestige -- rather (very slightly) the reverse, since it implies that this person handles secretarial drudgery themselves.

Is in the best interest of supervisors that his/her students publish their research results in Top journals?

All other things being equal, of course it is.

in my mind, there is no doubt that the results are good enough to be published in really good ones

With all due respect, I am willing to bet that your sense about these things isn’t nearly as well-calibrated as your advisor’s. Unless you already had several papers accepted at top math journals, the fact there is no doubt in your mind is essentially meaningless.
To be clear, I’m not saying you are necessarily wrong, just that your confidence is very likely misguided, even if you got great feedback about your work at conferences. Talk at conferences is cheap; referee reports recommending acceptance in a top journal are a much more rare kind of feedback.

Why would someone do this to his/her student?

There isn’t a rational reason why an adviser would want their students to intentionally undersell their achievements by submitting to a lower-tier journal than the paper is suited for. The (overwhelmingly) most likely explanation is that your adviser is giving you the best career advice she can, and simply does not think your papers have a high chance of being accepted in a top journal. Keep in mind that there is nothing offensive or negative about having such a belief; she may well think your papers are excellent but still have a more nuanced understanding than you of how few even excellent paper are groundbreaking and competitive enough to get accepted in a top journal.
As for the timing issue that Nate brings up in his answer, I think that could factor to a minor extent in the advice your adviser is giving you, but probably if she thought your papers were good enough for a top journal but thought submitting there might be risky because of the time delay this might incur, she would explain to you the considerations, tell you her opinion about the odds, and let you make the final decision yourself. So I’m guessing that’s not the main issue here.

Well, ask her how your advisor chooses the journals she wants you to submit. You can also include your past articles in this question. I am pretty sure you will learn from the answer.
Some reasons for how to choose a journal:

Better fit: Even if you could publish in Nature or Science, research about quantum optics might be better suited to some journal on quantum optics. Your peers won't read Nature or Science anyway.
Acceptance rate: Some journals receive way to many articles. This creates extra work if you have to resubmit to another journal.
Quality of review: Good reviews helps to improve a paper. You as a young researcher will profit from a thorough and constructive feedback. Sketchy or harsh review does help your writing and research.
Time to publication: If it is short, you will profit more from the earlier publication compared to the more prestigious journal. Especially as a PhD candidate this is in your interest.
Costs: In many cases a publication costs money, e.g., for color figures, too many pages or to make it open access. Your advisor might know this and want to save money.
Politics: They might want to support some group, they are friend to or disliked by a specific journal.

Why would someone do this to his/her student?

You're assuming bad faith. While that is not impossible it's also less likely than a failure of communication between you two.
You should tell your advisor you want to talk to her about the issue of choosing targets for publication. Tell her that you'd like her to tell you about:

The strategies different researchers in your field employ.
Her strategy - for graduate students, and perhaps even for her publications without grads.

At this meeting, after being given an explanation of the general policy/strategy - ask her something like "Ok, so I want to apply this as a thought experiment to some of the papers we co-wrote. Let's take paper Foo. etc. etc. etc. [try to explain how the strategy should have applied to your paper here ]. Does that sound reasonable?" ... and with this you've asked her to justify her recommendations on (one of) your papers in a rather non-confrontational way. Since your description ends up with a submission to different journals than the one she had suggested.

You’re assuming bad faith. OP is asking a question. I don’t see on what basis you can say he is assuming anything.

You should tell your advisor ... Tell her ... ask her ... How is this related to the question? OP did not ask for any advice on what to ask or say to his advisor.

@DanRomik: "Why would you she do this to me?" - sounds like consternation to me. If I misinterpreted OP's tone, let OP make a comment.


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How should you cite yourself when there are more than 15 authors?
I am not one of the primary authors of the article, and there are more than 15 authors for the paper I am referring to.
How should I include the paper in my CV? Should all authors be cited? How can I neatly highlight my contribution without having to enlist all authors?
(This is only necessary, so I can be as economical as possible with the available space.)

Why do you have space restrictions on a CV?

@Buffy I have a 2-page limitation for the application I am working on

Headline: Publications. I don't think anyone cares whether you are listed in the actual citation or included in "et al".

If you have space constraints, one way I have seen this done is:

First A., ... Plesca, A.-M., ... Senior A. (2022) "A title." Journal.

The first author lets someone find the full citation, and the senior author(s) contextualize whose lab(s) the work was performed in (if that's how your field uses last authors). If there are other important collaborators who don't fall in one of those roles, you can include them too.
If you are second or third (for example), you can list all authors until you get to yourself then insert the ellipsis.

Yes. This seems like the correct approach.

I thought that the OP's question is interesting and I agree completely with the answer provided. But I also think that there is something of a general nature that others can learn. Specifically, the many and various citation style guidelines (e.g., Vancouver, Chicago, APA,, etc) are all developed with journals, publication workflow, and standardization in mind. But there is no reason at all not to alter them to suit atypical situations when they arise. The adaptations are likely to be ad-hoc, but they can still be sensible, as nicely shown in the proffered answer.

I tend to use the standard "Jones et al., 2007" format, and then add a note to say it includes me. Something like this:

Jones et al. (including terdon), 2007

Of course, even this isn't necessary. If you are listing publications in your own C.V., then you are claiming authorship by definition, so simply mentioning the Jones paper will be enough: you wouldn't be listing it if your name were not among the authors so you don't even really need to highlight it in any way.

I have also seen i.a. for Latin inter alias/inter alios, meaning among others.
(Thanks to Denis Jardin for the correction!)
Something like:

First Author, i.a. John Smith. "A great Paper about something important", Science 3 (2022).

Doesn't inter alia (typically?) mean "among other things"?

It just means among others, could be things, people, what ever you desire.
And I've seen this used in this context a few times.

@d3lt4_papa Actually it does mean things (the correct form for people would be inter alios or inter alias depending on the grammatical gender). Not that I doubt that you saw it used like that, but it is a grammatical mistake...

Fair enough, I've only seen i.a. and assumed it stood for inter alia. Apparently it stands for inter alios.

Since it is your CV and this is a list of your publications, it is more than obvious that you are one of the authors. Going beyond the standard "Author. (year). etc." would appear somewhat unprofessional.
Publications where you are listed as a first author are the most important and these will be clearly visible.


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How do mathematics journals get reviewers for a very new theory?
Nowadays, getting reviewers is a challenging task for many pure mathematics journals. How would an editor be able to find a reviewer for a very new theory that nobody has worked on yet?
If the author is a big name in some field, an editor might consider doing the hard work in searching for referees and the referees will also agree to reviewing for them.
If it is from a not-so-popular author on the other hand, it might have a high chance of getting desk rejected. In such cases, how can we expect new theories to be developed with the current system?

If the people with a new theory can't explain it to others in their general area of research, what's the use in publishing it?

To be more explicit, if you "develop a new theory", then it's your job to convince the reader that it is an interesting and useful theory. People don't just "develop new theories" for the hell of it, they do it with a particular goal. For example, if you write hundreds of pages developing "inter-universal Teichmüller theory" because you think it's a neat generalization of Teichmüller theory (whatever that is), then it's perfectly reasonable that no-one would want to referee it. If you do it and promise that the payoff is a proof of the abc-conjecture, then it's a different story entirely.

@BryanKrause I am not telling that it can't be explained to others. But to verify it, one might require say for example experts from many fields then it would be a tough task to get those many experts to review it. So, one can simply put it in arxiv, I guess. Because the theory could be useful later on, who knows? Also to reply to Adam, if we have to convince readers at the initial stage itself, isn't it bad? Because nobody would try to develop new things and would work on current popular topics only

"The theory could be useful later on, who knows" is not on its own a good reason to publish or read a paper. "If we have to convince readers at the initial stage itself, isn't it bad?" No, this entirely normal. No-one has any obligation to read your papers, or listen to your talks. "Nobody would try to develop new things and would work on current popular topics only." But people do in fact develop new theories and other researchers do invest the time in learning them, provided that they can expect some payoff from this (see also my example of inter-universal Teichmüller theory).

@AdamPřenosil: In fact, IUTT has very little to do with the original Teichmuller theory. It is not at all a generalization of the latter.

@Moishe Kohan Thanks for correcting my misconception!

Part of the problem with your question is that it is not clear what a "new theory" means. Saying "that nobody has worked on yet" is not enough (for instance, most accepted papers are required to have some amount of novelty). Could you give a couple of examples of math from the last 100 years that, in your mind, would qualify as "new theories?" Personally, I cannot think of any theories which would be completely new. Even, say, Grothendieck's work in algebraic geometry  was not radically new: It is built upon earlier works of Zariski, Serre, Weil (just to name a few).

In pure mathematics, few new ideas are so unconnected with the past that there aren't people available. The new ideas normally grow out of existing mathematical threads and theories and there are probably experts available that are "close enough" to get the job done.
Occasionally something really new pops up. Not-Standard Analysis (epsilons exist as values) was once (previous generation) thought very radical. It was despised by many for a while, but it turns out to be just a different axiomatic basis on which to build - so, not so different after all. The underlying logical process is the same.
But some new things get a lot of pushback initially and it takes a while for the ideas of the inner core of researchers to reach a broader audience. But it isn't lack of expertise that causes this pushback.
If a paper cites others, an editor can look to those authors, perhaps. If it cites none then it is a harder problem, but might be solved by just guessing based on their own experience. And a reviewer who doesn't feel competent might suggest another person who might be a better choice.

(+1) One can flip through the entire metaphorical rolodex of the most startling theoretical leaps in 20th century pure mathematics, and every last one of them, no matter how formidably high-tech, interfaces deeply with some body of already existing mathematics whose experts were competent to review the work and then digest it. Not even Grothendieck worked in a vacuum! [Non-standard analysis, for instance, had an initial audience from set theory and then from functional analysis.]

Sorry if this is a dumb question, but is this what you mean by "epsilons exist as values"?

@JoshBone No, they mean infinitesimals.


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When should I use capitalization in presentations?
I am always confused with should I capitalize something or not. Take the talk by Jeremy Avigad for instance, on page 8, he uses "Formal methods" and "Interactive theorem proving", but not capitalization for "verified proof" and "formal search."
My understanding is that we should capitalize the first letter of the items which are sentences. If the item is just a short word, we should not capitalize it. However, my minds fail here. Could anyone give me some help with better rules about when to use capitalization? I always cannot decide on capitalization for presentation. For example, if I write something in a textbox of PowerPoint, should I capitalize the first letter?

The only real rule is to be consistent throughout the presentation.
Academics are generally pretty bad about following even this, so you will find lots of counter-examples, even from prominent researchers. The presentation you linked actually seems pretty good in this regard:

Most of the text is written in sentences, with periods at the end and normal sentence capitalization (first word + proper nouns). This is unusual for a presentation, but it's perfectly fine.
Some of these sentences have line breaks and bullets inserted, but the capitalization is not affected.
Some of the bulleted lists are lists of fragments (not sentences); these fragments have no periods and the first letter is not capitalized.

But even in this presentation, there are a few minor inconsistencies:

You mention slide 8. I think this one is arguable; the "squiggly bullets" at the bottom are in a different format than the regular bullets at the top; using different capitalization schemes for these different types of bullets is defensible. Using a different capitalization scheme for the outline or prologue is similarly something I would not second-guess, even if it's not my preference.
Perhaps the bullets on slide 30 are a clearer example of a mistake. Elsewhere (e.g., page 23) the author does not capitalize the first word of list entries that are not sentences, but here he does.

Well, another rule, probably, is not to use ALL CAPS. But, yes, the example given seems a bit sloppy.

Bullet lists are tricky to get right. It's often desirable to: // • keep items short // • minimise punctuation // • make items fragments continuing the sentence introducing them // • But sometimes, a related point fit the same way. // • It may even be a full sentence. // • Should we then continue the list, or what should we do? // • Points that need question marks don't help either. OK, a little contrived, to squeeze into a comment, but hopefully self-explanatory

This is virtually always completely up to you unless you've been given specific rules or a style guide by your institution or the venue.  But the trend seems to be toward less capitalization generally.  Consider, for example, the style on Wikipedia, where only the first word of a title is capitalized.

Yes, sentence casing on Wikipedia for titles (MOS:AT) and sub section headings (MOS:HEADINGS).

Jeremy Avigad seems to have got it right; his choices might well seem questionable from the perspective that there is something special about presentations, as compared to any other kind of text, but there is not.
Unless there are house rules then broadly in any text, the exceptions to normal rules about capitalisation are titles and lists.
Titles should have the same place in presentations as in books, magazines or newspapers: eg, each page or slide might have a headline.
Lists are different.
If the list is seen as text run-on without abnormal punctuation, made special only by its spacing, it should have normal capitals… broadly, none. Broadly, that applies when the items are separated by line spaces that would otherwise have been commas or semi-colons.
If the list is seen as a collection of separate items, each item should be treated a separate sentence, with normal capitalisation.