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Strict prohibition on overlapping data. Why?
The journal Immunity says on this webpage
"As a matter of publishing ethics, we cannot consider any paper that contains data that have been published or submitted for publication elsewhere."
Why not? What if the two papers analyze rich sources of data that overlap only partially? What if they have completely different ambitions? Is the only meritorious activity in science the act of gathering data, and to hell with the analysis? And what does this have to do with ethics?

It might be related to the idea that published data is the intellectual property of the publisher, such that publishing it elsewhere infringes upon the original publisher's property rights.  Presumably if Immunity wants to be able to sue others for publishing data that first appeared in Immunity, they'd have to ensure that they similarly don't reproduce others' content.  (Not an answer since this is speculation.)

Thank you for your perspective. I suppose that concern is vaguely ethical in nature (ha ha, nature), although it seems ethically problematic to claim ownership of data generated at publicly funded institutions, by state employees, using taxpayer money.

So you're suggesting this grants them ... immunity ... from copyright accusations?

I think it's also weird the way they use the word "contains". The paper doesn't "contain" my lab's data -- it analyzes them. No amount of squeezing and centrifuging, literal or metaphorical, will allow you to recover the data from the paper. Am I in the clear, then?

For some types of data, like gels, the paper does literally "contain" the data. Maybe the guidelines were written in that paradigm.

Yeah, you can probably infer their position from the Permissions section of that webpage.  I think that their concern is that, if you publish with them, then their unique value proposition as a business is that they're a unique source of that information.  If you then just post all of that same data and writing (or/and an updated/expanded version of it) to another corner of the web, then they're no longer a unique source of it.  And if people start doing that regularly, then their ability to demand high subscription fees is diminished.

Ah, here we go: "Rights, Sharing, and Embargo Policies".  They list that there's an "embargo period"; looks like the author isn't allowed to publicly share their article until after 12 months after Immunity first publishes it, then they're only allowed to share it on non-commercial platforms.  And apparently this 12-month delay can be waived for a $5,000 fee (if I'm reading that correctly).  Anyway, they probably object to republishing data for the same reason as they object to republishing the article.

They don't object to republishing data -- in fact, they require most data be made publicly available at no cost via repositories such as GEO. Am I missing something here?

Is the data published in those repositories particularly useful to someone who can't read the corresponding article?

Yes, and especially if this hypothetical reuser could read the preprint. Anyway, you argue that this restriction is driven by a profit motive. They call it an ethical concern. What's the connection there?

Let us continue this discussion in chat.

It is unethical to use the same control data for two different experiments without disclosing it, because it lowers the statistical power of the experiments.  This could be an additional factor.

@Nat That embargo period is quite common in biomedical journals, as it automatically complies with NIH requirements.

@Fomite Definitely; journals in general don't like authors making their pay-wall'd content available elsewhere.  Not even just scientific journals, but news outlets, too.

@AnonymousPhysicist : "without disclosing it". So what? Generally if you use data published elsewhere, as is often done, you cite the paper in which it was published elsewhere. But the bizarre policy appears to forbid that.

It has nothing to do with "copyright" it's to prevent salami papers, double submissions, and weak stats.

@Nat The pure data is nobodys property but the original authors, and since it is published, everybody is free to use it, as long as he gives the proper reference.

I think you're reading a little bit more into the policy than is actually there - many papers in this type of journal essentially contains data - gels, lab results, etc. That's what they're talking about. What they're not talking about is the corpus of data generated from a whole study.

Why not?

Because the goal is not to allow researchers to publish repeated analyses of the same dataset.

What if the two papers analyze rich sources of data that overlap only
  partially?

It's likely not what they're talking about - a rich source of data likely means that the data hasn't been published recently. What they don't want is "The same data as last time, now with a marginally different story".

What if they have completely different ambitions?

I'd be impressed if you managed to get two completely distinct analyses out of the same data - and indeed, the stance of the journal would likely be that they'd be willing to accept a decrease in salami slicing of papers at the cost of the occasional genuinely novel analysis that's built entirely off previously published data.

Is the only meritorious activity in science the act of gathering data,
  and to hell with the analysis?

That's reading way too far into the policy - the paper should also contain that analysis. What they don't want you doing is writing four papers on the same single assay or whatever.

And what does this have to do with ethics?

The idea that a paper should represent a single, stand-alone contribution to science, and that ever more esoteric spins on the same data is a net negative is an ethics in science stance. One you may disagree with, but it is one.

You'd be impressed if someone can get two different analyses out of the same data set??? Why?? That is commonplace. Even if there was no disagreement about methods of analysis, there might be entirely different research questions.

@MichaelHardy In my experience for journals like the one in question, "data" comes from a single experiment.

So what? How does coming from a single experiment mean that you cannot get two or more different analyses?

@MichaelHardy It's my experience that that isn't how the immunology-related projects I have interacted with have been run. "We ran a particular assay/gel/etc. to answer THING. Here is a picture. Maybe a graph and a t-test." Generally speaking, an additional analyses involves pulling in and/or generating some form of new data, which, in my answer, would I think be allowable under the stated rules.

I tried doing a lot of fact-checking on their policies and claims.  To me it looks like this is about the money; for example, they have a portal that allows parts of publications to be reprinted elsewhere for a fee.  This seems inconsistent with the claims in this answer; would you happen to have references for them?

Just to note it, I think that responsible data analysis and avoiding common analysis mistakes is a worthy objective.  Just, these polices look designed to ensure exclusivity rather than analytical integrity.  For example, if a policy intended to prevent the same data from being considered multiple times (which itself would be based on a fallacious premise, as the literature can't be considered a fair sampling of results to begin with), it seems like it'd be adequate to explicitly declare that the data was published before to avoid confusion, rather than strictly prohibit it.

@Fomite : But generally speaking, an additional analysis, either addressing a different research question or disagreeing with the original analysis, does NOT necessarily involve generating new data. That's why there are famous data sets about which many papers have been published.

@Nat That's likely about re-prints for inserts by pharma companies. I get dozens of them at certain conferences.

@MichaelHardy I am not saying all data sets universally - I'm saying that in the field for this journal it's my experience that there's a 1:1 mapping of "Data" and "Research Question" more often than not.

@Nat As mentioned above, most of their embargo period can be explained as "We abide by the NIH guidelines, which will cover most of the papers in this journal, and if you want it to appear faster, we offer an open access option."

@Fomite Also, they say that it's okay to reprint parts of it, just not during the embargo period, and not in a commercial venue after it (though non-commercial venues are fine).  Surely that's a purely commercial policy, right?

@Nat That's likely "You'll have to put your stuff on Pubmed Commons anyway, so knock yourself out."

@Nat Nearly all of their reprint stuff can be explained by "95% of you are NIH funded, and the other 5% are possibly the kind of thing that pharma companies are going to want glossy inserts of."

How's the NIH related?  The publisher's website said that the embargo period was about maintaining subscribers, and they'll waive it for a fee.  I didn't see mention of NIH guidelines, and wouldn't waiving the embargo period violate any guidelines that required it?

@Nat The embargo period conspicuously adheres to the NIH's timeline for when work funded by them must be made open access (you can do earlier, hence the faster fee). This is them talking a good game about their compromise. It's identical to the language used by publishers I've been in meetings with.

If the NIH's timeline requires them going open access within a certain period, then it seems more like they're maximizing the embargo period within the limits of what the NIH allows, rather than doing it for the NIH, right?

@Nat Basically, the embargo period that's been laid out in this journal (and most medical journals that aren't for-pay Open Access is the same - there's no incentive for them not to go out to the maximum allowable window. But this was a compromise arrived at between the major publishers and the NIH to make work more accessible without detonating the publishing industry.

This answer makes a lot of sense to me, but I am not convinced of your reading of the policy. They're in the business of editing, so I assume they choose their words carefully. If they wanted to disallow something that's essentially the same dataset as published elsewhere, they could have said that. Instead, they wrote a policy that could cover any intersection, no matter how minor.

@eric_kernfeld There are all kinds of people in publishing who are not editors and likely had a hand in things. It's also a far more compact expression - trying to lay out express thresholds is begging for someone rules-lawyering around things. Easier to have a blanket policy for most of the problem and pre-sub inquiries for the edge cases.

Good reply. +1.

@fomite You’d be impressed by someone writing multiple papers from the same dataset? I’m guessing your field isn’t anything related to machine learning, because that seems to happen fairly often there - the dataset that you use to train the machine learning system is itself a variable, so if you want consistent reproducible results you’d need to use the same dataset to control that variable.

@nick012000 This answer is rather focused on the specific journal - and thus field - the questioner asked about. Which you'll note has been addressed in a number of comments.

This is a guess but:
The sentence in question comes at the end of the paragraph entitled 'Related Manuscripts', which says they need to see any similar papers you have. This suggests to me that the primary point is that you can't publish the 'same' paper twice, even if you change the wording.
Another potentially relevant point, which I haven't seen mentioned yet, is that you have to be very careful when reusing a dataset - the more questions you ask about some data, the more likely you are to get a positive answer by chance, so that has to be taken into consideration in the analysis. If you present a single result from the data, it's therefore important to know what other questions have been asked.

I like your first guess, but I don't think this policy has anything to to with p-hacking, because if it did, there would be a lot more to say on that topic, and they don't mention it anywhere else.

Also, the policy as written covers a lot more than just the same paper twice. It applies to any intersecting data at all.

I think the keyword here is "contains". The paper should not present any data, that has already been presented elsewhere, because that would be considered double publication of the data.
I'm pretty sure that this is not meant as a restriction on using previously published dataset in your papers. For example, I don't think Immunity would want to restrict papers from using data from the Human Genome Project or public census data.
(Am not sure how this is supposed to work with significant extensions of previous datasets though.)

You must only write one paper in which you explain completely how you acquired the data and why exactly you did it the way you did.
The full story. A later paper which reveals that there were some additional motives, not to say parameters, turns the original one into a piece of scientific malpractice, eligible for retraction.

is "eligible" the right word?

Not wrong per se but you could choose another. I might say "subject to retraction" which carries a clear negative connotation; "eligible" is usually used in positive contexts. I disagree with your answer, though; there isn't a problem with a later paper that builds on or corrects a prior one, the problem addressed in the prohibition from the OP is representing work as if it is new when it is actually not new and has been published before. If that isn't clear, it's the later paper that is a problem, not the former.

@BryanKrause You can of course build on or correct an earlier paper. You seem to not have grasped the dire scientific problem with hiding relevant measurement parameters however.


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What should I do with a workshop paper which is peer reviewed, but only available on the workshop website?
I presented a paper in a workshop in CS. The workshop was related to a good conference, and they had a peer review system for evaluating the papers. But they do not have a proceeding and only made the pdf files available on their website.
Does it mean that my work is now considered published? or i should still try to send it to a conference venue for proper publication?

Works that appear in unpublished proceedings are not (generally) considered published, so such works can be submitted elsewhere, e.g., conferences with published proceedings, journals, etc. (Exceptions may arise if copyright was assigned, but workshops with unpublished proceedings shouldn't be asking for copyright.)

Can you elaborate on that, please? Does it mean i can submit it to a conference without changing anything from the content or i need to revise/add/improve it?

The work is not published, you can do whatever you like with it.

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Can I put a postdoc as one of the references in my CV?
I'm applying for a postdoc (Computer Science) and some positions ask for the contact information of 3 references. Beside my Ph.D. supervisor, I work with other departments in my institute, but mostly with the postdocs who were in charge of the projects. For instance, the professor head of department X knows who I am and that I did/do collaborations with them, but has no idea about the details. However, his postdoc researcher can provide much better detail regarding our collaborations and etc.
Nevertheless, I have a feeling that the employers (especially in academia) rely on professor-level references than a postdoc. So, I'm not sure about this situation.

You can nanage that with the postdoc without being offensive to him/her. The official professor of a department can well refer to collaborators opinion in a recommendation letter.  Be flexible.  But a post doc can be of value as well as independent subject

This depends on what is available to you. Yes, institutions value the opinion of well established researchers (professors), but they also value the honest opinions of people who can speak about your likelihood of success and how you "get along" in the research environment. The best are people who can do both, of course.
But you don't want lukewarm praise from top people. So think about who you have as possible recommenders and what they might say. Then just make a decision. I doubt that it will matter in a critical sense, actually.

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I left the acknowledgment part in a double blind review paper submission, what can i do?
I forgot to remove the acknowledgment when i submitted my paper to a conference with a double-blind review conference. And the acceptance notice would be 4 months from now. I think it's useless to contact the chairs asking for re-submission as in top conferences they are not flexible.
But if i can be sure that the paper will be rejected then i can send to to another upcoming conference in during that time. But i do not want to deliberately revoke my paper from the conference!
BTW, the acknowledgment part shows my affiliation.

Contact the organisers.

Send an email to the conference program chair right away and ask. The better conferences will be honest with you, either way. The answer could be no revision, but then you could move on.
These mistakes happen.

Yes, a clear decision soon is the best for you and for the organisers. It's better than the reviewers finding this out and ultimately rejecting the paper on formal reason after having had read through it first.

I do no know how serious is that nowadays, as in most cases the reviewer can find the authors based on the relevant works he has done, or even the arxiv version! So maybe they do not consider it seriously unless i insist on "hey! please reject me!". But your point it right as well!

Based on the suggestion, i contacted them and they replaced the submitted version with another identical version only without the acknowledgement. Apparently they were more reasonable than i though! Now everyone is happy again! ;)

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Is it appropriate to ask one of my thesis-committee members for a postdoc position?
I'm about to give submit my Ph.D. thesis to my selected reviewers. Among the external reviewers, I really like the research themes that one of them is working on. So, I want to ask him about any possibility of working in his lab as a postdoc by sending him my CV and a proper statement of interest in the specific research topic(s) he works on.
However, he is going to read my thesis during the next 3 months, and I'm not sure if my request is going to have an effect on that process or vice versa.
Also, I cannot wait for 3-5 months to see his feedback on my work (and the disputation outcome) before asking him about the position as it will be too late for me. So, either I should ask him now or not asking him at all! :D

I don't see any reason not to ask. I think that any "conflict" because he will review your dissertation would be minor. A post-doc position is a two-way affair in which you both give and get, so it isn't like pandering on the one hand or asking a favor on the other.
If your general relationship with the person is good, and you want to work in that area, then yes, ask. But be sure, before you ask, that you wouldn't be rejected out of hand. You need at least to know that you would get serious consideration even if you aren't accepted. An informal explorative meeting might be one way to broach the subject, followed up with a more formal (CV, SOP, ...) application.
On the other hand, there is also something to be gained from working at a different institution, with new colleagues and new ideas. Don't neglect to look for alternatives in other places.
But also note that this answer may not apply universally. My experience is in the US in mathematics and CS.

To make it clear for myself, should I first ask him for an informal meeting to talk about what exactly? I mean, shouldn't I mention that I want to work somewhere (or there) as a postdoc at all? Also, do you mean that I should not send my CV (or list of interests) to the person at the beginning?

My suggestion is just to talk it over first and get a sense of the reaction. Of course you know the person better than I do.  So my suggestion may just be an option.

I'll take your advice. In fact, he is from another institute and i don't know him in person. But i like the topic he works on.

In reality, it is definitely a conflict, but US academia accepts this level of conflict readily.  So I wouldn't worry about it.
In some European fields, the examination is done by truly independent scholars from other universities who are not collaborators of the advisor, etc.  However this level of effort in the Ph.D. examination rigor is rare.
In the US, it is really more about making your advisor happy that you did enough papers, writing up, and then getting out.  Failure of examinations is rare.
Net/net: I would go for it.

Well... I'm talking about Germany and the CS field! Would it change anything?!
BTW, this should be different than a collaboration during Ph.D.


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How can I refer to multiple equations with "Eq. (i)" style?
Assume the manuscript format for referencing the equations is as "Eq. (i)". How can I properly refer to multiple equations at once? For example which of the followings is more acceptable?

In Eq. (1), Eq. (2), Eq. (3), and Eq. (4)
In Eq. (1,2,3,4) or Eq. (1-4)
In Eqs. (1,2,3,4)
Any better idea?

If you wanna publish in a journal, you can check the author guidelines. Most journals have style guides for formatting and/or even style sheets, e.g. for LaTeX.

@FuzzyLeapfrog it's for a conference paper, but they only specified the single-form reference style.

My next advice would have been to check in previous papers of the conference what is used there but @jon-custer already included that advice in the answer.

Why do you use brackets when referencing at all? When numbering, the brackets assure that you do not mistake the number for a part of the actual equation and give some kind of highlighting, so they make sense there. But when referencing, they have no purpose and just add clutter, making your text more difficult to read. Writing “Eq. 1” already makes it perfectly clear what you are referencing.

In my old copy of the AIP style guides (American Institute of Physics) they use (Section IV Mathematical Expressions, C. Displayed Equations, 2. Equation numbers) the format of

Eqs. (3)-(5)

in their text.
But, as noted, check the author guidelines or style sheet of your particular journal (or just look in the articles for an example).

I will use that, and you want to use an EN dash between the numbering of the equations,, since you are enumerating a range


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Is the following publishing strategy convenient?
I work in the field of CS, and recently published one of my works (Algorithm A) in arXiv (not peer-reviewed), as a short technical report which introduces a new method along with relevant mathematical analysis/proofs/etc.
I want to submit a paper-X to a conference by using that method in some practical applications and in combination with other methods. I decided to not introduce Algorithm A in that work due to space limits and enough contributions that paper-X has by itself.
But i want to also submit an extended version of paper-X to a journal along with introducing Algorithm A with proper further analysis/proofs.
So:
1- Is this allowed?
2- Is it convenient / advised?

As you describe it, it seems like standard practice (more or less). Conferences prefer somewhat shorter works than journals due to time (reviewing) and space (proceedings) considerations. Journals don't have that limitation, though some won't publish long works.
You will need to be careful about citations of your own work, of course and you may have issues with the possibility of double-blind review in some situations.
However, if you publish the journal article first, there would probably be a presumption by the conference committee that it is "old work" and not suitable. But, in general, extending an older work is acceptable - even praiseworthy.
But, to satisfy journal reviewers, the "extended" work should be more than a restating of the earlier work. A summary of older work might not be enough, even if it brings together the, already published, algorithm, and the, already published, use. Reviewers may be looking for what is new.

Regarding the novelty of the extended work, would the reviewers consider Algorithm-A (from arXiv) an old work?  I have not yet introduced/analyzed it in any peer-reviewed publication, but i have only cited it.

And i will submit the short paper to a single-blind conference and afterwards i try the journal paper. So i would not be worried about blind-review issues.

@Bob, you have to assume that they might consider it 'published'. You can't control that. You have a chicken-egg problem here, I think. But that might depend on how much you put in the arXiv repository.

I like the chicken-egg expression :D!  Well, i need to put some minimum amount of analysis and proofs in arXiv to make sure the reviewers of the conference paper are convinced why i use it in the short paper.


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Is one year enough for a postdoc position to be a fruitful experience regarding my CV?
I have to decide about a postdoc position for which the contract would be only one year long. The position is in computer science and as a collaboration with an industrial sector. In fact, I want to join the industry after finishing my postdoc period, and I believe that collaboration can be add a bit of value to my CV.
My concern is that I'm not sure if one year is enough for doing high-quality research and publishing in top venues (which is important in CS).
Also, I have imagined that I have to spend the last 3-4 months of my contract on the job hunt to join a company. So, it remains me only 8-9 months to invest on research.

If you were aiming for the academic job market, the situation would be even worse.  You start the postdoc in August, prepare and submit your application materials by November, write and practice your talk in December, and (if you're lucky) interview sporadically from January through May.

Is it really only one year, or one year renewable? At my lab, the official policy on post-docs is they are one year contracts, renewable up to a total of 6 years. I don't recall ever actually having to act to renew a contract - it automatically rolled over. Heading past 3 years was a discussion between the post doc and me about their plans to get a real job (and the answer usually involved the academic hiring calendar not being well aligned to their original start date at the labs).

Why do you think publishing in top venues is important to your career in industry?  If you want to work in industry, why are you even considering a postdoc?

@AnonymousPhysicist - National labs have postdocs (but is that industry?). At least in the old days, Bell Labs and IBM Research hired postdocs, and often converted them to staff (as do national labs even now).

@JonCuster No it is not industry or academia.

@Babak - do you want to join the particular company that the post-doc would be at? If so, consider the one year your tryout.

@AnonymousPhysicist - I agree, but various national labs lie somewhere on the continuum.

@JonCuster Not sure at the moment if I want to work in that company or not. BTW, I'll be hired by the University and would work there mainly.

If you want to join the industry, the shorter you spend on your postdoc the better. If you are familiar with the project, then 8-9 months might be enough to have something out of your post doc experience. Also, one year postdoc is a great chance to focus on the skills that are necessary for you to get into your industry job category.


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What is the preferred order of information in an academic CV for computer science field?
I'm sending my CV for postdoc positions in computer science in Europe (esp. Germany). My special focus is academia rather than the industry.
I was wondering what is the preferred order of information to bring there?
For instance, is the following order acceptable/preferred?

Personal information
Education
Work experience
Research Interests
Teaching experiences (TA)
Computer skills
Publications

As a general rule, your CV should reflect both your strengths, in decreasing order, and the strengths that your target audience is looking for, in decreasing order.  If those two priorities don't coincide, you may not be a good fit for the position.
From the list in your question, I would guess that you have more teaching experience than research experience, and that you value your research record (= publications) less than your teaching record or your "computer skills".  This may be appropriate for a teaching-oriented position (in particular, one where you are teaching "computer skills"), but it's exactly backwards if you are aiming for a research-oriented position.
Assuming you're applying for a research-oriented postdoc position:

Personal information, including your work and education history, must come first, because that's standard, and you don't want to give anyone an excuse to think before they read the content.
Awards next (if you have any), because you want to convince people that you're good at what you do (or at least that other people think so), even before they read the actual content.
Publications next, because that's all that a majority of the hiring committee cares about.  Be sure to distinguish invited papers that carry extra prestige, because you want to convince people that you're good at what you do, even before they read the actual content.
Then grants (if you have any), because the people hiring you want to know that you can get money on your own.  This is less important than your ability to publish good research, but more important than....
Everything else, with whatever is more impressive and/or relevant for your desired position first: Presentations, teaching experience, advising/mentoring experience, community service (conference committees, refereeing, ...), university service (serving on an admission committee, organizing a seminar, ...)
References at the end, if and only if the position requires references in the CV itself.

I would omit "computer skills" entirely, unless those are actually the skills you want to be hired for.  (Then again, I'm coming from computer science, where "computer skills" are so necessary (and therefore boring) that actually mentioning them is weird.  Your community may have different expectations than mine.)

+1 When I was applying for jobs, once upon a time, I had multiple versions of my CV, all with the same content, but varying the order of sections based on the priorities of the position I was applying for.  And as JeffE suggests, the extent to which that matched my own priorities was a good indication of which positions I was a better fit for.

Thank you for all the information. BTW, I was wondering where should I talk about my skills as a computer scientist? For instance, if the job wants someone with skills in statisitacal analysis, then where in my CV I can mention that?!

You mention that by having publications that demonstrate your skill in statistical analysis, and by describing your specific contributions to those papers in your research statement.  Not in your CV.

I've also seen people putting their research interests in their CV. Is it unnecessary?

Yes, this is acceptable as are many other formats. It is not worth obsession about the exact format you use (though it should make it easy to find relevant information -- so use a good page layout). What does matter is the content of the CV.
But if you're unsure, look at the CVs a lot of people post on their websites when they are on the market for jobs. So look at the CVs of other postdocs you know and see how they structured theirs. Clearly, what they did might not have been the best possible format, but it was good enough to get a job!

Mine CV (in CS) is structured as follows:

Personal
Work experience
Education
Publications
Lectures hold
Teaching & supervision experience
Grants and fellowships
Position offers
Talks
Peer review & further community services
Languages spoken
References.

It depends on a position, but I think that contrary to what one might imagine, no one cares what programming languages and frameworks a later-term postdoc or a professor is fluent it. You obviously are able to get things done if you are this far. They'd be more interested in your funding experience and publication track.

Regarding the talks, is it the same as my oral presentations at the conferences?

Oral presentations at conferences, invited talks at conferences, invitations to colloquiums and hearings.


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Should I include a research in my thesis which does not have a direct connection to its title?
Let's say that my Ph.D. project was about mitigating the problem X (e.g. analyzing the specific type of datasets) from different perspectives (CS field), for which one of the methods I have developed was algorithm A.
However, I also developed the algorithm B, which belongs to the same overarching category that also Algorithm A belongs to, but it does not apply to problem X.
Even though I also published Algorithm B as an individual conference paper, I'm not sure if I can bring it in my dissertation. It is a novel method, but not related to the main problem (or the dataset type) that my Ph.D. project is about. So, I'm not sure if I can include it in my Ph.D. thesis despite the time I spent developing it.

This is something you should primarily discuss with your advisor.

I don't see how we can meaningfully answer this without knowing your field, paper and the algorithms in question.

In many cases, it comes down to your defense and the work you've done, and the thesis is a document for you. You can include, or not include whatever work you want.
I'm speaking from a PhD in the natural sciences in the US, different countries and subjects have different rules and guidelines.

I think its more "you can include what your advisor wants".


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Do I have to change the article name when it is the extended version of my published paper?
I'm submitting the extended version of my published conference paper to a journal that has a special issue specifically related to that conference.
Besides adding 30% of more material to the article (as the computer science routine), do I have to change the article name?
On one hand, everything is the same besides having more experiments and more formulations or extra explanations, and it is obvious that the article and the paper are the same works.
on the other hand, how people may decide to cite which one later on?

CS Prof. Jeff Offutt has an editorial on extending a conference paper to a journal paper (https://cs.gmu.edu/~offutt/stvr/26-7-October2016.html). To summarize, having the same title could be confusing for readers.

@kjacks21 I don't find that argument convincing.

To be extra clear, you could even name it "Paper Title (extended version)".

Most computer science researchers doesn't find his argument convincing.  Keeping the same title is significantly more common than changing the title.

Usually, there is no formal requirement that the title has to be changed. In fact, if the journal paper is just the long version of the conference paper, I would recommend not to change it, so that the connection between the two papers is obvious to people who see the paper in a list of references.
As soon as the journal version of the paper has appeared, it is generally assumed that this is the version that should be read and cited, rather than the shorter conference version. By keeping the title unchanged, you tell readers that there is no need to read the earlier one as well.

OP should check with the editor; some journals do not like this, even if they explicitly say that the journal paper can be an extended version of the conference one.

Then wouldn't that look odd having the same title being repeated in my list of publications (e.g. for CV or profile)?

@Bob No, since they are distinguished by where they appeared. On the contrary, having two papers with completely different titles that, on further inspection, turn out to be the long and the short version of the same paper might appear like cheating to some readers.

BTW, if you want to avoid such problems, you might name the confererence paper "A new result on whatever (extended abstract)" and drop the "extended abstract" in the journal version.


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How should I describe the "relationship" between me and postdoc in our lab if I am getting recomendation letter from her?
How should I describe the relationship between me and postdoc in our lab if I am getting recommendation letter for phD admission?
Advisor? Superviser? reasearch advisor?...
She has a title of research professor.

What is your position in the lab?  Undergraduate research assistant?  Graduate research assistant?  How do you interact?  Does she give you assignments and review your work? Do you collaborate? Work on totally different things? Is there another professor who is formally your thesis advisor? What I'm getting at is: if you want us to help you find the right word to describe the relationship, you'll have to describe the relationship to us.

Assuming this person is not formally your manager nor supervisor, the correct term would be colleague.

Or perhaps "colleague and mentor", if you want to emphasise the fact that the other person is in a position to judge your work.

Alternatively: "She is a postdoc in our lab."

Why not just ask her/your lab's PI if you're not sure? They are in the best position to answer the question.
Advisor is sometimes a reserved word for people listed on a thesis, so if I had to guess I would say that this is not the right word.
Also - to be honest it probably doesn't matter very much, you're focusing on an unimportant detail in my opinion. What matters is that her reference is positive and supports your application in the strongest terms. Having sat in graduate admissions committees myself, I cannot imagine a scenario where we look at an application and say "it's good, but they didn't list the referee's title appropriately, gotta reject it now!"


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sharing ideas for collaboration or working on them individually?
I'm in the middle of my PhD and published 2 conference papers only with my supervisor, But i always want to have collaborations with other academic people in the field. Unfortunately i am a bit confused there!

I met people in conferences who were working on similar problems in the area, but i do not know how to initiate/request for collaboration? especially with a professor or a high level researcher.
Sometime when i read a paper related to my work, an idea for improvement of the proposed approach comes to my mind. But i put it somewhere in the shelf to get back to it later. But i'm not sure if it is a good idea to share it with the author or keep it to myself and work on it alone? In case of the first, should i contact the student author or the professor?
BTW, my field is computer science and i deal with machine learning and data analysis! so the most probable scenario which comes to my mind is getting some specific data from someone and working on that, but then it wouldn't be considered collaboration!

You seem to have a couple questions in the text here.  On this site we aim for one question per post.  Can you focus this a bit more?  For your first question, see e.g., http://academia.stackexchange.com/q/271/19607 http://academia.stackexchange.com/q/3613/19607 and http://academia.stackexchange.com/q/6035/19607

i do not know how to initiate/request for collaboration -- There's no magic bullet; you just do it. "Hi, I really enjoyed your talk. I've actually been working on similar problems; do you have some time later to talk?"

should i contact the student author or the professor? -- Yes.

As a PhD student you are not usually required to have significant collaborations outside your school or department, although it does happen. So, if you are thinking about external collaborations, the first think you should do is talk to your advisor about it. Your advisor almost certainly has many connections in the field. Thus, they might be able to recommend someone to work with. But, they might also discourage this. You are (at least in theory) being trained by your supervisor. This gives them a greater responsibility for helping you develop your research abilities. Working with someone externally can put some of this burden onto them if your advisor isn't also involved.
For instance, I have a current PhD student that is part of an external collaboration, but I do some work to ensure that anything that we do that goes to our external collaborators is high enough quality that we aren't wasting their time. My colleague does something similar when his students work with me.
When I receive an unsolicited research request from a colleague's student, the first thing I do is contact their supervisor to see if they know about the request. I don't want to be seen as going behind their back to work with their student, and I wouldn't want someone else doing this with students I'm supervising.

So when i'm interested in someones data or have some idea for improvement of some other persons work, should i always CC my supervisor (and their supervisor) in the email communication?

@Bob If you just have a question about their work, you don't have to always cc your supervisor. But, if you are actively seeking collaboration, it is wise to keep your advisor in the loop.

I am also in the middle of my PhD and have published more than 4 conference papers.
1) I tried to make a collaboration with another PhD student, whom I met in a conference. Basically, talking with them and finding out what they are doing now is a good start. I am currently discussing with a higher level researcher than me after having some online discussions in a facebook group, which I make. I try to introduce my papers and my expertise in my field, while trying to have some fruitful discussions in the group. You may also try to use researchgate to connect with other great researchers.
2) I have no experience about the second question.
3) I think that this can be considered as a collaboration. I am currently participating in a project as the researcher side with a company. The company provides data, while I and my team creates the algorithm. Then, the company will evaluate the result I get because they understand well about the practical situations and are able to give insightful comments if my results seems to be quite different with the real situation.
Hope it helps.

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


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How to provide an anonymous copy of work online?
It may seem odd, but I need to put an anonymous part of my work (an algorithm) online.
Well, it should be available online as I want to refer to it in a single-blind submission (but not as the contributions), and it should be anonymous as I want to introduce it (as the contribution) in another conference submission (with double-blind review). Since the submission deadlines are close to each other I want to handle it like this.
Is there a way to put it online with anonymous author names (arXiv does not allow it) so it could also be searchable via google?
I will remove it or update it with full names after the paper review process is finished.

It might help understand what sort of solution is viable if we know why you need this. Usually, work is only submitted one place at a time.

@TobiasKildetoft it's an algorithm that should be introduced in a theoretical paper (with double-blind review) and should be used in a practical paper as well  (but not as a contribution). and the reviewing periods of conferences overlap!

Could you not just cite the first paper as "(in review)"? It doesn't break the double-blind anonymity unless the same person is reviewing both, which seems rather unlikely unless you are working in a very small specialism.

Why not GitHub?

@SylvainRibault is it possible to be anonymous on GitHub?

@Bob Just create an account without using your real name.

I added the work to vixra.org which is similar to arxiv but with fewer boundaries. Maybe it does not have the reputation of arxiv, but i used it for anonymous uploading of my work.

github (or gitlab, or both if you are afraid someone will try taking it down) lets you do the same as vixra but comes without the image of crankery. Then there is zenodo.

@darijgrinberg Those would make good answers.


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Can an algorithm tested only on artificial data be accepted in a high rank conference?
I devised a classification algorithm which is useful for specific complicated distribution of classes of data. The method works good on the artificial data which cannot be solved by typical algorithms in the literature. But yet i couldn't find any real data to evaluate the algorithm on real problems.
Also there is mathematical support for the basis of the approach and the way it works.
So i was wondering if an algorithm with good mathematical support but no evaluation on real data has any chance to get accepted in computer science conferences like SDM or CVPR?

This seems like a technical question — more about how to appropriately evaluate and test an algorithm than about the conference acceptance process.  Might it be better suited to cs.stackexchange.com?

In my experience in other related fields it is a rare exception to have anything tested on something resembling real data - so much so that investigations on a method using real world data can be a separately publishable result in and of itself (if the results are interesting). But I imagine this varies tremendously by sub-field, so I am unsure if this is true for your specifically stated venues.

Do you have significant theoretical results (i.e. proofs)?

yes i do have a mathematical proof for the basis of my approach.

But yet i couldn't find any real data to evaluate the algorithm on real problems.

There exist a good site on benchmark datasets in machine learning,  uci machine learning repository that you can use these datasets as well as the artificial tested data.

So i was wondering if an algorithm with good mathematical support but no evaluation on real data has any chance to get accepted in computer science conferences like SDM or CVPR?

The answer is yes if your approach is backed by theoretical support. On validation of attained results, you should provide intuition about how your proposed approach works on a class of problems via the theoretical results if you have such ones'.

my problem with UCI dataset is that, it is difficult to visualize most of them to see the structure of the classes and distribution of the data as they mostly are high-dimensional datasets.

You can use Iris datasets and also see this example to visualize this dataset effectively, http://sebastianraschka.com/Articles/2014_python_lda.html.  Furthermore, for most of the dataset you should have a preprocessing step to reduce the number of features to have an insight of the overlapping structure among the classes.


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Can I informally ask the special session organizers about the paper's decision?
I have sent a paper to a special session of a conference, and the notifications will be given in one week.
But I have to send my publication list for a postdoc position this week. Do you think it'd be a good idea to send an informal email to the organizers asking if the decisions have already been taken?

Yes, you can certainly ask, but you may not get an answer, especially in the time frame you need. So, when you ask, say why you need the information.
But even if you don't get an answer, you can list the paper in your CV as "submitted, awaiting decision", so that you get some "credit" for it. But, since decisions about the postdoc won't be made for a while, anyway, you can probably update your cv when you learn more.


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Should phd candidates spend time on extra-curricular?
Suppose an engineering PhD candidate in the USA is trying to pursue an academic profession (e.g. Assistant Professorship in a Research Institute), is it even worth-while to commit to extra-curricular activities like student leadership during PhD candidacy?
More specifically, are soft skills like leadership evaluated in isolation from your research portfolio during faculty recruitment? I understand that such skills will help with interacting with current faculties who are recruiting you, but could this kind of credentials ever elevate a research faculty candidate?

Whether or not you get a tenure-track job is going to be driven by your research credentials, and not by extra-curricular activities. However...

Interviews and job applications often ask about your service contributions; how you would go about managing people, budgets, and competing demands; etc. Extra-curricular activities such as student leadership may give you some concrete examples to draw on in addressing such questions. This is likely to result in a more compelling answer, which may help your overall application.
Your circumstances or preferences may evolve over the next few years, and you may not be able to find the right academic position when you graduate from your PhD. Participation in extra-curricular activities may help your CV when applying for non-academic jobs.
Networking is always helpful. You never know where you'll meet someone interesting or useful, even if it's just a fellow PhD student from a different department whose research connects to your own.
Variety is important. In my experience, it's good to have a reason to step away from your research regularly and think about something completely different. Often inspiration for difficult problems strikes when you're not actively thinking about them.

My university recruits faculties for two different tracks: (1) research-track and (2) teaching-track
For the former, the panel is least bothered about such activities of a candidate. However, for the second option, they indeed look for someone with such experience. It is not mandatory though.


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Why do people say you shouldn't do a PhD unless you want a career in research?
I know a PhD is supposed to be an apprenticeship for future researchers.  But what if you enjoy getting the PhD, but have no intention of doing research in the future?  It seems like being a professor is really stressful and there is a lot of competition, and same thing for other sorts of non-grad-school research jobs.  If you think this way, does this mean you shouldn't get a PhD, and why?

Do people say that?  I think the point is that a PhD probably isn't a requirement unless you want a career in research, rather than that you shouldn't do one unless you want a career in research.  Having said which, finding a PhD enjoyable is an indication that you would probably enjoy a career in research as well.

There are certainly many jobs outside academia that require a PhD, or at least where it is a big plus. For example the new data scientist job market.

Also electrical engineering and optical sciences/engineering both have large job markets for PhDs, many of whom don't publish much after the PhD is completed...

@DikranMarsupial I firmly advise students not to pursue a PhD (in math or  computer science) unless they cannot even imagine being happy without doing research.

Doing a PhD requires a large input of time and money. I'm told that some employers will see that negatively, if they don't require a PhD, as the time could have been spent learning the trade instead. For some people doing the PhD may be a good option, but there may be people who would enjoy doing one but whose career would benefit more from not doing so. In that case, perhaps doing one part-time would be an option (depending on subject).

@JeffE I wouldn't have done a PhD on that basis, but I am glad that I did!  Many students don't really have a good idea what research is about when they start a PhD (as this question shows); one of the most important things that you can learn during a PhD is that a career in research is not for you.  Many students are very able when being taught, but turn out not to be very good at coming up with new ideas, and the only way to test whether you are able to do this is to have a go.  I do agree though that a PhD requires a very substantial commitment and so warnings are a good idea (+1).

Answers to this question would be considerably more meaningful if we stick to a specific field. From the answers below it sounds like folks here aren't in any form of industry-related science or engineering field. It would be nice to get that out in the open explicitly if that's the case.

@JeffE putting aside the hyperbole, how would they know that before trying (i.e., grad school)?  And if that is they way you think, what about those PhDs who won't get a job in academia?

@xebtl a PhD is an apprenticeship for research, not an apprenticeship for academia.  There is plenty of research that is conducted in industry for which a PhD is the appropriate training.  FWIW andswering DanielSank's query, my PhD is in electronic engineering, but these days I am a computer scientist/computational statistician.

Related answer from a different perspective on Workplace - http://workplace.stackexchange.com/a/37067/2322

@xebtl It's not meant as hyperbole; I really believe exactly what I wrote.  If a student isn't sure they want to do research but want to find out, they should get an MS.

The general idea is monetary. Having a PHD can really hurt your future career, many people will not hire them. And most people after spending a hundred K or two on an education need to get a good job. If you are rich enough to pay for a PHD and never require a job, then there is no reason not to go for it, if that is what you want to do.

@JeffE a masters degree doesn't necessarily have a research component, there are taught masters and masters by research.  The taught masters generally has a project component, but it doesn't necessarily have research.  Perhaps this is an something that varies from country to country.

@DikranMarsupial  Right. Obviously I meant a research masters. In my experience, most course-based (aka professional or terminal) masters degrees in computer science don't even have a project component.

@JeffE I think it may vary from place to place, but research masters seem to be pretty rare in the U.K., however it was the case that in the U.K. PhD students would register for an MPhil and then be upgraded to a PhD after a year given satisfactory progress, so very few students tend to specifically enroll for a research masters, they tend to go for a taught MSc or a PhD with the exit route of an MPhil if things don't work out.  There is a distinction between a Masters by research and an MPhil, but I can't remember what it is (too long ago since last involved in the former).

Although the primary goal of a PhD is to train you in performing scientific research, a career in academia is definitely not the only possible result. In fact, more PhDs end up not staying in academia and not becoming a professor. A lot of people I did my PhD with either work for research institutes or consultancy companies. They are in positions where their abstract thinking skills serve them well, and are appreciated by their employers. I ended up in a data scientist like position, where my programming skills, abstract thinking, quickly learning complex topics, and data analysis skills really shine. In this field a PhD is not exactly a requirement, but a big plus nonetheless.
Where your PhD hurts you if you for example apply for pure programming roles. There you have to compete with people who have exclusively spent their time programming, and your research skills are not a plus. So, it is also a matter of finding the right position where a PhD adds value.
Note that this answer may depend on the country you live. My impression is that in the Netherlands the attitude towards PhDs is not very negative. The impression I get from this site is that this is less the case in the US.

People say it as a warning, not as a truth. Many bright young people, who love knowledge and discovery and love immersing themselves in a subject (even if the work is challenging), do a PhD because it feels like a dream job. But afterwards, they struggle to survive in the competitive research environment, and struggle to find a decent-paying job elsewhere. If anything it seems like the PhD set them back.
Nobody wants to see yet another young adult waste half a dozen years of their life. Hence this warning.
However, not all students are identical, and not all PhDs are created equal. The years I spent pursuing an engineering PhD (which I did not complete) were among the most valuable years in my life. Since I created my own highly innovative research topic, I quickly got a huge amount of experience in both pursuing, and communicating, controversial ideas. I learned how to manage the egos of the establishment, the critics who will shoot down anything that doesn't fit well with conventional ways of thinking, and how to express a complex new approach in a thirty-second or 2-page limit.
Now, I'm an entrepreneur and innovator, following my dream of bringing about real change in the world, and these skills are vital to me!
The warning is, you won't automatically get that out of a PhD. But if you're confident that you can make it happen, who is anyone else to say you can't?

I think doing a PhD in a subject you like for the love of the subject/topic is a completely valid reason for doing one. Whilst a PhD is primarily structured with an academic career in mind, you do learn very useful transferable skills that are of great value to the industry-problem solving, quick to learn, logical thinking, etc. A career in academia may not be for everyone who completes a PhD and depending on the subject, there are arguably better prospects in industry with better financial remuneration.
As mentioned, there are lots of jobs in industry that specifically require a PhD qualification-quantitative analyst, engineering, etc (see siam).
So in most cases, whether you remain in academia or not, a PhD qualification will still serve you well. I do however think this is very much subject dependent.

To take a different tack than some of the other answers: getting a PhD typically involves spending several years working your butt off for fairly low pay (or, in some fields, no pay, or even negative pay in that you may take on debt).  That's in addition to other more minor negatives you may or may not encounter depending on the personalities you interact with; you may be treated as slave labor, browbeaten, ignored, or looked down on.  For many people, the light at the end of the tunnel is the possibility of a career where they get to do research as a living.  If that's the career you want, a PhD is usually required.  If you don't want that, a PhD is a lot of work with limited payoff.
In short, the main difference between having a PhD and not having a PhD is that if you have a PhD you can be a professional researcher.  If you don't want to do that, you probably don't need a PhD, so why go to the trouble of getting one?
That said, I personally don't agree that you should never get a PhD unless you want a career as a researcher.  If you're in a good program, getting a PhD can be a fun and stimulating experience (although still a lot of work).  The main thing I would say is, you should be deriving some net benefit from getting a PhD.  You can benefit either from the experience of getting it, or from the doors it opens for you after you get it, but if what you're getting in those two areas isn't worth the effort you're putting in, you shouldn't do it.

Academia vs industry perspective
So most of these answers seem to be written from the perspective of academia - not industry.
This is a bit late, but I think it might provide that valuable perspective.

I know a PhD is supposed to be an apprenticeship for future researchers. But what if you enjoy getting the PhD, but have no intention of doing research in the future? It seems like being a professor is really stressful and there is a lot of competition, and same thing for other sorts of non-grad-school research jobs. If you think this way, does this mean you shouldn't get a PhD, and why?

Companies hire people mostly because those people can make them money. Which makes the decision about having a PhD purely a question of, "does this allow you to add more value to the company?"
For many jobs, the answer is, "no, it does not." And so a PhD doesn't really add value in those cases.
As much as we'd like to dream that everyone has a perfect job, many jobs are just jobs. Many (most?) don't need the skills
Hiring manager perspective
If I'm looking to hire someone for a position, and my options are someone who is early career (we'll assume it takes 5 years after a bachelors to get a PhD total, probably optimistic..) and they are:

Person with a PhD and 0 years industry experience
Person with no PhD and 5 years industry experience

Who is more valuable to me? It's almost always going to be the person with no PhD and relevant business experience.

PhD will want higher salary
PhD will have less experience in a work environment (which can be VERY different than grad school)
Huge negative stigmas in industry against people with PhDs and their ability to get stuff done
Most jobs will not require the detailed depth of research a PhD provides
Often a PhD means you are not using as much of your undergraduate practical experience during your PhD (for example, a PhD in Computer Science normally doesn't do as much programming as someone working) and so it can hurt your undergrad experience.

When is a PhD useful for industry?
Now there are situations where a PhD is useful. It's of course not fair to make blanket statements that "all PhDs are not useful for industry jobs." Here are some situations where a PhD can be useful even if you have no interest in research:

You love the subject matter of your PhD and want to work in the field doing that sort of work. In this case your PhD work may be useful because it means you have a depth of knowledge deeper than someone who may have worked in industry during that time.
Keep in mind that if someone has worked in industry in that field for a while, they may still be preferred by managers
You want to work for a startup. Skills required and learned while doing a PhD translate reasonably well to what is required for a startup.
Some companies like hiring people with PhDs (Google, etc). But keep in mind you are going to be competing with tons of people who want to do research-y types of work at this point.

What are the drawbacks of doing a PhD if you don't want a career in research?
There are drawbacks of a PhD for someone in this position.

More limited job availability
Opportunity cost. Even if you get a stipend, for many (especially in technical fields) you are losing a significant opportunity cost in your career earnings.

Working 4 years at $25k vs 4 years at $50k is 100k in "lost" earnings

Companies may not value your PhD experience (especially if it doesn't directly related)
You lose 4 years of experience
People will associate a negative stigma against you (no this isn't fair, and yes it happens)
You probably will work harder/longer on your PhD than an industry job

So what?
I would recommend you be aware of the downsides of doing a PhD if you don't really want to use the research long-term in your career.
Ultimately it's your life, and you can do whatever you want.
But you need to figure out your goals and see whether this sort of choice meets them. It may, it may not, ultimately no one can answer it for you.

You seem to have the answer already. PhD programs are training programs for research. We generally don't waste time and money training firefighters or cops that we don't deploy into service. The same reasons hold for PhD students. If you don't want to be a professional researcher, a PhD program is pretty inappropriate, a lot of work, and expensive for the funders.

+1 However if the student is funding their own PhD, it is fine for them to study for a PhD just for the fun of it.  I quite like the idea of going back to being a perpetual student, if I were rich, I'd seriously consider it! ;o)

I do not agree. The skills you learn as a PhD can also be beneficial to other, non-academic jobs. For example see the new data scientist job type, where a PhD is almost a requirement, and a PhD teaches you valuable skills needed for the jobs. Being trained a firefighter does not mean you have no transferable skills. It is just a matter of finding the right jobs.

Some of the best non-academic tech people I know have a PhD but don't do direct research anymore. They did some after their PhD, but don't now. I like the training that PhD students get, generally, but I wouldn't say it's cost-effective for sysadmins or basic data scientists.

From the point of view of many faculty members it's a waste of their time to train a PhD student who doesn't plan to go on to an academic/research career.  Much of the benefit to the faculty member comes from association with students who've gone on to successful research careers.

@PaulHiemstra a PhD is essentially an apprenticeship in research; not all research is academic research.  Note that Bill's answer does not make a distinction between academic and industrial (or other) research and hence already includes those other jobs.

I'd say even in roles that contain minimal research in the classical sense (developing new things), a lot of skills taught in a PhD are valuable.

@PaulHiemstra yes, but is a PhD a good way of gaining those skills, rather than having them taught more directly?  Probably not.

@BillBarth a better educated population is ALWAYS a plus. In addition it is very hard to predict which expertise or experience will be valuable in the future. Incidentally, there is no difference in funding a PhD student who goes into research and who does not - one does NOT obtain education to "become rich". Ironically, academics who stay in research, especially in academia, will generally earn less (!) than people in industry or even people who never went to university in some cases.

I disagree with almost every word of this answer. First, PhDs are about education, not training. Second, we do accept more PhD students than can possibly get jobs in academia. Third, the firefighter analogy just doesn't work. PhD students can and do make valuable contributions to research during what you're calling their training. In the framework of your analogy, even trainee firefighters put out fires and that is still a useful contribution to society, even if they quit after their training is over.

@DavidRicherby, yes, but going into a PhD program with the intention of not pursuing research after it has an air of fraudulency to it, especially if others are paying. There's a bargain or basic understanding here. You don't have to go on to be a prof, but there's some expectation that research will be pursued after. Bad feelings can be left around after if the student declares no intention to do research half-way through a program. Maybe your PhD program has no research training components to it, but I doubt that highly.

Although there are many different contexts, depending on subject matter, I really don't think that there's any general understanding that getting a Ph.D. is "exactly" "preparation for research". For that matter, there does seem to be a general misunderstanding of the various possibilities for "research", given the noise about "impact factors", and the belief system in which a clever person doesn't need to know much to "do research", and so on. (True, at the other end are the gate-keepers, status-protectors...)
In my field, mathematics, I think the idea is that engaging seriously with live mathematics, with a few years' preparation, is the most plausible way to develop a sense of what it really is... whatever one does subsequently. Being a teacher of mathematics is arguably enhanced by having some idea of what the subject is, although, indeed, this does not automatically make anyone a good teacher. My students who have "gone into industry" generally report that everything else is easier than was their PhD work. Perhaps a good PhD extends one's horizons, one's self-knowledge. Gosh! :)
There is also that "scholarship" thing, ... Organizing and preserving human understanding?
I myself don't find the start-a-startup or bring-a-product-to-market version of "research "very interesting", and that helps explain why I'm not rich. :)
If one is interested in something-or-other, while it is in-principle possible to learn much of what's known on one's own, on-line, whatever, in fact there are some senior people whose expertise is not made obsolete, nor eclipsed, by on-line stuff. Then it's useful to be in close contact with them, if the goal is getting a sense of the extreme cutting-edge of the thing, whether it's "products" or understanding of very old things.
(Also, I note that the objection that doing a PhD with no plans to "do research" in a conventional sense is somehow "wasting resources" overlooks the point that nearly all RAs and TAs are paid very low wages, hence, are a terrific bargain...)
In fact, I do claim that the miracle of "getting a PhD" is that if one has no serious financial obligations, that point in one's life is a marvellous opportunity to behave very idealistically... and worry about "making a living" later. (Yes, if the "opportunity cost" bothers you, then don't do a PhD.) That is, people with the luck to have the opportunity to be "pure intellectuals" for a few years when they're young and idealistic and energetic do indeed have good fortune. Worrying about optimizing job situations can wait, maybe?

In Sweden you have the possibility to become a industrial doctoral student. This might be a good compromize if you like to research but want to end up in industry and in an R&D department on a (larger) company.
Example from Chalmers University of Technology: http://www.chalmers.se/en/research/doctoral-programmes/becoming-a-doctoral-student/Pages/industrial-doctoral-students.aspx

Let's assume you are researching about information retrieval, and you found a better formula for retrieval documents based on a query and it is more effective than existing methods adopted by companies. That company will call you and ask you to come to their company and apply your research there. My point here is, it is not the degree or you that will make a difference, but the research that you made will make the difference in the exact place to be applied, so make sure about your research and do it in an active area, don't be like, I have a PHD.
Good luck!

Ok, this is how you can profit from a Phd, but not this was the question. The question was, why is it said so that Phd values its price only if one wants a career is research.

I have worked as a researcher for 20 years and studied part-time courses in business and information analysis but never did do a PhD. A colleague of mine who did do a 7 year PhD following a BA and MA said the reason he did it was to stay in academia, however he felt like that much work involved in carrying out research even though his subject was psychology would be better spent applying the techniques and tools that he had learned to work as a researcher, running large postal surveys, analysing statistical data, managing research projects etc. I realised that he knew SPSS inside out, could explain statistical terms and concepts to me that I couldn't understand and teach me certain research methods that I hadn't heard about. So with all of those advanced skills, I'd agree that research work would be a job that wouldn't be too difficult to get following University.

I saw an article today on my Linkedin Pulse titled: 24 Highpaying jobs for people who hate stress
At least half of them requires master to PhD level education.  So I'd say that's not true, but it depends on what your PhD is on.
Also, I'd like to point out an article from Hootsuite yesterday (though this may only apply to the tech industry for now).  The article claims that traditionally, the only way to climb up the corporate staircase is to slowly move up the manager position( and then manage more... a team, a department, a company, etc.)  However, they claim that companies like google (and themselves) long recognized that some highly skilled people are not fit to be managers and don't want to manage other people, so they set up alternative career track "the guru track".  This basically allows people with high knowledge in a specific topic to move up, without having to manage.  I don't know if this is the path the industry (and any other industry) is heading, but if this becomes more common, I suspect that graduate education will be more valued than they are now in the corporate world.

I don't see what this has to do with the question.

"People say you shouldn't do a phd unless you want a career in research" -- my answer is to counter that belief that says, no, you don't need a career in research to have value in a PhD.  Why does it not?

Because it doesn't address the question of why people say what they say. The question is "Why do people say X?" Your answer is "X is false." Great. but why do people say X?


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Can i put the submitted paper in the arxiv?
I'm not so familiar about the regulations regarding arxiv and conference papers.
Recently i submitted a conference paper and it's under review, but i couldn't put all the supplementary materials (proofs and extra analysis, pseudo-code etc) in the submitted paper due to the limited space!
So legally can i put the complete version in arxive, hoping if the reviewer search for the related works he might notice the complete version by chance. Then he can benefit from the extra supplementary materials provided there to better understand the submitted version?
Also arxiv is like putting the paper there, or you also need to wait for the acceptance from arxiv?

If you have just submitted the paper, chances are you haven't given the rights over to the committee. As long as you haven't agreed to some terms and conditions that gives temporary ownership to the conference, then you should still have ownership of the paper and can do what you want with it.
If you want to be doubly sure, a short email to the conference proceedings editor can erase any doubt you may have.

They have that in their submission page: "Papers submitted to this conference must not have been accepted or be under review by another conference with a published proceedings or by a journal". Would it include arxiv?  About the conference chairs, I think they are not so communicative based on my prior experience!!

I agree with the conclusion (namely, you can probably do it, but could write to the program committee to find out for sure).  However, I don't agree with the reasoning that it's OK because you haven't given ownership to the conference yet.  It's true that this means it's not a copyright violation right now, but it's not necessarily compatible with publishing in the conference (it would prevent you from signing the publishing agreement when your paper is accepted if they want exclusive rights).  In practice this is not likely to be a problem, but it depends on knowing the conference's policies.

@Bob: For a SIAM conference it should be fine.

When the conference paper is accepted, you should make a note of that in the arXiv. If it's just submitted, you don't need to mention it (after all, it could be rejected).

In the fields I work in (pure math and theoretical CS), it is completely standard.  Most conferences/journals have clear policies on what kinds of publication elsewhere are acceptable, which (a) generally do not allow submissions that have been previously submitted or published in other comparable peer-reviewed venues, but (b) often explicitly clarify that preprint servers (e.g. arXiv) and authors’ homepages are not included in that prohibition, since they’re not peer-reviewed venues.
So: it is almost certainly OK — but you can probably set your mind at rest by looking up (or asking about) the journal/conference’s policies on submission/publication elsewhere.

I did, they said it's ok! BTW, if i get rejected, and someone else uses my work and publish it before i do, then i'd loose my chance to publish my work?

Glad to hear they confirmed it’s OK!  At least in my field, you certainly wouldn’t lose the chance to publish your work — if you get rejected from one journal or conference, you can then submit to another, regardless of whether you or anyone else has made further progress in that direction in the meantime.

Can I put the submitted paper to arXiv?

That depends on the conference/journal policy: check with them. Sometimes the author instructions on the conference/journal website might mention the policy.
For example, ICASSP 2020 is ok if you upload to arXiv your submitted paper, but ACL 2019 is not ok (doing so will result in your paper being rejected).


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Is it appropriate/accepted for a PhD student to organize/coordinate a special session at a conference?
I have the possibility to organize a special track/session at a small computer science conference, however at the moment I'm in the last year of my PhD study.
The special session includes an introduction presentation (related to an editorial) by the organizer (me in this case), some accepted papers presentations, and an "Open Discussion and Closing Remarks" chaired by me as well.
Although I believe to have enough expertise/general knowledge on the topic of the session, do you think it'd be appropriate for me as a PhD student to do this? Or is everyone expecting a doctorate or a professor to organize such sessions?

I have done so, and the participants weren't troubled by it at all. As a PhD student you are supposed to become an expert on your (very narrow) topic, so near the end you are supposed to know more than most about that one topic than others in your field.
Susceptability to status differs quite a bit from country to country and discipline to discipline, so whether this works in your field depends. I am Dutch, but the conference was an international one, and my discipline is sociology.

Quite to the contrary, pulling this off demonstrates that you’re able to collaborate, work in a field of special interest, and affords you recognition with a group of leaders. No downside here aside from the time drain and general annoyance of having to do it.


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How to ask a professor via email about possible postdoc positions?
I have sent emails to a few (about 10) professors in different Computer Science research labs (related to my research interests) to see if they have any postdoc position available.
However, I received no response at all. Of course, one assumption is that there wasn't any open position in those labs matching my CV. However, I was wondering if the structure of my emails is good/proper enough to motivate them to look into my CV?
Generally, I use the following structure with proper modifications according to the research theme of the person:

Dear Prof. X
  I am XX, a Ph.D. candidate in the XX group of YY
  University, which is under the supervision of Prof. ZZ.
I am about to finish my Ph.D. study (in the submission phase) which is
  about using different XX methods (X, Y, Z) for the problem of XX,  and
  currently, I am looking for postdoc opportunities relevant to my
  skills and interests.
I studied your recent research and projects and I noticed you have a
  strong research theme in XX and YY.  I am really interested in your
  ongoing topics of ZZ and XYX, and I liked your publications which
  focus on DD and FF.
Therefore, I am really interested to know if you have any open postdoc
  position at your lab regarding similar topics in XX and YY. I have
  attached my CV for your view, but I also welcome any opportunity to
  discuss the possibilities with you in any formal/informal meeting.
Thank you very much for your time.

I write the third paragraph based on the research themes or topics in the research lab that I find interesting and relevant to my current skills.
I'm not sure if the length is good enough, and should I mention which specific topics from their ongoing/past research I am interested in? or I should let it be open to any possible discussion? Also, I have not met them before, so I introduce myself at the beginning.

How long have you waited? Were the professors hiring?

@user2768: Two weeks.

As long as you actually don't have a PhD, and you never met the Prof. in person or he/she saw one of your posters/presentations, looks like a waste of time maybe for him. Also, did you check thoroughly on the group website, if there are open PhD/Postdoc positions? It's often explicitly stated there.

@user2768: For a formal applicaton I expect a few months to hear an answer. But for an informail email I think this should not take this long. Isn't it right?

If none of those professors are hiring then you can reasonably expect all of them to ignore, especially if you don't know any of them.

My concern is more about the structure and content of my email in this post! :D

Most professors are extremely busy and, especially if they are very good, receive a lot of emails every day. So make the email as short as possible. Your email seems good, but you can probably trim it down even further.
Also, if you're planning on applying for a fellowship or other funding, mention it.
Finally, I think it's a good idea to attach your best recent paper.

Generally such positions are advertised and often must be. Adverts are placed in such places as professional journals/newsletters and in newsgroups related to the field in question. Given this practice, blind letters may not even be answered and you can assume that there are no positions available.
Alternatively, if your relationship to your advisor is good enough s/he can ask around among acquaintances about the possible availability or future availability of such jobs. The advisor probably won't be ignored.
A third possibility is to attend a suitable conference or other meeting and get to know people and explore the possibilities in person, making yourself known, but also trying to build a network of future collaborators.
Your letter is probably fine, but it will probably be trashed. There are too many blind appeals for people to spend any time on them. In the old days of departmental secretaries it was different, but no more.

Once you mentioned you are already retired, otherwise I'd have asked you for any possible positions as well. :D

I've noticed cases of postdoc positions in Germany (where I look) which do not have any public advertisements. That's why I tried to email some of the labs.
Also, a part of my question is how specific I should be when I ask about possible research topics?

sometimes, if a prof is esp. interested in you, they will point out external sources of funding one could apply for. They often have a lot of experience which agencies often grant funding in their field. One could add this as a question to the letter

Response to your technique
I would not expect any sort of response to an unsolicited email like yours when there is no formal application or position. I have worked at undergraduate only institutions (i.e., no graduate program and no postdocs) and received a half-dozen or more of these solicitations each year, many of them tailored to match my research interests as you describe. I would imagine that faculty at a research focused school would get many dozen email requests like yours. Much like other forms of spam, there is a similar format and emails like this are often quickly pattern matched and discarded.
You may also be doing yourself a disservice. If there is a postdoc position that you end up applying for, you may have created a negative impression with your initial email because you did not apply through the normal means ("Rules don't apply to me!" and/or "I can't be bothered to look at the website!"). I do appreciate your go-getter attitude, but I suspect that you'll have a very low success rate. Maybe 1 in 100 (or 1000) professors will reply.
Feedback on your email
The format of your email seems fine. I might change "view" to "review" and consider changing the immediately subsequent "but" to an "and".
Suggestion of alternate approaches
As others have stated, many (probably most) of the postdoc positions will be advertised via the web. This is probably your best route. However, if you do prefer emailing inquiries, rather than spamming faculty directly, I would suggest contacting the hiring, graduate admissions, or departmental administrative assistant to inquire about positions. These are people who have a job that is about communicating with people like you. You will likely get a much better response rate.
Edited to add: My comments are from a US perspective. I can't speak to norms and expectations in other countries.

Indeed, I did not mean to send blind spam-style emails. I studied the research and publications of the group to see if they are in my skill area and if I like what they do ... But, based on what you and Buffy said, it may sound the other way around to the faculty! So, I'd better change my strategy! Thank you!

Lots of negative responses here, but it is possible that you will find something via cold emailing. When I got my first postdoc (in the US), I did it this way. In fact three profs offered me an interview, one turned me down after interview, one offered to support me to write a fellowship, and a final prof gave me a 2 year position on the understanding I used that time to apply for fellowships. This was 12 years ago, so perhaps things have tighten up since then, but its not so long ago.
As for your email.... I might be tempted to switch the second and third paragraphs around. The most eye catching thing for a prof is to see someone who really understands what it is they do.
Also, I wouldn't expect an answer from people that arn't interested - it would be polite, I know, but thats just how it is. Also, I'm not sure 0 out of 10 is such a terrible hit rate.
Finally, you might like to think about your targeting. You want to target profs that are going to a) have spare money b) are not so famous they get a million such emails every day. One place you might try is newish faculty at Ivy league or top level state universities. New faculty might well have start up money left that isn't preallocated to particular purposes. Also, being new, they are likely not to get so many such emails, particularly ones that really get what it is they do.

As with many things related to hiring, it is not what you know but who you know. The form-letter approach is very obvious (as other answers have mentioned) and professors who aren't actively looking are not likely to give your mail a serious look. Therefore, you should focus on working your advisor's network and your personal network. Then, even if there is a requirement by the country or university to have an "open" application, the job posting will be tailored to fit you and your experience.
If you don't have a network or nobody in your advisor's network is hiring (or you and they don't have a great relationship), then it's time to attend a conference or two and give some talks or posters based on your work. Then through coffee breaks or Q&A sessions you can get some personal interaction with some folks you're interested in working with and briefly inquire: "Hey, Prof. X, I really loved your (student's) talk! I'm actually getting my PhD on a related topic, working with Prof. Y in a couple months and I'm thinking about my next steps. Would you happen to have any positions open?"
Failing that, then it's time to hit the job postings, which are for faculty and projects where their internal network didn't have anybody suitable graduating soon and they have to start an actual blind search process. The hit rate here will probably be low, but at the very least you are explicitly applying for open positions and not risking sending an unwanted spam e-mail.
A final point to consider is that in your form-email you phrase something like "I noticed you have a strong research theme in XX." This screams out to me that what you really mean is "I did a cursory Google search and your name came up." If you do write an e-mail like this in the future (possibly as a cover letter or cover e-mail to an official application), you should never use verbs like "noticed" or "happened" or some other word that indicates some element of chance in your discovery. "oh i was just thumbing through Journal X and I noticed your name." The e-mail should seem more purposeful than this, that you are e-mailing them because of their eminence in the field and this is a carefully considered decision that you know will help you reach the next phase in your career path! In other words a little flattery will help you a little, but a little anti-flattery will kill you!


text:
Admission to Master's degree in Data Science without a STEM Background?
I became really interested in data during my 2nd year of college (I’m currently in my third year) and I recently began to think that undergoing a masters degree in data science can greatly help me study the field more professionally.
However I am not majoring in any STEM degree in my university right now (I’m an Economics major) And I’m very likely to graduate without any changes.
I’ve heard that people from very different backgrounds who have nothing to do with STEM are often successfully admitted to data science graduate programs, but how do people from a non-STEM background get admitted when all the requirements I’ve been reading in various university websites state that if you are from a non-STEM undergraduate background you need to prove in a different way somehow that you are ready (in terms of mathematics, statistics, etc) to undertake the program?
In other words, my question is this: How do people who have non-STEM bachelor’s degrees get accepted into these programs? Is there a really a certain way to prove your analysis capabilities besides your degree for non-STEM undergraduate students that can convince the admissions committees? If you know anything about this I would sincerely appreciate it if you could share your knowledge/experience and offer helpful advice.
I’m not thinking of phD (not planning to stay in academia for that long, I simply wish to study this field a bit deeper to the point where I can apply it professionally) so hope you can consider this as well when replying :)

You haven't told us anything about your background in mathematics, statistics, and computer science- as an economic student you might well have studied some of the important prerequisites such as calculus, linear algebra, probability, statistics, and programming.

There is a huge diversity of data science programs, from those that are preparation for a research career to those that are intended for retraining of non-stem mostly business folks who find themselves in a pile of data at work or trying to switch careers.

You said that you were studying Economics, wouldn't that be considered a STEM area (or at the very least, certain segments of Economics). I know that for instance, financial economics have been considered a STEM subject by certain institutions (e.g. Columbia)
Having that said, it really depends on the program that you are applying to. I've seen people from a broad spectrum of backgrounds enter into what would traditionally been known as quantitative sciences. I've seen psychologist and linguists working at an engineering departments for instance. What I would do is to consult the person in charge of the program that you are interested in, and ask about the specific requirements. From your own perspective, it would certainly help to take on additional courses that deal with data science so you have some experience with it, even if your primary degree isn't in a "STEM" field.


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Citing photographs shared through social media in thesis
I have included two photographs related to my research in my thesis. The photographs were shared with me by my connections on Facebook as private messages. How can I state the source of the photographs in the thesis? My idea is:
Figure 1. Caption. Photograph taken by NAME, reused with permission.
(I requested my connections over Facebook message if I may use the photographs they sent, and they had no issues.)

Yes, that’s fine. Get written permission, though. Some such things are marked as “private communication.”
Save the permission. Don't depend on FB to do it for you. Cache it, or get an email with permission, perhaps.


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Permission to use video still (screenshot from a video) in a research article
I need to use YouTube video stills (images taken as screenshots from a video) in an article I am currently writing. How should I take permission to use the images? The email or other contact information of the channel owner is not provided in the videos. Would it be sufficient if I properly cite the source of the image in the article as if citing a website (example below)?
Figure 1. Caption (Author, year)

Define 'article' - is this going to be published by a journal or magazine? In that case they will require you do explicitly have obtained the right to include that material in the publication. If for a class assignment, ask your professor.

The article is for a scientific journal.

A proper answer would probably need to come from an attorney versed in copyright law. Perhaps your university can provide such assistance. With no notice to the contrary, assume that the work is copyrighted with all rights reserved. This implies that normal fair use and scholarly and/or critical exceptions apply, and these can vary.
But, informally, it would depend a bit on what you wish to capture. If it is a constructed graphic then that might be construed as a "complete work" within the larger whole, making it less likely to be acceptable. And if there are several such screen captures from the same video it also can be problematic.
On the other hand, copyright law (most places, but variable) permits some exceptions for scholarly work; looser interpretations of limitations. And the same is true for works of criticism.
However, I think you need to be more specific than you suggest in any citation if it is permitted. The date that you accessed it needs to be included and probably a time within the video in which it can be found.
In general, you don't need permission to cite from a work, but you do need to be careful about how much you "quote" and its relation to the "whole work."
But for specifics, talk to a lawyer versed in IP law.


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How to organize the thesis chapter on literature review when there are only a few past works on the topic?
I have to include an individual chapter on literature review in PhD thesis. While going through the theses of seniors in the library, I found that typically the chapter is 30-40 pages long and covers a review of more than 30-40 articles.
The chapter is generally organized into different themes, each related to a specific aspect and a review of studies that address the theme.
There are only about 15 research articles relevant to my work. Moreover, when I arrange them into four themes, I get only 3-4 articles in 2 of such themes. I am not sure how do I organize the chapter with scarce literature.
When I brought this issue to my supervisor, they suggested adding reviews of other studies that are of the same broader domain as my work but are not directly relevant. I think this suggestion may negatively impact the thesis review.

Why do you think your supervisor's suggestion may negatively impact your literature review?

Because when the thesis is submitted for evaluation, the examiner may think that the review included in the chapter is not relevant (maybe tangential) to the work.

Your issue seems to be bigger than what you're explicitly asking about. If you are able to understand and communicate connections between your narrow topic and the broader field, as well as possible implications of your results in that broader field, it normally is a big plus. By contrast, narrowing the scope too much is usually crippling and indicative of someone lacking either understanding or metacognition (being aware that something they know is not totally trivial).

@Lodinn Thanks for the comment. Could you elaborate on 'metacognition'? I didn't get it.

Gladly. In this case, I mostly it's usually wrong to think something is too well-known to talk about it and be concerned with that talk being perceived as "padding" the thesis. While you are working on a narrow topic, it feels like everyone knows a whole lot about it. This does not hold true for even the thesis committee. Dissertations have this above average tendency to escalate really quickly from "1 + 1 = 2" to differential topology in a couple of pages, that's true, but they do still include "1 + 1 = 2". Came up recently, relevant: https://xkcd.com/2501.

Talk to a fellow student generally familiar with the field but working on something notably different. You'd be surprised how much context you need to bring up before you even start explaining your own work. Aim at approximately that level of competence, not your supervisor's one, when writing the thesis. In other words, you should know that others don't know as much of what you know about the topic. Duh, that metacognition stuff does lead to confusingly sounding sentences...