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How should I reference my Github repository with materials for my paper?
I am writing a paper describing some of the research I have done. As part of my work I have developed an open-source library and made it available on Github.
How should I link to it?
Should I cite it in the bibliography or make a footnote with the link to the software?

See this paper (published full text and arXiv version) by Kruse & Agol, they linked their repository on GitHub in the acknowledgments section.

Related: How do you cite a Github repository?

Such resources, especially if they are a supplementary to the paper, i.e. in some sense a part of it, should be referenced in a footnote and not in bibliography.
Do include not only the URL but also a short description; and do try to keep that URL valid - once you publish that link, it's frozen forever.
It also helps to include the opposite citation. In the readme file of that library, include full citation information of your paper. This will allow others to gain extra information about the methods, and also give you citations if/when others build upon your work.
Some publishers also support binary attachments for supplementary information. If they do, you should use it - prepare a package of the current stable version and upload it there. It allows for reproducability, as a specific version is referenced which relates to the actual paper, and not some improvements done in 2020 that change everything; and it also attaches all relevant information together with the paper at the publisher's site, which will stay valid even if that github repository goes away for whatever reason.

Github may disappear in the future, you may close your account. I think it is the journal's job to keep a snapshot of the code for the record, plus a link to the repository for folks to get updates.

This depends.  I develop software as a mathematician and in all the papers I have been a a co-author, we have cited the software (it's widely used in our community).  I would probably cite the software on your university page, as this would be fairly stable and around, unless you are not tenured, and then on your site, link to the latest version of the library on github (see http://homepages.math.uic.edu/~jan/download.html) as an example.  The other option is to write a 'software' paper and find an appropriate journal to publish the library, at which point you can cite that paper from then on.

@Davidmh In an ideal world it might be the journal's job, but I've definitely had code I wanted to share for an article where the journal wasn't going to or couldn't handle archiving it.

In previous papers, I've used something like (source code available at github.com/fomite/brilliantwork) when describing the software methods used.
However Figshare now allows you to directly import a repo from GitHub, which will give you a DOI you can reference as a citation. This also provides a benefit for having a "snapshot" of the repo at the time of publication, for repositories that will continue to be worked on. That, plus the ability for me and other people to cite the repo directly (and thus be able to get some traditional citation metrics to show the impact of the software), is what I'll be doing in the future.

+1 for snapshots; I would say in general, when citing, be very sure you give a version. git provides hashes corresponding to particular commits/versions: That's what I'd include in a citation (having not used Figshare).

@MatthewG Agreed as to the version. The Figshare upload process will actually create an entirely standalone code base that will be frozen in time.

I was going to post the same thing for the same reasons.

An alternative view on Figshare. It doesn't mean that Figshare is bad but one does need to be careful with the fine print when uploading there.

A citation is a reference to a research object.  Prior to the DOI, this reference contained the information somebody would need to physically locate the research object, whether it was a book, journal article, or dissertation.  Although the jury is still out on how to provide general references to digital research objects, a Git repository contains a canonical reference, the SHA1 hash of the commit.
If you would like to refer to your Git repository in such a way that it is easy to locate in the future, you should provide not only the URL where it is now, but also the short name for the repository, the lead author[s], and the SHA1 hash of the commit that you produced your results with.
Here is an example URL from GitHub that contains the commit: https://github.com/hashdist/hashstack/commit/4c72950a0f6eb9cc1cf63cd640f3e6b82c9ce9c0
I don't recommend uploading your code to Figshare until they've fixed their ability to accept code licenses.
Update 14 May 2014
GitHub and ZENODO have partnered together to upload code for a DOI under a flexible license.  Obtaining a DOI for your code should be as simple as following the instructions on the GitHub Guide to Making Your Code Citable.  Here's an overview of the instructions:

Choose your repository
Login to ZENODO
Pick the repository you want to archive
Check repository settings
Create a new release
Check everything has worked
Mint a DOI

It's really that easy.  There's no current reason not to use this as a default approach for citing your software.

The specifics depend on your field, and I think most areas have very loose guidelines as to what to do in these situations. The only hard-and-fast rule is that you need to get it past your reviewers and your editors, who will tell you if the style is inappropriate. Other than that, the most important thing is that you yourself are happy that the citation is getting your GitHub repo the most visibility possible. Finally, you should consider your paper from the perspective of readers that may want to use and cite your code, and who will naturally look to your writing for how to do that.
In general, I would recommend citing the code in the bibliography, as one more reference. The advantage of this is that your article's references will be listed and indexed separately, and (with luck) this will register as links pointing towards your repo, which will become more of an advantage if more people cite it. Such a citation should have

the name of the programme,
the URL of the repository, and
a clear indication of the version cited and its date.

An example citation is

G. A. Worth, M. H. Beck, A. Jackle, and H.-D. Meyer. The MCTDH Package, Version 8.2, (2000), University of Heidelberg, Heidelberg, Germany. H.-D. Meyer, Version 8.3 (2002), Version 8.4 (2007). See http://mctdh.uni-hd.de

You should also describe the code within the text when you first cite the code, and provide a complete enough description within the paper that readers do not need to go read any additional information to continue reading your paper, because it needs to read like a single, coherent piece of work.
Alternatively, you can cite it in a footnote, indicating the name of the code and its location. This would be a good place to include the description if it is brief. Another choice is to do this in the acknowledgements, as giordano points out. However, I think these make your code less discoverable to both humans and search engines.
As I mentioned before, you should mould your citation of the code in the way that you'd like others to cite it. It is also desirable that you include, within the pages describing code online, a description of how you want people to cite the code. Some examples are GAMESS UK, MCTDH and MOLCAS, or the ones in this question. Having such a description will strengthen your position should referees or editors not like your preferred style. You set the terms on which your code gets cited!
Finally, as others have mentioned, you should make sure that the URL you point to is stable, as you will be unable to change the link in the published paper once it goes out. This is a separate question altogether, and there are a number of ways to do this - including supplementary information to the article itself, separate repositories for academic code, and of course GitHub itself - and you should strive for the most stable solution possible. Is it likely that the repository will someday get closed or moved? If so, you should consider alternatives.

In addition to referencing the repository within the paper, you should also make sure that the link between the paper and repository is part of the paper metadata. Specifically, if you are depositing the paper on arXiv, you can use the latest feature that integrates with Papers with Code to record the link between your paper and its implementation.


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Should academics allow student to submit a possibly flawed paper?
I let a student associated with me (but who is not really my PhD student) submit a paper to a fairly-local meeting (some people do come from abroad). Because I'd been tasked with supporting the student, I let him put my name on the paper and also helped with the presentation, but I found the content unconvincing.  However, he was exploring an area that I knew little about and had little interest in, so I'd hoped he'd get reviews & (if accepted) discussion that would give him more guidance than I have been able to.
In fact, no one seemed as sceptical as I was of the work, and it got into the meeting and was presented both in our department and at the meeting with if anything positive comments.
However, yesterday I got an email from a postdoc who was cited in the paper (the student compared himself to the postdoc's work, the meeting's papers are on a website, Google Scholar alerted the postdoc) and was incensed at the paper's low quality and inaccuracies.  I think the postdoc is being a bit paranoid, but is broadly right on the technical issues. I have also previously noticed that one of my more-successful colleagues I've been collaborating with recently had a less tolerant attitude towards student publication than I do.
I'm wondering if I should put more effort on quality control.  The cost would be possibly stifling a student unnecessarily if I'm wrong, and allowing fewer students to have fewer presentation experiences since I'm already working flat out keeping up with giving feedback on their journal articles & dissertations.

Strongly advising a student against publishing a paper with major technical issues isn't "stifling" the student, it's helping the student avoid a mistake that can significantly harm their career.

Why do people reply in comments instead of answers?  Surely that defeats the purpose of stack exchange, which is to allow the community to weigh in on answers as well as questions?

I'm not attempting to answer your question (Should academics allow students to submit...). I only wanted to point out what I believe is a fallacy in the question (the cost would be possibly stifling a student).

@user14470: Short, one-sentence responses are not considered answers in the Stack Exchange format.

I let him put my name on the paper...I found the content unconvincing — What? You were unconvinced by your own content? Or worse: You put your name on a paper without contributing any content?

I'm confused about one thing.  Did the paper get accepted?  If so, then maybe the conference's standards are also a concern.  If not, then how does this postdoc know about it?

@user14470 Weak (i.e. technically correct, but the content is cutting-edge or considered an important topic) or wrong (from inaccurate or sloppy to incorrect)?

Letting a student who is interested in submitting a paper do so if it is not up to your standards is dubious but debatable. Putting your name on a paper you did not contribute to is unethical. Putting your name on a paper you don't even agree with is crazy.

The paper used some of my ideas, and presented a working system, but the analysis and worth of the system are what's in question.  Ironically, the postdoc just admitted that the idea if not implementation was great and that + the weak quality has helped him find a new collaborator who wants to fix it.

@JeffE yes it was accepted. I was not certain of the value of the contribution, and trusted review to sort that, but basically the argument is that having a known author and/or institutional affiliation affects the prior on the quality of the work & skews the review process.  I've never bought that argument, but am using this question to reconsider it.

@aiesmail I was trying to get ff524 to put the effort into a real answer that could be debated. The fact you can't reference more than one person in a comment shows there isn't meant to be debate here.

@PiotrMigdal the system works, but I suspected (& the postdoc proved) the analysis is wrong and it wasn't really beyond the state of the art.

I'm confused by your phrasing "submit and possibly publish" which seems to suggest that you'd consider submitting something and then decide not to publish it. Why would you submit something without the intention of having it published? Doesn't that just waste the journal and referees' time? If you want to get comments on your work, mail it to colleagues and put it on the ArXiv.

@user14470 I edited the question. Do you like the changes?

If you believe the paper to be flawed, but don't understand it well enough to be sure, one solid option is to tell the student exactly that (and exactly why), and to refer them to a colleague who can answer the question more definitively.

What field are you in? I suspect this makes a difference here. Personally, coming from an artificial intelligence / artificial life background, I would say that when you publish at a "fairly local" meeting it's expected to be preliminary work, and that getting feedback is the primary purpose of presenting it, especially if it's student work. So I would regard this as a case of the system working correctly. But I suspect it rather depends on the norms of your field and the nature of the specific meeting.

In my opinion, since you aren't the supervisor, it's not your role to stop the student from publishing.  If you are an expert in the subject of the publication and you don't think it's strong, you should advise them not to publish and explain why, but that's as far as you should go.
However, you "let him put [your] name on the paper" -- a paper which, by your own admission, you don't know much about!  Regardless of whether the co-author is a student, and regardless of the quality of the paper, the behavior you're describing is wrong.  In my field, it's actually forbidden by at least one of our professional societies:

All the authors listed for a paper . . . must have made a significant contribution to its content.

This is generally construed as meaning you should have done a significant part of the research and a significant part of the writing.
Now, violating this rule for a paper that you don't even have a high opinion of seems not only unethical but foolish.
I realize this is a harsh-sounding answer, but you have posted the question anonymously so I hope you don't mind me being frank.

I agree with the sentiment, but most of the text of this answer does not address the question at all.

@ff524  You're right.  But if I ask whether I should do X or Y, and the right answer is to do Z, I hope that someone will tell me so.

I would upvote this, but I don't have sufficient reputation on this account yet.  I think I am making an error in agreeing to treat the student like one of my own, and should keep my name off his papers even if that does discourage him.  Thanks for being blunt, and I agree that Z is often the issue with an apparent X|Y question.

I haven't said the question is "answered" though yet as there may be more things I haven't thought of.

Whether he is "your own" student is utterly immaterial. Either you contributed to the paper, (therefore) deserve coauthorship, and (therefore) are responsible for its content, or you didn't, you don't, and you aren't.

Standards of authorship are completely different in mathematics vs. the life sciences. This level of involvement would not be totally unusual for a co-author in the life sciences who was the student's mentor, for instance. However, even there, adding your name to the author list makes you (at least partially) responsible for the paper's content. I wouldn't call it "unethical" to add your name in this situation but given your reservations, it seems unwise. A less formal conference or meeting, without published proceedings, might be a better venue for this type of "trial balloon."

@PatrickB., that in some fields unethical behavior is standard does not make it any more ethical.

@Kostya_I That's tautological. Unethical behavior is of course, by definition, unethical. I think most people would agree with the underlying principle that it is unethical to misrepresent your contributions to a paper. However, my point is that what authorship is generally understood to represent does demonstrably differ across fields. Therefore, whether you are in fact misrepresenting your contributions to the paper may depend on what exactly is expected of authors in that field (in the life sciences this will also depend on your position in the authorship list).

@PatrickB. the list of journals adhering to ICMJE guidelines, which explicitly prohibit the last authorship practice you are alluding to, seems to range across all life sciences I can think of. By contrast, I've never seen the practice of authorship based on subordination codified as accepted by any professional body. And yes, if it is generally understood that the king is naked and everybody is just lying, it's still a lie.

@Kostya_I What last-authorship practice exactly do you think I am "alluding to"? It is entirely possible from what the author wrote that they did in fact fulfill those four ICMJE criteria, especially given their clarification below (there is of course not enough information to make an unambiguous determination): they contributed to the study design, revised the paper critically, approved the submission, and recognize that they are accountable for the publication. My point, again, is that those criteria are not uniform across fields.

I think we can distinguish two situations here:

The work is not convincing because it is still preliminary. In this situation, even if the results might prove wrong latter, I would see no objection to send the student to present a talk or a poster about it, as long as the preliminary nature of the work is clearly stated. I might give him the opportunity to meet other researchers, find new ideas or even build collaboration based on these first results.
The work is not convincing because the results are flawed, the protocol is not robust or the techniques might not be adequate. In such situation, as @ff524 said, I would advise not to let the student present the work in public meetings. It will be unproductive both for the student and the advisor (as you experienced).

This assumes you can reliably discriminate the two cases.  Preliminary results may be flawed, and you may not have detected that flaw.  Is it worth stifling the student until you can invest the time to be certain there are no flaws, or do you engage the academic community in that checking?

@user14470 If you can't invest the time to tell whether there are flaws, you absolutely should not be an author of the paper.

Well, I disagree with that – again, an arbitrarily brilliant paper can have arbitrarily subtle flaws, you invest some time in any paper, but how much for which target audiences.

@user14470 I think as long as you state clearly that the results are preliminary, it is not a problem to share them, even if further experiment prove them wrong. The preliminary sharing might bring some interesting discussions.

@William If you'd put that as an answer instead of a comment I wouldn't have had to add my own (see below).

@user14470 sorry for that... I edited my answer accordingly.

As a mathematician, I had the same first reaction to this question as several other answerers and commenters: It's wrong to be a co-author of a paper that is just someone else's work.  But I need to temper that reaction with the fact that other fields have rather different standards. In particular, in some (maybe even all?) of the experimental sciences, it is standard practice for the head of the lab to be a co-author of everything that comes out of that lab, whether or not the head actually did any of the research or even understood the research. As far as I can tell, the rationale for this is that the head of the lab gets the grants that make everybody else's work possible. The question here suggests that this sort of thing might be involved here ("I'd been tasked with supporting the student"), so co-authorship might not be quite as crazy or as unethical as it looks to a mathematician. Even in the experimental sciences, though, the head of the lab is (as far as I know) expected to make sure the work is good (and is held responsible if it is not).

Good answer.  It isn't only money though, the money comes from ideas.  Lab members benefit from an intellectual atmosphere, direction, a set of literature other lab members have identified as being worth extending, etc.  But anyway (as I've clarified in the comments & in an answer I've floated) I did contribute to the paper.

Sorry to break the "don't answer your own question" norm here, but I want to float an answer.  First, I did contribute to the paper (suggested direction of research) and the system presented does work.  My issue was whether this new system was a real academic contribution, which I left up to the academic process to determine.
Overnight, the postdoc who initially complained a) admitted that the idea was great if not entirely well executed and b) told me he's found a new prestigious collaboration writing a better paper.  In my mind, this is how academia is supposed to work.  So my proposed answer is "yes".  I leave it to you folks to vote up or down.

suggested direction of research — This is not universally regarded as sufficient contribution for coauthorship.

You answer is Academics should allow students to submit a possibly flawed paper because I did it once and it turned out not to be so flawed?

My answer is "students should be allowed to use the review and presentation process the way it's intended to be used, to contribute plausible ideas to the community and let the community correct, improve, or reject them". As to whether even weak students deserve the branding of an academic institution including its faculty, well, students probably suffer as much as institutions from bad admissions decisions, and once in probably deserve a level playing ground to try to prove themselves.  But I for one am not letting myself get dragged into this situation again.

My answer is, to what end? What purpose are you serving by allowing such work to be published? The community does not benefit, as the work is subpar and (apparently) flawed. The student does not benefit, as (a) the experience is different from actual publishing with more rigorous review processes and (b) their reputation is tarnished. You receive no benefit for the same reasons.
The only possible benefit I see is that the student gains experience in informal writing and presenting, which they can already gain through group lab meetings without the possible repercussions relating to their reputation. In short, this appears on all fronts to be a pretty bad practice.

The benefit is getting the opinion of more people than are in the lab, especially since it is a new area for us.  I now consider (see my floated "answer" / update) that I did get a benefit, since the postdoc is now in a new collaboration, writing a better system AND provided a critique of the student's work that the student couldn't have gotten in the lab & didn't get from the reviewers.

@user14470 I guess that this comes down to how much risk you're willing to take for the possible good outcome. I would venture that the risk here far outweights any possible benefits. Do remember that you still can't observe possible negative reputation effects, and you may never know the full negative effect of this paper.

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Reusing someone else's code in my own publication
I am about to submit a paper in which one of the algorithms I used is heavily based on the code available on one of the TensowFlow tutorials. In fact, I mostly copied the code from the page and made the necessary modifications for my specific case. I did cite, in the paper, both TensorFlow and the page, and disclosed that the neural net architecture I was using was based on the one on the page. The licensing terms of the code (Apache 2.0) mention that the user is free to build upon the code and redistribute it.
I am not in CS, and am applying the model to a specific problem in my field. However, in copying the code (which I believe will not be disclosed) I am afraid I might be doing academic misconduct. However, on the other hand, if that was the case, using open-source libraries would also be frowned upon, given that the user is essentially copying code.
Will I be committing academic misconduct or anything that is ethically frowned upon in academia by submitting results parts of which were based on copied code?
PS: In response to a comment, I cited TensorFlow and the webpage in the paper, which will be published if accepted, but the code itself (which was heavily based on the code available in the webpage) won't be posted anywhere (as far as I know).

I'm confused. In the first paragraph, you say you cited the page but I the second paragraph you say the copy will not be disclosed. Those seem mutually exclusive to me. Could you elaborate?

You are doing the right thing as long as you clearly cite your sources. In other words, as long as you say that your code is based on someone else's code, and as long as you clearly cite what that "someone else's code" is and where a reader can find it, you are ok.
The fact that the basis for your work is open source does not require you to share your code as well (though that would clearly be in the spirit of open source software). Open source software generally just means that if you give someone else an executable of your implementation using an open source package, that you also need to give them the source code of your implementation. (The details vary based on the license in question, but that's the idea.) As long as you write the code only for yourself, there is no need or requirement to share it with others.

I would recommend very strongly to make your own code open source. It makes your "experimentation" reproducible. IMNSHO every software funded by public research money should be open source.

Note that journals or the terms of your grant may require you to share your code. Plus it is good practice to do so. It can't be too horrible to show other people if you copied most of it from a third party anyway.

If you include other people's work in your paper, you need to reference it.
"Other people's work" includes - but is not exclusive to - the following :

words you're quoting from a lecture, a conversation or other oral context
words you're quoting from a paper, book or other publication
photographs that you include from a paper, book or other source
graphs that you include from a paper, book or other source
source code that you include from a paper, book, Github page or other source

The Apache 2.0 license allows you to legally build upon the code and redistribute it in pretty much any context. Just attribute any borrowed code to the original author(s) either in the comments of your code or as a normal source reference and you should be fine.
Remember that we're all standing on the shoulders of giants. There's nothing wrong with borrowing other people's code if that code is properly licensed (as it is in your case). Just don't pretend it's your own and you'll be fine.

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What are the cons of studying in a Ph.D. program that takes much time to confer the degree (about 1 year and a half or 2 years, after dissertation)?
Currently, I am in the second year of a Ph.D. program. However, I am really worried about the amount of time Faculty takes to confer the degree, since It could be detrimental to my academic and professional future.

Could you clarify why you think "it could be detrimental to my academic and professional future"?

Tthis is a potentially useful question  but I think the answers might be really  country or institute dependent.

Admittedly I come from a system (the US) in which the PhD is always conferred at the end of the semester during which it is successfully defended, but....2 years?!?  That seems like an unreasonably long time.

Many thanks for your interesting answers. I think It could be detrimental in terms of salary (probably universities pay more if I have the degree). At the same time I think it could be a problem if a want to get a tenure track position or to apply to a postdoc position. However, your answers have helped me to understand different solutions and perspectives. (Excuse for my English. I am a native Spanish speaker)

Many academic positions after graduation are dependent on proof of degree completion, so this could potentially be a problem.  The NSF math postdocs, for example, require a signature from the school that you've completed your degree, and some positions explicitly require a copy of your transcript or diploma that indicate your completion of a Ph.D. program.  The institution where I completed a postdoc was in this latter category, and would only issue an informal offer (and not an official contract) until they received final documentation of my degree, which in my case took several months to arrive after graduation.  One practical problem I faced as a result was difficulty arranging for housing at my postdoc institution; most rental companies want to see their tenant's signed contract as proof of income.  I am also aware of tenure track jobs where the contract specified that the tenure clock only started after conferral of a degree.  I suspect some institutions would be willing to bend the rules a bit in unusual circumstances, but it certainly could be a problem.  (Note that this answer is based on experiences of myself and colleagues in the US.  I have no idea how this practice varies in other countries.)

my post doc employer (US) did not require such certification. So it really is employer-dependent.

It's understood that it takes some time from the date the thesis is turned in until the examination, and then again from the examination to the final degree conferral. Consequently, most employers—business, industrial, and academic—who hire Ph.D.'s will be aware of this, and will allow people to start working once they have finished the defense (and sometimes even before then!).
The date of conferral has some impact on when you're eligible to apply for certain programs, but even then, it's usually a window of X years after the conferral date, so you're not going to "lose out" on opportunities just because your faculty takes longer than average. Even in the event where it's after the date of defense, usually schools can issue documentation that the defense took place and was successfully passed.

In which countries is this true?

Once you've passed your PhD, ask your supervisor to write and sign a letter (on headed paper) that says you've passed.* (Explain to your supervisor why you need it.) That letter should be convincing enough to anyone that asks, even though it isn't a formal certificate.
*Selection of the right words is crucial. All statements should be honest.

A letter from your supervisor will not be convincing to (e.g.) many North American universities.  For instance, in order to register as a postdoc in Canada I had to submit a copy of my PhD.  I know the same is true at many (though certainly not all) universities in the US.

@PeteL.Clark There are many ways to make a letter look convincing. Especially when a supervisor doesn't like bureaucracy. This method mightn't always work, but it should work in many cases. (Thinking of failure cases increases my confidence. E.g., a North America university questions the document's validity, verifies the supervisor's identity, and calls them. The supervisor confirms validity.)

I had the occasion to inquire into the rules at my own university.  In order to hire a postdoc, they either have to have their degree or a note from the registrar saying that all the requirements have been fulfilled and the degree will be conferred on such-and-such a date.  I have been involved in cases where this forced the postdoc to get the degree earlier than was convenient for them.  Again, this is not the case at all North American universities, but it will definitely be a problem in certain cases.

There are of course cases when this won't work. In case of the requirement for "a note from the registrar saying that all the requirements have been fulfilled and the degree will be conferred on such-and-such a date," that seems possible to resolve: A student can draft a note that the supervisor agrees upon, and the supervisor can urge the registrar to sign. Of course, the registrar may refuse. But, it's worth a try.

¡Many thanks for your interesting opinions! You have helped me a lot to analyze possible solutions and cons.


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What is the difference between 'withdrawn' and 'retracted'?
I think both withdrawal and retraction are done when there is something wrong with the journal article.
What is the difference between them?

This is one example of Latin synonyms for Old English words of Norse or Germanic origin owing to this dual heritage: Value/worth, cow/beef etc.

Withdrawal happens before the article is published, during peer review. Retraction happens after it is published.
By the way withdrawal does not imply there is something wrong with the journal article - it could e.g. mean the authors think the journal is taking too long with peer review, and they'd rather submit elsewhere.

Also, withdrawal does not necessarily mean there is something wrong with the paper. Authors withdraw for all kinds of reasons, including that the journal took too long to assess the paper.

Withdrawn can also mean the authors were unable to comply with some required step for the journal like presentation of the work in person at a conference, or a publication fee.

The distinction may not be always the case, e.g. "Publishers withdraw more than 120 gibberish papers".

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How common are interviews for Computer Science PhD programs?
I am applying for admission to several CS PhD programs (in the US), and I am just wondering how common it is for schools to request interviews with the candidates. I will be out of the country for much of January which seems like a prime time for interviews to take place, so I am wondering if I should start making alternate plans. Thank you!

Extremely uncommon
I understand that interviews are fairly common in other fields and in other countries, but they are rare in computer science in the US. In my experience (student, postdoc, or faculty at five different American CS departments; multiple years on graduate admissions committees; dozens of recommendation letters for CS undergraduates applying to graduate school), very few (if any) American computer science departments include interviews as a standard part of the PhD application process.
There are rare exceptions, though, usually involving prospective advisors calling up applicants directly.  The most common reason for a phone interview in my department is to assess their English fluency, especially when the applicant's test scores are borderline, or there seems to be a discrepancy between their test scores and the fluency of their statements.  I'd be surprised if this happens more than 10 times a year, and we get 2000 grad applications each year.
When I applied to Berkeley's PhD program, I was already a PhD student at UC Irvine. My future advisor and the director of grad admissions called me to ask why I wanted to move when I seemed to be succeeding in my current program (good advisor, good research progress, and so on).  I suspect they also wanted to understand the discrepancy betwen my grad school grades (good) and my undergrad grades (terrible).
In short, as long as there's nothing borderline or non-standard in your application, it's unlikely that you'd need to be available for an interview. And even if someone does want to interview you, they're much more likely to want to do it by phone or skype than in person.

JeffE's experience in this area is much more extensive than mine, so please weight his answer much more heavily than mine.

Thank you! I had suspected as much, but I was confused when I heard friends from other fields had gone through numerous interviews. Greatly appreciate the help.

From what I've seen, some form of interview is not uncommon (and a good sign, since it means you've passed most of the initial filters and are being considered seriously).  In such cases as there is an interview, however, it can often be conducted over the phone or internet. Thus, your travel is unlikely to be a major issue as long as you will have good connectivity and be able to receive messages sent to the contact information you provided.


text:
How and why would I "claim ownership" of a paper on arXiv?
My colleague has just published a paper on which I am co-author. This is the first paper that has my name on so I am not familiar with the systems yet.
He has received an email from arXiv to confirm his submission and it gave a "paper password". It recommended he forward this to the co-authors so "They may use it to claim ownership."
What does this mean, why would I want to do it and how do I go about it?

By claiming ownership the paper appears in your arXiv account, and you can manage it there (e.g. uploading a new version or adding a journal reference). It also means it will appear in the list of papers associated to your arXiv author identifier, which is especially useful if you have a common name.
You can claim ownership by logging in to your arXiv account (create one first if you don't have one yet), and then click on the "Claim ownerhsip" link. It will ask for the paper ID and the password.
Edit: As Wrzlprmft pointed out in the comments, it will likely also add to your endorsement record, meaning that you are less likely to need to be endorsed for future submissions you make yourself, and will sooner be able to endorse others.

Just for convenience, here’s the “claim ownership” link directly: https://arxiv.org/auth/need-paper-password

Note that the "claim ownership" feature isn't restricted to the authors of the article, and access to articles can be given to other people in some workflows (e.g., the copy editors of epijournals).

For anyone as confused as me: the paper password was sent in the confirmation email to the article uploader. It is not available via the webpage.


text:
Getting a dump of arXiv metadata
Prompted by discussion in this post on Meta.MathOverflow.net I got interested in comparing usage of tags from MathOverflow to submissions in the respective disciplines of arXiv. (Vide a similar idea of language popularity, GitHub vs StackOverflow (or one from 2015).) Moreover, as people often use their real names on MO, it may be interesting to check the overlap of mathematicians.
The question is, how to get such data?
There is arXiv API, but for bulk downloads of metadata they recommend Open Archives Initiative (OAI). Yet, as I see, it can query one article at a time, and one needs to know its id. So without knowing arXiv ids beforehand, it turns into a guessing game.
There are some plots in arXiv usage statistics, yet I don't see this exact data.
Also, one can get total submission to math from links in Mathematics -> Article statistics by year, but it misses the splitting into subdisciplines.

Looking at the OAI, it looks like there's also a cap in place to prevent a high amount of leeching (503 after a certain period).

@Compass In any case, do you know what are the relevant queries?

I have been playing with this a little bit, using the Perl package Net::OAI::Harvester.  The OAI API does provide a way to download all records (ListRecords), rather than just one at a time (GetRecord).  It looks like arXiv will give you 1000 records and then return a 503 asking you to retry after a delay, so it's not to prevent excessive leeching as @Compass says but only to rate limit.  Net::OAI::Harvester knows to how to retry (listAllRecords) but not how to delay, so that may need to be done manually.

+1; I've been wondering the same thing.  I downloaded the entire database of arXiv articles via the dump on Amazon S3, but I haven't found a simple way to get my hands on the associated metadata.  It'd be a big help for the sort of linguistic analyses I've thought of running.

@Mark : you might try asking the question on http://opendata.stackexchange.com .  (disclaimer : I'm currently a moderator there)

@Mark See my https://s3-eu-west-1.amazonaws.com/stared/arxiv_math_metadata_2015022.zip and https://s3-eu-west-1.amazonaws.com/stared/arxiv_metadata_dump_20150223.zip.

Arxiv metadata and fulltext have been made (more easily) accessible in 08/2020.

https://blogs.cornell.edu/arxiv/2020/08/05/leveraging-machine-learning-to-fuel-new-discoveries-with-the-arxiv-dataset/
https://www.kaggle.com/Cornell-University/arxiv

The full set of PDFs is available for free in the GCS bucket gs://arxiv-dataset or through Google API (json documentation and xml documentation, gsutil). They are grouped into several .tar.gz files in the tarpdfs folder, the complete set is about 1.1TB in size.

So something good is coming out of machine learning at last! (Though I'm a bit surprised to see the PDFs rather than the source codes in the dump. And wow -- the metadata alone is over 4GB??)

My main confusion was not realizing that The Open Archives Initiative Protocol for Metadata Harvesting is a separate protocol, not a subset of arXiv API.
In this case, the relevant queries are ListIdentifiers (10k items per query) and ListRecords (1k items per query). To get just identifiers we need to write:
http://export.arxiv.org/oai2?verb=ListIdentifiers&set=math&metadataPrefix=oai_dc

It results in 10k identifiers in the following form:
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2015-02-16T19:28:22Z</responseDate>
  <request verb="ListIdentifiers" metadataPrefix="oai_dc" set="math">http://export.arxiv.org/oai2</request>
  <ListIdentifiers>
    <header>
      <identifier>oai:arXiv.org:0704.0002</identifier>
      <datestamp>2008-12-13</datestamp>
      <setSpec>math</setSpec>
    </header>
    ...
    <header>
      <identifier>oai:arXiv.org:0712.1769</identifier>
      <datestamp>2011-06-23</datestamp>
      <setSpec>math</setSpec>
    </header>
    <resumptionToken cursor="0" completeListSize="249546">760571|10001</resumptionToken>
  </ListIdentifiers>
</OAI-PMH>

As there are more results, to get next batch we need to specify resumptionToken, in this case:
http://export.arxiv.org/oai2?
   verb=ListIdentifiers&resumptionToken=760571|10001

and so on.
Other useful parameters are from and until, e.g. as in
http://export.arxiv.org/oai2?verb=ListIdentifiers&set=math&metadataPrefix=oai_dc&from=2015-01-14&until=2015-01-14

To directly get categories (bear in mind that set=math specifies mathematics, but there are no smaller subsets), one can write:
http://export.arxiv.org/oai2?verb=ListRecords&set=math&from=2015-01-01&until=2015-01-31&metadataPrefix=arXiv

It's important to set metadataPrefix=arXiv, so that subdisciplines will be listed:
<categories>
  math-ph cond-mat.other math.MP nlin.CD physics.class-ph
</categories>

EDIT:
I used delay as Nate Eldredge suggested, in my case - 25s. Yet, while trying to get all math (250k items so in 250 queries) it gave error at 70. I did continue it (with even higher delay) but sometime around 110 the query was not longer available.
So, the way to go is in getting smaller chunks - e.g. by month (or for mathematics - at most by year).

When making multiple requests using a resumptionToken, you are required to wait a little while between requests; otherwise you get a 503 error with a Retry-After: header telling you how long to wait.  In the tests I have made so far it has always been 20 seconds, so adding an unconditional 20 second delay between requests might do the trick.

This looks good. Here you can see the code we are using to get metadata. It is fetching only on page of it though for now.

Shameless plug: I wrote a generic OAI harvesting tool, that will harvest Arxiv just fine. It's called metha and consists of a few commands:
$ metha-sync https://export.arxiv.org/oai2

This will download all data up to the last full day (it will take a couple of days). The XML API responses are compressed and placed under ~/.cache/metha directory. Metha will use monthly windows by default and a resilient HTTP client to ensure downloads succeed, while not stressing the server. It has been tested in the wild on thousands of OAI endpoints.
After (and during) download, you can inspect (already downloaded) records with:
$ metha-cat https://export.arxiv.org/oai2

For any further processing you will have to use your favorite XML tools.

Update: Additionally to the metha (incremental) harvester, I wrote a small tool called oaicrawl, which does no caching and just fetches records off an OAI endpoint one by one. This create more overhead, as there's an HTTP request for each record but can be useful, if the OAI endpoint does not support selective harvesting (e.g. by date) or is otherwise broken and you are ok with having a best effort data set harvested from the service.
Syntax would be similarly simple:
$ oaicrawl http://export.arxiv.org/oai2 > arxiv.data

Note, that this will concatenate the raw responses from the API and hence won't be valid XML out of the box.

A torrent for a metadata dump "collected from the OAI-PMH API endpoint using the 'metha-sync' tool" is available at: https://archive.org/details/arxiv-bulk-metadata
NB: This dataset contains metadata also for non-math articles.


text:
Internship for PhD students
I am a PhD student and I would like to do an internship at an American university or institute. How should I proceed?

Contact the university or institute at which you want to intern, and find out what their requirements are for international students.

If your advisor has contacts at any of the universities you'd like to work at, I would ask him or her to reach out for you. If you are looking to work with a specific faculty member, that is probably the trickiest route, as faculty members often use the summer to travel themselves, and it is rarely worth the hassle to hire an unknown student for the summer, anyway.
If you don't have a networking contact through your advisor or other faculty members at your school, my next suggestion would be to start networking at a conference, or through a professional organization that you are affiliated with (e.g., IEEE, AMS, etc.). Start getting the word out that you are looking to spend a summer at a U.S. school, and see if anyone has suggestions.
If you are looking for a paid internship, you may be at a disadvantage as an international student, and you are probably more likely to find something if you can fund yourself. If that isn't an option, you might also consider looking at internships in industry, as they tend to have more money for summer interns.

If you are in a STEM field, there are lots of options available to you. There are about 20 national laboratories that take students from every level of education as summer students, including international students. Personally, I've interned at Los Alamos National Lab and at Pacific Northwest National Lab. It's generally very applied work, but it's a great atmosphere for students and is also extremely helpful for making connections with potential future employers. I would look up national lab websites and look at their respective research areas, and contact individuals you would be interested in working with.
The internet is your friend -- many companies and government agencies have programs listed on their website, and many of them don't require US citizenship.

Be careful, though—many of the programs at national laboratories do not take international students, which the OP appears to be.

I was at Pacific Northwest last summer, and many of the other interns were international students. International students (as well as international staff) were issued badges that had some access restrictions. The OP may be ineligible for some opportunities, but there are many internationals that do get internships at labs.

It definitely differs between labs. The Office of Science labs (Argonne, PNNL, Oak Ridge, etc.) definitely have a lower threshold than the National Nuclear Security Administration labs (Sandia, Los Alamos, Livermore).


text:
Part time industrial work during a mathematics PhD
I am in the second year of my PhD in mathematics and I have recently decided I want to go into industry after I'm finished.  I feel like a lot of what I'm learning in the program does not have direct, real-world applications, and I'd like to remedy this problem by getting some work experience while I'm still in graduate school.
I have some programming background (but not as much as a CS grad) and a couple years experience doing simple data analysis programming.  I also have a BS in physics.  It seems like there should be a place in industry for me somewhere, but I don't know how to go about finding it.
I would like to work part-time, or as a consultant, so that the job won't greatly hinder my progress towards my degree.  (Of course, some time will be lost that could have gone to my dissertation, but this is made up for by gaining marketable skills.)  I'd like to work in science or in software.  I am aware of internships, but it seems like most are either for undergraduate students or engineers.

Can a math grad student work part-time in industry during his PhD?  If so, how would one go about finding jobs?

I think your options (in the U.S. anyways) will be limited, since, even if you were to find a good fit, working part-time may not be an option. You might want to consider doing a summer internship instead to get more industry-relevant experience.

Why are you getting a PhD in math if you're concerned about direct, real-world applications?

I think it would be very challenging and unusual to work part-time at a job and be taking graduate classes or doing thesis research.
But it's quite common for graduate students to work full-time (as interns) at government laboratories or in industry during the summer (or at another time of year) for perhaps 3 months.  Mathematics students I have known have worked at

Google
Microsoft
US Dept. of Energy labs (Sandia, Los Alamos, Livermore, Oak Ridge, Argonne, etc.)
Oil companies (Schlumberger, Total)
US NSA contractors (cryptography)
US Geological Survey
NOAA

and many other places that I've forgotten.  Talk to your advisor and to other faculty in the program.  Your university may also have someone who coordinates these things.

I can say that I am currently doing this and it works very well for me, but you have to discuss this with your supervisors.
My consulting work is in optimisation/operations research (part mathematics, part computer science) and directly relevant to my PhD. I use these experiences to motivate my research and I'm convinced it makes me a better researcher.
I work for the company I worked for before starting my PhD, but part-time consultants can be valuable to smaller consulting companies if you have the flexibility to work when they have more jobs, and not work when they don't.
I will second the call to apply for internships: a couple of the interns I worked with at Google last summer were mathematicians who could code, and one or two of us were PhD students.
It should be noted that I'm doing my PhD in Australia, where PhDs nominally take 3 years and teaching/research assistant work is not a condition of any scholarship, so your mileage may vary in other countries.


text:
Still waiting for comments on my draft - what to do?
Some weeks ago I completed the first draft a paper. I then decided to approach one of the experts in the field, who is working at the same school as I. She seemed interested and were willing to read the draft.  However I sent her the draft ~ 5 weeks ago, and I have not heard from her since then.
I am now wondering if this is normal? Can I send her an e-mail and ask if she has looked at it yet (or is that considered inappropriate?).
I am getting quite stressed about this, the reason being that my advisor expects a draft of a new paper soon, and it would be valuable to have her comments before I talk to my own supervisor (who knows less about the subject I am writing my paper about).

You don't say what field you're in and how long the paper is. Reading a short paper that's mostly text doesn't take long; reading forty pages of dense mathematics can take weeks or even months. Also, why on earth are you writing papers that your own supervisor doesn't know about?

After 5 weeks, I think you can safely assume that she has forgotten about it. You can either accept that you will not hear back from her, or send a polite reminder.
Most people do not mind a friendly reminder about tasks that they have committed to but forgotten about. It is definitely not inappropriate. However, it is of course possible that she simply has no time after all, but then the right thing for her to do is to just tell you.

Thank you, what confuses me is that she seemed interested the first time we met, and encouraged me to contact her if I had more ideas. But maybe she has just forgotten as you say.

Maybe I should add that 4 weeks ago I sent to her a new draft (corrected some errors). I do not know if she got put off by this.  Would an friendly reminder still be appropriate?

@Phd Yes, a friendly email every few weeks can hardly be over the top.

ok, thank you! I really appreciate your help.


text:
Where to search for conferences and workshops in computer science?
I'm a master student in computer science. I'm still working on my thesis and probably I will publish a paper, however I really have no idea where to look for workshops and conferences. I often hear my supervisor talking about some conferences and workshops and mentioning them, but I get no idea how he knew about them. Is there such a central website or search engine for this?
I'm new in research, never published a paper before.

(1) Ask your advisor.  (2) Look at where the papers you cite are published.

I found some decent ones here: http://www.nsays.in

One more that hasn't been mentioned yet: http://confsearch.org/ The nice thing about confsearch is that you can filter by sub-field.

A very good site for CS conferences and journals call for papers and their respective deadlines is A Wiki Calls for Papers.

similar website: http://eventseer.net/

There are lots of different websites and mailing lists for different subjects.  Some researchers also maintain webpages of conferences in their area.  I mostly learn about conferences through two mailing lists I subscribe to and two researchers' webpages, as well as emails forwarded by my mentor (I'm a postdoc).
Probably the best thing would be to ask your supervisor how he finds conferences.  He'll probably know the best sources for your research area.
One fairly general website I know about is www.conference-service.com, but in my area it's far from complete.

I may be biased (since I am working on that project) but I would start looking in the dblp computer science bibliography:

http://dblp.dagstuhl.de
http://dblp.uni-trier.de
http://dblp.org

However, much more important than just searching for "any" conference and workshop in whatever repository is talking to your advisor, colleagues, etc and asking them for their opinion on which venues are most relevant for your specific topic.
Another way of finding relevant venues is just looking at the references in the literature you are working with. If there are some conferences or journals that come up over and over again, then they are probably interesting for you to consider.

Most of the conferences in Computer Science publish proceedings with Springer, IEEE or ACM (even though there are several dozens which self-publish or publish elsewhere).
All three publishers have lists of upcoming conferences:
Springer

LNCS: http://www.springer.com/computer/lncs?SGWID=0-164-12-73665-0
CCIS: http://www.springer.com/series/7899
LNBIP: http://www.springer.com/series/7911

IEEE

http://www.ieee.org/conferences_events/index.html

ACM

http://www.acm.org/calendar-of-events

Other great resources are mailing lists:
Mailing lists

DBWorld https://research.cs.wisc.edu/dbworld/
AISWorld http://www.aisnet.org/AIS_Lists/publiclists.aspx
ACM SIG-IR list http://www.sigir.org/sigirlist/
ECOOP info list http://web.satd.uma.es/mailman/listinfo/ecoop-info

Beware of conferences which do this just for money and not for disseminating knowledge:

http://scholarlyoa.com/publishers/

This being said - of course the best way is to ask colleagues working on a topic related to yours - like your supervisors, post-docs from your group etc.

Another very useful site is Conference Partner (myhuiban). The site lists conferences by deadline, and shows useful information such as rankings by 3 different sources, years, views and so on. In each conference page, it also shows the call for papers, acceptance rate (when available), related conferences etc. Note that for some reason, full access requires (free) registration.

Downvoter, care to explain? To make it clear, I'm in no way affiliated with the site I mentioned, and other useful sites were already mentioned in other answers and comments.

I found some decent ones here: http://www.allconferences.com/
I also receive a number of invitations through a number of mailing lists that I subscribe to.
You can also find a number of them through technology publishers like O'Reilly for example: http://strataconf.com/strata2014/public/content/home


text:
How should I go about doing meaningful academic outreach as a graduate student?
I am a Hispanic student (senior undergraduate) from a socioeconomically disadvantaged background pursuing a graduate degree in Physics in US (I just finished submitting my applications). I do not know anyone from my neighborhood that attended college, much less pursued an advanced degree, and I find myself abhorred by the educational inequality in the US (and the world) on a daily basis. I've undertaken some personal outreach endeavors thus far, I act as a mentor via email for minority students in K-12 (for example, I send them updates about what projects I'm doing and links that I think are suitable for their age group) and I stay in contact with minority undergraduates in STEM whom find themselves faced with (socioeconomic) obstacles similar to those that I have faced.
My parents were hard workers, but they had little time (or money) to stimulate me intellectually as a child, and they also had no idea what it meant to pursue a PhD in anything besides a medical doctor. Thus, I really only became familiar with the notion of pursuing a career in scientific research when I arrived at college; prior to arriving at college, engineering was my idea of what it meant to be a "scientist", or, to "do science" as a career.
I would say that over the course of the past three years I have become a relatively successful researcher, and in some ways I have made up for lost time, but to this day I can only cringe thinking about where I (or someone in my same shoes) might be today if I had been exposed to science at a younger age.
Does anyone have advice as to how I can begin making a significant impact in scientific outreach as a graduate student? I have a knack for programming, and the majority of my research is computational in nature, so I think I might be well suited for helping individuals learn programming remotely, or something of that effect.

As a data point, ASU launched last year a outreach program with special focus on the Hispanic community and where a good part of the work is being done by PhD students. You may want to look at what they do, and the results they are getting.

Outreach comes in two flavours: creating interest (wow, this science thing is actually cool!), and educating (this is how you program). I think both are necessary, so take your pick (or both).
Also, don't even think of making it a one man effort. I don't advise to do it alone. There will be times where you will be swarmed in work, and then your outreach will just fall out of priority. Instead, team up with undergrads, schools, youth centres... whoever is willing to help you. If your university has something in place, you should probably go for it, as it will probably suit your primary needs the most. If you cannot find anyone, you can always start alone and get people on board along the way, but do make this a priority to ensure continuity.
Story time: when I was an undergrad, I joined the astronomy club at my university and we did a lot of outreach, mostly talks and workshops about different topics. It was a very good starting point, as I met people that have been in the business for some time, and could help me avoid some of the beginners mistakes. To give you an idea of our success, in some years, we (a dozen of undergrads), single handedly organised half of the events for the Science Week at the Physics Department, usually getting fully booked in less than a week since opening the registrations.
My experiences showed that:

Many teachers are overworked, underpaid, and frustrated; and they would take your activities as a way to relax for an hour. A few others are still passionate and will go out of their way to get you in the classroom and make the most of it.
Teenagers are difficult to begin with, but once you break the toughness layer, they have a bunch of interesting ideas. Also, beyond that point there are not many differences between posh and underprivileged schools.
Ideally, all fronts should be covered. This includes schools, family activities, talks for adults, elderly centres, giving teachers ideas... But of course, you don't have to do them all.
Be engaging. Get small groups, if you can. Prefer interaction in person. I wouldn't recommend teaching programming remotely, as it is too easy to ignore it. It would only work with kids that are really interested, but then, there are tons of online resources to learn.
Sometimes, kids behave like mushrooms, and there is nothing you can do about it. Don't let that frustrate you. Don't be impervious to (self) criticism, though.
Talk to professors active in outreach, even if they are out of your expertise. In my university there were a couple of them from the Optics department, that taught me a few nifty low budget experiments.

+1 for everything but "Also, don't even think of making it a one man effort.". I mean, in many cases it is good to have more people. But one can be totally fine with running a site, or giving lectures, or 1-1 mentoring as single person (and it many cases there is an advantage of not needing to wait for, or adhere to, others).

@PiotrMigdal doing anything worth requires continuity. A PhD student cannot commit so much, there are times where other demands take over. Maybe I was a bit strong, and one can definitely start it alone, but to keep it alive, one would need support.

Well, keeping something alive is great. But it's better to start from scratch something worth one's time that continue something not worth it (for various reasons). (Things here are not theory - rather my experience over last ~9 years. Certainly, the best things are ones which I run with others and are now still alive. Yet, most things needs some starting impulse, who will lead and take responsibility. Waiting for others is the best way to forfeit an opportunity.)

"Sometimes, kids behave like mushrooms, and there is nothing you can do about it." How do mushrooms behave?

@FaheemMitha they subsist doing nothing.

Outreach - to start with, once you know something worth sharing (and for sure you do, as you are a PhD student), the biggest thing is your motivation and drive for it.
There are various styles of doing outreach, and for various audiences (of different age, specialization etc).
In some sense its up to your taste to choose topic and the audience. As you are from a disadvantaged group you should know the best what kind of help and stimulation would had benefited you the most.
It's important to look at the "added value" - i.e. how much they benefit from it. (Sometimes a spark of inspiration can be better than concentrating knowledge; but also sometimes a lecture can be cool but provide little long-term value.)
What you can do?

Giving lectures.
Doing workshops.
1-1 mentoring.
Running some blog (but this is less sure, as there is less direct feedback).

In any case, it's mostly practice, practice and practice. You will learn which things work the best, and which - do not. Which things are interesting for the audience, which - not as much as for you.
If you are in programming, it is a good way to start, as it gives them tools they can play with by themselves, and it is beneficial in many different job scenarios.
It's of course good to contact people in your department, who are involved in outreach - very likely they can provide some advice and it may be possible that there are organizations or events you can join (whether run by univ. or something external).
Also, some of my experiences are captured here: An independent camp for high school geeks (some lessons can be used in different settings).

I can completely relate to you. There are plenty of programs out there where you can do some outreach. You can check out Code2040 : http://code2040.org/ . Its a program that helps minorities get their foot in the door in the tech world. It might not be exactly what you had in mind but I would try to network with the coordinators maybe they can give you better advice on your journey. You can also try doing outreach through your local library. I know that some libraries offer tech literacy courses so that maybe good place to start. You can also try to create a non-profit organization for your cause as well.


text:
Is there any research on applying Agile/Extreme/Lean methods in academic research?
I've been enjoying quite a lot of literature, all published within the last ten years, on how to handle projects, ideas, development, thinking, and so in with processes like Extreme, Agile, or Lean.  I have searched for similar literature on academic research and projects, but I couldn't find anything modern and relevant that links various parts of the research process, such as project management, hardware/software/experiment development, or refactoring of collaborative writing.
Are there any recent publications about applying these methods to academic research?

Some people think the question is too broad. Can you please make it more specific, as it risks being closed.

The question is not focused and is not clear what to answer, Please rewrite it so it remains open and active for answers.

The paper Adapting Scrum to Managing a Research Group by Michael Hicks and Jeffrey S. Foster, describes the experiences of adapting Scrum to running a research group.
For those who don't know about Scrum, wikipedia has the following:

Scrum is an iterative and incremental Agile software development
  framework for managing software projects and product or application
  development. Its focus is on "a flexible, holistic product development
  strategy where a development team works as a unit to reach a common
  goal" as opposed to a "traditional, sequential approach". Scrum
  enables the creation of self-organizing teams by encouraging
  co-location of all team members, and verbal communication among all
  team members and disciplines in the project.
A key principle of Scrum is its recognition that during a project the
  customers can change their minds about what they want and need (often
  called requirements churn), and that unpredicted challenges cannot be
  easily addressed in a traditional predictive or planned manner. As
  such, Scrum adopts an empirical approach—accepting that the problem
  cannot be fully understood or defined, focusing instead on maximizing
  the team's ability to deliver quickly and respond to emerging
  requirements.

Scrum is built around the idea of a sprint, a short burst of activity. Again, from wikipedia:

A sprint (or iteration) is the basic unit of development in Scrum. The
  sprint is a "timeboxed" effort; that is, it is restricted to a
  specific duration. The duration is fixed in advance for each
  sprint and is normally between one week and one month, although two
  weeks is typical.
Each sprint is started by a planning meeting, where the tasks for the
  sprint are identified and an estimated commitment for the sprint goal
  is made, and ended by a sprint review-and-retrospective meeting,
  where the progress is reviewed and lessons for the next sprint are
  identified.
Scrum emphasizes working product at the end of the Sprint that is
  really "done"; in the case of software, this means a system that is
  integrated, fully tested, end-user documented, and potentially
  shippable.

The emphasis is thus on highly dynamic, short cycles of work, and interaction between the team
members and the customer to get the right product delivered by rapidly ensuring that the development team is on target.
Now, returning to the paper: it  advocates 2 or 3 short meeting per week with the entire research group, rather than infrequent meetings between supervisors and PhD students. In these meetings goals, progress and achievements are reported on frequently. One outcome is that it helped quickly identify when a student was off-track, unmotivated or stuck.
I used the technique for my research group for a while. It was good for the various members to learn about what the others were doing, but as everyone was doing quite different things, in the end it didn't create any synergies I'd hoped for. Now that I am in a different institution and the new research group works on topics that are closer together, I think it could work, but we've decided to use a different approach to our research meetings.

Thanks for this answer.  Would you consider adding a few lines explaining "scrum"?  Until I saw this answer I had never heard of it.  Worse, the idea that there are highly formalized models and/or software for managing interactions with PhD students was also totally unknown to me.  (And I have PhD students...)  Please help?

@PeteL.Clark: Done (albeit somewhat lazily).

Dave: thanks very much.  I admit that I am so clueless that "this is a thing" that I didn't even think to check whether there was a wikipedia article.  (Although eventually I would have googled it, of course.)

@PeteL.Clark: It's a neat idea. It works well in small software development teams, I hear, though adapting it to research still needs work. Research does not so easily break up into small chunks and you often get people saying "Same as last week", though of course this could be an indicator of problems.

Generally I find that agile approaches might work for some research project deliverables (e.g., if your project includes the development of a mobile app) but are unlikely to work overall, at least when it comes to academic research. The issue is that a key risk for large research projects is that team members will have divergent ideas about what the project is intended to produce, and agile's frequent meetings focused on the immediate upcoming work are unlikely to help with that.
You also have the issue that many research project team members have other responsibilities, including teaching, coursework and other projects, so it may not be practical or useful to have a team meeting every few days for two reasons. First, you may not be able to find times that everyone is available to meet. Second, because people have multiple responsibilities, work on the project doesn't progess all the time so the cadence of academic research often doesn't match the normal cadence of agile approaches.

While this might be useful as an anecdote, it doesn't really answer the question, which asks for recent research.


text:
Differences in student load at liberal arts colleges vs. research universities
Do students at liberal arts universities have 'harder' courses than students at research universities?
Computer Science curricula at large research universities have 5 to 6 courses per semester. The Liberal Arts model dictates roughly 4 courses per semester. If the load on the student is considered to be equivalent, there must be something special to the teaching in the Liberal Arts model.
How is it that a 4 course Liberal Arts semester is as intensive as a 6 course research university semester?
UPDATE: Many of the comments below say the course load I mention above is inaccurate. I have obtained the figures as follows.

The Liberal Arts Computer Science Consortium (LACS) has released 3 LACS curricula in response to ACM/IEEE CS curriculum recommendations. The first in 1986 in response to the 1978 recommendation, next in 1996 in response to the 1991 recommendation and the most recent in 2007 in response to the 2001 recommendation. The 4 year course breakdown in all the LACS recommendations is roughly the same:

4 courses per semester
30-35% CS courses, 10% math, 5% science, and the rest, i.e. 50% or more courses on arts, humanities and social sciences.

A typical graduation requirement at a research university is at least 120 credits, which comes to 5 3-credit courses per semester. Many require more than 120 so 6 course semesters are not uncommon.

What country are you specifically talking about?

I am using the generic US Liberal Arts model as a reference.

I don't understand. Are you claiming that most students in computer science at large research universities take 5 or 6 courses per semester? That does not seem accurate: 5 seems a little high and 6 is definitely a stretch.

Yeah, I'm skeptical about the premise here.  In my experience, students at both liberal arts colleges and research universities take 4-5 courses per semester.  There might be differences in the average number of courses taken, but not by 50%.

Even if those numbers were correct, who ever said the load was considered to be equivalent?  Programs at different institutions can vary widely in their requirements.

It's also very difficult to generalize about "large research universities" and "liberal arts colleges" in this way.  My undergraduate institution considers itself a liberal arts college, and requires students to average 5.33 3-unit courses per semester in order to graduate in 4 years.

The maximum allowable courseload at my university is 17 hours = 5+2/3 courses per semester.  12 hours = 4 courses per semester is considered full-time.  In my experience, most students tend towards the lower end and pick up extra credits either through advanced placement or summer courses, if necessary.  Moreover, overall probably less than half of these courses are in CS (or any other one declared major).  I wonder where these counterfactual premises are coming from?

The title of this question does not match its content.

"A typical graduation requirement at any U.S.  university is at least 120 credits, which comes to 4-5 3-credit or 4-credit courses per semester." (The document you cite in support of your claims about LAC explicitly describes several classes as being 4-credit).

You are making several unfounded assumptions:

That courses are always 3 credits, so that "4 courses per semester" means 12 credits. I have taken courses that were worth 1, 2, 3, 4, and 5 credits. Many of the science courses I've taken, including math and computer science courses, have been worth 4 credits. Basic sciences that involve a lecture, lab, and recitation have sometimes been 5 credits.
That most liberal arts colleges follow the LACS recommendations to the letter.
That the LACS recommendations somehow suggest that less than 120 credits are required for graduation. Here is an example of a liberal arts college following the LACS recommendations for CS and requiring 120 credits.

I did half of my undergraduate degree at a liberal arts college and then transferred to a large research university for the other half. There was virtually no difference in my courseload between the two - I took exactly one credit more in my two years in the research university. I just pulled up my transcripts, and this is what I took each semester:
Part 0
I transferred in 30 credits in humanities, etc. from college courses taken while in high school.
Part 1 - Liberal Arts College

16 credits, 4 classes (4, 3, 4, 5)
13 credits, 3 classes (4, 4, 5)
(Summer) 3 credits, 1 class (3)
19 credits, 5 classes (3, 4, 5, 4, 3)
12 credits, 4 classes (3, 3, 4, 2)

Part 2 - Research University

16 credits, 4 classes (4, 4, 4, 4)
19 credits, 5 classes (4, 3, 4, 4, 4)
16 credits, 6 classes (3, 4, 1, 3, 3, 2)
13 credits, 4 classes (4, 3, 3, 3)

(My undergraduate degree was in Electrical Engineering, with a minor in Computer Science.)

Thanks. So what then was the difference between the Liberal Arts College and Research University? Was there a higher proportion of humanities courses at the Liberal Arts college?

@wsaleem Why do you assume there was a difference? The curriculum requirements were very similar at the two schools.

I assume a difference in the CS curricula because of the fundamental difference between the two types of institutions - in teaching vs. research and in depth vs. breadth.

There were differences in things like UG advising, class sizes in science courses, availability of research opportunities, etc. but none as far as curriculum requirements or teachers' expectations.

That is very interesting to me. Was the CS curriculum then an anomaly at your Liberal Arts college, in that it was the same as at a research university? Other commenters have pointed out how Engineering programs at Liberal Arts colleges are anomalies.

The science and engineering majors at the LAC all required more credits for the major than the humanities-type majors. I don't think courseloads were different (120 credits were required for graduation in any case) but humanities majors had more freedom in choosing electives.

@wsaleem: It sounds like you have some broader questions about "What are the differences between a research university and a liberal arts college" that might be better addressed in a separate question.

As with other commenters, I think the premise here is flawed.
I teach at a public liberal arts college which is typical of many. (Certainly our curriculum and requirements are in line with other schools in the COPLAC Council of Public Liberal Arts Colleges.)
Our students are required to complete 120 credits to graduate, which over four years averages to 15 credits a semester, or five 3-credit courses. I don't believe there is any serious difference in student course load when compared to large research universities (but maybe someone will prove me wrong).
The main distinction about teaching at a liberal arts college is that the overall curriculum is broader, and students do not focus on specialization as much as integration of diverse subject areas. Instead of taking 70 or 80 credits of (say) computer science, our students take only 40-45, with the other two-thirds of their degree consisting of courses in other areas. In this way, students build a broad, integrated perspective which incorporates their major into a study of the world at large. The focus at liberal arts colleges is to help students become better critical thinkers, decision makers, and problem solvers, rather than becoming subject matter experts in a narrow discipline.

I think you may be overstating the curricular differences between liberal arts colleges and research universities.  I teach at a research university, and I just looked up the degree requirements in CS: it is 18 courses or 54 credits.  This includes freshman level courses for non-majors that I imagine many CS majors would place out of.  In my own department (math) the major requirements are less.

Overall I don't know of any US research university where students are required to take more than 50% of their coursework in the area of their major.  As someone who advises undergraduates, I can say that it is fairly common for a student to change majors during their third year, and with careful planning they can probably still graduate in four years.  Also, with all due respect to my undergraduate students, very few of them are graduating as "subject matter experts" in any discipline, however narrow.

120 hours is also the requirement for most majors at my research university, although engineering majors are slightly higher (128 to 134). My department's engineering major requires 64 hours of coursework within the major, out of 128 total.

The exception is engineering: my school required 36 classes in four years to graduate, but engineers needed as many as 41 to finish the major and the general requirements. [Twenty-nine classes (70%) were the major and its prerequisites.]

@aeismail: Yes, I agree that engineering is exceptional.  In fact, many (most?) US research universities have a "college of arts and sciences".  That's what I was describing (and that's where CS lives), and that model is equal to first order to the liberal arts model.  If you are studying engineering or architecture or are in a conservatory, things will really be different.

@PeteL.Clark ...and that's sometimes where CS lives. I'd guess a bit more than half of American CS departments are in engineering colleges.

@JeffE: Really?  Well, you would know better than I.  I tend to think of CS as being recently seceded from mathematics (when I was an undergraduate at University of Chicago, 20 years ago, CS was still formally part of the math department), but that is probably not the case at most places.

Teaching at a Liberal Arts school (in the United States), there is a lot more "teaching" expected. Largely, you do not have TAs for your classes, meaning you not only do all the lecturing, but also the grading and lab work associated with the course. Furthermore, Teachers at Liberal Arts Schools are expected to take a serious interest in the undergraduate body since there usually are no graduate students (hence few TAs).
I am not sure if they are equivalent, and certainly my advisor my research university only teaches 4 classes a year as well. I'd say his teaching load is far less than my professors at the Small Liberal Arts school I attended for undergraduate. That being said, he also has 4 graduate students he advises, which I think end up being a lot more work.
So from a class prospective, teachers at Liberal Arts schools tend to spend more time teaching. But from an advisement perspective, research universities tend to be more intensive. It probably evens out, though I have no first hand experience.

Thanks. My question was from the student's perspective. Is there something special in the way the Liberal Arts student is taught (by prof or by TA) such that her 4-course semester "load" is comparable to that of her research university colleague doing a 6-course semester?

@wsaleem: The average course load is not 6 courses, even at schools like MIT—or in majors like engineering.


text:
What are the typical research requirements for an application to lecturer position to be successful?
When someone is applying for a position as a lecture at an average ranking university in Sweden (equivalent to associate professor elsewhere), what is the typical number of research papers and of what quality he is expected to have in order to have a realistic chance of getting the position?

"Lecturer" in the United States is very different from "Lecturer" in the U.K.  Can you let us know, preferably by editing or tagging the question, in what country you are applying?

I meant a lecturer position from a European university.

The research track record is only one parameters among many, and it's not the most important one. Even if it was, there are so many differences between different fields, institutions.

Do you think its possible for someone with only a Phd and only one paper at an A* conference (but several papers at A* conference workshops) and teaching/mentoring experience to get a lecturer position in Sweden(its also called associate prof btw)?

In theory everything is possible, it depends how competitive the specific position is. If you can find out information about past similar positions in similar institutions that could give you an indication (also the profile of the accepted candidate). In general I'd say it's likely that these positions are very competitive, with hundreds of applicants, some probably having more experience than you. But it would also depend a lot on your research project and how well it fits with the particular research group.

The skills to teach are more important than a list of papers...
Having had to suffer "good" researchers who proved to be poor at explaining things, then the focus should be on the lecturing ...

The positions seem to  list research as the most important requirement. What is the typical number of applicants that you have seen at these positions?

Pick a number greater or equal to zero... Really that is not answerable... some positions may get hundreds of applicants, others one or two... just depends on so many factors : location, subject, salary...

Think about the factors I mentioned and the others... Such as who you may be working with...

Since OP clarified this is in Europe, and based on the comments, this does not seem to be a primarily teaching position.

@BryanKrause Tge OP stated that it was a “lecturing position” unless you know different and it is a research post?

For example in the UK a "lecturer" is what would be called an assistant professor in the US. OP stated in a comment that research was listed as the most important requirement which is consistent with that type of position rather than the "primarily teaching" implication of the title in the US.

I agree "lecturing" vs "lecturer" makes it a bit unclear but I think the former was just a mistake, the title and context suggest lecturer.

I am specifically interested for a lecturer(associate prof) in Sweden.

Edited question to include "Sweden" and "associate prof".  Please double-check that I haven't hanged your meaning, and edit the question to clarify if I have.


text:
Is it a bad idea to do research with multiple different professors at the same time?
I want to go to graduate school to study computational molecular biology and biochemistry.
During my first three semesters as an undergraduate I finished my distribution requirements and started doing research with one professor.  During my fourth semester I started doing research with two more professors (working in computational biophysics and  computational linear algebra).  My work with two of these three professors has already led to work that will be published soon.  I am considering starting research with yet another professor (in statistical genomics).
Is it wise to begin research with yet another professor?  I fear that having engaged with too many professors makes my resume read like that of a dilettante rather than a promising computational biologist.

The sooner you learn to express yourself without going to wall-of-text, the better.

I can be concise when necessary, but I typed a ton because I had to go somewhere and didn't have the time to decide which details were irrelevant.

The level of detail is not as much of a problem as the fact that you didn't use paragraphs.

I see your situation as a perfect recipe for burn out. Please, be careful and take care of yourself.

didn't have the time — Huh?  StackExchange doesn't impose deadlines.  Graduate schools will definitely look down on poor writing.

There comes a point where a person will be spread too thin – that's inevitable. Only you can really say how much you can take on before your workload starts to interfere with your academic goals and achievements.
If you're already doing the work of two students, I'd be cautious about expanding that to three.

I'm spending around 9 hours a day on research right now with ~3 hours in class, 1 hour eating, and 4 hours free time, on week days, and even more free time on weekends

You make it sound like those four hours of free time are being wasted, when they might not be. Perhaps your brain is appreciating that rest more than you know. And I don't see any time allotted for physical fitness; besides being good for your health, fitness can be good for your mind.
If you decide you want to try a third project, you should be honest with yourself, and up front with the sponsoring faculty member from the outset. "I'm already working on two other projects, so, to be honest, this might turn out to be too much for me. But I'd still like to give it a try." Such a disclosure might make it a little harder for you to find someone willing to take you on, but it might help save your reputation if you find yourself in a position where you've bitten off more than you can chew.

I'm not really doing the work of two students, although last semester I definitely was because of my course load. (I also had a job last year which I quit). Thing is, a lot of other students are involved in research along with some kind of clubs, or something.
I guess physical fitness is something I do not pay attention to, but I never have. I could pick it up now or in graduate school I suppose. But other than that my free time is wasted, for reasons I won't go into I'm not having a great time at school outside of academic stuff and I don't have many friends to do things with so I'm bored.

It sounds like a good idea to notify the third guy I have two more projects, I'll do that. After reflecting, four seems ridiculous even to me.

First of all: congratulations and good luck. It seems you are quite enthusiastic about research. That said: be careful not to take too much on your plate, especially initially. Your time schedule seems very stringent which may lead to a burn-out.
I have the feeling that you are too erratic in your current research: there is no overall story to connect the different pieces you are (or plan to be) working on with different professors (computational biophysics, linear algebra, statistical genomics, mathematics, ...). It's true that there is likely some overlap but to do effective research you need to have a single point of focus, which you can tackle from different angles. What is your main focus?
I would say less is more. Having so many different professors in such diverse fields will inevitably hinder you from digging deep in each one. An important part in research is choosing your battles: deciding what to focus on and planning your work/collaborations in a meaningful way. It seems to me that you just want to do everything, which (while commendable!) rarely works out in the long run. You can't do everything, even if you want to.

I haven't figured out what my ultimate research area will be. I know it will be something in computational biology (and definitely not, for instance, computational EVOLUTIONARY biology) but I don't know what yet. The algebra guy I work with actually does research with connections to computational biology which is why I picked him. The other two are of course computational biology projects. The last guy is a numerical analysis professor but he is also interested in applications and a friend said he was great to do research with. Although if I pick three I will likely pick the other three.

I don't think I will burn out, I also took a very heavy course load in high school juggled that with a part time job and playing in a youth orchestra. I guess I am just wired to enjoy working all the time.
But I appreciate your comment and I will probably pick three and not four. If I double the 9 hours I currently spend on research it would be 18 on average which is clearly not realistic even for me who likes working.

Just because you can doesn't mean you should.
Your schedule reminds me of my marathon days of thesis writing and of my 24-hour calls on surgery. In both cases I accomplished a lot in the moment, but retained little. College is your first formal exposure to academia. Racing through it could shortchange your longterm foundation. Just as with muscles, you need to alternate periods of stress and relaxation otherwise the adaptation to that stress never occurs.
Publishing one paper doesn't indicate mastery of a field.
Few outstanding scientific issues are definitively solved in one paper. A string of publications on the same topic is more common and, for a CV, more impressive. Someone who can attack the same problem from different angles with different approaches is a promising candidate.
Form an informal committee; don't add to your collection
Instead of being a human ping-pong between PIs, why not ask a few of the professors to guide you in writing papers at the intersection of their interests. Those sound like interesting discussions even if no paper comes from them.