text:
Value of unpublished (i.e. submitted) papers?
It's a normal case for researchers to have some manuscripts under reviewing. So, can such only-submitted and currently-unpublished papers add some value to CV of whom apply for a PhD program? Should they be included in CV at all?

The rule of thumb with writing a CV is to keep things relevant. If what you are writing is relevant for the purpose you are writing your CV (you should really have a template CV, and customize it for each application), then yes add them.
So a couple of ways how I could see that being relevant for you, specifically:

you mention applying to a PhD program, so it might be relevant to show whoever is taking the decision that you have worked on something similar before
in a similar way, it could be meaningful to show that you have some research experience, as opposed to other graduates that have 0 research experience.
if your manuscript in review has been uploaded to a preprint server (like arXiv or BioRxiv) then you might even be able to showcase the work you have done.

Unpublished texts are not only OK, but sometimes essential. When considering applicants for a PhD position I want to have samples of the applicants' scientific writings. In the absence of published articles, these have to be unpublished texts, or even internship reports. (Reports are sometimes better than articles, because articles can have several authors, while a report is typically written by the applicant alone.)
So the answer is: yes, list all your relevant writings, published or not. Give links to the full texts that are available online, otherwise be prepared to send them on request.

What if there are some published works? Can under-review manuscripts be regarded as a determining factor?

I would not make a big difference between published and under review, provided I can get the text. (In my field of mathematical physics, everything is on arXiv anyway.) What matters is whether the paper is good, and how important your contribution is.

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Publishing in Open Access journals
I heard somewhere that publishing in open access (OA) journals is not a good strategy and such papers may be less valuable (specially for who want to apply for a PhD program in EU and North America)! However, I think this is not a strong remark but publishing in low level and paid journals form unknown publishers is thought to be less constructive for the resume. Specifically, I mean journals and magazines by highly esteemed publishers such as Elsevier, Springer and Hindawi.
What's the fact about OA journals?

@eilia, dont worry not all open access journals are the same, mpdi, PLOS and scietific report are OK.

FWIW, MDPI is considered a predatory compnany by some as is Hindawi.

@RichardErickson, So you mean OA journals are not good?

Some OA journals are garbage. Others are respectable.

There are several posts on OA journals here. How do these not answer your questions? You've changed your question to be very broad.

@RichardErickson, specifically I want to know attitude towards such journals among scholars and their affect on CV that I could not find any answer on other posts.

@RichardErickson - the article you linked actually has this to say about HIndawi - 'Imperfect English or a predominantly non-Western editorial board does not make a journal predatory. An interesting example is Hindawi, an Egyptian publisher once considered predatory that improved its practices and standards over time.' Your comment therefore seems misleading.

There is some evidence out there that papers with open access tend to get more citations... make of that what you will :)

This question is similar to asking whether a degree from a public university is worth less than one from a private university.  There is no general answer; you can only compare specific entities, since there are good and bad examples from either group.

Strategically, it gives better chances to aim for journal that are recognized by your peers. This need not be related to openness of the journal, but it is true that there are fewer well-recognized OA journals than subscription journals. Ask around (to several peoples, taste can vary in a field).
Do not forget that you can in many cases publish green Open-Access (self-deposit into an Open repository) your manuscript even if you publish at a paywalled journal. This gives a good compromise between openness and strategy for beginning careers. Information about what you can do publisher by publisher and journal by journal is available at Sherpa/Romeo.
Last remark: judging a paper by the journal it is published in is quite bold, but judging it by the publisher of said journal is far worse. Elsevier has crappy journals in its portfolio, as most publishers do.

To add to this, not all open access journals are equal. Just the same way that not all journals are equal. An established organization that decides to start an open access journal due to demand from that organization's members is quite different from a fresh start up open access journal.

Your question is really broad. I suggest reading e.g. the Wikipedia article on open access to get some sense on what it's about.
Traditional journals operate by subscriptions. They publish articles, and would-be readers must subscribe to be able to read them (or they can buy individual articles, which is similar). This model has been criticized for making it difficult to disseminate academic knowledge. After all, if you can't pay, you can't read the paper either and so won't be able to build on that work. Since academic publishing costs money, someone has to pay, and OA proposes to make that "someone" the authors. The authors pay the publishers, who then makes the articles available for free.
The main problem with OA is that it introduces a conflict of interest. Since the publisher makes money every time it accepts a paper, it (and by extension the editorial board) is incentivized to accept papers. This has led to so-called predatory open access publishers, who basically accept everything that's submitted with no thought for quality control. Subscription journals cannot do this because if they publish junk people won't subscribe, but OA journals can.
That is not to say that all open access articles are junk or that all open access publishers are predatory, but you can imagine the tension. Not helpful is that some people consider X predatory while others do not. Hindawi and MDPI are examples. Both were listed by Jeffrey Beall (the authority on predatory OA publishing before he retired) as predatory at some point, but they also had vocal defenders.
Practically speaking, open access papers do have more usage than subscription ones, but whether that translates to more citations is hotly debated. Whether or not to publish open access is up to you, and possibly your funding agency.

What I have read, although I can not offer bibliographic references, is that the publisher charges for an accepted article and requires a transfer of copyrights to the publisher. That is the source of the dissatisfaction that motivates the trend towards free access journals.

@djnavas authors usually retain the copyright for an open access article. It's not normal for a publisher to simultaneously charge for OA and require a transfer of copyright.

"Thanks to the great success of OA publishing, many conventional print publishers now offer a so-called ‘Open Access option’, i.e. to make accepted articles free to read for an additional payment by the authors. The copyright in these hybrid models might remain with the publisher, whilst fully OA usually provide a liberal license, such as the Creative Commons Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/)."
Albert Krewinkel in "Formatting Open Science: agilely creating multiple document formats for academic manuscripts with Pandoc Scholar".

Some OA journals do not require authors of any APC. What about them?

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Will the PhD admissions chances of siblings in Italy be affected if they have coauthored papers?
The following question maybe the case for any country but I mentioned Italy to know if there are special conditions or rules.
Is this possible for a sibling to get admission from the same university and in the same program. They are working on different research plans (e.g. software engineering and data science but both are in the information engineering department/program) but they are coauthors in some publications.
Specifically, I mean is this possible that their relationship affect the committee's decision on accepting both of them (if they are qualified based on their own records) in the same program?
Or may the simultaneous applications make their chance lower, at least for one of them?

FYI: Both Christopher Musco and Cameron Musco did PhDs at MIT's in Computer Science between 2013 and 2018. They have coauthored several papers together, although I'm not sure if any are from before they were admitted.

@JimmyK4542, Thank you for this, but the main question here is what about Italy? Since I heard from a professor that there is a hard bureaucracy in Italy and it may complicate the situation.

Edit: None of the below is Italy-specific. It would be my general understanding from working in several places across Europe, often along side Italian colleagues who previously worked at Italian Universities. If I am correct in assuming that asking you to confirm whether your co-author is your sibling would be a privacy breach, this would apply across EU. Any Italy specific insight would be great.
As long as none of the publications look out of place on either of the applicants' profiles, I wouldn't expect it to affect either applicant at all. Just make doubly sure you highlight your own name in each of the author's lists to make it easier for the committee to know who is who. And ensure that if asked to describe your contributions, you can do it for each of the publications.
In fact, it might be some sort of a privacy breach to even ask the applicants whether their co-author is their sibling (people with same last names do exist outside of family relations after all!).
If some of the publications look really out of place on one, or both, profiles (i.e. they are on topics which bear no relation to how that person presents themselves), and they frequently happen with the same co-author, this would look bad regardless of whether the applicants/authors share a last name or not. (As it might indicate somebody was "trading" publications and inflating their publication count artificially and not through work and contributions).
However note that if you are applying to a doctoral programme, a fairly diverse publication profile is not unusual (as is no publication profile up until this point), since you are after all still often "finding yourself" during your MSc.

I have a favor to ask, since you don't identify a place in your profile. Do you have the experience to answer this specifically for Italy?

Thanks for your answer, as @Buffy said, how is the condition for Italy? As I know, things may change drastically from one country to another.


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Options with a Diploma in Theoretical Physics (A failed MSc)
To put this bluntly a number of our class are facing a real prospect of failing their finals myself included. By fail I mean not obtain MSc. and instead obtain a Diploma.

What are our options if we instead obtain a Diploma in HEP Theory and not an MSc?

Will any other programmes take a student with a diploma for a repeat?
Is a PhD still a reality for those of us holding offers?
How can we explain this to future employers when "no-one fails a masters degree if they put the work in"

I am unsure if the reality of the volume of work we have undertaken nor the academic credentials (on paper) of those attending and potentially failing, will strike a chord with any reader who has not been in this exact situation. Consequently, I will save our despair for the MSc. coffee room chat and keep the question on its intended topic.
The reality...

Out of those risking failure the grade average is between 70%-95% (mine is 86%)
This includes a number of scholarship recipients with perfect undergraduate grades (GPAs)
No resits

I emphasise that if any of the concerned did fail this would be highly unexpected and possibly a bit embarrassing for the programme due to the merit of several high calibre students in this group (I would not consider myself in this list) not to mention the time invested by all. This does not change the outcome of a fail for the student.

More of a side comment. I'm an active participant on SO, Physics and Mathematics and wanted to keep complete anonymity to avoid connections between the programme any of us are enrolled in or the potential institutions any of us might end up in. Several of us have quite significant research roles lined up and would not want these concerns - if never materialised - to be associated with them.

I must add that I won't be able to respond for a few weeks on this topic until finals are completed as I must do all I can to secure my future. I hope that I will never have to utilise the answers myself nor have to share them. However, inevitably this post will be highly valuable to several people and so any feedback is highly appreciated.

https://academia.stackexchange.com/questions/11765/how-to-effectively-deal-with-imposter-syndrome-and-feelings-of-inadequacy-ive

I realize this is an old question (feel free to give us an update if you read this!), but for anyone familiar with the (presumably European) system -- what is meant by "obtain a diploma rather than an MSc"? Are you doing an integrated bachelor's + master's program, and the concern is that you would only get the bachelor's degree? Or is a "diploma" some kind of lower-ranked master's degree?

To be blunt: not good. Top school or not top school, if you can't get an MSci, maybe you are just not cut for it. Yes, HEP theory is intellectually the most demanding field, but it is also currently in least demand and I've seen some very very good people leaving the field after pushing two postdocs. So really, if you insist on doing HEP theory and can't get an MSci, maybe it is good that you get cut of years of depression before it is too late. Another observation is that in HEP theory very few people push the field forward and the rest are completely irrelevant (i.e you write some stupid lagrangian, get eom, write not a very good paper and repeat; it is really not helping anyone. Very few other fields have this dichotomy)
Now, some people get into other fields and flourish. I know a person who had 1.5 postdocs in HEP theory, doing so so, some papers, some citations, but no real permanent job offers and he switched to biophysics, applied some easy peasy mean field QFT methods to neural networks and to them this was science fiction and now he is a star in that field. I also know another person, a stundent. who would do mind-boggling two loop integrals and wanted to do some data analysis and was so hopeless that it was embarrassing for all involved. So not all transplants are successful.
My advice would therefore be: if coding/data/algo is something that excites you, think seriously of moving into data science while it is still hot. If deep questions excite you but you can't get an MSci, think of transitioning to something a bit softer, maybe cosmology, nuclear physics, with some pheno/data component. But note that those fields are still very competitive. If you insist on doing HEP Theory, go ahead, but be warned that most likely you will be jobless.

Some hard-hitting but potentially useful advice here. My only concern is that this institution seems to be failing a large number of otherwise-qualified people, so it seems like there might not be enough evidence to conclude that "you are just not cut for it."

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Can I still publish a paper in an IEEE transaction journal after uploading it to the arXiv?
I read often that preprint servers such as arXiv are changing how we publish. However, no one in my area of medical imaging uses them and they are regarded in my lab with suspicion.
I currently have a finished manuscript that I am about to send to an IEEE transaction journal. I would like a preprint of this manuscript to be publicly available for several reasons. However, my supervisors are worried this might invalidate my ability to publish this manuscript. They don't want to take the risk that I do this wrong and can't publish.
So here is the simple question I never seem to see a simple answer to. Here is a finished manuscript, fully formatted for the journal I am going to submit it to, in PDF. Can I submit this  PDF as a pre-print to arXiv right now, or not? Will the publisher still consider publishing it? To play devil's advocate, why wouldn't IEEE say, this has already been published in the arXiv, it is no longer new work?
Edit: Here is what IEEE says: "Does IEEE consider an author posting her paper on preprint servers or on her company's web sites to be a form of prior publication, which may then disqualify the paper from further editorial consideration? No. IEEE policy allows an author to submit previously posted papers to IEEE publications for consideration as long as she is able to transfer copyright to IEEE, i.e., she had not transferred copyright to another party prior to submission."

Yes you can. If/when they accept the paper you need to perform the actions as described in:
From https://www.ieee.org/content/dam/ieee-org/ieee/web/org/pubs/author_version_faq.pdf

Can an author post his manuscript on a preprint server such as ArXiv?
Yes. The IEEE recognizes that many authors share their unpublished
manuscripts on public sites. Once manuscripts have been accepted for
publication by IEEE, an author is required to post an IEEE copyright
notice on his preprint. Upon publication, the author must replace the
preprint with either 1) the full citation to the IEEE work with
Digital Object Identifiers (DOI) or a link to the paper’s abstract in
IEEE Xplore, or 2) the accepted version only (not the IEEEpublished
version), including the IEEE copyright notice and full citation, with
a link to the final, published paper in IEEE Xplore

On the author center of IEEE it's restated here, that prior to publication one can share it on ArXiv
http://ieeeauthorcenter.ieee.org/publish-with-ieee/author-education-resources/guidelines-and-policies/policy-posting-your-article/

Prior to Submission to an IEEE Publication
Authors may post their article anywhere at any time, including on preprint servers such as arXiv.org.

Authors seem to get a lot of leeway from IEEE, including reuse of their previously submitted work in new works. Only thing is that your preprint must match the accepted version by IEEE. Not any rough draft or anything.

Can you comment on whether particular IEEE journals/transactions have different preprint policies than the general IEEE policy you cited above? (As someone who publishes in the IEEE regularly, I was under the impression that this varies according to the particular journal/transactions you are interested in publishing in.)

It seems to be an IEEE wide thing, also in the author center it's repeated in several places, with nowhere a mention of "With the exclusion of.." Also all links on the different journals lead back to the author center so far i've seen. "Submission guidelines" etc..

Yes, that seems reasonable. (Perhaps it might still be a good idea for OP to check with the particular journal/transacations, just to be sure.)

While I generally agree with this, if the publication is of very high quality or is expected to cause high impact, I would be hesitant to publish it anywhere else before it is fully accepted and (electronically) published. It may happen that arXiv version is being widely shared and cited, but everyone forgetting the journal publication. At my university, it matters - citations of non-journal and non-conference papers are not taken into the account when evaluating one's work. May not be relevant to you, but could be for your supervisor or other coauthors.

@xmp125a So basically you're saying, keep in mind if your future aspirations will have elitist mindsets to deal with or not. Personally I'd find it poor, perhaps even spiteful mindset to discard citations on an open arXiv publication that references the original accepted paper. But that's life and the rules you get to play with.

@Tschallacka I, unfortunately, have no influence on the rules our University uses to grade academic achievement. And it is not even "elite" university.

Check with the journal you are trying to publish in. Different journals might have different views on things and there is no general answer that is true for all of them.
Try looking up the regulations for copyright when publishing with said journal, there should be information there on what you are still allowed to do (upload to arXiv, upload to your personal homepage, send it to people only on request, etc.). Maybe they don't want you to upload the exact version you publish with them, but rather a preprint (e.g. without the journal formating).
And btw, this is not a simple question, as the correct answer differs between journals, fields, etc.

Ok, today is my nice day:
Googling "IEEE Arxiv", the first hit is
this FAQ, which has a point:

Can an author post his manuscript on a preprint server such as ArXiv?
Yes. The IEEE recognizes that many authors share their unpublished manuscripts on public
sites. Once manuscripts have been accepted for publication by IEEE, an author is required to
post an IEEE copyright notice on his preprint. Upon publication, the author must replace the
preprint with either 1) the full citation to the IEEE work with Digital Object Identifiers (DOI)
or a link to the paper’s abstract in IEEE Xplore, or 2) the accepted version only (not the IEEEpublished
version), including the IEEE copyright notice and full citation, with a link to the
final, published paper in IEEE Xplore

I'd say the question is simple, but it doesn't have a simple answer ;)

Here is what IEEE says: "Does IEEE consider an author
posting her paper on preprint servers or on her company's
web sites to be a form of prior publication, which may then disqualify the paper from further editorial consideration? No. IEEE policy allows an author
to submit previously posted papers to IEEE publications for consideration as long as she is able to transfer copyright to IEEE, i.e., she had not
transferred copyright to another party prior to submission." Again there is a lot of superstition around here regarding this, and I want to make sure this is an answer to my question.

Although it might not answer the question exacly, googling revealed a useful Wikipedia list of the policies of some journals.

I don't know of any reasonable publishers in my field (theoretical CS) who would refuse to publish work that has been submitted to arXiv as a preprint beforehand. However, there is one important exception which hasn't been pointed out by other answers: double-blind reviewing.
For venues that employ double-blind reviewing, submissions are required to be anonymized, i.e., not give away the identity or affiliations of their authors. Some employ so-called "lightweight double-blind reviewing" where this requirement only applies to the submitted paper: the idea is to avoid biasing the reviewers, but not to prevent them from determining the identity of the authors if they search independently.
However, some venues are more strict about double-blind reviewing and want to ensure that reviewers cannot find out the authors even if they search. Some of these venues (or some reviewers at these venues) will outright reject any submission if they manage to deanonymize the author in this way. (In my opinion, this is stupid, but I have seen this happen at a respectable conference.)
So, in the case of a venue which is double-blind and not in the lightweight sense, it may be a problem if you have your work posted on arXiv before submitting, because a reviewer will probably be able to deanonymize you by searching for your work (the title, abstract, or some random sentence) and finding the arXiv page with your name.
As arXiv submissions cannot be deleted, I think it may be better to wait for acceptance before posting your work on arXiv, in cases where you are planning to submit your paper to a double-blind conference with such a policy, or if you might be resubmitting it later to such a conference.
See also a related question and discussion of this problem.

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What can a letter writer say in a teaching letter to make a candidate stand out?
When applying to faculty positions at teaching-focused institutions, recommendation letters that speak to a candidate's teaching ability are (presumably) very important. But often the person writing the teaching letter is a faculty member at a research-focused institution, where teaching experience of faculty applicants is not emphasized, and so they may not know how to write a good teaching letter (due to lack of experience evaluating such letters).
For example, Pete L. Clark said in this answer:

Many teaching letters are not worthy of more than a very brief reading. They simply do not separate out the candidate or provide any really incisive or useful information. If teaching letters are to be believed, then approximately 99.5% of all candidates are above average teachers. Why do I find myself skeptical of this?

What are the characteristics of a strong teaching letter? What can a letter writer do to emphasize a candidate's teaching abilities in a way that makes them stand out?
Of course it will depend on the candidate... but I'm interested in hearing from users who have been on search committees at teaching-focused institutions, regarding what kind of things they look for or appreciate in teaching letters.

I've served on many search committees in a mathematics department.  At our institution teaching and research are both important, and I wouldn't describe our institution as entirely "teaching focused."
In my experience, most recommendation letters that address teaching don't show that the letter writer has actually observed the teaching of the job applicant and don't say much about what the applicant actually does in the classroom or how effective this was in promoting student learning.  I'm generally impressed by letters which describe direct observations of the candidate's teaching.
e.g. "Jane had average student evaluations of 4.0/5.0, which is very good in my department." isn't very helpful (especially since I can read the teaching evaluations that the candidate submitted with the application.)    On the other hand, I'd like to see something like "As the calculus course coordinator, I met with John on a weekly basis and regularly observed him in class.  His lectures were well organized, and he regularly incorporated pair-and-share and other active learning strategies.  In interacting with students he consistently has a positive and encouraging attitude.  I have never had to deal with complaints from students in his sections.  John's students also performed well above the course average on common final exams."

The problem, I think, is that what you would like to see probably rarely happens: How many professors go observing other professor's lectures?

@MassimoOrtolano it varies a lot from one institution to another.   For graduate student teaching assistants in the US, the kind of supervisions that I've described is widely considered to be good practice.  See for example the article on teaching TA's how to teach in this month's issue of the MAA Focus magazine.  I agree that in many places the actual practice is that graduate student TA's and tenure track assistant profs are effectively unsupervised.  That's unfortunate.

I'll add that if you're a graduate student TA or teaching postdoc you should consider asking the people who will write your letters of recommendation to directly observe your teaching.


text:
What is "shared governance" and how does it work in practice in academia?
I read the Wikipedia page and this article on the subject and frankly, I am still clueless. I don't understand what shared governance means in practice, how it applies, who it is for, and how it actually works. (In most universities - I understand the details are university-specific.)

What kind of issues are decided by shared governance?
Who participates? (I understand this can vary by university; just asking about general trends)
How does it work, on a practical level? (I understand what a session of the U.S. Senate looks like, I have no idea what a session of a faculty senate looks like.)
What are the benefits and disadvantages of this approach to governance?
What kind of hierarchy typically exists? (Who reports to who?)

A related question is What is the usual role of a faculty senate? Does it typically have any real power, or is it a rubber-stamp for the administrators?, but that only questions the extent of their power. I am trying to understand how this kind of system looks and works in practice, the way my high school Civics course taught me how the U.S. government functions in practice.
For context: I am most interested in large research universities in the U.S., though I welcome answers from other perspectives.


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My new advisor didn't reply to my email, is it a sign that she doesn't care?
I am a 2nd year graduate student in physics, at one of the top 20 universities in the US. This semester, I am supposed to start a research project with a professor who agreed already to be my thesis advisor.
So, I emailed her at the beginning of the semester -10 days ago- but I didn't get a response yet. Nothing. I emailed her again yesterday and I hope for a response. I tried to go to her office, but I couldn't find her. She comes to the university only to teach and then she disappears.
I am really worried. Is this normal or is this a sign that the advisor doesn't care at all? Should I try and change advisor? She is a top scientist but I am very disappointed...

Maybe you could find out if she has 'office hours'? As a instructor she should have them usually. As for the emails, perhaps they got filtered out, or buried or maybe she just is busy or backlogged with stuff to do?

Related: Is ignoring emails acceptable in academia?

Related: What does it mean if a professor does not answer your email in time?

Just out of curiosity, what the facts that you are in a top university and she is a top scientist have to do with your question?

Curiosity is a good thing: I think these facts may explain why she seems to be so busy. Also explains to me why is hard for me (at least psychologically!) to change advisors.

Perhaps there is a serious personal issue that your prospective advisor is dealing with that she doesn't feel comfortable sharing yet.

Flippant version: It is normal AND it is a sign that the advisor doesn't care at all.
Less flippant version: I would wait a couple more days. Then, if still no response, I would ask around to see if the professor is sick/having a major life crisis/etc. If not, then I would conclude that they do not meet a reasonable standard of availability, and I would choose to work with someone else. There are few things more depressing in academia than colleagues who drop the ball and ignore you, especially when they are senior to you and you rely on them to make progress on your work. Life is too short to be working with someone who is not interested in working with you.

@duboce: Note that you can edit your own answer at any time.

@duboce: thank you for your helpful answer, and especially for your comment.

@jigg While it is true that it is a speculation, duboce is right that 1) total ignorance of lesser beings is rather common in academia, and that 2) the situation easily can be a major red flag about what the OP can expect.

@duboce: DO NOT EDIT ANYTHING. It turns out that you were right!  After 3 months, I realised she doesn't care. I wasted my time with her. I cannot go into details but I can say that now I am trying to start working with somebody else. This didn't have to do with me though, she doesn't care about any of her students.

This is, unfortunately, a not too uncommon occurrence in my limited experience in top US graduate schools. Some professors are over committed;others might be having a real life emergency/crises - yet others are just not good at replying to emails. There are a number of different strategies that you could employ at this juncture.
1. Email again, early next week:
There are a few variations to this strategy. First, if the professor has just missed your email due to a veritable deluge of emails in her inbox then this could be a timely reminder. Second, if there is another collaborator, temporary graduate chair or other, initial faculty that you worked with (since you mention that you are a second year graduate student), then an email with such a person cc'ed is usually good incentive to get at least, an initial reply.
2. Meet with the department chair/other goto faculty:
Every department will usually have one faculty who serves as a point of contact for students in any kind of a predicament. This is a predicament. You need to kickstart your research agenda with this professor (potentially) but have not been able to do so. In our department, we call them "Director of Graduate Studies". Your department might have a similar or different nomenclature for such a person. An email sent from this person (cc'ing you) is usually enough to get a reply.
3. If all fails, lie in wait to surprise them IRL:
Figure out their office hours. If they teach, then they must have office hours and usually, a website for the course that they are teaching. Ask their TA's (if they have any) when the office hours are. If you see this faculty in the corridors then stop them and chat with them.
I am a fifth year graduate student and over the years, I have had to (unfortunately) employ most variations of the three broad strategies I mentioned.One reason why I chose the dissertation committee that I have is that these professors always make time for me if I want to chat with them about some academic (and sometimes non-academic) issues.
I hope you succeed in your quest to locate said faculty. Good luck!

+1 Adding to your 3rd point, I've had success with waiting outside of the individual's class room and entering once class adjourned. It's at least useful for getting important forms signed, etc.

Excellent point. I've had to do this myself (to get important forms signed. :) )!

 This is sad, truly sad. :-(

@RoboKaren I know! Sometimes its such an ordeal. This is exactly why I made sure that my dissertation committee members are generally responsive to my queries and available to meet!

The important thing to always to remember here is that in fact the Advisor/Supervisor is also as interested in his/her students finishing their studies as students themselves.  Mutual Benefits and Mutual Respect.


text:
Is it possible to have a PhD advisor from another university?
I am a graduate student, 2nd year, in a STEM field at a US university. I have passed my quals.
Is it possible to ask from a Professor who is not at your university to be your advisor?  Did anybody hear of a similar situation?
I am very interested in the research of a professor who is at a nearby university, I really want to work with him but I don't know if this is even possible. (Maybe I should also note that his university is not necessarily better than the one I am now.)
Any suggestions?

Extrapolating from the very few cases that I've seen, I think you'll need an official adviser at the school where you're actually enrolled.  If it's OK with that adviser, then you could also have a "real" adviser elsewhere, on an unofficial basis.  Your school may have some paperwork to be completed if your real adviser is to serve as a member of your official thesis committee.

I've never heard of that except in the case where a prof left one university to go to another. During the long transition, some of his students physically moved with him but continued their PhD at the old school. I believe the professor retained some sort of appointment at the old university in order to make it fit within the University's rules.
That being said, almost every PhD committee is required to have an external member. That's usually outside the department, but it is often outside the university. So, it might be possible to find an advisor at your current university to be the nominal head of your committee, but to include the other guy on the committee and have him be the de facto head. The biggest problem will probably come down to how your research is funded. If funding is not a problem, then you might be able to pull it off.

I know a few examples of this happening in math, though it may be even rarer in other fields.

Not that rare, professors do occasionally change universities and that will often impact any students that  they are advising.

This actually happens on a regular basis between the math departments of Harvard and MIT, in both directions.  Harvard has no combinatorics, for example, so interested students end up working with Stanley (typically, or Postnikov) at MIT.  And my advisor, Gaitsgory, had a student at MIT.

Glad to know there are more reasons for this to happen than I was aware of.


text:
What productive academic work can you do with minimal attention in a small (<30 minutes) block of time?
Ok, here is a major issue I have with much of my work (which is experimental/computational research): I am a graduate student, and spend much of my day doing 5, 10, 20 and 30 minute experiments (both biological and computational), which require set up and monitoring, but result in small to medium size hunks of time when I'm really not doing much (essentially just checking every 1-2 minutes to make sure everything is still working). During these periods of time I either a) attempt to do other work or b) procrastinate. Both are not great, since either a) I'm constantly shifting my focus from the latter project and end up making mistakes in it or b) I'm procrastinate (usually by reading articles, twitter, etc.)
Does anyone have advice for how to deal with these small, awkward period of times (< 30 minutes) - is there something which you find useful to do that you can also shift your focus from incessantly?
As an example: http://xkcd.com/303/

While this seems like a very specific situation, I think this question is more broadly applicable; e.g. to teachers and TAs who have small blocks of time between students in office hours, for graduate students who have short breaks between classes or meetings, etc.

Why is reading articles considered procrastination (assuming they are related to your research)?

By articles I mean, like, NY Times.
And yes, to ff524, I think this is a pretty widely applicable phenomena, especially in the sciences:      http://xkcd.com/303/

Why not just do the non-science errands like checking emails that would normally compete with scientific work when you are not running experiments?

How about moments of quiet, stillness, reflection and deep breathing? These have benefits too, and are easily forgotten.

As another example: http://on.fb.me/1whNDOx

Visit the SE network. Oh wait

@ff524, At work too. 10 minutes to a meeting, and etc.

Grading papers.

ff524's answer is awesome as usual, but the core problem for you may be that most of these suggestions are not, or at least not directly, useful to your research. If, as you say, most of your day is spent in this way, even "productive procrastination" may be too much procrastination and too little actual progress.
In that case, you have two options:

Learn how to get actual work done in those short chunks. Being able to context-switch without getting thrown off completely is definitely a skill that can be learned. You will probably never get as efficient as when you can devote your full attention to the task, and you will likely need to double-check what you did while multi-tasking, but getting things done slower than usual is much better than not getting anything done at all.
Automate better (and, hence, increase the time between needing to check up on your results). In my experience, if you need to actually check every other minute or so what your experiments are doing, then your tooling is not good enough. Many things can be scripted so that they basically run from beginning to end on their own. Further, you can configure a system monitoring tool so that it (for instance) sends you an email when something abnormal happens. Of course this requires non-trivial IT skills, but in my experience most students working in experimental sciences are able to grok these things quite quickly if they devote a few days to it (believe me, the time necessary to learn how to automate pays off manifold in the long run).

In practice, you probably want to go for a combination of both of these options. Try to increase the time your experiments are chugging along on their own. In parallel, train making the best use out of this time.
Edit:
This question has been added by the OP in a comment. I think it is interesting, hence I added it to my answer:

what can you do in the case of active code development? For example, when I am actively developing a piece of code to analyze data, that code may take a few minutes (up to 10) to run, after which I assess if it is working. How do you automate that process?

This has actually more to do with standard software engineering practices than the sciences, but I think it is a helpful concept nonetheless. When you are trying out different implementations, with a large possibility of error, make sure that your application fails fast. That is, make it so that your application does not take ten minutes to fail, but does the complex, error-prone stuff directly in the beginning. Two simple examples from my own research:
Example 1:
Say you do research in Machine Learning. Your application first trains an artificial neural network (ANN) on your data (easy as you are using an external library, but takes ~15 minutes due to algorithmic complexity), after which you do some postprocessing (trivial, executes fast) and a statistical analysis of the results (executes fast as well, but relatively complex, error-prone code). If you now always run the entire application and have the code fail during the statistical analysis, you are always losing 15 minutes for every run for a step that you already know works. A better solution would be to train the model once, and store it to disk. Then write code that only loads the ANN from disk and fails directly after that. Almost no dead time anymore. When the statistical analysis is working, you can revert to do everything in the expected order.
Example 2:
You have written a complex, multi-threaded testbed, which is running distributed over multiple physical servers. You know you have a synchronization issue somewhere, as your application non-deterministically dies every couple of minutes. You have no idea where exactly. Hence, you repeatedly execute the entire application, wait for the error to happen, and then debug from there in different directions. Given that the error only happens every few minutes, you spend most of your time waiting. A better way is to take a page out of good software engineering practices. Make sure to unit test all components in isolation before throwing everything together. Specifically try to cover exceptional cases. Learn how to write good mock objects. Some amount of debugging of the integration system will still be necessary, but you will not spend hours debugging components that are fundamentally broken.

w.r.t. point 2 - learning the skills behind this and sketching out on paper ow you might go about it are useful tasks (see ff524's answer) even if you can't put it into practice.

The automation part is definitely one that I think about the most, but what can you do in the case of active code development? For example, when I am actively developing a piece of code to analyze data, that code may take a few minutes (up to 10) to run, after which I assess if it is working. How do you automate that process?
Getting actual work done in those chunks is certainly my long term goal, it's just something that I struggle with. Any ideas on that front?

@DannyW. Make it fail fast. I have edited my answer with two examples of what I mean. I hope this helps.

The automation part is not necessarily applicable to biological experiments though...

Regarding the ability to learn to task switch frequently, I personally don't do it particularly well, but I have seen secretaries & receptionists who are excellent at it.  It's almost in their job description.  Maybe ask one of them for advice?

@nickalh, I task switch every day all the day (currently have ~40k tabs opened). The trick is to divide your job into small discrete chunks and either do the whole small chunk at one time or don't do it at all. Also, imagine yourself as 20 separate entities and don't fool yourself that you can read your own (previous) mind. Write instructions down for yourself in a context-free manner readable by a third party. Allocate the sole role of manager to one of......................................................................................................

.....................................your entities because you'll get to a point where you have so much instructions and of so many varying types that it's good to have instructions to make proper sense of all those instructions. When you're at the point where you can confidently get a car crash anytime, lose all your memory, including all your passwords, and still be able to resume work immediately after, you can give yourself a pat on your back.

Given that you can't do something that requires your full attention during this time, I would at least advise to follow Matt Might's advice to procrastinate productively on meta-work:

Read articles (or answers on Academia.SE!) about how to be more productive as a grad student.
Read a few pages of a book on data analysis, visualization, academic writing, or some other research skill.
Use the time to review your schedule, make to-do lists, schedule a meeting, etc.
Try a relevant Coursera course that has video lectures broken into small bits
Put some thought into a tiny bite-sized aspect of your research or research presentation: How should I organize this manuscript? What is the best way to visualize this data?
Unfocused literature search: check in on your favorite journals or conferences and identify interesting papers (to read later, when you can give them your full attention)
Do a favor for someone else: Even if you're not teaching or grading this semester, I'm sure someone in your department is, and would be happy to give you a pile of quizzes to grade.
Update your CV and/or web page.
Start preparing a presentation on your current work. You'll present it to someone, somewhere sooner or later, right?
Think about your ultimate career goals.
Use the Internet to look for promising potential collaborators at other institutions.
Try out a new programming trick, LaTeX package, or software tool.

These are great - definitely the kind of stuff I was thinking about.

"Start preparing a presentation", "Try out a new...LaTeX...software tool": Write up some notes on your current work in LaTeX, especially if it's new to you; Learn to use inkscape a draw publication-quality diagrams of your current work.
Within a few weeks you've gained a few pages towards your thesis.

Reading articles seems ideally suited to the time frames that Danny W. mentioned. That's enough time for me to read/skim an article and highlight the main points. If the article is particularly relevant to my research, I then put it aside for a second, in-depth reading. Similarly, short chunks of time are good for searching for new articles to read.

@ChrisH "Learn to use inkscape a draw publication-quality diagrams of your current work" leads to: Learn to use TikZ and pgfplots to draw publication quality diagrams :-)

@darthbith, true, but my tikz is only up to simple stuff like flowcharts while I can draw quite complicated figures in inkscape -- hence my more modest goal.

@ChrisH +1 TikZ is quite complicated, but pgfplots (and the related pgfplotstable) are much simpler... You can find some examples of nice scientific drawings with TikZ here: http://tex.stackexchange.com/q/158668/32374 and http://texample.net/tikz/examples/ among other places :-)

As a guy with a full time job, a wife, two kids but also aspires to release an app someday, I can tell your bullet about "bite-sized work" is golden. Not only does it show you have an understanding of your project and the individual components of it, but it gives you microrewards that motivate you to continue working on your project over time.

A great deal of things we have already learnt are slowly being lost by our brains. Small chunks of time like this are ideal for a bit of memory consolidation. I would suggest a quick revision session on things you want to know better.
It's best to select knowledge that will be most useful if remembered. Ignore any knowledge that is easier to just look up on Google (e.g. 5 Biggest Cities) and focus on knowledge that will help in your day-to-day (e.g. Chrome Keyboard Shortcuts).
How to create a summary of key information for learning

Keep the 'bits' of information small and 'easily learnable'
Write a dot-point summary of a topic you wish to understand better
Highlight favourite passages on your Kindle then download them from kindle.com
Look up book summaries from websites like getabstract.com
Use Evernote to collect information you wish to learn in notebooks (their Web Clipper is  awesome)

Once you have a summary

Use small chunks of time to review the material

Don't spend too long or your brain will switch off

Use Spaced Repetition methods

Material you find hard to learn should be repeated more often
Material you find easy should be repeated less
You need to wait between sessions to allow it to sink in

Good luck!
Disclaimer/Self-Promotion:
I am the creator of www.revisy.com - a tool that helps automate all of these steps.

+1 learning language vocabulary is another example of something that is great to learn this way. Here's a good article from a language-learning blogger on ways to go about doing this.

Good suggestion. If only all infomercials were useful... is there an app for that?

I'd like to add a point of view from experimental science. I do spectroscopy of biological samples and I have experiments like that as well.
Abstract:

do not underestimate how strenuous experiments can be: if done well they often require you to keep up a high level of concentration. This is exhausting, and particularly if awkward timing is involved.
Decide your priorities: do experiments only or start an experiment whenever that fits your other work schedule.

My experience is that there are some people who cope with such situations relatively calmly: they vanish into the lab, do their experiments, don't go into fits because of the lost time, and reappear after the experiments are done. Other people (e.g. I) absolutely hate the situation, because tons of other stuff needs to be done, and don't get done while you are there waiting for the experiments in minute time chunks that don't relly let you do anything.
Personally, I found 2 feasible ways of dealing with these situations, and the big idea is to decide beforehand where the priority is.

Experiments have high priority: In that case, I consider the time and concentration being reserved for the experiments. Any other things that happen to get done are a surplus. I find that most items from @ff's list shift my focus too much. Which in the end leads to chaos in the experimental data (e.g. slightly differing names of files which require manual adjusting afterwards etc.)
But setting up the measurement schedule for the next experiment, getting the notes and the samples ready for the next experiment, etc. is OK. (Though many of my experiments are in the dark, so preparations to be done in the same lab are not really feasible). Other than that, I do things like checking (e)mail, schedule meetings, preparing TODO lists and working off tiny tasks. If the waiting time is more like half an hour I may clean up my desk/the lab, but e.g.
I don't like reading papers or doing a literature search on an alarm-clock schedule. Such work I do only when
Experiments have lower priority: I often have experiments where I need to change samples after measurements are finished after, say 30 min, but nothing really bad happens if I change the sample 30 min or 1 h later. In that case, I often decide to run the experiments on a lower priority, while reading or writing papers, doing literature search, etc.
So I do something, and when a natural break occurs after a chunk of work is done, I go and change samples before I start the next chunk of work.
This way, I get maybe only a third or a quarter of the experiments done compared with the "high priority" experiment situation, but other work is possible as well.

All in all, the first scenario is the more emotionally exhausting the more you dislike the breaks and fret about all the work that doesn't get done. It is the more feasible the better you can arrive also mentally in a state where you concentrate on the experiment only. Experiments are serious and exhausting work as well. One reason is that you have to be far more concentrated than at the computer*: If I make a typo, there's backspace. If I type an awkward sentence I can change it later. If I pipette wrong, I have to start from the beginning or even take out everything and clean my instrument. And this is the more strenuous if awkward schedules are involved. And weird little waiting times are in my experience as exhausting as an experimental schedule you can barely keep up with.
* most times: obviously, data analyses need to be set up correctly. But even there, e.g. literate programming allows to double-check later that everything was done correctly. This is not possible for the experiments - there I have to be concentrated so I'm sure afterwards that I did everything correctly even though I have at most very limited possibilties for double checking.
If you have to do your experiments in that way, then the best way to deal with it is to also mentally accept this and make it your task. Make sure that the experiments really stay top priority if you decided that way: don't read SX if you realize that this in fact leads to the experiment waiting for you when you decided that you should wait for the experiment.
The second way is of course only possible if if the experiments allow this. And if the lab has low enough use so your colleagues don't kill you for not making fullest use of the vaulable lab time you reserved.

In terms of automatization of (biological) lab experiments, I for example print out "experiment forms" that have a predefined structure for my note taking so don't forget any of the parameters. This helps me to make fewer mistakes.
In some cases I even write custom measurement programs, but this effort is usually only worth while if you know that large series of experiments follow (for totally non-scientific and awkward reasons: the instrument software often allows only very restricted programmed interaction, and if there are not many experiments following, it is not worth while to go down to the low-level control the SDK offers - if there is an SDK available at all).

+1 for no backspace key when performing experiments. :')

If you are

a) Constantly shifting your focus from the latter project you overcharge your brain with additional tasks in the moment and possibly what your brain really needs from you is some rest, that's why you end up making mistakes because by the time you get to the lab projects your brain is already preoccupied with new tasks and have used up a lot of memory doing new operational memory tasks
b) If you procrastinate by reading NY Times and Twitter you are also overloading your brain with new information that requires some analytical work(although it is of course incommensurate with the amount of work you need for academic journals, but precisely because the Times loves using such words as incommensurate on its pages you have to constantly query your memory and thus, keep your brain busy), so here we are, getting back again to the same problem-you are using your operational memory, the brain gets tired and by the time you get back to the lab work it is nearly impossible to focus.
c) I hate to suggest that...but...is there a possibility of a short nap or just sitting quietly in your office and not exposing your brain to any additional flow of information? Plus, Twitter, email checking, Facebook, are all good examples of instant stimulation but that's a topic for another conversation.
d) In a nutshell, trivial things like smells, sounds from the street, someone's voice etc. are all examples of additional information that you are exposing yourself to that inadvertently bombard our brains on a daily basis and they make it even harder to focus and get back to the actual projects.

I do like naps.

How does that address the question?

@xLeitix , yes, as the question was "Is there something which you find useful to do that you can also shift your focus from incessantly?" while another underlying issue is also the fact that when they go back to the original task, the concentration is lower and the problem is, instead of finding an effective way to work overall and have "down periods" when the brain is exposed to the minimal amount of tasks and data, they are looking to overload it if additional tasks and yes, certain things can be learned & automated,there is nevertheless the need to change the overall structure of daily work

@xLeitix I guess my main argument is that if someone procrastinates actively it happens for a reason and not merely because this individual is thoroughly enjoying being merely "lazy" or unproductive. There is a lot of literature on time-management and procrastination books and very few of them address the actual neurobiological processes that are happening when someone is performing tasks that require a lot of concentration and fewer mistakes. So if the question was "how to deal with these small chunks of time," I believe I've addressed it thoroughly.


text:
What is the optimal balance of time spent on research vs. volunteering/leadership/organizing in grad school and beyond?
In graduate school I have found that along with being a producer of good research, fellowships/post-doctoral advisers/faculty search committee are also often interested in what one has done outside of research (or teaching/TA-ing) as well. Examples of such activities include (but are not limited to) volunteering with younger students in science, organizing a journal club, mentoring undergraduate researchers, etc. In some cases, the importance of these activities is immense: for the NSF CAREER award, for example, having outreach is a substantial part of the application.
However, clearly, spending all of one's time on these activities is not the best idea, since then no research gets done.
So, my question is:
What is the optimal fraction/amount of time to spend on "outreach" activities like those mentioned above, which are explicitly not research or teaching activities, for someone looking to pursue a career at a research university?

I suggest rephrasing the question. In its current form there might not be an actual answer insofar as 'optimal' time varies from person to person, and depends on many variables such as skill, experience, history, etc.

Optimal in what sense?  Optimal for your happiness?  Optimal for getting tenure?  Optimal for making the world a better place?

Would you consider changing the question to "How do I know if I'm spending too much time on volunteering/leadership/organizing vs research?" - I think that might make it more answerable (and I really want to know the answer :))

Yes, I guess in some way that is my question too, but I also am not sure if it's answerable.

I believe the optimal amount of time spent on these other activities is as little as possible.
Graduate students are notoriously overworked and underpaid. In essence, they are trying to see how much work they can get you to do for them, for free. In situations like this, where they attempt to get you to prove how much of a 'team player' you are by essentially providing them valuable work without them having to pay for it, you need to be on your guard and make sure you aren't taken advantage of.
You will need some of these 'extra-curricular' activities. But they are a means to an end. Essentially you must demonstrate your willingness to go 'above and beyond' the explicitly listed duties of your job without neglecting the official duties. It can be a tight balancing act.
There isn't a hard and fast ratio, like 70% research/ 30% leadership. Instead, you will need to be skilled at determining your peers/superiors expectations for you in this regard and meeting/exceeding them, and this will vary from institution to institution and individual to individual. A lot of potential feedback is likely to be of the 'you must read between the lines' variety.They are unlikely to say "You aren't going to get the position unless you do more work for us for free". Instead they might hint "you are doing great in your research, but it would be very nice if you would take more initiative and perhaps be more of a mentor to younger students" - indeed, anything phrased as definitively as that is equivalent to being posted in bright red flashing letters.
In general you will be pressured to spend more time on these activities than you can afford to, or is necessary for your career. Finding out where that level is (somewhere between 'nothing' and 'everything they ever ask you to do') may be a matter of trial and error.
I would spend time on the research unless you get the distinct feeling you are not meeting expectations in the extra-curriculars department.

Short answer: You should spend "some" time on volunteering and leadership activities.
Unlike an undergraduate student, a graduate student is primarily focused on research, so you don't want to spend "a lot" of time elswhere. Even so, proficiency in other activities is something of a "tiebreaker" against another candidate with comparable research activities that doesn't have "outside" activities.
This is in contrast to undergraduate work, where "leadership" is the "whole game" for some students (for later life)--provided they pass their courses.

See, actually this is what I disagree with. I have applied for numerous fellowships/grants etc. where the volunteering was explicitly NOT a tie breaker, but was required (and these were big money, multi-year science grants).  But, graduate school is often sold as being about only research. So I'm really just confused.

@DannyW. The problem with tie-breakers is, if you can count on many people being really good at the primary selection criterion, having good "tie-breakers" becomes mandatory. (and if those were reputable grants / fellowships, you can assume that most applicants had grand research CVs)

@xLeitix So, it's hard to call them tiebreakers, in that case. Maybe "The criteria formerly known as tiebreakers"?

It has to factor in what your goals are for 5 years from now.  Are you truly interested in the field in which you are doing research?  Or do you actually need to think about changing tracks to make some of the volunteering your career?  What would give you joy in daily work?
If you are committed to the research field, then think about the relevancy of the volunteering.  If your goals include highly competitive academic positions, you will need to be publishing 2 solid articles per year to survive.  Solid research even now is the foundation without which nothing happens.
The volunteer work should be meaningful ideally to both you and those who hire you either academically or in industry.  Joining too many organizations with little contribution may make you feel you are doing something but won't add to your credentials.  Focusing on perhaps at most 2 and rising to some official capacity or being able to document accomplishment is deemed essential according to many who have succeeded.
What kind of "cake" do you seek to make out of your work life?  Volunteering is frosting for which you feel good.  But you need to work on the cake foremost.

One point I'm missing in the answers so far is that you learn skills with many such activities that you need later on. As a postdoc, people will expect that you can teach and supervise a small group as well as doing research on your own. The same is true if you want to become a group leader in industry.
So unless you're the natural born group leader and teacher, you'll need to put in a certain amount of time to try out how these things work. When and where you do this is maybe less relevant*: I learned a lot of this volounteering in a student group as an undergrad. You may also convince people that you learned teaching by fincancing your studies giving private lessons.
I have learned more about group leading looking after students doing research practicum, and after students doing their bachelor/master/diplom theses. In terms of getting research done, research practicum students typically require a multiple of the time I need to do the work. (And I find it important to be clear about that beforehand, and also to communicate this clearly to everyone, in particular to supervisors and students.)
In terms of learning how to formulate a decent research project, how to find someone capable and willing o work on it and how to supervise such work, I don't see many alternatives to putting in the effort.
I'm less enthusastic about looking after a 101 labwork practicum. I do get out a thorough update on my general professional knowledge, but the time put in to knowledge gained ratio is really bad. You can still learn skills like teaching and grading, though.
Note: during most of my PhD I've not been paid for the research, the wage I got was for looking after a labwork practicum. However, this was non-negotiable in the sense that while I could have refused the contract, I'd still have been assigned as TA. So at the end of the day, there wasn't much volounteering about this. It was part of "everyone has to make their contribution to keep the institue alive". Where I'm now, teaching practicum is officially required during the PhD in some faculties.
* With the exception that starting to learn these skills as a postdoc is very late.


text:
How can we perpetuate reproducibility?
Two years ago, Roger Peng discussed in his blog the ramifications of requiring reproducibility for the first producers of research objects, but not for reusers. He then proposed

What I think a good reproducibility policy should have is a type of "viral" clause. For example, the GNU General Public License (GPL) is an open source software license that requires, among other things, that anyone who writes their own software, but links to or integrates software covered under the GPL, must publish their software under the GPL too. This "viral" requirement ensures that people cannot make use of the efforts of the open source community without also giving back to that community.

I agree that having such a perpetuating policy element is very desirable, but would try to establish community norms around that, i.e. to get community organs like funders, publishers, institutions or learned societies to express support for the idea that reproducibility should trigger reproducibility. How best to phrase the core elements of such statements?
This question was originally posted at the Open Science SE private beta, due to be closed tomorrow.

Again, this question seems to actually be a springboard for discussion on the topic of perpetuating reproducibility. I apologize for being a pain in the backside... I'm honestly impressed with the effort you guys are putting in to make this site merge work. Can this also be edited to make it more of an answerable question as opposed to an invitation for discussion?

The first and only answer I got over there was very productive and to the point - hope it will be posted here too. In order to comply better with scope limitations, it would be helpful if you (or anyone) could point me to a place where scope - or criteria for holding or closing or protecting or deleting questions or answers or comments - are spelled out. Haven't gone through all of http://meta.academia.stackexchange.com/search?q=scope yet.

The original thread is now available through https://github.com/Daniel-Mietchen/SE-Static/blob/master/openscience/how-can-we-perpetuate-reproducibility.html , and I have found some help pages.

I agree with eykanal. I think there is an interesting question in here, but it needs to be more clearly articulated. What aspect of reproducibility are you asking about? How are you defining perpetuation? is it more than just having an effect of increasing reproducibility?

the common practice of framing plagiarism primarily or even exclusively as a copyright issue – Are you talking about academic plagiarism or plagiarism in general here? In the former case, I am not aware of a single case supporting this statement.

@JeromyAnglim What I mean is that if someone builds on some work that meets some reproducibility criteria (e.g. as laid out in a given institutional or funder policy), then that derivative work (be it a corroboration or a harsh critique) should be held up to the same or stricter reproducibility criteria.

@Wrzlprmft I am talking about academic plagiarism, and an example would be this one from Die ZEIT: "Ex-Minister Guttenberg habe zwar gegen Urheberrechte verstoßen, doch der wirtschaftliche Schaden sei gering, stellt die Staatsanwaltschaft fest – und das Verfahren ein." The Telegraph wrote about "copyright infringement" too.

@DanielMietchen: Yes, and on levels other than copyright, Guttenberg lost his doctor, had to resign as a minister and is stigmatised. So, this is a typical example where plagiarism is not primarily treated as a copyright issue. It is actually one of the few cases of plagiarism that was also taken to court as a copyright issue – which was only a sidenote in the whole affair of things.

@Wrzlprmft I removed the plagiarism-related part from the question, as it was just tangential.

@DanielMietchen: Is there anything I can do to improve my answer, or are you waiting to get more answers?

@jvriesem I am waiting for further answers, and commented on yours.

A few ways to perpetuate reproducibility:

As a stipulation to publication, publishers could require the software and data used for a research paper to be made publicly available so that others could reproduce the results.
Academic institutions could provide repositories to allow its members to publish their code or data, to encourage making the code or data publicly available.

Difficulties regarding reproducibility requirements:
A major downside--perhaps a dealbreaker--is that there are significant issues with intellectual property. Large numerical models often take years to develop, benchmark and test, but once they are completed, they can often be used several times for several publications with minimal modification. Sharing the code they spent years developing in many cases would allow others to use the code to make discoveries before the original developers had a chance to do so. Allowing the original developers to protect their code gives them incentive to develop it.
Many data sets (especially in the social sciences) are protected/restricted because they contain classified, confidential, or personally-identifiable information. Moreover, many data sets that contain no personally identifiable information can actually be de-anonymized though careful computer analysis, so even protected data sets that have been "anonymized" are not necessarily safe to be released to the public.
Another difficulty is that some codes/scripts/routines may use commercial software (e.g. MATLAB, SPSS, etc.), so while the code themselves may be freely available, they might not necessarily be usable without paid software. This would exclude certain people from being able to reproduce results.
Some data sets are so large as to be an unreasonable burden on repositories. For example, I collaborated on a simulation that produced over 50 TB of data -- and there are other data sets that make 50 TB look tiny! It could cost a lot to maintain repositories on the order of hundreds of TB or several PB.
Summary
There are a few things that could be done to strongly encourage reproducibility, but they come at a cost. Releasing code, exact methodology, data sets, and other intellectual property would impact different fields in different ways and may not always be possible from a legal perspective. My guess is that data reproducibility will need to be treated on a field-by-field basis.

Your points 1 and 2 are key elements of reproducibility policies, and the issues you describe are important and frequently stand in the way of reproducibility. I also agree that field specificities need to be taken into account. However, my question is not about that, but about how we can make reproducibility viral, i.e. if I share my stuff reproducibly and you want to make use of it, you should share reproducibly too.

@DanielMietchen: Gotcha. Thanks. I'm not sure about that last bit.

It's an economic problem, not a legal problem.  Make the experiment cheap to do, and it will be widely copied.  Provide funding or career advancement for reproduction, and people will do it.  An unfunded legal mandate will just cause scientists to go elsewhere.

It's not just economics, though: some problems are inherently complicated, limited to certain researchers, are expensive, or are just plain difficult so that they cannot be reproduced easily. I agree with your last statement for sure, though.


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How to make data management plans machine readable?
More and more research projects are now required to have a data management plan, sometimes already upon submission of the proposal, sometimes early into the then funded project.
These plans lay out the kinds and amounts of data to be generated by the project, as well as information about their storage, archiving, accessibility and related questions. There are also software management plans or more generic project management plans, which serve similar purposes.
A big issue with these plans — which so far are usually just text documents, and often not even public ones — is monitoring compliance, i.e. if statements made in the plan have been followed up by appropriate action.
I think this problem could be eased substantially if these plans were both public and machine readable, which would also allow them to be used for things other than monitoring compliance, e.g. as a discovery tool — imagine you could subscribe to the feed of all data of kind X acquired from sources of kind Y.
What I mean by machine readability is expressed quite well in the introductory part of the Wikipedia article "Machine-readable data":

"Machine-readable data is data (or metadata) which is in a format that
  can be understood by a computer. There are two types; human-readable
  data that is marked up so that it can also be read by machines
  (examples; microformats, RDFa) or data file formats intended
  principally for processing by machines (RDF, XML, JSON).
Machine readable is not synonymous with digitally accessible. A
  digitally accessible document may be online, making it easier for a
  human to access it via a computer, but unless the relevant data is
  available in a machine readable format, it will be much harder to use
  the computer to extract, transform and process that data."

Any advice on how best to go about achieving machine readability for data management plans?
I am aware of https://dmponline.dcc.ac.uk/ and https://dmp.cdlib.org/ but as far as I can tell, their machine readability is limited to non-existant.

If you haven't already, I'd recommend getting in contact directly with the Digital Curation Centre as they're actively developing DMPonline and I think this is one of the areas they're interested in exploring.

Thanks. I am in contact with them, and they are interested, but it will take some time, and I've posted it here in part to get in contact with others interested in the matter.

I'm voting to close this question as off-topic because it's not about academia: it's a computer-science question about how to make a particular category of information machine-readable.

Thanks for explaining your vote. The question may appear to be about computer science, but just imagine we had machine readable data management plans - how would that affect academia? For one, compliance with policies could be monitored, while on the other hand, we could use the information in the plans to build new data discovery tools. For instance, you could be notified automatically every time a dataset of a specific kind is announced in a plan or deposited in a relevant repository. We're not there yet, but I think it's relevant to academia and the life of researchers.

What do you mean by machine-readable? Obviously, most DMPs are digital documents.

@Wrzlprmft What I mean is expressed quite well in the introductory part of the Wikipedia article "Machine-readable data": "Machine-readable data is data (or metadata) which is in a format that can be understood by a computer.
There are two types; human-readable data that is marked up so that it can also be read by machines (examples; microformats, RDFa) or data file formats intended principally for processing by machines (RDF, XML, JSON)."

@Wrzlprmft They also have a paragraph about your second question: "Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for a human to access it via a computer, but unless the relevant data is available in a machine readable format, it will be much harder to use the computer to extract, transform and process that data."
Should I add that into my question?

@DanielMietchen: Maybe I am too unfamiliar with the terminology here, but let me briefly summarise my understanding: A DMP is an actual plan (we do this; then we do that; if X happens, we react with Y; …) detailing how you deal with your data. And this is what you want to make machine-readable, not the data governed by this plan itself, correct? (If yes, it would be good, if you elaborated this a bit more in your question; if not, your question needs to be rectified.)

I think that one of the optimal solutions to the problem of "achieving machine readability for data management plans" could be as simple as specifying procedures of data conversion from source formats (human-readable or semi-machine-readable) into desired machine-readable formats for further data analysis. Those procedures might be coded in natural language instructions or, alternatively and preferably, via algorithms for benefits of automation and reproducible research.
Conversion from machine-readable formats to human-readable is an optional step, based on projects' requirements (while optional, this is a typical step for most projects, implemented in their reporting phase). The types and details of data conversion functionality IMHO obviously depend on projects' specifics and, therefore, should be determined and specified on a project by project basis.


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Is it ethically defensible to attain consent on an opt-out basis in a newspaper?
I frequently find notices in my local newspaper from researchers who are in the process of doing follow-up research on some study that they conducted a few years back. There, they provide an overview on the research topic at hand and give the participants a chance to opt-out of the follow-up piece. Otherwise, they are included by fetching data from their medical records.
To me, this practise seems questionable. If I were to guess, only a minority of the people involved in the previous study are likely to come across the notice.
Is this ethically sound? If not; what are the alternatives?

I would hope that all other avenues of attempted contact have been attempted before this is, i.e. they made their best effort and this is their last attempt.

Of course, this may have been covered in the consent to the original study. Some level of 'public notice' may be required, and you would not be party to any private communications.

It seems to violate informed consent

I strongly suspect that this is the situation @Compass mentions.
Making a newspaper announcement after previous attempts at communication have failed a) means that you've made a good-faith attempt to notify the relevant people, even if it's unlikely to succeed, and (just as importantly) b) provides a public demonstration that you've done so in case of future objections. If you can reasonably assume they would have consented had you been able to reach them, you can now say "we've done all we possibly can" and go ahead.

In California, if you want to make a certain class of corporation, you are legally required to take out a newspaper ad for a certain period of time (on the order of weeks). I wouldn't be surprised if somewhere, someone has a guideline that says that you need to do the same for certain studies.

You'd need to obtain the first informed consent and examine the wording. If the informed consent does state that the collected samples or information may be used for other research-related purposes and the new projects will be announced on some specified channels, then the researchers have fulfilled their due diligence.
The problem being the general participants are really not very well-informed when it comes to providing biological specimen or medical records. For example, most of them may not realize that their samples can reveal a lot more as technology evolves. Unlike a questionnaire or an interview, the respondents know clearly what's written and said, but they may not realize what their blood or DNA can reveal, or what they can reveal a few more years down the road when molecular technology further develops, and the researchers want a re-analysis.
The researchers are usually not to blame, however, as they are probably equally clouded by the ever-changing hot topics. There are many occasions that a study was set up to test compound A, but fearing that compounds B, C, D... etc. may be the next scientific focal points, the researchers may opt for writing in the "opt out from reanalysis" statement into the consent form even they are not entirely sure what are to be reanalyzed and even if that analysis will ever happen. As for why opt out but not opt in, part of the reason is cost control: It's extremely difficult to track people down and collect a consent form again. For that, I can empathize.
If you will potentially be one of these studies' participants and are worried about this opt out mechanism, do realize that you don't owe them anything and you are completely free to walk away or require an amendment to limit the use of your samples, or request your sample or copies of record be destroyed after the study. The worst is that they will say no and you'll walk away without a chance to participate or receive the stipend. However, you'll have your peace of mind.
So to answer your question, this practice is defensible as other mechanisms can be cost-prohibitive. However, I personally do not feel it is entirely fair as I am doubtful that the participants fully know the consequences and their rights when it comes to giving biological samples or related medical records. Guidelines are being developed, and hopefully more education on how to be an informed participant will happen.


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Must one state min/max values in addition to mean(+-std) when writing a publication?
Is it 'a must' to state min/max values in addition to mean(+-std) when writing a publication?

I think we need more context about your field to provide a usable answer. In general, look at other publications in your field to learn about the customs.

The field is medical imaging. To be more concrete, it is related to cardiac segmentation and distance error between obtained 3d meshes.

No, SD and min/max are not bound to be reported together.
If the variable is normally distributed, -3SD and 3SD will include about 99.7% of the readings, indirectly giving an approximate value for minimum and maximum.
A concerning bad habit is reporting mean and SD without carefully checking if the distribution is approximately normal. For instance, if the variable is highly skewed (long tailed to either end,) then that interesting feature of -3SD and 3SD will disappear. In those cases, reporting median and inter-quartile range may be better if you want to describe the central tendency.
Reporting minimum and maximum has its merit if you wish to highlight the presence or absence of extreme values. In most of the analytical scenarios, min and max don't share as much spot light as mean; whether to report them is really context-specific.
There are a couple situations in which reporting min and max may be helpful:

You reported mean and SD already, but the variable is not normally distributed. (or alternately, report non-parametric summary such as median.)
You use a measurement tool that is rather unfamiliar to the readers. For instance, you developed a life quality scale and the mean and SD of the sample is 17.2 (+/- 3.6)... but out of what range? In that case, reporting both sample min/max (the min/max you actually observed) and possible min/max (the min/max score the instrument can actually give) will be helpful.

No, it's not a universal "must."
The statistics you should report in your publication depend on conventions in your discipline, the kind of data you are describing, your experimental methodology, and many, many other factors.
Consult your advisor and/or similar publications in your discipline to find out more about what's expected for the kind of research you are doing.


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Is it ok to mention my upcoming post during my conference presentation?
I was recently appointed to a new post (at a much better university) and my affiliation will change in a couple of months. I have to attend a conference next week and present work carried out with my current affiliation.
I would like somehow to let the audience know what my next position will be, and the project I will be working on, as it is very relevant in the community.
My questions are:

Is this common, or should I just stick to the research findings?
If it is indeed common, how to let the audience know? I was thinking about including a slide in the end of my presentation, about my upcoming post and project. But then, is there a chance that my new employer would have a problem with that? Should I let them know? Or maybe just forget the whole thing and just present my current work?

I am asking because the presentation will be permanently online, so I would like to make the most out of it.

Not really worth an answer, but: There is nothing more boring than speakers talking at length about their affiliation and the affiliation of their collaborators. Should I be interested in contacting somebody, I can obtain the name from the program and use it to find a working e-mail address (if it’s not already given in the program).

I have seen people advertise their upcoming affiliation changes across a number of different fields, and it is almost always appropriate to do.  I see it as a simple matter of courtesy to the audience, who may be interested in contacting you in the future.
The one caveat that I would add, because you sound (appropriately) excited about "upgrading" your affiliation, is to not make a big deal out of it, but simply let your affiliation speak for itself.  You can do this as simply as embedding the upcoming change information in your affiliation on the title slide, doing something like:

Universal University (moving to Prestigious Institution in Fall 2015)

Likewise, if you use an institutional slide template, keep the one of your current institution, don't switch to the one you haven't joined yet. There is no need to contact anybody or anything like that: you're not advertising, you're simply stating a fact of an upcoming transition that will no doubt appear in publicly available information (e.g., your LinkedIn profile and professional webpage) when it occurs.
The point of all this is to avoid publicly insulting your current institution or being perceived as bragging about your new institution.  It feels like a big deal for you (and it is a big deal in your career), but you'll be just one of a very large number of people at Prestigious Institution and part of moving up in your career is learning to be comfortable with an environment where you will likely spend a lot more time around people who also are at prestigious institutions.

This answer is based on conference experience from CS subfields.

a) is this common, or should I just stick to the research findings, and

Yes, it is fairly common for presenters to stick in a little bit of info on their affiliation/current research environment, especially if they see themselves in a position where they want to be contacted by members of the audience (for research collaborations, etc.).

b) if it is indeed common, how to let the audience know?

Mentions of the respective information are sometimes (briefly) inserted at the beginning of a talk, but more commonly at the end (along with saying something like "If you have any cool ideas for .../If you're looking for a project jointventure in ... contact me at ...!").

But then, is there a chance that my new employer would have a problem with that?

If both your old and your new employer are institutions that see themselves dedicated to the public benefit, e.g. universities, rather than to enhancing their corporate profile, e.g. private enterprises, none of the employers should have much of a problem with that (even more so as switching between universities usually does not mean you cut all ties to your former institution).
If, however, one of these employers is indeed a private enterprise, you may want to ask. In a way, I see the primary concern here rather that the old employer might have a problem with what you are planning, less so the new one. After all, you carried out the work presented during the talk while being affiliated with the old employer - mentioning your new job in such a presentation could be seen like using the resources of the old employer to advertise the new employer.

The last comment is a good reason to mention both your current and your future employers. People move around, letting people know that your old contact information is about to become obsolete and that they should search for you at your new place (even if you don't have an email address there yet) is helpful.


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PhD admissions: Overlap between B.Sc. and M.Sc. transcripts
As I wrote in my previous question, I am currently applying to PhD departments in the US (from a German institution). Beyond the language problem, there is another aspect that worries me:
I finished my degree back in March 2015 and then decided to fill the time from then to Fall 2016 (start of PhD programs in the US) by starting a 2 year M.Sc. in the same subject, which I planned to finish in 1.5 years (and will also manage to do so).
Officially I enrolled in that degree this September. Nevertheless, I "unofficially" started with the degree already during April 2015, by staying enrolled as an undergraduate and taking "additional courses" (i.e. graded courses beyond the required number of credits, which also do not influence the final grade). Since the Master and Bachelor degree were offered from the same university, they then simply approved all those additional credits as part of my MSc degree.
Now, my problem is that these courses are mentioned twice in my transcripts, once as additional courses of my Bachelor's degree and once for my Master's degree transcript. Do you think this could confuse the admissions committee? Should I maybe refrain from submitting the M.Sc. transcript since officially I am enrolled only from the Fall semester and the universities do not require transcripts from the Fall semester? Also note that currently the M.Sc. transcript does not contain any information about courses not included in my B.Sc. transcript. Or should I try to mention this somewhere (I am particularly worried about some universities which do not provide any opportunity to leave such comments/remarks).

Your two degrees sound reminiscent of the "3-2 combined" programs that are common in the U.S.  Read up on a couple of those and I think you'll start to relax.
Please do let the admissions committee know what you're doing this year.  Perhaps that information can be fit into one of the standard documents -- perhaps your CV, perhaps a cover letter or the statement of purpose.
No matter how streamlined an online application process is, I would think you could contact the department and ask them what email address to use for submitting an additional document that didn't fit into any of the pigeonholes provided.