text:
Do references count towards the word limit for Science Magazine?
Quoting from Science Magazine's General Information for Authors,

Reports (up to ~2500 words including references, notes and captions or ~3 printed pages) ...

This seems to suggest that references are to be counted towards the word limit.
But the reference style guide says,

Science includes titles in references. These are displayed in the online HTML version, but not in the print or the PDF versions of papers.

The printed (and PDF) version of papers do not have full titles in the references, which makes the reference list much shorter than in the HTML version.  In the end this makes a huge difference in the word count.  If I include titles, the word count of just the references makes up more than 30% of the stated word limit.  This doesn't seem very reasonable!
In general, I am finding it very hard to navigate the author guidelines.  Can someone provide a link to where the word limit (and what counts towards it) is most clearly stated for this journal?  In particular: do references count towards the word limit, and if they do, does that include the titles?  What about counting things like formulas, which might contribute significantly if counted variable by variable, as a naive word processor would do?
I'm seeing much higher word counts in already published Reports than what the guideline says.  For example, this random report appears to have ~4000 words without references, as counted by pasting to Word from the HTML page.

So I imagine this work count includes the abstract?

If you want to get published, follow the guidelines for authors meticulously.
If you're not bothered about getting published, then you are free to reinterpret them as you wish.
But if you do want to get published, read the words, obey the words.
You've already given us the definitive link for authors. The guide says 2500 words including references. So that's your limit. Keep to it. Yes, it does include titles. References include titles.
It doesn't matter that published articles are longer: they've been through peer review and editing. Your article hasn't, yet.
So if you want to get published, follow the guidelines for authors meticulously.
If there's still any doubt in your mind, talk to the editor. Remember, you want the editor to treat you fairly, so treat them fairly, be polite, don't waste their time, and don't ask them things that are already explicit in the guide for authors.

While I mostly agree with the answer saying that you need to follow author guidelines carefully, my experience with submitting to these types of high-level journals is that there's a little bit of play in the requirements.
If the requirements give precise numbers of words, figures, references, then you need to be close to those numbers.  In the first pass, the handling editor doesn't care enough about your paper to bother determining how many words an equation is going to count as.  Their actual threshold is more like "Does this look like it's likely to be able to fit in an appropriate size after editing?"  As such, it's less about exact counts and more about whether you force them to take notice of the difference between your document's size and their preferred size.
My advice then, is this: don't sweat whether the titles count or not.  If your document would be under without titles, it's close enough that size is very unlikely to be a reason for rejection.


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How to implement contradictory comments/suggestions from collaborators?
Every paper that I have worked on originated from the following timeline: a small team works on data and obtains results; one member is designated first author (always the one who did the biggest chunk of work); first author elaborates a draft version of the paper (may request other members to write one or more sections) and sends it to the rest of the team; all the other members send comments/suggestions; first author implements comments/suggestions and sends a new version; iteration of the process until everyone is happy with the paper.
Background: My field is Astrophysics. Even though the methodology behind writing papers varies from field to field, this question should be useful and easily applied across multiple fields. The viability of this methodology should not be the focus of this question.
I am currently writing a paper as the first author for the first time. Whenever I get comments/suggestions I always try to implement them and if I get more than one suggestion for the same thing, I try to find a mid term that allows me to accommodate every contribution. However, sometimes this is just not possible. As an example, I recently got contradictory comments regarding one paragraph in the introduction: one of the collaborators said "Delete it, we don't need this", the other said "add X, Y and Z". I cannot possibly implement both, as it is not possible to both delete a paragraph and add more information to it.
So, I can summarize my concerns in the following questions:

What factors should I take into account when prioritizing a comment/suggestion (e.g. personal preference as the first author, expertise or reputation of the collaborators who made the comments, ...)
How to professionally address comments/suggestions that were not implemented/had to be ignored? Will it offend a collaborator if I don't use one of his contributions? Should I contact that person stating that I did not implemented comment A because B?
Does the answer to this question change in case of trivial (such as rephrasing and paper structure) vs non-trivial (such as results or other relevant scientific contributions) comments/suggestions?

(I find this question to be significantly different from this one as I am looking for insight regarding collaborators that are contributing to your work and that you intend to collaborate again in the future. The mentioned questions are about (anonymous) peer-reviewers who are not working with you and are trying to make your work publishable.)

I handle conflicting suggestions as follows: Consider all suggestions for this point, work out a suggestion based on the other suggestions and send an email to all collaborators and write something like "In this respect I got conflicting suggestions namely... I would suggest to proceed as follows... because... ".
It would be best to collect all the points of conflict in one message.
You may reduce the number of conflicts a bit by adding another step to your workflow: Before starting to write the paper have a session/discussion about the structure of the paper, its focus and the notation. This costs some time and be difficult, but can considerably reduce the number of conflicts. Especially you may point out that some issue has been already discussed in this session.
Oh, and by the way: I would not suggest to encourage all coauthors to cc their comments to the whole group of authors and have the discussion like that. Then the thread may loose focus pretty fast and things may get messy.

Yes, it's the lead author's job to collate and respond to comments - but unless you're the head of the research group (in which case you're out of luck), always talk it over with the supervising author.

Notice the importance of people management here. It is part and parcel of doing big (or even biggish) science.

Thank you for the advice. Adressing all the conflicting points in a single email with a brief explanation for each point should solve most issues without offending anyone. Also, discussing the paper structure and notation before starting is definitely the best way to start.

In some cases both co-authors can be right even if their suggestions are opposite:  If a detail is included, it may be necessary to include further details, so either cut or expand.  Which you do may depend on the journal you are targetting - you do have one in mind don't you?  Some have a tight page limit and thus a lower expectation of the amount of background, while others expect everything to be fully explained.  Some work may never fit in the former.

+1. Thank you for pointing out that I need to keep in mind the journal that I am targetting. And that both collaborators may be right even with opposite suggestions.

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How to reconcile with an old mentor after (unintentional) plagiarism accusations
The question is not about plagiarism, it is about its aftermath. I am presenting the whole situstion.
Three years back, in my undergraduate years (summer after sophomore, to be precise), I did  my first major project with a professor in India. The project came out well and we had two publications next year.
Obviously happy with my work, the professor offered me another project, next year, which was entirely different and of higher level of difficulty (he had his graduate student working on it). He asked me to do simple simulations and mostly literature survey so that he could later use it. He also proposed some survey papers (all by same author, call him X) to start with. I wrote the survey section based on the papers mentioned in the citations by X, and added reviews of another dozens of paper. I cited all survey papers by X and cited others too. I also did certain simulations.
Meanwhile, he provided me references to land into a Masters program at one of the top 5 electrical engineering programs in the USA, and I will be forever indebted to him. After I came to USA, he had his graduate student worked upon the project and submitted a paper (I was surprised to be listed as co-author) including the survey section and simulations I did an year ago.
One of the reviewers pointed that the survey section has been almost a replica of X's works. The reviewer added that, although X's works has been cited, word plagiarism is still relevant. I got disappointed and wrote my mentor and email apologizing for letting him down. He didn't reply.
Now, it's been six months since the review came, and I really wish to reconcile with him. What can I do ?
few clarifications
The plagiarism was using 3-4 sentences from various papers without double quotes but properly cited. Also, half of my citations were same as X's works. It happened because, my survey was about major techniques in a field while X's surveys were about techniques in a subfield of the field.
I emailed the professor only once. He didn't reply and I never emailed him again.
I never knew how my section was going to be used because the project-work was incomplete. When his graduate student finished the work, he immediately submitted the paper and informed me after the submission. I should have got time to review something I did an year back.

Your title says (unintentional), but your last paragraph sounds like you copied the text.  Did you copy the words or not?

I believe I copied a few sentences without double quotes and cited the source. However, my question is not about plagiarism. Rather, it is about how to get back to my old mentor.

the reality of what you did is very relevant to how to get back to your mentor.  If everything you wrote was original and unique, except one line was written without quotes, the way in which you need to approach your mentor is very different than if you had copied the entire structure of another paper as well as not quoting multiple parts.  How you explained your work being finished is also relevant.  If you made it seem you had checked your work before leaving, it is different than if you thought you left unfinished work.

I never thought my work would be featured in the paper. So, even though, at that moment, I thought, I was done with my part; I should have reviewed my work before submission. I also think, I could have asked him to review that section even after submission.

Remember, proffessors are often very busy, and receive a lot of emails. Just because he didn't reply to one email doesn't mean he's snubbed you: its entirely possible (and even likely) that it simply got lost and/or forgotten in his inbox

reading the title, i thought the accusations (not the plagiarism) were unintentional and you directed them at your prof.

Nobody is going to mention that the OP's old mentor put undergraduate work directly into a publication (i) without checking that it met professional-level standards of academic honesty and (ii) without informing the undergraduate that this was being done or that he was being added as a coauthor?  In my view, these transgressions are equally bad or a bit worse than including a few sentences without quotation marks from sources that have been cited in a survey paper.
In my opinion there is a good chance that the mentor is embarrassed that these lapses of his own have been exposed, which complicates the OP's situation.  I am surprised that other answers seem determined to pin this all on the OP.  @Scott Seidman suggests that the OP contact the editor:

I think an email to the editor involved, cc'ed to your mentor, taking responsibility (without qualifiers) and stating that none of the other authors were in a position to recognize or correct this prior submission,

In my view the mentor was in the position to recognize and correct academic cultural errors made by his undergraduate mentee, and since the OP was not involved in the preparation or submission of the manuscript in any way, the responsibility for this clearly lies at least partially (and I would argue, primarily) with the mentor.  Sending such an email to the editor could just call attention to his poor mentorship and supervision: i.e., it could actually make it worse.  (Pro tip: don't contact an editor about something unless you're sure all your coauthors are on board.  If there is any real doubt, clear it with them explicitly.  If someone is not returning your emails, you can't do this.)
The OP is not blameless in the situation: he made a mistake, and a mistake involving what sounds like a borderline instance of academic dishonesty.  He should apologize to his old mentor -- which he has done -- and move on.  I would recommend that the OP continue to treat his mentor like an old mentor: i.e., send him occasional emails keeping him up to date on his current academic life.  I would certainly not assume that someone never wants to hear from me again based on a single unanswered email.  But ultimately the mentor should know that he made mistakes as well, from a position of far more experience and responsibility.  It does not fall on the OP to take any kind of heroic or (especially) self-abnegating measures to remedy the situation.  One hopes that eventually these two people can move past one negative interaction amid many other good ones.

I agree, although my answer is based on lack of knowledge in the situation.  It seems strange the advisor would actually do this, and I only can assume it is more likely that the undergrad was considered a researcher on the project and expected to be fulfilling duties as a co-author (or maybe this is how the advisor felt) and as such, chalking it up to a misunderstanding on the undergrads side of lacking responsibility might just be a safer approach

An interesting perspective. However, I believe, he had some confidence on me because of the first project (original idea, no survey kind of thing) which resulted in two good publications. Hence, I don't want to blame my mentor. It just turned out that I am not as reliable as he thought me to be, and this makes me sad. I am going to send an email to him, probably a long one. And even if he doesn't reply, I will be sending him info about my academic life in future.

@GKS: I'm not asking you to blame your mentor and certainly not suggesting that you allude to such matters in any contact with him.  But the truth is that he bears some responsibility here.  Whenever you include material from coauthors you have some responsibility to check that it is correct.  If you can't directly check the work to your satisfaction then you need to have careful conversations with them about what they did.

Grabbing someone's work and putting it in a paper without contacting them about it at all is really not acceptable, no matter who the person is.  In my opinion it's worse if it's an undergraduate -- by definition this is someone who is still getting training at the most basic level -- but that's really a matter of degree (pun not intended).

Absolutely agree with this answer. It's not okay to submit a paper without a coauthor's consent or knowledge.

If you are in the US and you professor is in India, I don't think a viable option is to visit him (at least for now). That leaves you with email and phone. If your professor doesn't answer emails, you could try to call him. However, that might be really awkward, given that he doesn't even send you an angry/disappointed/indifferent email. I don't know how many times you tried to email him, but I would try at about 2-3 times. If you get no response, leave it be, you won't get any benefits from spamming him.
If you get the chance, visit him and try to work it out eye-to-eye. Ultimately, though, I wouldn't go out of my way to reconcile with him at all costs. Some people just don't forget/forgive some slight, regardless if it was intended or not. In my opinion, if you already reached out and explained your "unintentional mistake", that's as far as I would go. Now it is your professor's turn to reply. If he doesn't want to, you can't force him, nor let that fact run your life.

Thanks for the reply. I just added a few clarifications.

"Ultimately, though, I wouldn't go out of my way to reconcile with him at all costs." This is very important. Sometimes, we screw up and everything we can do is learn from it and move on. True for all kinds of relationships.

It is possible the e-mail didn't reach the professor, or it could have been automatically sent to the spam folder.  It's also possible that the e-mail address is no longer valid.

It is hard to know for sure how 'bad' your plagiarism was without personally reviewing it, but from what you explain, I do not think your situation is that bad.
As others have mentioned, professors are busy, and as you emailed once, it could be that he does not have time to reply, is working on something before replying, or just forgot.
If you would like to follow-up, I would suggest apologizing while giving your explanation you gave here.  As long as it's honest, and how it sounds to me, you can apologize not for plagarizing only, but more about misunderstanding the situation and the work you left behind.
Explaining it as you have here, something along the lines of (but in your way, I'm not the best at writing these letters, nor understand the cultural nuances you will have to include):

Dear Professor,
I am sorry to find out I have caused problems for you with the
  literature review I had done.  At the time, I had not felt it was
  completed as there were outstanding issues such as the lack of
  originality and the heavy reliance on past reviews.  I was surprised
  to find I was being included as a co-author on the submitted
  manuscript, and realize I should have mentioned the problems with my
  review at that time.  I hope you do not feel I had done this
  intentionally.  If you would like me to redo the review properly in
  order for it to be submitted again please let me know.
Sincerely,
GKS

I am afraid that the reviewer was right. Be aware that direct quotes (which, yes, must have the quotation marks or the indented separate paragraph) of more than a dozen words are rarely if ever necessary. You should have paraphrased.
Now as for your old mentor. S/He may be upset, but since you were just an undergrad, s/he should have caught the plagiarism and correct it. It was not your fault (although you did something wrong). H/She may be angry with themselves, or with the editor, whatever. But they have no call to be angry with you (at least not over this!).
Try to email them a few more times, with intervals of a couple of weeks. There could be lots of reasons why you have not had a reply, including innocuous ones.

Paraphrasing is not a defense against plagiarism. It can be a defense against copyright infringement, but not for plagiarism, which is about ideas, not their explicit expression.

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Paper rejection
Recently, one of my papers was rejected from a prestigious journal published by a well known publishing house. I received two reports. Both of them made the same MISTAKE by claiming that I assumed a restrictive condition (which was assumed only in an AUXILIARY result, not for the final result of the paper). This INACCURACY was the main reason for rejection. Is there a possibility to bring this complaint to the attention of the publishing house? P.S. I must add that both reports (that I suspect that is from only one person) say that the paper is very interesting and contains many relevant results.
P.P.S. It looks like all the answers you suggested deal with very honest editors. What about cases in which they publish their collaborators and their students papers. By the way their students papers are not subsequently cited in the literature, but they keep publishing those authors. Besides, they kept my paper almost one year before sending me the so called reports.

If you think the reviewers have badly misunderstood your paper, the usual course of action would be to contact the editor who handled the paper, explain the mistake made by the reviewers, and request that the editor either have the reviewers reconsider the paper or send it to other reviewers.  This is not the sort of thing that one would normally escalate to the publisher, since they are not usually qualified to review technical issues or professional opinions, and in any case would (should!) be loathe to interfere with the journal's editorial independence.
Be prepared, however, that the editor may simply let the rejection stand, on any of the following grounds or others:

It was your responsibility to write the paper in such a way as to clearly state its results.  If the reviewers misunderstood your result, then that indicates that it is not clearly written, and hence not suitable for publication.
The editor feels, based on other points in the reviews or on his/her own opinion, that the paper is not strong enough.

As such, you may want to just drop the matter, revise the paper to state your results even more clearly, and submit somewhere else.
Your suspicion that the two reviewers were actually the same person is a much more serious allegation, as that would be an abuse of the peer review process.  If you honestly believe that is the case, you should contact the journal's editor-in-chief with your evidence.  I would only escalate to the publisher if the editor-in-chief does not appear to investigate the matter appropriately, or if the handling editor is the editor-in-chief.  However, you should clearly separate that from your paper - if it seems to those in authority that your concern is mostly with your paper, rather than academic integrity in general, then your claims may be taken less seriously.  (The indiscriminate use of words in all capitals will also not help your claims be taken seriously.  Just a suggestion.)
And keep in mind that even in this case, you may well still want to submit your paper somewhere else.  The outcome of that process won't be the automatic acceptance of your paper - at best, it will be a long investigation, a shake-up of the editorial board, and then, maybe, an offer to reconsider your paper from scratch.

Thanks Nate. I just did what you suggested and I am waiting for their answer. What if they don't answer?

Thanks again Nate. The reason why I want to make a "little fuss" about this is that I have reasons to suspect a very cliquey behavior and I want to stop it if I can. My younger colleagues deserve a more honest academic environment.


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For early researchers, how does the ranking of a university relate to future career in high ranked universities?
I am a longtime user of SE but posting as anonymous for the (not so) obvious reasons.
I am an early academic researcher (though I have been working in industry for a while prior to academia), and my PhD is in Information Technology, from a fairly high ranked university (Top 100 in my area). I have been offered two tenure track positions.
Position A: Large, established, and high ranked research intensive university (research focus but limited options)
Position B: Small, young, and low ranked university (some alignment with my existing research, university is not research intensive but teaching load is attractive, some interesting research projects going on).
Salary, benefits, and package in general is almost equal. I have a good publication pipeline, and both universities are aware that I will focus mostly on my research.
My main question is, for early researchers, how does the ranking of a university where one begins the career relates to a future career in high ranked universities?
If I accept position B, would my affiliation with the low rank university affect my future progress?
Another related question is, are there any pros and cons of joining a small and young university (for early researchers such as myself)?
UPDATE: This is supposed to be comment.
I don't strictly intend to use a tenure track job as a stepping stone to a more prestigious school, but as an early academic, in the initial two years if there are opportunities in more prestigious school then I would like to move (I know many people do, I don't see why this is seen as a negative thing)

I'm not sure I understand the question. You intend to use this tenure track job as a stepping stone to a job at a more prestigious school?

Let me tell you what I told a friend who was struggling with a decision between a great department without people in his area and a good department with people in his area (including collaborators):
Go to the better school.  They're not in the same league, and you may have a chance in the future to get more people in your area.  You can still travel to the other school to collaborate, but you'll get much better students and postdocs at the better place.  You'll be in a better environment exchanging ideas with top people.
This answer of JeffE to a related question discusses the "better environment" aspect more.
He did in fact go to the better school, and afterwards was happy with his decision.  One thing he remarked was just because of the what university it is, they had top people coming in all the time and he got to interact with so many of them in a year, which wouldn't have been possible at the other school.
While I won't say this is universally what you should do (there may be other factors that matter to you here), I don't see much advantage of B, unless the people at B are truly top people in the area (in which case they might leave soon) that you want to work closly with, and wouldn't be likely to if you went to A.  Since you say University B is not research intensive, you may not have as much time as you would for research at University A (e.g., the teaching expectations might be much more per class, or more administrative duties).  Note there can also be tension in a department when part of the faculty is research active and part is effectively not.


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Is there a "Rate my Advisor" type website?
I am not sure about the incentive effects of websites such as ratemyprof.com, as Professors with tenure may or may not care about undergrad students. However, they may be more serious about graduate students (supervision, co-authorship etc).
Does a ratemyadvisor type website exist?
This is important (I think) because relationships between supervisor-supervisee are probably more profound (given the time investment) and have arguably more ramifications on the future (reference letters bear more weight).
Any thoughts are welcome!

There would be no anonymity; the number of advisees is sufficiently small in most cases that it would be obvious who the author of the review is.

The review could be written ex post...

@ ChinG There is not really an ex post in academia, unless you leave it forever.

If you are applying for grad school, and will work with a professor, simply ask his/hers current graduate students what they think.

It takes a lot of work, but have a look at the department site (if without former members also the Internet Archive) and find out what happened to the former PhDs. Some PhDs genuinely do not want to work in science anymore, but if it happens to (almost) all, it paints a pretty ugly picture for some department heads.

Having seen RateMyProfessors, I think a RateMyAdvisor site would be a terrible way of achieving the intended aim.

@ff524: In many cases, you don't need anonymity: Your advisor was other great enough that you're happy to write a glowing review, or awful enough that your academic career is ruined anyway.

This is not as much an answer, as a potential suggestion. Perhaps instead of creating a forum as subjective as Rate my Prof for advisors which may be subject ao anonymity concerns, a more objective plaftorm could be constructed. For instance, an algorithm that calculates a score for an advisor based on a weighted average of votes by his/her students, the placement of his/her student etc.

@ff524: True, and even with ~100 undergraduate students I've immediately recognized certain complaints on RateMyProfessor as coming from certain students. Which blew other students' minds when I mentioned that offhand later on.

@YemonChoi the site would be different because graduate students are more serious (or better, enthusiastic!) Students.

@Insulin69 I'm afraid I don't share your confidence. To be clear: I am not denying that the majority of graduate students are more serious; but RMP is a prime example of a site where one is getting a biased sample of responses, and I believe a RMA site would be susceptible to the same. YMMV

I don’t think preserving anonymity 100% towards the PI is a big issue. Let them guess and retaliate at random. Not disclosing the name publicly avoids a lawsuit , and building a face reputation as a troublemaker. This works the same as with evaluating managers and small companies.

Please don’t be discouraged by unsupportive reviews of such websites. I think these are mainly from professors instead of students. They certainly enjoy their power over vulnerable students and do not want to see a change.

I spoke recently with someone who is building such a website/community, and I doubt they will mind me giving a brief overview of how they intend it to work.
Basically there are 2 issues that are difficult to overcome - assuming all the technological issues are minor:

Skepticism
Slander

Skepticism in the sense of 'Oh, I see this PI has a bad review from 2001, but who's to say that will happen to me. Maybe this one student is an odd-ball'. It also has parallels with the TornadoGuard problem.
And we see this a lot on academia.stackexchange too. We only ever hear 1 side of the story, and that story is rarely impartial. You know, occasionally people like to rant. People often omit the times they made mistakes. It's human nature. And so any website of this nature has to somehow overcome this issue of he-said-she-said, and theres simply no technological device we can implement to get around this. Particularly if you allow anonymous submissions.
The second, slander, is what I believe most people in the pubpeer.com debate are worried about. A deliberate attempt to hurt someone's reputation under the flag of mob-justice. Nitpicking and emotive words, but no real substance, etc. I actually only found out about pubpeer a week or so ago when a colleague at a party showed it to me, and I was shocked to see people I respect being picked apart on there for silly little things. Still, those anonymous criticisms do have some validity, and the concept of post-publication peer review is something I deeply agree with. I think only time will tell what impact sites like pubpeer will have on science. For a PI-review site, it could have disastrous consequences. Potentially legal consequences.
The website/community I referred to up top seemed to have spent a considerable amount of time fleshing out all the technological methods that could help here, and in the end i'm told they settled on the fairly low-tech idea for Skype-based interviews, potentially on a weekly release schedule. The idea is to mainly cater for people who hate their PhD and feel relief knowing they're not the only ones going through hell. So these Skype interviews generally are with post-docs or no-docs who had a bad experience, and they talk about what the end result was, what they'd do differently, if they still feel they were treated unfairly many years down the line (I watched 3 interviews and they were all totally different, so i'm finding it difficult to generalise here). But the big issue for the team putting all this together is finding PhD/post-docs/no-docs who are OK with talking about their experiences. They offered to interview me but I turned them down because, frankly, i'm not that brave. They offered to pixilate my face, but even so... as disappointed as I am with my treatment during my PhD, there's no one specific person that really let me down. It was a system of failures, I think, and one that is unlikely to happen again. So that particular PI-review project wouldn't really help me or people like me.

Since the question's been bumped to the top: +1 for several considered points, in particular: "as disappointed as I am with my treatment during my PhD, there's no one specific person that really let me down. It was a system of failures, I think"

As this question got bumped to the top again, I'm wondering: are there any updates on that attempt to build a website/community?

Yes, see my answer to this question. There are two websites.
Professor Abuja Sharma from Iowa State University developed one: https://ratemygradadvisor.com
Two PhD students developed one: https://pi-review.com/
The first one primarily requests ratings from one star to five stars, accompanied by a brief comment. The second one asks for more detailed comments in response to specific questions.

@scientist Could you provide a link to the webpage of this professor? I cannot find anyone of that name working at Iowa State.

To add to the issues mentioned in Wetlab Walter's answer: one of the common reasons why advisors and student relationships break down is a mismatch between their personalities. It causes unrealistic and often unsatisfied mutual expectations.
Therefore one's perfect advisor can be another person's nightmare, and vice versa. Even a good track record with several past students does not reliably predict unqualified success with the next one - partially because typically the sample size is very small, but mostly, I think, because of the high degree of subjectivity about what a "good advisor" really means.

The only feasible situation of 'nightmare' vs 'perfect' match is when the advisor clearly pushes a protégée to the top while shoving other(s) off the lane. Which is clearly an unprofessional relationship which I am afraid might be more common than many believe. Otherwise, any reasonable advisor should feature as 'just OK' and 'so and so' to almost everyone outside of their perfect match.

Students should be encouraged to consider personality conflicts and explicitly mention them in their reviews.

Such a website will never work.  Most professors have fewer PhD students in their entire career than they have undergraduate students in a single semester.  They will never accumulate enough reviews to be useful.
However, you can get the information.  Simply email the professor's former students asking them for advice.  Often a list of these students is on the professor's website.  If it is not, ask the professor for the list.

In my experience ex-students typically remain fearful or dependent of abusive supervisors. Particularly in small towns and social circles, or societies relying heavily on social relationships (e.g. 'guaxi' in China). In addition a bunch of abusive supervisors I've had seem to avoid informative professional websites and public CVs. I will avoid listing names here, but I find it remarkably hard just getting a full list of publications from some PIs in China, where names may look similar and ORCID registration isn't common practice. Plus, I find 1-3 reviews already better than naught.

The issue is that some students were forced to leave without obtaining their degree, so you have no way to obtain their contact information. If you ask your advisor for references, they will only provide names that they are comfortable with, which could be a small portion of the list.

Yes, there are such websites.
I know of the following two:
(i) http://www.qcist.com;
(ii) https://www.ratemypi.com
However, at least at this moment, these websites remain useless and mostly abandoned. Note (i) is free and (ii) is paid.
This is because to, in addition to a number of issues related by other respondents, academics are remarkably overreactive to criticism. This works directly against open criticism, as public relations play a central role in defining academics careers, and petty individuals are frequently empowered enough to take revenge on others. I am sure a number of PIs would exploit (public) institutional resources just to try and tear such websites down should they take root.

@NajibIdrissi I am sticking with the question topic, i.e. PI-specific relationship public evaluations. Again: In my view this is completely unrelated to technical peer-review, institutional evaluations, up/down-voting. Surely not about anyone specific (e.g. I or you). I am rephrasing to make it clearly, thanks.

Neither of these seem to exist anymore.

Yes, there are two functional websites. See my answer to this question.

Yes, there are at least two websites.

Professor Anuj Sharma from Iowa State University developed one: https://ratemygradadvisor.com

Two PhD students developed one:
https://pi-review.com/

The first one primarily requests ratings from one star to five stars, accompanied by a brief comment. The second one asks for more detailed comments in response to specific questions.

Thanks, though it doesn't seem like either of those sites has enough reviews/functionality to be too helpful yet.

They are relatively new. There will be more and more.

@scientist Could you provide a link to the webpage of this professor? I cannot find anyone of that name working at Iowa State

@YemonChoi Appears to be this professor: https://www.ccee.iastate.edu/directory/profile/anujs/ I only found the connection to the website on LinkedIn, however: https://www.linkedin.com/posts/anuj-sharma-link_rmga-activity-7052144170538340354-cDmC

@Anyon The original version of this answer gave a different first name, which is why I couldn't find it. Thanks to the author of the answer for the correction/clarification.

I strongly disagree with the opinions here. If you're worried about people slander-ing their PIs, then implement one or more of the following ideas:

Allow them only a Likert scale of predefined queries about their relationship with their PI (i.e., no free response answers).

You can make them identify the top and worse qualities of the PI, and if they fail to select top qualities, not let them submit the form.

You can make the reviewer review more than just 1 PI to get a sense of their attitude/outlook tendencies. You can flag students who frequently underscore their collaborators so that viewers making judgments about prospective collaborators will contextualize the feedback.

You can make the reviewer specify how many hours or years they spent working with the PI being rated, which adds further context, and to label their relationship with them (advisor-advisee, collaborator, committee member, in my department, talked to them once, interviewed with them, etc).

Allow reviewers to report the theme of the abuse, but not free response details. Categories can include criminal-oriented offenses (sexual misconduct, physical harm), moral offenses (verbal abuse/yelling), personality disorders (narcissism, borderline), or something more subjective like "doesn't trust others", "doesn't show respect", "is controlling", "is a bad listener", etc.

Allow an optional identity verification for past students who don't care if their advisor knows it's them who reviewed them. For PhDs who leave the field after they graduate, I doubt they'd care if their advisor knew they rated them poorly.

It is sooo easy to implement a fair enough reporting system, and I resent the opinions that say otherwise. I feel you are just supporting the hazing, abusive nature of many advisors in academia. In fact, bosses should not be afforded more protection than their underlings (including tenure, imho, but whatever, it's not my money to disburse). If a PI behaves badly, they behaved badly. I don't CARE if the student made a mistake or was negligent, and that this elicited the PI's behavior. Mistakes do not warrant abusive behavior from a boss. Negligence could be a flight response to the PI's abusive nature, and fight or flight is a survival instinct. People in positions of power must be held to higher standards, and instead, we are protecting them.
A web app that allows students to give feedback on their PIs'/collaborators' leadership qualities will not fundamentally determine hiring/firing decisions - but hiring managers should be given more accountability/motivation to do something with student feedback. As a grad student, I did an exit questionnaire where I exposed my PI's abuse, and nothing was ever done. I suspect it was because this feedback was not made public. In fact, I don't think anyone but my graduation officer read it, and he has no motivation to do anything about it, nor the power to do anything about it. Did my PI commit a crime? No.. but abuse is a moral crime, and people should be warned against giving bad PIs more power, more money, and prospective employees should RUN FOR THEIR LIVES. We deserve that kind of transparency.
And it's simplistic to tell someone to ask current graduate students about their PIs, and ironic because one of the first criticisms for a web app feedback system in this thread was that it was likely not anonymous due to the small population size of possible reviewers. If a first year asked me about my PI prior to me graduating, I could not tell them the full truth because them changing their interest in joining the lab would have directly implicated me. You can't criticize an online portal for the same deficiencies your alleged solutions possess and call it a better system.

Completely agree. Please don’t be intimidated by the strong vote of the objections of these rating websites. Please bear in mind that this website is dominated by established professors. The voice of students is relatively weak.

As I knew such website doesn't exist yet. It would be a nice idea for creating an app for this work:)
Anyway about getting to know the professors in a given department is not a difficult task and you can do the following,

See the website of a given advisor to learn about recent publications, grants, and number of current students
If you can visit the depatment, go to it and speak with the current students and also his(her) students taken a course with the advisor
If you cannot go to the department, find the email addresses of the current students of that advisor and ask important questions about its course works, research process, active topics in their lab
If you are decided to work with a advisor based on your findings and interest to his works, contact with him and just ask some formal questions to see its rules on admissions (if a busy supervisor reply your email, you are luckyman!)

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


text:
How to negotiate with collaborators on adjusting procedures to interdisciplinary projects?
This is an issue I frequently face. As a biologist I like doing interdisciplinary science. In fact I believe interdisciplinarity is the key to producing any truly insightful observation, at least in my field of work.
Typically I deal with arthropod morphology, allied to some biochemistry, with some conclusions relevant for general biology, ecology, evolution. Since recently I've added some molecular biology (i.e. DNA analyses) to this.
Now my problem is that my main expertise lies in morphology and natural history – because I have seen and read a lot on my bugs. But because of my interdisciplinary approach, I have some insights into what’s happening around. I just don’t know their protocols, don’t have their reagents, lack the experience and background know-how to plan and analyse their results.
However frequently I run into (minor) conflicts over interpreting their results in face of the bigger picture. Usually they get upset when I come up with a different final conclusion, and instead of trying to logically convince me, they get all evasive repeating I am not qualified enough to discuss their work.
It will try to illustrate some typical situations:

A group of collaborators do some proteomic analysis of an organ, and come up with exciting compounds therein reported for the first time. But I do not understand why, out of a 15-lines-long list of potential candidates, the most exciting one is surely the correct identification. (I do have superficial understanding of database identification scores, but likely not as much as they [must] have, and would be delighted to learn more).
Some skilful biochemist isolates a compound as I suggested and starts on further bioassays but while using solvents and plastic labware. From my general experience around GC-MS machines and organic solvents, there will be an uncontrolled amount of phthalate esters released in the extracts which may account for significant effects. Even when alerted of this problem prior to procedures, plastic and solvents are used because “this is how we do it”. Some guy protested “ethanol isn’t exactly a solvent” when shutting his ears and going all offended.
A chemist will use antibiotics solutions which did not really solubilise, categorically stating “suspensions of medicines work”.

There are countless such cases. I try as best to be always prepared to receive their questions and skepticism around my own specialities as I feel confident enough to explain the rationale. And alas, they may be seeing something I did not. However I feel almost everyone else is unwilling to discuss and question routine protocols, and this makes doing interdisciplinary science very hard!
How can I avoid such conflicts, adjust procedures, and at the same time reach an agreement with collaborators?

@86BCP2432T In fact I would gladly have a beer with almost any of my collaborators, who can be fun when they're out of the lab. Actually the lousiest professionals I know tend to be quite good beer mates. I don't usually (want to) punch anyone for disagreeing with me or making some possible mistake, but I start feeling like it when they suddenly claim to be above any reasoning or questioning.

@Scientist are this people in China? south china uni?

@SSimon This question is a general one. I have collaborations from several different countries. If you meant the examples, one of them happened with two different sets of collaborators, one of them from China. No specific university pertaining.

I don't see any concretely answerable question, here. Your post seems to really just be an invitation to discuss the difficulties of interdisciplinary research, but this isn't a discussion forum.

Could you be a bit more specific about how you ask your questions?

"Usually they get upset when I come up with a different final conclusion, and instead of trying to logically convince me, they get all evasive repeating I am not qualified enough to discuss their work." - Is it possible you have made a big enough error that they don't feel like explaining their entire discipline to you? That what they might be evading is responding with calling you an idiot? I'm having trouble understanding what your criticisms are to judge if they are appropriate criticisms or not.

@DavidRicherby I think you're right. Maybe I have a wrong approach to this community. Would you have a suggestion of a community where I could actually discuss such matters? [Maybe I will turn this into a question].

@NajibIdrissi No, not all of them. In fact I suspect the conflict is biased towards older professors. I get a much better time dealing with postdocs, who seem way more flexible and open about their procedures.

@Designerpot I think you have a point. As DavidRicherby mentioned I may have approached this thread/community on this to see points of view instead of an authoritative final answer.

@DavidRicherby Thanks for an insight into their likely mindset. Note I didn't mention some error I made but a (belatedly perceived procedural detail on their in-house protocol/rationale that might be questionable from an interdisciplinary point of view). Maybe I will design some preliminary test in deciding who I should pick as a fitful collaborator.

It seems to me that the problem lies on a psychological aspect of their lives and your way of questioning is triggering this response on them. Maybe they're insecure about their academic formation or have been challenged in the past in a way that made them develop a very defensive stance when questioned. My suggestion is that you change your way of obtaining this info to a more friendly one. usually lowering your guard creates the same behavior on others. You could go for something like "I have no knowledge about this. Could you please teach me X? I feel it's important for my formation."

"Please teach me about X" is asking for a substantial amount of their time, which is pretty demanding if you're not already on great terms. Perhaps a better start would be "Can you recommend some accessible books / papers / courses on X where I could get started?"

Thanks, it always helps adjusting a tone. Just that "formation" might not be the best terminology as I am past the degrees part. I believe that actually the timing seems a major issue, and also the selection of collaborators. That's likely where I am erring the most. I perceive a problem after it took place because I didn't expect it (e.g. using solvents with plastic releasing bioactive dioxins) and then it probably feels like "gotcha" + "now you've screwed it up". From sheer experience I may be able to predict issues and better select future collaborators...

@NateEldredge I don't feel any question on my protocols takes "a substantial amount of (...) time" to answer because I already know a simple explanation. I think enlightening a collaborator may feel like an unnecessary waste of time for those who are not really interested in the project contents (likely just looking forwards to its end, on a CV). In interdisciplinary research concepts need to be equalised as routine protocols are adjusted to local/focal views.


text:
How to deal with low quality software project that needs to be evaluated
I have evaluated a software project which has been created by a consortium of top scientists in the field. However, the project itself it doesn't really work, and has only been developed as a proof-of-concept rather than an end product (i.e., it works only with 2-3 scenarios).
This software application should perform 4 steps in order to successfully execute. Each step takes an input file, and produces an output file. The output file from the previous step is used as input in the current step.
Initially, you start with 1 file. This file is used as input for step 1. After step 1, another file is produced. Let’s call the input file general_input_file, and the output file general_output_file. When general_input_file is loaded into the application, general_output_file should be produced. Now, I have an input file which I will call my_input_file. I expect the application to produce my_output_file. However, the application only accepts specific_input_file and will produce a specific_output_file. This means that it only works with 2 files which has been previously generated. Both files exist in the project. When I look at the part of the project which should process the general_input_file, there is a statement which looks like this: if the name of the given input file equals specific_input_file, then return specific_output_file. This is a file inside the project. Otherwise, try to process the generate_input_file and generate general_output_file. At this point the software breaks. A number of exceptions are thrown, and debugging and fixing this is beyond of the work that I’m doing.
The question is: in the paper, how do you approach this issue? And how do you argue, in the paper, that the reason you're unable to evaluate the software in a different scenario is due to the limitations of the software? What is the best wording to be used, without being offensive to the authors?

"only been developed as a proof-of-concept rather than an end product" - this is normally the highest aimed for goal of any software development that happens in research. That in itself is not a sign of "low quality", it is by design, as research is not conducted to produce full-fledged, marketable products in most cases. And this can well extend to your observations about the coding quality, as maintainability or top performance are not necessarily a priority.

I'm not sure we can answer this question without more context, since the appropriate level of development for software depends entirely on the goals associated with the project.

It seems that this depends on at least three questions that you don't talk about here: (1) what is the goal/audience of your paper?, (2) what does the consortium claim their implementation can do, and how does it stack up?, and (3) why is it obvious, and why does it matter, that coding was done by someone inexperienced? For (2), if the project claims the code can transform any input_file to a correct output_file and it can't, this is part of (2); and for (3), I hope it is more than style; but only you know.

I am still confused as to why this actually matters for your paper or why you are writing a paper on evaluating a software project. Could it be that you are peer-reviewing the project in some way? If a paper on the respective project is already published, I suggest to write a comment/reply to that paper, if the journal allows for that.

When I look at the part of the project which should process the
  general_input_file, there is a statement which looks like this: if the
  name of the given input file equals specific_input_file, then return
  specific_output_file. [...]
  Otherwise, try to process the generate_input_file and generate
  general_output_file. At this point the software breaks.

By your description, it sounds like the code you are looking at is cheating by returning a precomputed output known to be correct if the input matches a single sample input the authors included with their code (presumably to demonstrate its correctness and because including a sample input was required to get their paper accepted). For all input instances other than this one sample input, the code does something entirely different, which doesn't produce correct results.
This sort of behavior would without question merit a failing grade in a programming course homework assignment or exam. If done in the context of a commercial product, similar behavior would justify the resignation of the CEO and a multibillion dollar scandal. So, saying that the code is "of low quality" seems to me so euphemistic as to be itself a dishonest statement. The way I would describe it is: if the authors are claiming that their code is correct, then they are lying.
Now, I could discuss your actual question of how to discuss this situation in your follow-up paper "without being offensive to the authors," but honestly, I don't see the point. I would ask instead, why would you want to not be offensive to the authors? It's not just that the authors' algorithm may be wrong and that their code is of poor quality; they are seemingly committing academic fraud by submitting an incorrect algorithm with code dishonestly tweaked to make it seem like the algorithm is working. I'm willing to leave a 3% chance that some other more innocent explanation can be proposed, but given the description you've provided I really can't think of one. I'll be happy to reconsider if you provide a bit more detail on how the authors' claims are presented in the paper.

It is hard to understand what is their actual claim about the code. The project page is as follows: they have a bunch of deliverables where they discuss the approach they take into this project, and present a few examples (based on their scenarios?). Another part of the project presents the code of this project (or at least some components of the project). There is a possibility that the code might be out of date (i.e., the last version might not be posted). This project ended a couple of years ago, and a follow up project started this year (I expect they will build on/extend this project)

Well, the if clause that you described that matches a specific output to a specific sample input file sounds by its nature dishonest. But we're obviously missing a lot of details so it's hard to say anything more substantial.

"it sounds like the code you are looking at is cheating by returning a precomputed output known to be correct" - at first, I thought so, too; but note that as per the question, this is only one out of four steps. Hence, it is also possible the actual transformation of that file is beside the point and the proof of concept software is only meant to demonstrate (e.g. towards non-CS-savvy people who would not make the conceptual jump from, say, "open an application-specific dataset document" to "open a Word file") that a given file format can be loaded. Hence, it is not clear that this is fraud.

@O.R.Mapper well yeah it is not 100% clear, as I said we'd need a lot more detail to reach an authoritative conclusion. But lazy and dishonest researchers do exist, and I personally have seen examples of papers trying to get away with blatantly dishonest claims of this sort. So the opinion above represents my best guess at what's going on based on the available information. Of course OP should be very careful and make sure he is confident (and possibly consult a colleague who can look at the situation in detail) about any claims he makes accusing the authors of dishonesty or incompetence.

@O.R.Mapper I should add that an if clause testing to see if the input matches a known input that the researchers "expect" their code will be tested against is a huge red flag IMO. It is completely analogous to what the Volkswagen people did in the recent scandal. I've also read somewhere about certain chip manufacturers doing this to make their chips appear to perform well in benchmarking tests, and probably encountered similar behavior in many other contexts. So, this behavior falls squarely in the "gaming the system" category. Again, I honestly cannot think of an innocuous explanation.

Here is some information on cheating in chip benchmarking tests: http://bgr.com/2013/11/27/samsung-benchmark-cheating-banned-htc/

@DanRomik: I agree it's a red flag, but I can easily think of an innocuous explanation. The subject of the research might be, for example, a specific algorithm to process certain data or extract some information. The input for this algorithm could be required to (if only for reasons of simple implementation) be in a specific custom format X. To CS people, it might be obvious that Excel documents can be converted to that format, if someone writes a converter. To non-technical people, however, connections of this kind are often not evident. They are not satisfied if they ...

... see the prototype open and process a file in format X and are told that a converter can be written to process Excel documents. They typically have no concept of the complexity (or lack thereof) of a transformation between structure-equivalent data. Silly as it may seem, they want to see how an Excel document is selected and processed. Consequently, unless one wants to spend time for discussions about irrelevant topics such as concrete file formats, the prototype will be adapted appropriately for a demo whose actual point is to show the workflow and the core algorithm.

@O.R.Mapper I thought more about this. It's a subtle point and you nearly had me there, but I disagree that this is "innocuous". If I understand correctly, you're saying that it's okay to dishonestly claim that your code includes a working Excel-to-format-X converter to satisfy a stupid requirement by a referee or editor, in order to get your paper accepted. Well, actually, no. An honest researcher would not lie in such a way, even to get around a silly or incompetent referee. Once again, we come back to my point that the if clause is just dishonest. So: keeping an open mind, but try again...

@DanRomik: I am saying it's okay to describe a concrete workflow that a contribution could be used in, and that a demonstration of this workflow does not require a fully implemented application. You can go ahead and claim that the audience of the demonstration needs to be told explicitly what is implemented and what isn't, but I think you are failing to understand your audience with that. To an audience without the right background knowledge, remarks about parts of the system being mock-ups will just add confusion and blur the actual message of the demonstration. And to be clear: I was ...

... not so much thinking of referees and getting papers accepted, but of representatives of funding agencies (who often have only a financial/administrative background and need to be explained and shown in the research results in (from the domain perspective) layman's terms), or other audiences who are not so much interested in the "how does it work?", and more so in the "what do those university people do with our money; is it useful for the real world in any way, including to people who have no doctoral degree in the domain?"

The least offensive way to indicate that you tried to use system-X (and it didn't work) is to say something like

We also attempted to process our data using system-X [1023],
  but were unable to do so successfully due to run-time errors.

That avoids assigning any blame, whilst acknowledging the existence of the project. I am guessing that there is a very high chance that at least one of the scientists associated with system-X will be a reviewer for your paper.  If the system doesn't work, they have still gotten a cite.  If there is a newer version of the software that does work, you may elicit a useful comment.
You might also try sending an email to the authors to see if there is a way around the error, or if there is a newer software release.  Including the exception log as an attachment, and avoiding mentioning having looked behind the curtain may be diplomatic.

Does it need to work?
I know the question is weird, but researchers are not software engineers. If the objective of the project is to create a fully functional piece of software, then the low quality of the software is worrying. In the other hand, if the objective of the project is to devise new ways of doing stuff, then this software is a prototype, a proof of concept, as you mentioned, then working in a few cases is a remarkable feat.
Once the proof of concept is done, the software engineers can come and turn this into a product, which is a fully functional software. Don't make researchers do engineer's work, they are bad at it :). To each its own.
Understand the context to understand the results..

I don't believe this addresses OP's question. He is not asking whether he needs to be critical of the software he is evaluating, but rather how to be critical without offending anyone.

"Don't make researchers do engineer's work, they are bad at it" - I find the second part of this statement untrue as a general rule, and even slightly offensive (given that at least in applied CS, a significant part of people switch to the industry as professional developers right after receiving their doctoral degree). Lots of research code is not of low quality (when measured to code of marketable products) because its developers cannot do any better, but because priorities in development are simply very different.

@O.R.Mapper Writing code is very different from developing a fully functional software. And that takes experience and a skill set broader than just good programming skill. You can go from academia to industry and vice versa, of course, but you won't be a senior anything. Both problems are more complicated than that and need experience. More than that, a proper software needs documentation, use cases, etc. For a researcher, that would be a colossal waste of time, better spent writing articles...

@FábioDias: All right, so you meant something like "Don't make researchers do engineers' work, they are sometimes inexperienced at it (similar to engineers who have started working only one or two years ago)". As it stands, I first read your statement more like "Don't make researchers do engineers' work, they are hopelessly incompetent at it (similar to cooks or gardeners who are asked to develop a software)", hence my slight disagreement.

@O.R.Mapper yeah... Let's just call it an unfortunate and incompetent attempt to be funny :)

Personally O.R.Mapper I find your retort that researchers are "like engineers who only started working one or two years ago" quite offensive. This sort of thinking makes software engineering look like a lesser, more trivial subject, when in fact if anything it has far more moving parts to consider as Fabio mentioned. I think if more researchers acknowledged their inability to write usable code, the world would be a better place - and I am a researcher!!


text:
Getting a Masters in computer science but I am without any undergraduate degree
I am a programmer working 6 years in the industry. I am proficient in Javascript/JQuery, PHP, Java, c/c++, MASM Assembly, MySQL/SQL, together with accessory tools/technologies like Git/Github, UML, XML, JSON, etc and the Drupal and Zend CMS/framework.
Is there any way of getting accepted into a computer science, Masters degree program in universities in Europe, USA or Canada?

Why you want a Msc degree and not a bachelor one?

A bachelor in computer science is not just programming. It is mostly Math and other subjects.

Do you actually want to attend a masters program? In the first version of this question, it sounded like you didn't want to be a student right now.

@ff524 I do want to get an academic certification for the skills and experience I acquired in the industry. But it doesn't seem to make much sense to start fresh and do 4 years of computer science courseworks, trying to learn things I already have expertise in. I am looking for fast track routes, with exemptions on skills I'm already proficient in.

I strongly suspect you'd be happier in software engineering than CS -- and no, they are not at all the same.

@okeyxyz - It's possible to sometimes "test out" of courses if you have relevant experience.  This could let you skip over the early CS courses and jump into the more advanced undergrad courses (though you'd still probably have to complete your general education requirements - i.e., Calculus, writing courses, etc.).  It's entirely university dependent, though.

@okeyxyz I don't think, without any bachelor degree anyone can get admission into masters program because universities always have some weight of the candidate performance in bachelor degree program, or they conduct their own test. But in most of the programs bachelor degree is the minimum requirement to get admitted in master programs.

What You study in the bachelor in computer science is different from what you do in a working environment. In the bachelor's degree they teach you the basis to achieve a master's degree, like math, science and other subjects that probably you've never studied from an academic point of view.
I live in europe and I believe all the european universities require a bachelor's degree in order to be admitted to the master programme.

You probably don't need a bachelor's degree in computer science to be admitted to a master's program in computer science, but you probably need a bachelor's degree in something.
If what you're looking for is academic certification for skills you've acquired (and honestly this seems unnecessary given your extensive proficiency and experience) you probably wouldn't want to go the route of B.S. then a Masters.
I'm aware there are some software certifications you can acquire as proof of proficiency, but they're perhaps controversial.
What about sucessfully completing an online course like those offered by coursera in one or more of the languages you already know?  Some courses provide certificates of completion and even grades.

You need a bachelor degree in something if the institute that is going to give you a Msc is accredited. (e.g. by ABET or similar provisioning institutions)
And a MSc degree from an institute/university that does not require a BSc would probably will not be standing on the same foot when it comes to be used as a university degree.

No, I don't think you'll find anyone who will admit you to a Master's program without some sort of Bachelor's degree.  But I will offer some ideas specific to your field.
If your goal is to have something to frame and hang on the wall, you might consider getting a Certificate in Computer Programming (sample description).
If your goal is to satisfy your intellectual curiosity, just enroll in whatever graduate level course(s) you're interested in, as an auditor.  But if you'd like to have the credits to show for what you did, you might be able to enroll as a non-matriculated student.  But note, I'm not at all sure this latter idea would work without a Bachelor's degree.
If your goal really is to get a Master's degree to frame and hang on the wall, here are some ideas about how to get a jump start:

Find the right institution -- it may be a two-year college to start
with; it may be a university that wants you as a Master's student
and works with you to help you speedstart; and it may be an online
program (but stay away from the private schools that charge an arm
and a leg).  You're looking for a place where

Very few non-major courses are required for the program.
You can sign up for a wheelbarrow of credits each semester, and churn out the homeworks and projects with minimal attendance;
ideally, they would allow you to simply sit a final exam and then
give you the credits.

Note that the best way to sound out a department to find out how flexible they are going to be would be by meeting with a professor
or dean in the department.  Yours is not an uncommon problem in
your field, and most professors will not laugh you out of the water
if you come in and state clearly that in order to advance in your
profession, you need a degree to substantiate your expertise.
You'll want to target primarily departments that have more of an
Information Systems focus than a Computer Science focus.
Consider getting the non-major credits you need with some CLEP
credits.  However, note that this may not be necessary, since
frequently departments will have their own internal procedures for
waiving prerequisites or giving credit by examination.  (CLEP has
some disadvantages, compared to the internal route, such as cost of
exam, the challenge of finding an institution that offers the exam
you want, and the limited list of exams available through CLEP.)
As a first step, you might want to enroll as a non-matriculated
student in an upper-level undergraduate course that you are truly
interested in, and give it your all.  The professor may be blown
away and mentor you based on what you did in that course.
Note that in this option, you would have to convince the instructor
and/or the department to waive the prerequisites.  This is quite
doable in your situation, however.

In Canada yes. You have at least two options:
1) if you're willing to learn French, you can be accepted at UQAM  in Montreal. See here for the conditions for the Master in computer science(Bachelor or two years + experience).
What they usually do is that they might ask for what they call a "propedeutique". It means a one or two semesters at undergraduate level with specific courses chosen  by the department  to put you up at the proper level (in your case it could be French and maybe pure Math, for example). Application to this university cost less than $100, try it.
Note that for the French part, it's not as hard as it sounds. Programming language are the same, the books and resources will be in English and all of your teacher and most of your fellow students will speak some English. Montreal is a very active city computer wise and is bilingual.
2) If you want to go the Master with Thesis route you can study at ETS, a top school for Engineers very industry oriented, again in Montreal.
For nearly all their Masters the requirements are

Hold a Bachelor’s degree .../...
  OR have acquired the required knowledge, appropriate training and experience that is deemed to be relevant.
  You can write your thesis in English and as for the French requirement you only need to pass a ridiculously small 3 credit course.

No, all higher academic institutions in the US would require you meet prerequisites before being able to attend. This means that even English 101 and such would need to be completed before even being considered. That said, I do know of situations where if you already have your B.S. you can go straight to Doctorate's without Master's.


text:
Sending manuscript to a journal that rejected an earlier paper
As a beginner researcher, several years ago, I sent some (2-3) papers to a quality journal (with ISI IF). The papers were rejected.
Now I am preparing a manuscript, which is in my opinion much more mature.
In the meantime I had some papers published in other journals.
Now I am considering the same journal again, because of the topic.
Is it a good idea ? Or is there going to be some bias because of earlier rejections ?
Just to clarify: this is a completely new manuscript.

I have just done this myself. A paper on one topic was rejected in 2010, submitted a different paper to the same journal in 2013 and it was accepted. I think editors are too busy to hold grudges.

There seems to be some confusion in the answers, so I think you should edit the question to address this explicitly: is the manuscript you're currently preparing an updated version of one of the papers you submitted to this journal several years ago? Or is it a new, unrelated paper?

Apologies, it was a different paper.

This is perfectly legit. You are not forbidden to submit ever again to a journal that has rejected one of your earlier manuscripts. Indeed, rejections happen for everyone. It is best not to take them too badly.
Now, it would be a different story if you resubmitted the same paper that was previously rejected, or a version with only minor updates. Sending an entirely new manuscript, or a much more mature version, is unproblematic.

Or is there going to be some bias because of earlier rejections ?

Important journals get many submissions, the majority of which they have to reject. I think it is very unlikely that the responsible editor even remembers that you already had a paper rejected a few years back. And even if (s)he does, it is unlikely that (s)he holds it against you. As I said, rejection is something that happens to every researcher, at least now and then.

In addition to xLeitix's answer, which hits on most of the main points, I would add one further suggestion, if the manuscript is a revised version of a previously rejected manuscript.
As part of the cover letter to the editor, you should indicate that this is a resubmission of the previous paper. You should also outline clearly what has changed from the previous manuscript to the current one: what have you added or removed, and how the present manuscript is an improvement over the previous version.

I don't see a clear place in the OP that suggests that the paper is a resubmission?

"A much more mature manuscript" implies it's the same topic.

I disagree with this interpretation of "a much more mature manuscript".  I certainly hope that my recent papers are much more mature than, say, the ones I wrote as an undergraduate, which were on totally different topics.

Not only is this a good idea, but its relatively common, and a decent publication strategy overall. Given a particular topic is likely to have more than one journal, a way to decide where to submit is to submit it to the "top" journal that you think you have a shot at getting into (you shouldn't submit work that has no chance - its a waste of your time and theirs), and then letting the paper "slide" down the rankings as it gets rejected.
Rejection doesn't even imply that you submitted to the wrong journal, or that the work wasn't worth trying to get in there - the vagaries of editorial discretion, publishing cycles, and reviewers mean something might have had a legitimate shot at getting in but just missed this time. The next paper? Start the process over again.
Unless your submission is egregious enough to be memorably bad, the odds of the editors holding rejections against you, or even remembering you specifically, are pretty low.


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Bad data - how to move on successfully
I work in an interdisciplinary field. My input is not generated by myself, but by talented people that I trust and that trust me to analyse their data and generate fascinating insights.
But here I am, once more stuck with a project where the input is bad. There is no use for blame and finding a scape goat, we are in this together. And people learn. But I have been stuck in projects with big promises and bad/insufficient input since the start of my PhD. I moved to another place for a PostDoc now, but this situation seemingly haunts me wherever I go.
There is not much I can achieve when most of my projects stop after the input quality control. But if I want to stay in academia, I desperately need to step up my game in actual output, not just in my ability to troubleshoot, right?
How do I move on from this? What chances do I have if I can just never land a "prestigious project" that results in a valuable publication? Is there a chance to still build up a good scientific reputation without those? Should I try myself at writing a review? Take on projects until one finally works out? (But how long will I succeed in getting another job if they don't?)
I could work with published data for a bit, but it is often not comparable between studies and severely lacking metadata.
The question is, do I have to accept that success (as in, basically being able to stay) in academia is to a large degree based on luck and I am not one of the lucky ones or is there anything major I can do? I really love the work I do, I would like to keep going.
So far I have taken on additional projects, tried to do my own 'side projects' on at least roughly usable parts of the data in the hope I will find a better dataset eventually where this could come in handy and kept in touch with collaborators in an effort to troubleshoot and eventually produce better input.
EDIT:
To address some questions:
I produce analysis pipelines, partially based on my own methods. Without "real data" application it's hard to publish those in my field.
Yes, it is "real world data". I do not expect perfect data at all. But I do expect technically correct, usable data. If the input is random/to few features to be statistically relevant there is nothing I can do, though.
Imagine trying to do a statistical test on the similarity of blog posts based on word usage written by different groups of people but many "groups" only are represented by two authors, the text is sometimes just one sentence long and quite a few of the posts looking like they are produced by a random letter generator, not having any actual words in them. While I was promised at least 5 authors per group, minimum 5000 words per text and of course the post actually written by the author assigned to it.

What do you actually do? It seems you take input and produce output, but how are you producing that output? Presumably, you are applying some method. Is that method your own? If so, What do you actually do? It seems you take input and produce output, but how are you producing that output? Presumably, you are applying some method. Is that method your own? If so, then you could publish the method.then you could publish the method as a research contribution. Perhaps you can expand on your question to add some more details.

What types of outputs are made; do they include papers; are you an author; etc

Do the inputs come from "the real world". If so, than you should not expect perfect data; the real world is a funny place that does not care a whole lot about what we require... The methods you use to process such data needs to be able to cope with those imperfections. If that is not the case, then the only solution is to find other methods than can deal with imperfections. If you wait till the world becomes perfect, then you can wait a very long time.

Can't you be involved in the data gathering process?

Can you use existing public datasets, rather than using "new" datasets that may turn out to be problematic?

The world is full of existing datasets. Why do you need new ones?

@Karl: OP may be employed by people working on a particular application (= real world question) to develop data analyses for their data.

Okay, to address some of the comments: I sometimes am involved in the planning of the data gathering, but my input has been "overwritten" and ignored before. The existing datasets lack crucial metadata, so are insufficient to answer the novel questions we have. In some cases, there is not a lot of published existing data, so instead of working with 5000 data points I would have to work with 45 if restricted to them.

Summary:

Real research life with real world data is messy*, and there will hardly ever be sufficient samples (my very personal prediction).
There are huge opportunities (and needs) in working about small and messy data. Maybe that could become your area of research?
Good data analysis work requires close collaboration. Actually already when planning the experiments, but for sure during data analysis.
Close collaboration will allow you to make them aware of your needs and that data analysis cannot work miracles. It is also necessary for your because otherwise you may be employing inadequate analysis methods.
* When I say messy I don't mean bad curation (although I do see opportunities here as well - though maybe more business than research) but reality sneaking in with lots of influencing factors creating structure in your data where many (most?) data analysis approaches assume nicely independent data. I think this is a field that not only deserves more research but also that this has high practical importance.

I feel your pain. Been (almost) there as well. Actually, I still am (just my PhD is long done): so far with ≈ 15 years of professional experience in chemometrics, all the real world data I've encountered so far has one thing in common: too small sample size (even if it may look nice at first glance).

One consequence I drew for me is that I started to do research on such less-than-ideal situations I encounter in practice, e.g.

small sample size situations: knowing that I have far too few cases (some orders of magnitude below rule of thumb recommendations), how to diagnose when things break down, how to stabilize models, what will break down, are there hard limits, etc.
in terms of messy data in the sense above (with lots of influencing factors):
I had situations where it turned out that biology doesn't really obey the disease classification medical doctors use (that was developed for totally different purposes, as I learnt later on) how to adapt data analysis methodology to these situations (that were somewhere in between classification and regression)
how to adapt validation/verification procedures in such situations
(I work very much along the lines that you can to whatever you think may work for modeling as long as you do a honest verification and validation of that model)
I see tons of similarly important questions that are unanswered.
To the extent that if you need such research ideas, I'd happily supply you with questions ;-)

In my field, I think, it is going to stay like this: well-characterized samples are expensive.
In some respect you may even say that basic restearch isn't meant to have comfortable sample sizes. It's meant to find basic knowledge and point out promising possibility but the leg work of obtaining (and paying for) large sample sizes to make a method robust for routine use is something applied research/industry is supposed to do (and pay for). That point of view would say that taxpayer money should not wasted on work that industry could and should do.
On the other hand, I often see unnecessarily too small sample sizes in academic research: too small here meaning that given the sample size even without any experimental data it is (or would have been if one had bothered to check) clear that no knowledge is gained because the study is too underpowered. This is clearly just bad science, and a total waste of experimental and data analysis effort.
If that's what you refer to in your question, it's going to be hard work to improve this - but don't give up! Science needs people like you pointing this out.
My experience with that is that as PhD student or fresh postdoc how much you can actually do to improve the data may depend very much on how much weight what you say has with your supervisor (or even top level director).
What you can (and should) always do is to clearly discuss the limitations in terms of possible interpretation of the results of your study - including in the manuscripts you write.
To be fair, there are practical limitations. If we study a rare disease where the big university hospital gets maybe one sample per year, I tend to think that working with very few cases is necessary (but again: spell out the limitations). After all, one has to start somewhere.
Whereas, if we're talking easily accessible measurements of no particular ethical concern of a disease where the hospital sees 10s of cases per week then of course a thesis on 5 cases looks somehow lazy (not necessarily on the PhD student's side, though: The PhD student may not have been able to change pre-existing sample plans)
One consequence for my PhD thesis was: as I did not only data analysis but also sample preparation and measurements for my thesis, I put in substantial effort to have more samples (fortunately I had access to a comparatively large data bank, but in the end also that approach was limited by availability of the more rare conditions).
I'd recommend to at least take a decided interest how the data are generated (get a lab tour, get the collaboration partners explain how things work and what the data mean).

 If the input is random/to few features to be statistically relevant there is nothing I can do, though.

Yes. Again, this needs to be clearly communicated: I do have the experience that applied groups may expect miracles from data analysis (and you may even have a particularly uphill fight here if this group in the past got data analyses that were badly overfit and thus looking overoptimistic and noone realized this).
In addition, you'll have to document that it isn't your "fault" that no nice results come out of this data. It's doable though (and again in my experience something that is needed in everyday data analysis work as well: I'm just having such a situation on my desk right now again).

[...] "groups" only are represented by two authors, the text is sometimes just one sentence long and quite a  few of the posts looking like they are produced by a random letter generator, not having any actual words in them. While I was promised at least 5 authors per group, minimum 5000 words per text and of course the post actually written by the author assigned to it.

A few thoughts about this. I "smell" some communication/collaboration issues here. Again, they are typical everyday issues in my research and data analysis work:

I've met similar things due to fundamental communication issues (e.g. between statisticians talking about necessary sample sizes in the 1000s to answer a particular question and experimental scientists "translating" this to "many" ≈ 7.
"quite a  few of the posts looking like they are produced by a random letter generator"
Your experimental collaboration partners may have no clue what you don't know about their techiques (again communication issue): unless you have a background in these techniques you have no chance to recognize what is going in those noisy measurements and how to deal with them.
They could be anything from artifacts that should be deleted because the underlying mechanism that causes them is well known and can be disregarded over "the signal is hidden under this noise and you data analyst surely have some magic to get it of there" (not going to work, but typical expectation) to "your outlier is my most interesting case" - without the help of the experimental/data supplying folks you won't be able to adequately deal with this.
Seeing all this makes me think whether you do have sufficient information about the background of the study to even decide which data analysis approach is suitable?

Congrats for a genuinely insightful/useful narrative. Enlightening for us all.

@paulgarrett: wow, many thanks.

:) We should remind ourselves that other peoples' excellent contributions are too often greeted with silence... :)

Thank you so much! I will mull over this information for a few days and hopefully come up with a plan. For a first: I try my best to be in close collaboration. I used to have lab tours, even help out a little in small experiments, but now the lab is working with more dangerous pathogens and I am not allowed in there. I try to ask many questions, though. It's just such a slow process... When I mean "messy data", I actually know it is random. I did quality control and talked with the experimentalists. It's RNA in this case and the base composition is literally random.

If you are the data analyst/scientist/statistician, you need to be aware of the limitations of your approach given the data you are provided. If you do not have sufficient data, you should not even run the analysis - if you do so, you are more likely to accept the results if they are what you "expect" and discard them otherwise.
This is dangerous.
A big part of data analysis is knowing your data and knowing their limitations. If you are given data that is insufficient to make the conclusions you are asked to make, you must say so and refuse to do the analysis. Especially in the ridiculous case you used as an example, where you are expected to make generalizations about groups from two authors. This has nothing to do with luck.
You would never conclude in a scientific setting that Group A is taller than Group B based on n=1 in each group. Don't let yourself get into a trap where you attempt to make the same level of conclusion in another context.
I think you already know most of this, because you talk about stopping at the quality control stage, but if this is how all of your projects are ending then you are putting in way too much time towards a project without having access to the data that shows you the project is feasible. As soon as you are given data that is unsuitable, you need to tell your collaborators that it is not sufficient, explain why, and move on. This step should take 15 minutes if the data are truly as poor as you are describing.

I totally agree with this if clear cut hypothesis tests are involved and/or DoE has obvious flaws. OTOH, if the problem is "only" sample size and signal to noise ratio, I've found that running analyses and doing e.g. perturbation tests can provide "results" that allow more easily to communicate in a convincing manner that the resulting uncertainty doesn't allow any conclusions. This is particularly helpful if you find yourself in a position where you better demonstrate that the problem is really the data and not you/your ability/laziness...

This is a common issue and it's why I recommend to get more into the weeds and to be more choosey about who you work with.  (I basically always took leadership of any collaboration...then again this is easier as the synthesist...people like you are used to being support...then again, I checked their work and sometimes found issues...none of them ever bothered to ask me questions or learn/criticize my methods!)
Similar issues happen when people ask for PDE mathematical models and analysis, but the engineering assumptions are incorrect.  You actually add way more intellectual value by "asking five times" and checking input quality, assumptions, etc. than by just cranking the stats machine or the diffyQ solver.  Ideally you should try to be involved even with the study design.
One serious area you might look into is US oil/gas.  There's a lot of interest in optimization, neural nets, big data, etc.  Also, they have a lot of money.  (Even when they say they don't, they do.  Are used to paying a lot for services, travel, tools, etc.)  The data is not always perfect, but they have experience with making do and dealing with missing points also.  Of course you need to become more involved yourself in scrubbing, inspecting, correcting, etc. the inputs.  But I don't think they will be put off by a questioning approach, only if you tell them to reshoot the seismic or get in a time machine and drill better test verticals in 1950.  But I suspect your tools can still often add value even with imperfect data AS LONG AS the imperfections are known before the analysis starts.
P.s.  Even the questions on SE often suffer from this.  People ask for help with outcome X bounded by conditions 1, 2, 3.  But they would really be better served by questioning of what their real output aim should be and of the restrictions.

In science we investigate the unknown. As such, it is impossible to guarantee that your project will produce positive results. If you know that your hypothesis is true beforehand, there would be no point in doing the project. However, if all your projects are turning out bad, that also sounds unusual. The best place to look would be peers and colleagues who work with similar things - is it really the case that they are just luckier than you? Or maybe they are doing something differently?
Almost any scientific project will have strengths and weaknesses. It is not necessarily your job to provide an exhaustive account of the weaknesses. Starting with reviewers and even before, there will be no shortage of detractors who will point these out. Selling the strengths, on the other hand, is something only you can do. If there are 99 features that have no statistical significance, it is not productive to get hung up on those. Obviously don't deny them when presenting your results. But the 100th feature that does have significance is the most interesting and bears mention, in addition to features whose insignificance by itself is striking. From there, more significant features can be uncovered.
As you get experienced with analysis you should cultivate a feel for good projects and bad ones. Poorly conceived experiments, lack of controls, experimenters known for carelessness, crazy hypotheses that have no foundation in literature, are all examples of giveaways for a project to stay away from. If you are backed into a corner and all of your prospective projects have crap data, then you can look outside your immediate collaborators. Successful analyses get made all the time so surely there is data out there that is not useless. As you mention, reviews are a good way to at least publish something, they can also attract new collaborators and help you understand the field better and get more capable at detecting bad projects. You can also try to re-analyze data from other researchers or papers.
Another option is to improve matters with your collaborators. Even though a negative result is not helpful for publishing, it is still useful information. It keeps them from wasting time on a red herring. If the data you get is bad after all, you should try to show this convincingly ASAP, so you can quickly get back to your collaborators and start coming up with a solution. In fact, you can use your experience of previous failures to guide them on study design and point out errors they might be making that gave you problems before. If too little is known about what it takes to produce good data, then projects should be small and quick, so you can iterate many times as you find the correct parameters. Big projects should be avoided until you're confident you've taken care of all the basic mistakes.