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Doing research outside of academia, without a PhD?
I love learning and solving problems (I'm in math/ computer science) and I'm also very motivated. However, I don't have fantastic grades and so I don't expect to get into any top graduate program. This may seem like a discrepancy, but I think it is partly a personal issue (which I'm working on).
Is it possible to conduct research and therefore further any field without being in academia? This post is primarily targeting people who are doing their PhD.
I want to get the opinions of as many people as possible, so let me know if there's another place (forum) where I can ask.

This might be relevant: http://academia.stackexchange.com/questions/385/do-scientific-journals-in-general-have-any-kind-of-policy-regarding-papers-submi

Short answer: Possibly in computer science, definitely not in math.

Yes, of course it's possible, even in math.  You want to do math research?  Just do it. The only thing preventing you from publishing a paper in Annals is an Annals-worthy result.  Sorry, what's that?  Oh, you actually want to get paid to do research?  Well, that's an entirely different question!

As Nunoxic noted, doing research as a part of your job is possible. Also, doing research at a graduate program that isn't at a top school is possible. But doing research on your own, and hoping to bring innovations to a field... is highly unlikely to work, in my opinion.
My opinion is based on experience with a bad schooling system (undergrad and Masters in Serbia) where publication is mostly irrelevant to survival of the teaching staff. Access to research journals is also severely limited, and, as a consequence, access to international conferences, international collaboration and all that. Essentially, supervisors would offer minimal guidance, and students are always free to choose their research topic. This is still more guidance than you would have on your own.
Then I got myself into good schooling system (a second Masters - to catch up - and now my PhD studies), in The Netherlands. Neither of the two Dutch universities I've been studying at are top schools in the Shanghai sense: their rankings fall between 100 and 150. Still, in comparison with Serbia, the difference in what I've learned and achieved since being at these universities is staggering.

I don't spend months of my time sifting through articles in order to slowly begin comprehending which papers/names are the most relevant ones in the field.
I have someone to immediately tell me if a research question is worth pursuing.
I have someone to immediately point out similar research.
I have someone to immediately correct small mistakes that would prove important at the end of data collection
I meet important people in the field, and get feedback from them, with no cost to myself
I work with technology that costs a great deal of money, with no cost to myself

The essence boils down to having access to people who know what my work is about. This speeds up my understanding of the subject matter by an order of years. Science is so fast-paced nowadays that it's very difficult to make breakthroughs without collaboration.
In sum, my advice for someone in your position would be to give priority to non-top graduate schools, if it doesn't work put with top ones, instead of trying to be a solo researcher.

Although your fundamentals are strong, Internet makes possible to do research in some areas like computer science. It is not solo research in the sense that you are in contact with other people via "virtual" social networks. Anyway, physical place and contact, and speed is much more stimulating and efficient.

How is doing research on your own different from doing research at grad school - industry? Besides group/lab projects, research is essentially you and yourself figuring things out.

I think at the end of the day it depends on the person, but I would like to generalise that for most people doing research on your own is both harder and more time consuming than being able to bounce ideas with fellow colleagues within the same field of research, cause at the end of the day you're writing an idea to expand the field, so from the above post it is arguing for proximity.

You can get a CS support job in a research lab. I worked as a programmer at various government and edu labs for a decade between bad grades and phd. I got my name on papers as a coauthor and helped do research.
Can be a great gig. You get to help solve problems, but don't need to worry about writing it up or worry much about funding, something PhDs do constantly.

I'm a former professor. I could no longer stand the elitism, tiresome institutional structure, and narrow mindedness of working in a university. After quitting the ivory tower, I still desired to continue my research, and discovered the pros and cons of being an independent researcher.
The most obvious plus is that I can research whatever I want, whenever I want, and publish in whatever publications I want. No more colleagues frowning on my choices. No more administrators denying my funding requests based on personal bias. No more crappy vanity research in unethical publications to stroke the egos of a tenure and promotion committee. It feels a bit like being in grad school again, where my creativity and personal curiosity can shine - only with more maturity and structure as guidance.
The cons are painful, though. Gone are the days I can submit 30 interlibrary loan requests for materials in a day. My public library only allows three requests at a time, which is practically useless. I'm spending a lot of time and money buying materials and traveling to libraries to get at resources. But, the traveling is fun at least. Even though it's all on my dime (that travel budget as a professor was a privilege I took for granted!), true research freedom is priceless!
Besides a lack of access to materials, the major con is lack of access to respect. Filling out a form - be it for access to an archive or submission of a publication - I'm always asked for my "affiliated institution." Well, I don't have one anymore. They aren't interested in where I used to work, or my publication history, or even the quality of research I do now. It's a major hurdle. Academics are often fueled by their egos, and they often only have respect for others at their perceived level. Don't have the right letters after your name? Go away! Aren't on the payroll of a university? Get lost! Urgh. I saw serious independent researchers get turned away and mocked frequently by these elitist pigs.
It is possible to make progress in your research without the credentials of an academic, even while being under scrutiny of many academics you'll come into contact with. You'll have to be a more efficient researcher and do everything the hard way, but it will be rewarding!
In the hard sciences I imagine it would be costly, too, to gain access to equipment you might need. It all depends on what your goal is. But give it a try, and live your dream! You have nothing to lose.

The original post asks about doing research without a PhD. Perhaps you can [edit] your post to comment on that. Presumably you earned a PhD before starting your unaffiliated research; do you think your work as an independent researcher would be better/worse if you hadn't?

I like this answer.

@ff524 The post might have asked about research without a PhD, but researching without being at an institution is a closely related topic that offers critical insights. This answer is extremely helpful.

Indeed, this answer is still really useful, as an undergrad trying to get into research I face the same hurdles as described there, so that's quite on point even if you don't already hold a PhD

I'm currently working on my math PhD. I think that it's incredibly unlikely that someone without graduate work in math could do much math research (I don't know about the computer-science side, however).
Now that I have put myself out there, I should clarify my point. Is it actually impossible? Well, no. In particular, there are many combinatorial or graph theoretic problems that don't really rely on previous work to be done. Some fields of math aren't as profoundly cumulative as others (on the other side of the spectrum, I might place something like algebraic number theory or elliptic curves, both of which I find highly removed from pregraduate work).
But what I'm really saying is that it's prohibitively difficult, not impossible. It's time-consuming no matter what, and I think the greatest advantage an academic has is that research is what we're paid to do. But it is possible to get 'research-style' jobs without a PhD. But they are limited in scope.

There is 'doing research' and then there is 'publishing papers'
You most certainly don't need a PhD to do research.  If you want to make an advancement, like a better algorithm, then you can create your own experiments to show how your idea works better (on the problem you tested) and you have created a meaningful advancement in science.  If you had a PhD you should have learned how to create solid experiments and how to analyse the results (which often leads to more experiments).
This may sound a bit jaded, but publishing papers is often about understanding what a particular field cares about (and who the influencers are) and becoming part of that community.  There is definitely a clique element in most fields - by getting your PhD you are invited into the clique under the wing of your supervisor.  It might not be impossible to publish coming from outside a community, but I think it is certainly harder.

If wanting to get admitted to a specific institute for a PhD, would it help to ask a PhD or Professor from this institute to have a look at the self-conducted experiment description and collected results, to summarize it in a paper and publish it together, so that one is not unknown anymore in the institute when applying later?

@Lucas - sure, that sounds like a good idea!  and if they say no, then it can also be interesting to get feedback on your idea.  most likely, they would want you to refine the experiments before putting their name on it.

I like this answer. If one comes up with a great enough advancement, then it is not at all unreasonable to assume that PhD or no PhD people will listen and take notice. A PhD is merely a certification to show you learned certain skills. It doesn't mean one cannot possess them on their own or just get lucky and have a break-through. After all, considering how long the concept of "PhD" has been around... 90% of all mathematicians who did research over all these centuries had absolutely no certification.

On some universities there are programmes to encourage more people to join the research. You might want to check them and ask (They might be called UROP, research internship etc.). In such way you can work a bit with research to try if it is for you.
Depending on your work and findings you might be coauthor of paper. At least 2 of my friends coauthored the paper in such way before they finished their undergraduate studies.
PS. I am not a PhD student (and I do not have PhD) but I participated in such programme. I would recommend you to search for one as it allows you to try it.
From what I heard it also helps getting onto PhD programme as you have shown that you have an experience with research (and know what you are doing).

I am doing research outside of academia because being in business shows where the real problems are, because being given a job assignment is what motivates me to take action, because my research is very far outside the mainstream, and because I wanted to develop my thinking very far outside the mainstream. I would prefer to be in academia but for these four things. I have made slow but steady progress in my area of research, and if I had to make a guess, I will have something useful to present about five years from now. I do not recommend doing research outside of academia unless you have some serious reasons to do so, as I have such reasons but most researchers do not.
On the other hand, now that I have developed my research focus and direction over the last 20 years in a way that is solid and unique, I am now starting to think seriously about starting a Phd program. With a strong direction to my thinking and research, I can now interact with other academics without being lead outside my research direction. We'll see what happens.

Philosophical issues with no right or wrong. Bottom line, for me, after masters level is doing research that brings innovations whether for social or academic or personal reasons. Fame should be attached to success in life, career etc. not necessarily to acquisition of a 'prestigious' title.


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Etiquette for effective workshop comments
After a few years of graduate school, I've noticed that I may not be asking questions and making criticism in the workshops in the best way. By "best," I have two goals for comments. The first (selfish) goal is to develop a reputation for being a intelligent, insightful colleague with good comments. The second goal is to effectively communicate my concerns to help others improve their research. Luckily these goals are mutually reinforcing.
Observing others in my department's weekly workshop, the questions that usually get the most attention are those that are phrased quite strongly, even aggressively.
My tendency has always been to be polite and not try to put someone on the spot about their work. I gently communicate the problems I see with the work. As a result, presenters often do not listen. They brush off the questions as if they are unthreatening and thereof of limited concern.
I do not want workshops to be a threatening or aggressive environment, but it seems like only questions that get the presenter's adrenaline flowing regularly warrant proper consideration and response. Maybe I'm going too soft and I can find a middle ground without being one of the aggressive (sometimes rude) ones.

I am not sure that your questions at a conference are necessarily a major concern. If you look at how science is propagated, conferences are typically a step before publication. Therefore, it is noble of you to think of it as a point where you can provide constructive comments to authors about their work. However, the most critical point comes when work is published and peer-reviewed.
Based on your experience at conferences (which I to some extent share) you can ask yourself the following: Is the author/presenter interested in your comments. If he/she is, then it would be counter productive to ignore your questions. The question-and-answer format, on the other hand, is too brief and fragmented considering the time constraints of a presentation schedule to be the forum for constructive criticism. That is probably also why the more aggressive questions dominate. In the end, there is nothing wrong with approaching a person during a break to initiate a deeper discussion. you will then also get a better feeling if your comments are welcome.
A final thought. I tend to think as the saying: it is sometimes better to be silent and seem a fool than to speak and prove it. You can exchange "fool" by whatever trait you wish.

Just a couple of thoughts on this (not so recent) question:

The first (selfish) goal is to develop a reputation for being a intelligent, insightful colleague with good comments.

If everyone went into a workshop or conference talk with the primary goal of "developing a reputation for being an intelligent, insightful colleague with good comments", then...yikes.  Chaos would ensue.  Luckily I've never seen this; the closest I have seen is in faculty meetings, where often I feel that being a good colleague requires me to say something from time to time just to be heard, rather than because I think I am contributing something absolutely essential to the discussion.  It is, frankly, one of my worse habits: I shudder to imagine what would happen if all of my colleagues behaved the same way.
You should speak up in a talk because you think that something you say will be directly helpful to the speaker or to others, or because you didn't fully understand something the speaker said and you have reason to believe that a quick question and answer could set you back on track.  I don't think that you should aim to make yourself heard specifically to impress your colleagues: that seems too likely to backfire, either because what you say is not as insightful as you think or because the speaker and the audience will not appreciate your commandeering more than your fair share of the speaker's time.

The second goal is to effectively communicate my concerns to help others improve their research.

Yes, that's a good reason to speak up.  What you say about "too soft" questions getting brushed off by speakers sounds like a "local phenomenon" rather than a general truth about such talks.  Where I come from, too-pointed questioning is more likely not to have the desired effect: a speaker either becomes flustered and the entire talk becomes (at least!) temporarily derailed, or to avoid that they say "I don't know" and move on without really thinking about the question.  For several years this type of thing happened often in response to questions of mine that I didn't even realize were so pointed, so I have (somewhat) learned to correct in the opposite direction: by trying to ensure that my questions are friendly and do not come off as quizzing or challenging the speaker.  But mine is not a universal truth either: I think it just depends on the local culture involved, maybe even the specific group of people in the room.

I gently communicate the problems I see with the work.

If you feel like you see a problem -- rather than just asking a question about something that you may not properly understand -- then pointing this out during the talk itself may not be ideal.  The following is in my experience a universal truth: people don't like being told they're wrong.  Do you want them to stop short and try to "fix" their work on the spot?  I think that if you really have something to say which vitiates a substantial part of the talk, it will be better for all involved if you wait until after the talk and speak solely to the speaker about it.  (Admittedly, j'accuse moments in talks can be pretty entertaining for the spectators: see e.g. the beginning of this wonderful story.  Spoiler alert: as amazing as this beginning event is, yet more amazing is that it is not what the story is really about at all.)
I might go so far as to say that it is not really "fair" to raise a serious objection during a fixed-duration talk.  The speaker has other things on her mind and plate besides understanding and addressing your specific concern.  Often in a talk it turns out that the brilliant professor's lightning-quick refutation of the speaker's work is not actually a refutation of the speaker's work: it was based on some kind of misunderstanding or miscommunication (e.g. people using terminology in subtly different ways or elided technical hypotheses).  When this happens the majority of the audience cannot really follow what happened and goes away with a vague impression that something was wrong with the speaker's work.  That's not really fair.

+1 Thanks for these great comments. My question is really about workshops, which -- in my discipline at my university -- means everyone reads a paper in advance and discussion with the author is the goal. I wasn't really thinking of talks, where a speaker intends to present to an audience. Your distinction between the two, and the corresponding etiquette, is well taken.

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How to format code for IEEE Journals
I'm working on a paper for IEEE Transactions on Computers, and I can't find any guidelines on how to format code (C++) that is included in the paper.
I found some other papers with short code snippets, and it looks like they just used Courier New for the font, but I'm not sure of the font size. Do I need to include line numbers? I haven't found any with long sections of code (so for something with 4 lines I wouldn't expect line numbers anyways).
If someone has published a paper with code in it in IEEE Trans, and could give me some advice, I would really really appreciate it.

The IEEE does not generally have any strict guidelines on how to format code for papers in their journals or conferences, so you have some freedom of choice in how you do so.  The general guideline is simply to use a monospace font and formatting choices that are not blatantly incompatible with the rest of the paper.
Now, if you are writing for IEEE, you should probably be using LaTeX.  This means that there are a number of excellent packages, such as these, that will format your code in a nice and professional manner that is generally quite familiar to IEEE reviewers and editors.  If you pick one and use that, you should not expect any problems.

My supervisor is unfortunately very opposed to LaTeX due to the difficulty in exchanging feedback and tracking changes, which is why I'm stuck with Word for the time being. I use LaTeX when I write up labs for my class, and I really like the options and appearance. Thank you for the information, though. I will stick with Courier New for now and maybe try to switch to TeX again, though that has never gone well so far.

@Mewa: Not that it sounds like it would convince your supervisor, but: Unlike Word documents, LaTeX source code can be properly used and merged with versioning systems (something you should be familiar with anyway if you are publishing papers with code), in particular, if you adhere to the simple rule of using one line per sentence. Rendering nice can be as easy as latexdiff-svn -r 42 paper.tex --pdf. You can exchange feedback by just changing what you think should be changed, your collaborator silently accepts what they agree on and the you discuss everything else.

You can also use LaTeX to prepare the code as as figure images for inclusion in your Word document.  But, PS: your supervisor is wrong.

Just as an update, I wrote it in LaTeX, and the code looked beautiful. The paper, however, got rejected on the grounds of having too much code (which I agree with), so there is that.

Unfortunately the ieeetrans latex templates make the use of algorithmic latex templates very complicated.

Word's style definitions are really useful in situations like this. As long as everyone is using Word 2007+, one person could set up what code ought to look like (e.g., +1 left indent, +1 right indent, Courier New, single space, do not spell check, etc.) and save it as "CodeSnippet" or a similar style by highlighting some of the text, right clicking, and looking under the "Style" sub-menu for "Save selection as new Quick Style." Saving it to that document (rather than a template) will at least keep a consistent code style in everyone's document. It's also a little easier to go back and "clean up" someone else's code later (highlight it, click the code style in the Quick Styles bar) or modify the style rules for all instances of code later on in revision.
One caveat: aside from the style definitions common to all general use word processors (like blue-text + underline "HTML Link" style), I've never seen user-defined styles make the jump between Word and anything else (or vice versa).

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Do postdoctoral studies have official certificate?
I have seen that some universities give postdoctoral fellows an official certificate (similar to the PhD diploma) after finishing their postdoctoral job. On the other hand, some of postdoctoral positions are just simple research (or even academic) jobs. I have even seen that some professors pay the postdoctoral fellows without contract (from their grants).
My question is: How common is a certificate of postdoctoral studies? Do future employers expect applicants to give them certificate of postdoctoral studies as well as PhD diploma?

Answers to this question varies from one country to another.

@EnthusiasticStudent so give us your experience of your country.

Certainly not in the US.  I never got any kind of certificate for the postdoc I did, and in fact I'd never heard of such a thing until your question.
My current employer never asked for any formal verification of my postdoc.  They received letters of recommendation from my postdoc supervisor and other colleagues at that institution, and I think most US academic employers would consider that sufficient.
Then again, they never asked for my PhD diploma either.  In the US, we don't use the diploma for official purposes; it's just a decorative piece of paper.  The official document of choice is a transcript sent directly from the university (and my employer did ask for one of those).

Likewise on all counts, except I don't think my employer ever asked for my official transcripts.

The one thing that needed a scan of my diploma was a US government grant. Everybody else was fine with a degree verification (a transcript without grades, just degrees and dates of attendance).

I'm in the UK. I've never heard of a "certificate of postdoctoral studies", not least because postdocs aren't studying towards a qualification: they're working. I've also never heard of a postdoc not having a formal contract of employment. In fact, I suspect it would be illegal to do so in the UK (and probably most western countries).

I work in academia in the US and have never, ever had a formal employment contract. That being said, I never did a post doc either.

@BillBarth Er, boggle. So on what basis do you work and on what basis does the university pay you?

I come to work and they pay me. We agreed on a salary and benefits when I started (via email or maybe a paper offer letter). I am reappointed to my current position annually. Sometimes I negotiate a raise or promotion. The university (a state agency) has a handbook of operating procedures that explains their HR policies for people in my position (Research Scientist) and their procedures for hiring and firing. I've been working for this university for nearly 20 years in one capacity or another and never had anything that looked like a formal contract with negotiable terms.

As a specific counterexample, my postdoc (at a major US private university) had no formal contract.  I signed a one-page appointment letter each year that described my job title, salary, etc, but there was nothing that imposed formal obligations on either party, nor specified damages for failing to deliver.  The US generally has at-will employment, so I was free to quit at any time if I didn't like the working conditions, and they were free to fire me if they didn't like my work.

@BillBarth - can't this absence of contract be abused if you get into a tiff with the authorities?

@New_new_newbie I think the point here is that, to Europeans, employment law in the USA is completely alien.

@New_new_newbie, yes, but also if I don't like the job, I can walk away with no notice, and my employer can struggle along without me. Also, if I am not fired for a good reason, then I can claim unemployment benefits which costs my employer money.

@New_new_newbie If you want to have a discussion, please take it to chat.

@BillBarth - In accordance with David's wishes, I'm removing my comment from here and reposting in chat. If you feel like responding, please see the comment here.

You are expected to have publications while working as a post doc. These publications document your institution, topic and success while working as a post doc.
You can also ask for recommendation. I do not think it is a separate "diploma" because unlike PhD, post doc is already more work than studies.

In general, the only "certificates" of post-doctoral research that I am familiar with would better be classified as certifications: that is, they indicate the dates for which the postdoctoral associate worked at the given university, and the kinds of duties performed. I know that some universities also give certifications for particular courses taken, but otherwise, I am not actively familiar with "certificates" in the style of a diploma.

I would like to think that since "post-doc" is not a formal designation but "research fellow" (or similar) is, and that since the latter is not a degree but a job just like any other ones, it is understandable if an institution does not issue a certificate of completion.
But coming from China, I know that in that system, completion of postdoc position is subject to evaluation, often requiring a research report at the end, examed much like a candidate defence. The certificate is important, being a prerequisite to some job positions. People often mistaken postdoc as a higher academic degree, I think owning to this (and the fact that there is the word "postdoc").
I just completed my postdoc at the University of Michigan, USA. And just a few days ago, they sent me a certificate which is not by my request. I wasn't expecting this, as I understand that they likely might not do this. I'm not sure if UMich is doing this because it has had enough Chinese scholars who asked for a certificate, needed for finding employment in China.
I now work in a Chinese company in Canada. They know I did a postdoc but never bothered to make me prove it (I may have sent my postdoc offer letter?) Neither did they ask for my diplomas, but they might have verified them through some third-party service.
With diploma already mentioned, I think that when proving degrees, one typically use a letter of degree completion (aka. completion letters). The diploma itself is a one-of-a-kind document: my Alma Mater defines it so [1] and I'd be heartbroken if I'd ever loss the original copy. And since diploma shouldn't be copied, I'd rather send my completion letter or transcript (or ask the university to send them). But again, in my case my current employeer never asked me for those things.
[1] Why do some universities refuse to issue duplicate diplomas?

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How to deal with an unhygienic human subject?
The human subject that I really depend on is extremely unhygienic. Not simply sweaty way, but full-on stench, black teeth and dirty clothes.
I feel responsible for the lab equipment and space that I am going to use for my audio recording with him and I don't know what to do in such situation.
Anyone encountered such problems in their research? Is there any way professionally and politely ask someone to clean themselves up?
Any advice on how to deal with such a situation will be very appreciated.

Regardless of the ethics/etiquette of it all, something to keep in mind is that if you make being clean a condition of participation then your participant may withdraw. This may not be desirable if you "depend" on them. Sounds like you might just have to accept their poor hygiene.

I'm voting to close this question as off-topic because it is not about academia. It may be on topic in workplace.SE.

@DmitrySavostyanov is research on human subjects on topic on the Workspace SE? I doubt so.

@DmitrySavostyanov issues regarding human subject research are definitely on topic here. This question is not about interacting with a coworker, but rather a participant where all interactions need to be approved by an IRB.

@CapeCode Thank you for clarification - I misinterpreted the meaning of a "human subject" in this context. I retracted my vote to close.

I'm voting to close, not because it's an inappropriate question for this site, but because to me your question is not clear.  Please edit the question to explain how the subject's poor personal hygiene puts the lab equipment and space in jeopardy.  Are you concerned about exoparasites, or what?  If you are experiencing a feeling of disgust, and there is no actual danger to the lab or equipment, that is a separate question.  Bottom line, please clarify.

@aparente001 My question was how to communicate non-insultingly the "please wash up for my experiment" message.
I will have to record in our EEG booth with the air conditioning off (too noisy for recording), since it has the professional audio equipment and sound isolation. Since the recording is ±3 hours, I am concerned that the smell will persist afterwards. I had an interview with him in a seminar room and I had to intensely air it for a complete day, since my colleagues who entered the room after were feeling sick from the smell that lingered.

That's helpful clarification.  I think you should edit the the question and especially the title, to focus clearly on the smell and the effect it has on you and the others who use that space.

@aparente001 Thanks for your tips! Sorry, I'm new to the community. I thought that not focusing on the smell makes the question more general, and hence more helpful to other people, who might have problems similar in some way.

Well, I do understand the smell being the main problem, or perhaps the only problem.  The part I was having trouble understanding was the hygiene.  I was taking it to mean "conditions or practices conducive to maintaining health and preventing disease, especially through cleanliness."  I think you should edit that word out and just focus on the smell.


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How can I avoid being "the negative one" when giving feedback on statistics?
Results get sent around a group of biological collaborators for feedback. Comments come back from the senior members of the group about the implications of the results, possible extensions, etc. I look at the results and I tend not to be as good at the "big picture" stuff (I'm a relatively junior member of the team), but I'm reasonably good with statistics (and that's my main role), so I look at the details.
Sometimes I think to myself "I don't think those conclusions are remotely justified by the data".  How can I give honest feedback in a way that doesn't come across as overly negative? I can suggest alternative, more valid approaches, but if those give the answer "it's just noise", I'm pouring cold water over the whole thing.

Perhaps consider that it will hurt a lot less coming from a colleague rather than a reviewer. Sure, there's no guarantee that a statistically-minded reviewer will be assigned the paper, but still, it's better to improve the paper before trying to publish. If the results aren't supported by statistics, they are likely to fall apart under further scrutiny, and you wouldn't want to build a research program on a false finding.

Also applies when replacing "statistics" to "choice of colormaps" ;-)

The "magic words" for this are not the "please" and "thank you" you learned as a little kid, but "I don't understand why blah blah blah". If you like the explanation you get, the person giving it will feel good about being smarter than you, and if you don't like it, the other guy started the technical debate not you, and you never accused him/her of being "wrong".

@alephzero When my supervisor says I don't understand why you do X, I tend to interpret it as X is wrong, but there is probably a degree of imposter effect in that interpretation.

As a student in the field you ask the professor to explain in further detail their teaching so that you gain understanding.  As a professor, you ask the student to explain their answers so that they gain understanding...

In support of @alephzero's comment, in pedagogy / communication courses I came across the term I-message, which I really like. It's all about: "I don't understand why..." vs. "You must be wrong...". See here for further information: https://en.wikipedia.org/wiki/I-message

What's wrong with "I don't think those conclusions are really justified by the data currently."?

@Trilarion Some people devoted many hours to form that conclusions and they are rejected without any question how (and why) they did it? Question "How did you conclude the [claim] from the [data]?" is much better - You show our interest in understanding, not in denying, and it allows you to figure why the [claim] is not justified by the [data].

@Crowley I agree that asking is the better tactic but for me the difference between the two approaches is rather small. Stating your opinion using seems/might/could ... is not the same as an outright rejection and almost the same like asking. In the course of the conversation you anyway might be forced to come up with your opinion in order to proceed.

@Trilarion If it is said during, say, 2hour discussion on that topic and trying to understand it it is completely different from stating that question alone.

@alephzero, while keeping the same basic idea, you could be less self-deprecating with e.g. "I don't see how the data supports that conclusion," or a little more firmly with "I don't see that the data supports that conclusion."

I was just at a conference where the swag bag included a soft foam ball.  We were instructed that whenever we felt a presenter was bullshitting we should throw the ball at him/her.  Can you throw a ball at your colleague?

I would suggest approaching your colleague in a humble and inquisitive way (especially since you're a junior member of the team). If you start the conversation with "your conclusions are wrong and here's why" you're going to set a combative tone for the rest of the meeting. There may be reasons that they interpreted the data the way they did that you're not aware of.
Instead try approaching the situation with something similar to "I looked at the data and came to this interpretation, can you explain your interpretation to me?" You're a researcher in your own right, so junior or not your opinion should be valued. But at least with this approach you indicate that you are open to the idea of being wrong, and hopefully that will start a constructive conversation where you can debate the merits of analysis type A over type B, etc.

I came here for interest in this question, and this answer just seems wrong to me. I have tried the equivalent of paragraph 2 and gotten as answer to the initial question something obviously unsubstantiated, basically a repeat of the same error. Yet I seek how to avoid exactly this.

@Joshua the idea is to start the conversation as I suggested, then take it from there. For example, if they say "I used Foo's method to solve the widget" and you think that Foo's method doesn't apply to this situation, tell them that and see what their justification is. I can't predict everything that is going to happen in the conversation, but if you approach it with the right mentality you can discuss your differences of opinion until the truth emerges

If they don't provide any justification for the analysis methods they used and refuse to discuss it with you, then there's not much more that can be done. It's their research after all (unless you're coauthoring a paper with them, in which case your name is on it so you can push a lot harder for answers)

General feedback rules apply.
Here are some of them:

There is no need to criticize any person.
Stick to facts.
Describe
things, especially describe what you think about things, e.g. don't
write "the assertion is not justified by the data" but "I can't see
how this is explained by the data" (and probably give an example of
some claim which you find equally "unexplained" by the data).
Don't be negative. Instead, make a suggestion for something better.
If you don't know an improvement, ask a question (e.g. "I could not
figure out, how this conclusion was drawn, could you please
clarify/provide further explanation").

Last tip: Sandwich your criticism, i.e. start with something good, end with something good and put the meat in between.
(You may also want to google "feedback rules" or "how to give feedback", but there are some rules/tips which may not apply…)

I would also add: be open to other people's opinion, and realize it is possible that you are wrong - regardless of background and qualifications.

I have to dispute the next-to-last point ("Don't be negative. Instead, make a suggestion for something better.") Saying "Why don't you try X" when you mean "Y doesn't work" is very confusing (I've been on both ends of this exchange).

Isn't "I can't see how this is explained by the data" the same as "the assertion doesn't seem to be justified by the data"? The use of could, might, seems, maybe, ... should be enough to indicate you might be wrong as well. Of course it never hurts to be as polite as possible.

As a former biostatistician, I regret to say this is not the way to address this problem. Bio projects have lots of money behind them, creating extremely intensive pressure for positive results. Bad statistics is often the result of trying to "will" a positive result. Not being negative and sandwiching criticisms is a sure fire way to have your insights ignored in these situations.

That's bad news from the biostats community (I mean, ignoring feedback if it's sandwiched or phrased in a friendly manner).

@Dirk: being a biostatistician can be very difficult. Doing things correctly doesn't always make you a lot of friends, although that's very lab dependent.

@CharlesStaats I can relate to that, "Why didn't you use X?" can also be a pretty hard / time consuming question to answer if the person getting feedback isn't that familiar with X, and the rest of the setup is complex. It's more polite to be positive but it's a much longer path to the real issue than "Y doesn't work" or even worse "Don't depend on Y, it is a false positive in your case because..." (Note: My field is computer science related)

It would be nice if statistics was always about the truth, and there was a right answer or method to every question.  That simply isn't the case though, and many elements have room for debate.  I'm an economist, and I've seen this first-hand in three different areas.
First, I did some interdisciplinary empirical research where I worked with a sociologist for a while.  I was struck by how our assumptions ran contrary to each other; a few times I suggested things that were completely standard in the field of economics, only to discover that he couldn't fathom why we would do it that way.  Then at least three times it was him proposing standard methods in sociology that I couldn't fathom.
Second, I moved into the policy research field.  Holy cow, the policy field does things with statistics and econometrics that would make an econometrician roll over in his or her grave, while they were still alive.
And then it just got worse, because I started working alongside some data scientists.  I won't even get started, except to say that they accepted as completely normal things that made me roll over in my grave.
My point in sharing my anecdotes is to suggest a more humble approach than "I don't think those conclusions are remotely justified by the data."  Offer criticism, certainly, but don't be the person in the department who is a complete pedant about every statistical detail.  My points were mainly about what I ran into when venturing outside of my field, but I think the lesson is still relevant within your primary field.  Be particularly cautious about things you find objections to that are, regardless, widely used in your field.
None of this is to suggest that you should just ignore things you find incorrect.  Rather, try statements like:

"I'm familiar with using method A as you did here.  The issues raised in (somepaper, someyear) look possibly relevant, so you might want to address their points here too."
"What made you decide to use method A over method B?  Maybe method B would be a good robustness check?"
"A line or two about how you verified the data fits with assumption X might be good here."

In short, approach it as if you're assuming they know what they're doing, then asking helpful questions to lead them to your desired points.

Being a complete pedant is actually the right thing to do, esp. in statistics. That does not mean being a complete jerk. It means that any additional assumptions should be stated and only claims that really follow from data and used theorems are valid. Even that one slight generalization, which sounds really reasonable can be only a hypothesis or potential explanation (which is still worth including in a paper), but not a result.

I think this is a good answer: in practice, whether the data justifies the conclusion has a strong academic-cultural component.  This means that you can come off as an ignorant jerk if you just walk in the room and assume you're right and everyone else is wrong.  Of course, whether the data justifies the conclusions is also a statistical issue.  So I think the OP's goal should be to try to contribute to the discussion of the validity of the results in a useful way.  (It seems unlikely that the role will be to convince the rest of the group to go back to the drawing board...)

@dtldarek There's a difference between being pedantic and being thorough and correct in your work.  And since the OP's question was about how to best engage with his colleagues, it's an important difference.

@Jeff Could you give an example that makes the distinction between being pedantic and being thorough? One I can think of is whether to check validity of some widely accepted work that we are basing on, but in some cases even being thorough would mean going deep into that too, so I am not sure. In particular, if that work makes some implicit assumption (e.g. otherwise some theorems authors use do not work), then we should include that assumption in our paper (e.g. similarly to [42], for Theorem 7 to work we assume that XYZ). If you don't then you get just a bunch of false results.

@dtldarek Pedantic is a pejorative term.  It implies, for example, arrogance or superiority in your approach.

@Jeff I stand corrected. I knew "pedant" is a pejorative term, but understood it as "excessively concerned with formalism, accuracy, and precision" (quoted from Wikipedia) and that was mine intended meaning (I did mention not being a jerk). Only now I have checked there is another meaning which is probably the one you intended (one who makes an ostentatious and arrogant show of learning; also from Wikipedia, but lots of dictionaries agree).

As a "data scientist", this answer did make me chuckle.

"If I were a reviewer..."
The easiest way to accomplish this is to frame the critique as something a reviewer would want to know.  In this way, you are positioning yourself as a very valuable team player, who is protecting the team from an imaginary adversarial reviewer.  I frequently give critiques like this:
"if I were a reviewer, I would want evidence that this couldn't be the result of [null model X/violated model assumption Y]."
or
"I can imagine a reviewer wanting [some robustness check] -- let's just run it"
or
"if we get a reviewer in [academic subcommunity], they would probably want [some more rigorous technique]"
Everybody loves to hate reviewers, and anticipating all of the horrible things reviewers complain about is usually seen as a valuable and helpful contribution.  (Incidentally, it's a good exercise to ask these questions of oneself as well.  It's easy to get locked in to one perspective on the research and it can be a good skill to empathize with a skeptical reader.)

Science is about truth, not hurt feelings.  If the data don't support the conclusion, say so.  You are doing nobody any favors by dancing around the subject.
I don't know if there's office politics or something else involved here, or if this is your first real job and you're having trouble finding your place in the pecking order.  Is that part of the issue?  Try to speak to one of the collaborators, face-to-face, and ask about how to bring up your concerns in an appropriate manner.

Just because "science is about truth, not hurt feelings" doesn't mean that you should ignore other people's feelings just because you think you're right. Science is also about collaboration and cooperation; try to work with people, not against them. Plus, the OP clearly wants to tell them their interpretation is wrong, they just want to present it in as constructive a light as possible. If you can critique their research and avoid anyone getting upset then it's a win-win

One way might be to suggest that the section on the statistics might be misconstrued by a statistically-minded reader. And, offer to teach a short seminar on statistical techniques, using the various papers sent your way as examples of how to describe the stats or do the stats in a clear and defensible way. Don't be the naysayer, teach them the right way.

@Jon I think, given the context that the OP is a junior and the people they disagree with are senior members of the group, that offering to run a seminar to teach them stats might be taken as being quite presumptuous. It should be enough to explain your reasoning to them, you should definitely not imply that they need to relearn statistics

Science is about truth, yet speech is about impressions and feelings.

Help them prove the truth
You are on their team, so to not appear "too negative", act like you're on their team - help them.
For work in progress, a preliminary conclusion will often be a hypothesis that is not yet fully covered by analysis or even the available data. "I don't think those conclusions are remotely justified by the data" would be something that a reviewer can reasonably say about a finished paper. However, you're not a reviewer for this, and the paper is not finished - what you should do instead is use your knowledge of statistics and describe in very specific terms what analysis and metrics would be required to properly support or deny that hypothesis which they're targeting.
It may be that this requires additional data. It may be that this requires simply a more thorough analysis. It may be that this particular hypothesis cannot ever be verified with the process and type of data that you're gathering, so there need to be significant changes - in any case, it's better to start handling this early, instead of waiting for a formal review.

I think this is a good answer. I will add that I have had good luck describing my own objections as the potential objections of a reviewer. Something like, "As it currently stands, I think a reviewer might say X about Y . I think we could overcome this objection by doing Z."

"so to not appear "too negative"" They way not to appear too negative without ignoring the mistakes would be: "Hey, I have discovered shortcomings in the draft, but I hope we can fix them."

If your main role is to provide a statistics perspective to the review process, then you have to weigh in. Imagine what would happen if an outside review for publishing came back with adverse conclusions that you might have been expected to identify in an internal review.
Politics aside, I assume the collaborators want their names on a well received paper, and they will appreciate that you are making an effort to get into the groove of working with them. If you start off without assuming someone has made any fundamental or conceptual mistakes, you will have them listening to you more easily.
Keep in mind that, in general, when you don't come to the same conclusion that your colleague came to, it may be just as likely due to a poor choice in how the material was presented rather than a fundamental error in thinking. This is especially true in an internal review prior to publication. I would assume that you provide a set of eyes that are unbiased from having been buried in the production of the paper and, as a reviewer, you can find areas where the communication of the concepts has broken down, if that happens to be the case.
If at all possible, you should arrange to discuss it with the collaborator who is closest to the issue that you are concerned with. That person should be already aware that you are experienced in statistics. (If not, you may have to introduce that point to them.) You might suggest that this may be presented in a way that you are not accustomed to (that may be true or not), and that you have consequently been unable to draw the same conclusions. Let them have a chance to present their concept while you ask questions related to your expertise.
If that person is unavailable to discuss it, at least make sure you have communicated to them that you have questions specifically about the statistics and how they are presented. Then try to discuss it with one of the other collaborators In the same way. (Do it in this order to avoid any objections that you bypassed the key collaborator on the matter.)

How can I give honest feedback in a way that doesn't come across as overly negative

Don't be overly negative. For an internal review, you should focus on both the strengths and weaknesses of the manuscript, not just the weaknesses. Obviously, you want your colleagues to know what needs to be improved, but it is also helpful to know what others think are the strengths.
You also want to be careful about how you say things. Instead of saying something like

I don't think those conclusions are remotely justified by the data

which I view as very negative, you could let the author know that they have not convinced you of the conclusions for reasons x, y, and z.

Hi Senior Member Bob,
I have been working to improve my skills at reconciling the data to draw accurate conclusions but I keep on getting stuck at conclusion XYZ for this set of data. This troubles me because I see that you've concluded QRS. Would you have a few minutes to show me how to arrive at QRS?

Now, let Bob speak and try not to interject. If he is professional then he will want to hear you out on XYZ after you hear him out.
This is a classic example of "seek first to understand then to be understood"

+1, but I think that the first clause sounds a little forced. OP should not pretend to be completely clueless. How about "trying to derive conclusion XYZ from the data like you have, but I keep getting stuck." ?

@einpoklum Agreed, OP should definitely modify it to suit their situation and personalities. I mainly wanted to show a non-combative way to get someone to elaborate. Working in IT, I find that merely trying to get someone to reproduce their "issue" in front of me solves their issue because they missed a step so IF OP is correct and conclusion QRS is wrong then hopefully that gets uncovered while the senior member expresses their conclusion process.

tl;dr: Do not aim to prove their way wrong; aim to find the right way instead.
Do not make yourself a remote verifier or something like that.
If you write I don't think those conclusions are remotely justified by the data. You are stating that their approach is wrong. Period. You don't need to wrap it in nice words either.
I can read from your question that You were hired to improve usage of statistical tools in your team. Do your job.

Do not be rude.
State your concerns and points without mocking them or using rude words tec. During chit-chat you can mention where you were wrong etc. Some people are offended when you use overpolite and defensive language - they want hard facts, not digging out them from 2hour monolgue. Some people are crushed by emotionless facts - they took their work personally. Allways try to find something right and mention it.
Try to understand.
Ask them to explain their work to you. You all will find why they come to that conclusion, that the conclusion is wrong, why it is wrong and you can suggest different approach. Ask appropriate questions: "What does that mean..." "Why was this parameter neglected?"
Help them.
Suggest different approaches and methods. Offer them your assistance with such tools.
Be open.
Be ready to say that You did't get their picture or made wrong conclusion. Sometimes the deeper discussion reveals all the hidden points that were missing in the text, because they though "it is easy to see", but it isn't.

On the other hand, if you want to help them to prepare for a conference, the colder and nastier questions and notes you have he better they will be prepared for the actual questions from the audience.

"Do your job." Sometimes this means being the bearer of bad news, if for example you tried to understand and finally arrive at the conclusion that there is indeed something wrong which cannot be made right. Communicating this would probably be a very unpleasant duty.

@Trilarion If you prove why they are wrong it is less unpleasant than stating they are wrong. And offering help and assistance will improve the workspace as well.

I have - unfortunately - found that in many cases people tend to be very resistant of conclusions that you have made and are offering them, almost universally, while being much more likely to accept the exact same conclusions if you give them the chance to make the last rhetorical steps. In other words:

Is B true? Yes, it is. This is due to the fact that A->B and A.

is likely to get you responses like:

"How can you say something like that?"
"You're being overly critical"
"There goes OP again with his/her outlandish claims."
"You're just saying that because C"
"You are a terrorist/communist/chauvinist, we should expel/fire you"

while if you write, or say, something like:

So, I've been thinking about the question of B. I think the fact that A is something we need to take into account.
  ...
  I was talking to X about the B question, and she reminded me that A->B holds. I think she makes a good point. But where does that lead us?

and then you get the:

"oh, oh, wait! I've got it! B! B! I knew it all along!"
"You know, since we're stuck, let's just try assuming B and seeing what happens."
"Hmm, I suppose B could be an option"
"I have always known that B, and I've said it all the time."

kind of responses. It sounds dumb but it just happens a lot when B is something that's not easy to stomach - socially, psychologically, politically etc.
Now apply this to your specific case :-)

I have been in a similar boat (doctoral student working with faculty), on a project I was working on. It was working with a colleague with a different disciplinary background, with different norms about what was considered convincing evidence. Based on the standards I was used to, I wasn't convinced of the results, and didn't think reviewers with backgrounds like mine would be either. I did use a bit of the future reviewer argument, but also had to say that I wasn't yet comfortable putting my name on the paper - that I would need to see X, Y, and Z. I had to do a lot of that myself. One thing I wanted to know was whether the selection of controls had driven the results (of an archival study), since there were many researcher degrees of freedom there. I had to write a program to run the models with every possible combination of control variables to convince myself that we hadn't just subconsciously played with combinations until we got something we liked (I was actually surprised at how robust the results were - though of course that isn't proof, but it does rule out one concern). My co-author actually proposed adding an experiment to the study, which made the results even more convincing. I don't think anything less than expressing my discomfort with putting my name on the earlier version would have yielded the result I'm now comfortable with.
Other thing I did, to give myself legitimacy: I had to learn a lot more about the methods he had applied - which was not easy. Some of the commands were part of a widely used (in his field) macro incorporating algorithms based on some older methods papers. I had to go back and read through several of those papers until I actually understood what the algorithms were doing (and it did seem like something that could be useful in my own field, with more transparency). A detailed understanding was required to express why I wasn't yet convinced. It's easy to say, "I'm not convinced by this." It's more convincing to others if you can explain what explicit sources of bias exist, or which specific assumptions are violated.


text:
How do I start a new peer-reviewed open access journal?
I'm an academic and would like to start a scholarly journal. My reasons are manyfold: primarily I want an open access journal, and think that my field needs an own journal.
How do I start a new open access journal?

Have you served as an editor on an existing journal?

Editor and editor ... I've peer reviewed some articles.

Related: How to create an editorial board for a new journal How to start a new journal is actually a pretty good question - I'm surprised we don't yet have a question on this. +1.

Anyone can create a journal - all you need is a website to host papers and an email address for submissions. However, if you want it to be taken seriously, you need to convince respected people to serve on the editorial board and/or submit papers. That can be very difficult as it requires you to be well-connected and respected yourself.

@StephanKolassa sorry, when I googled it I couldn't find anything about it. And to be honest: the titles on the other questions don't sound like how to start a open access journal.

@AmabileScientius You might like to read this: http://www.nature.com/news/leading-mathematician-launches-arxiv-overlay-journal-1.18351

This new journal uses "scholastica": https://gowers.wordpress.com/2015/09/10/discrete-analysis-an-arxiv-overlay-journal/.

Once your journal is up and running, I would suggest applying to DOAJ https://doaj.org/

You might try contacting other academics who started their own journal.  For example, in the last year the Open Journal of Astrophysics launched.  I don't know much about it, but they likely went through / are going through similar issues: http://astro.theoj.org/about

@StephanKolassa we have already a question on this: Steps for creating a scientific journal (duplicate) marked as duplicate of How is a new academic journal born?.

There is already a wealth of journals. More than we could possibly need. Are you positive that your work cannot find home in an existing, respected journal?

I have enough reputation now to post comments on topic (don't know how that happened though). http://megamozg.ru/post/7612/ — This article, written in russian, covers topic of creating scientific journal from technical side (software, typesetting, registering in things like DOI and ISSN).
Should i delete same message posted previusly as an answer?

Starting an open access journal will require a significant amount of time from a dedicated group of people with expertise in diverse areas. A great resource I found that explains this process in detail is "Starting an Open Access Journal, a step-by-step guide".  In addition, there are many books on the topic as well, and online resources which I will list at the end.
Why do you want to start an open access journal? To promote scholarly research in your field, to allow dissemination of knowledge, or to increase your own prestige within the scientific community? It may be possible that you will find your efforts more rewarding and fruitful if you are able to contribute to an open access journal. I have listed a few resources below:
ArXiv.org - "e-paper" of scientific papers in math, physics, computational finance, computer finance. De Facto publishing platform for many fields.
oatd.org - Fantastic collection of Open Access Theses and Dissertations, free to search, download. Great resource for any open access researcher.
Wikipedia Directory of Open Access Journals - Before you begin further development on a new open access journal, be sure to check that a journal with your current focus is not already in existence. In addition performing this research will help you to form your open access journal if you decide to pursue starting an open access journal.

The free open source Open Journal Systems (OJS) (GitHub) software enables one to easily create and manage all the stages (submission, peer-reviewing, etc.) of an open access journal. It will even aid submitting it to the Directory of Open Access Journals (DOAJ).

See the list of "OA journal launch services" maintained by the Open Access Directory.
http://oad.simmons.edu/oadwiki/OA_journal_launch_services
If you learn about other, similar services not on the OAD list, you could help others by adding them. OAD is a wiki open to public edits.
Good luck.

I am not having enough reputation to just post comment on topic.
http://megamozg.ru/post/7612/ — This article, written in russian, covers topic of creating scientific journal from technical side (software, typesetting, registering in things like DOI and ISSN).

Do you expect us to read Russian?

Check the section "Provide context for links" at http://academia.stackexchange.com/help/how-to-answer.

@scaaahu I expect that users can choose what translate tools they prefer (google.translate and translate feature of some browsers are not only options).
Google translated link

At the very least, please explain what's in there in English in a brief manner. Please read the link @silvado provided above.

@silvado Is "This article, written in russian, covers topic of creating scientific journal from technical side (software, typesetting, registering in things like DOI and ISSN)"  not enough for "fellow users will have some idea what it is"?

No. Not enough. You need to give us info about how to start a new peer-reviewed open access journal if you want to answer the question.

You've read to the second sentence as well, right? It starts with Always quote the most relevant part of an important link.

... few days off the computer and the bounty accidentally went to this. First time I have "awarded" a bounty for an answer that I have downvoted ..

@scaaahu See this machine translation of the article. The article is very helpful.

Here's a book:
Developing open access journals : a practical guide.
Solomon, David J.
https://books.google.com/books?id=sbIVAQAAIAAJ&source=gbs_ViewAPI

I downvoted this answer because it is not self-contained and therefore not useful. It could be posted as a comment instead.


text:
Experience with DeepDyve group plan?
I am currently looking for alternatives / provisional solutions to traditional journal subscriptions. The research institute (200+ researchers) I am working at has contracts with all the big players but they do not move in the currently ongoing negotiations. We consider cancelling one big subscription completely but the scientists would go berserk on us. So, we need an interim solution to supply them quickly with the articles they want and DeepDyve is one possibility. Unfortunately, I got no reply to the inquiries I sent via their online form.
Has anyone experience with DeepDyve group plans? What are the costs and is the access reliable?

I've no experience with DeepDyve specifically, but there are a few things that stand out as issues from a quick look -
a) Coverage. They seem fairly broad, but it would be worth double-checking they include, say, the top-fifty most used journals by your researchers. (It's a little fiddly to produce such a list, but quite an interesting thing to have available.)
b) Access rights. In my experience, people get quite frustrated by read-online-only access models with no print/save capability, and this seems to be the DeepDyve approach. Downloading is apparently an additional cost (the plan gives you "20% off" without saying how much the basic price is)
c) Accessibility/usability. Requiring users to go to a specific service to read the paper (rather than the websites they're used to) will cause friction and probably a bit of confusion.
None of these are insurmountable, but they're all things that are worth considering. On the plus side, their basic prices seem competitive to standard document-delivery services, assuming some kind of a bulk-purchase discount, and access seems to be immediate rather than delayed.

While it is convenient to be able to download articles on demand, I am not sure all researchers would go "berserk" on losing that ability. Simply switching access plans without consultation will likely piss people off. Why not have a meeting with the researchers where you open a conversation about the cost of instant access versus delayed access. You would need to have the relevant data about subscription costs, individual downloads costs, and number of downloads. Remember that the number of articles downloaded is probably an overestimation of the actual numbers because some articles are downloaded by multiple people multiple times and others are free in other places. A slight delay (48-72 hours) might be acceptable for many articles. Finally, telling the researcher how the savings will be passed on to them would be helpful. They will want to know what else can be cut from the budget to save the privledges.

Passing the savings on to the scientists would be a good incentive. Unfortunately, it is not possible to transfer money from this fund to another. My hope was to use the money to pay for gold Open Access fees and, thus, to support the transition from subscription to Open Access. We will see if this is possible to at least a small extent. Government regulations are very strict on this and we will probably have to transfer the (small) savings back to the ministry and ask them for a rededication.

After fishing an email address of someone responsible at DeepDyve from the internet and contacting them directly, I got a number:
usual cost per year per user: 12 * 40 $ = 480 $
group plan for 200 users: 54840 $ (274.20$ per user)
No idea how much this is negotiable. It includes reading as many article as one want and a 20% discount on the publisher price when buying the article (to be able to download and print it).
The price (including the amount of articles our scientists would want to buy) would be feasible if we were to cancel more than one big subscription package. As it is currently, we considered only one cancellation and too much overlap in coverage of DeepDyve and our remaining subscriptions would result in 'double payment' for too many journals. We have to think this through.
If anyone has experience with this kind of transition or with DeepDyve as supplier for research institutions, I would still be grateful for some reports.


text:
Accepting an Assistant Professor position at a low ranked university
I recently completed a postdoc at a prestigious R1 university, in a high profile lab. My publication record is not prolific, but those that I have published as first author are well respected journals, and a handful of middle author publications in top journals as well. I have a consistent teaching record throughout my graduate and postdoc training as well.
I applied to faculty positions at ~30 schools over the past two years, including several that I was not very excited about, but were in my target location and had open positions, so I applied. In total, I've had two on campus interviews and have been offered a faculty position at a low ranking liberal arts university (awards Master's but not PhDs) that seems like somewhat of a good fit for me personally in terms of the teaching I would do and the research interests of the other faculty, but lacks the prestige and resources of larger/more research intensive schools.
Would it be wise to take this position in the hopes that several years of faculty experience will allow me to jump to a different college/university that is a better fit for my professional goals (i.e., more research, higher caliber students, more resources for faculty)? Does this happen often, where someone takes a "starter" appointment and moves to a more fitting situation when it arises, somewhat like a Visiting Assistant Professor position?

The current version of this question looks very broad. Would you mind telling us your location/country and your discipline?

Depending on your circumstances, it might be wise for you to take this position (you seem to like several things about it). That said, it is rare (at least in my discipline) for people to move from faculty positions at liberal arts colleges to more prestigious research universities. In particular, "several years of faculty experience" will almost certainly not help you. Some amazing publications might.

In my field (CS) there are often good years and bad years for job hunting. Sometimes schools are hiring in your area and other times they aren't. You should see if that's the case in your field and, if so, what the job market looks like for your area.

It is true (we can see just from the numbers) that most of those with Ph.D.s and postdocs at top universities do not end up with tenure-track positions at top universities.  So your case is not unusual.

It sounds like this older question, and the answers to it, might be relevant? http://academia.stackexchange.com/questions/18078/how-frequently-do-pre-tenured-assistant-professors-switch-institutions

@GEdgar Yes, but what the numbers you talk about refer to is that one usually graduates from a top school and then goes to a lower-ranked research university. The problem with going to a liberal arts college is that it is hard to conduct best-in-the-world research without PhD students and with a high teaching load.

It is important to think about non-ideal scenarios, because all of them are much more likely than the ideal one.  If you took this offer and never got an offer from a research university, would you stay and be happy, or would you leave for industry?  On the other hand, if you declined this offer and never again got any offer for a tenure-track job, would you regret having turned this down?  The specific numbers depend on your field, but you have to decide if raising your chance of a research university job from 10% to 20% is worth dropping your chance of any tenure-track job from 100% to 60%.

I think the really important question here is what your other options are.  Obviously, the job you describe sounds superior to living under a bridge, so what would you be doing if you don't take it?

If you have options you can choose. If you don't have options you have to beg. Beggars can't be choosers. If you have the resources to wait for what you want, that's what you should do. if you need to put food on the table the situation is different.

@DarrinThomas: Even if OP has other decent options in the short term, such as continuing his or her current postdoc or getting another postdoc position, there is a significant chance in most fields that he or she will never get another offer for any kind of tenure-track position.

@AlexanderWoo : this is tru but there is also a signficant chance that OP might get another offer that's even better. Since we can't predict the future we can only go by what we already know.

@DarrinThomas "but there is also a signficant chance that OP might get another offer that's even better" -- why do you think there is a significant chance? and does this mean 50%? 30%? 10%?

The chances of successfully "moving on to bigger and better things" depend on one's discipline, and also what topic one studies within that discipline. Some topics look much more attractive than others to departments/faculties seeking to boost their standing; note that this is, erm, less than perfectly correlated with the actual depth/worth of those topics.

Would it be wise to take this position in the hopes that several years of faculty experience will allow me to jump to a different college/university that is a better fit for my professional goals (i.e., more research, higher caliber students, more resources for faculty)?

This strikes me as risky, if you feel those professional goals are essential to your job satisfaction within the next 4–7 years. It's also the case, at least in my discipline, that people hiring in smaller places are not keen on applicants who give the impression — rightly or wrongly — that they are using such places as stepping stones.
Moreover, it may not be as easy for you to move on to "better things" as you might hope; you'll be up against younger people applying for similar jobs, and your rate of publication — if that is important in your discipline — will almost certainly be lower than those people, especially if you start work somewhere that has fewer resources than the place you did your PhD.

Does this happen often, where someone takes a "starter" appointment and moves to a more fitting situation when it arises, somewhat like a Visiting Assistant Professor position?

I'm afraid I don't have anything more than anecdotes and a very partial sample based on my own discipline.
My own experience, which may differ from yours, is that permanent positions in my sub-discipline were hard to come by in my home country. Consequently, I ended up taking a job in North America which, although not in a liberal arts college, was not too far from the situation you describe as

somewhat of a good fit for me personally in terms of the teaching I would do and the research interests of the other faculty, but lacks the prestige and resources of larger/more research intensive schools.

At the time I did not do so with the intention of moving on, but it was always a possibility I kept in mind, and subsequently I did indeed quit that job to take up a new one elsewhere, which unexpectedly arose. So I guess you could view this story as some evidence to support your initial idea — start somewhere and then move on — but I'd also counsel caution.

This happens a lot (job hopping for professors) but it's mainly depending on your research whether you get hired.
If the infrastructure is not there for you to do decent research, you're probably only going to do a lot of teaching without getting many grants or publications that would get you a subsequent position. I think you're better of staying in a postdoc position where you have less teaching responsibility. Get a big grant and you can probably choose where you want to go.

I think this is dangerous advice.  Most postdocs just aren't going to get a big grant.  Most postdocs just aren't going to get R1 tenure track jobs, and certainly not one in their "target location."

Gosh, why did all these people leaving academia never think of just getting a big grant?

You describe the resources as likely insufficient to garner the publications that will fuel the leap up.
Academia abounds with professors who were generally useless at a top lab for a few years and then got hired lower down the food chain in the mistaken hope that some of the magic would have rubbed off on them.
There are not so many people who jumped up because they did something remarkable at a low-prestige uni. One reason is the lack of resources as noted. Another one is that the top places will not hesitate to copy your work without acknowledging you. (Yes, Kevin, this happens, and it happens a lot, and there exists no effective recourse.)
The jump "upwards" is very much easier from industry, especially if you manage to be sitting on funding or acces to (or knowledge of) some new instrument.
Another warning: low prestige places are often run by people who are - how to put this politely - mediocre prima donnas. Your experience at a much better place may inspire some unpleasantness on the part of your new colleagues.

"Another one is that the top places will not hesitate to copy your work without acknowledging you." That strikes me as an absurdly strong claim. You really feel that academic research is rampant with blatant plagiarism of entire bodies of work, and that there's no recourse for the plagiarized?


text:
Apply to math Ph.D. programs
If I were to apply to a Ph.D. program for mathematics and in the application mentioned that my intended field of study would be something in algebra, would the admissions committee be interested in a paper on graph theory that I wrote and gave a talk about at an AMS conference for undergraduate research?
I think I did some good work (for an undergraduate), but I don't know if application reviewers would value work in graph theory for somebody looking to go into an algebra related field. If it matters at all, the paper was on various forms of dominating sets.

Yes, a graph theory paper is just as relevant as an algebra paper for graduate admissions, even if you plan to specialize in algebra.  There are several reasons for this:

Many people applying to math graduate school don't really know what they will specialize in.  (This is true even for the very strongest applicants.)  It's OK not to know, and everyone realizes that you may change your mind in a year or two even if you think you know now.
Undergraduate research is generally limited by the availability of supervision.  You may not have had the opportunity to work on your favorite topic.
Undergraduate research is not intended as a head start for graduate school.  Even if you had written an algebra paper, you would almost certainly not end up building on that work for your Ph.D. thesis.  Instead, the real purposes are learning about research and demonstrating your talents, and the research topic itself is not so important.


text:
How are citations used to fairly compare researchers: fewer publications and fewer co-authors versus more publications with many co-authors
As a recently graduated researcher in the field of physics, I am in the early stages of establishing my scientific career, which involves being hired by senior researchers and professors. Much of the likelihood of being hired in a reputable group relies on my publication record and, for good or for bad, on my citation count.
I have always played a significant role in all the papers I have written or coauthored, which justified my inclusion in the list of authors. Thus I feel that I have earned the many or few citations to my papers.
In the context of a competitive mindset in which your quality is often (perhaps unfairly) measured by the number of citations your work has attracted, it has always bothered me how endless author lists from large collaborations boost the citation counts of those people. It is just a matter of common sense to assume that not all of them have significantly contributed to a paper. I am thinking especially about particle-physics collaborations, such as LHC at CERN. As an example, take this highly cited paper with a couple of hundred authors sharing authorship. The same or very similar author lists appear in several other highly cited articles. I am sure most of these are highly capable and competent researchers, some of which are leaders in their field. But did they all really contributed enough to be credited as authors? Another question: how much is enough?
How can one trust the citation count model (whether it’s fair or not) when there are these collaboration-enhanced players in the game?
How much do professors and PIs rely on citation counts in order to make a decision on hiring a researcher?

I am not into physics, but I always assumed that any hiring committee in the field is fully aware of the special practices regarding large collaborations in physics, and adjust their expectations / valueing of such papers accordingly.

@xLeitix That adds an extra perspective: long author lists may undermine the contribution of significant authors, whose names get lost among the other ones in an alphabetically ordered fashion.

A general rule of bibliometrics is that they shouldn't be used to compare people (or projects, or papers, or research projects) across different fields, because publication culture can vary wildly even within disciplines and between closely allied fields. To begin with, the size of the field - the number of papers published per year, for example - has a direct impact on how many citations each paper gets.
Your quandary is an example of this. In certain fields such as high-energy physics, astronomy, or parts of biology, a lot of the science is concentrated in very large collaborations, which produce papers with many citations and many authors. It is indeed unfair to use citation counts to compare such a CV with, say, a mathematician's, since papers there tend to have few authors and, in many specialized fields, be read by very few people indeed, even for high-quality papers.
Whether such bibliometrics are used in practice by hiring committees - well, that obviously depends on the field, the institution, and the specific people involved. If all the applicants are from similar fields then this may not be a huge problem, but the numbers need to be treated with some distance to avoid the problem you point out. If a hiring or review process places a large emphasis on citation counts (or other bibliometrics) for applicants from different fields, then that is indeed a problem.
One final thing you should keep in mind is that applicants with a high-citation-count, large-collaboration paper in their CV are likely to get asked at interview questions like

So, what was your role in this collaboration?

in any case, as part of the interview process.

"Field" should be narrowed down even more.  For example, within high energy physics, this is only typical for experimental papers, not theoretical one.  (You can spot an experimental paper in the reference list: the first author always has a name starting with "A").

In biology these papers have become extremely common. These papers are often results of high-throughput data generation projects (e.g. genome sequencing projects). Since they generate a lot of data, their data is often used thus generating many citations (this is why journals like these papers - they are impact factor boosters).
However, I think this is not such a big problem.
In many cases where it is important for people to understand your contribution, there tend to be means of doing this. For example, some funding agencies may ask you to specify verbally or numerically what your contribution to each paper was. If these are the only papers you have, the relevant people will want to know what your exact role in the project was. You will almost always be able to explain or emphasize your role in a cover letter.
When it is less important for people to understand your exact contribution, I find that people will usually give a very low weight to such papers. I guess this comes from an underlying assumption that without prior knowledge we can assume that the amount of contribution is the inverse of the number of authors (maximum entropy?). That said, in biology the first and last authors have special status and I think this is also the case in these papers.
So the main problem, I think, is not getting more recognition then you deserve but actually less if you are some author in the middle of the list. However, as I mentioned, you will usually have some other venue to explain your exact contribution. The only way I could see these papers being very useful for a CV is as an indicator that you can get collaborations and funding (these projects are typically well-funded).

At least in the example I gave from CERN, the authors are listed in alphabetical order, so not too much to make out from there. As you say there are also negative aspects for authors in these long lists. Maybe we should resort to a movie-like credit system where stars, executive producers, director, etc. are listed first in bold big letters, followed by the rest of the crew - just a joke (or is it?).

Where I work at a academic computing center, when hiring at the postdoc and research associate (junior researcher) level, I never look at citation counts. We are very different from an academic department, and citation counts aren't all that useful to us. We are looking for a certain skill set which includes good publications, but it also includes lots of other things.