text:
Why am I requested to compile my figure files with 300 DPI?
I will submit an article to Geoscientific Model Development, and the submission instructions requests that I ensure that my figure files are "compiled as *.pdf, *.ps, *.eps, *.jpg, *.png, or *.tif files with a resolution of 300 dpi" before I submit the manuscript for peer review.
What does it mean in practice that my figure files are compiled with a resolution of 300 DPI? How is this relevant when it comes to PNG and PDF files? Do the DPIs of PNG and PDF files have any effect on the final document, and is this even information that is stored in such files?
I know what DPI is and the difference between a raster and a vector image. That's not the issue.

In my experience when people say to use 300 dpi what they really mean is use a high resolution image suitable for printing and not a lower resolution suitable for web or screen.

Don't forget that PDF can contain raster data instead/as well as vector data.  So can SVG and probably EPS.  This is typically used for annotated photos

PDFs store information about the physical size of the pages they contain. You probably don't realize it because almost every PDF says "I'm 8.5x11" on every page (or whatever the standard outside-the-US size is, I guess), so you never noticed it had the capability. Once you know it has an intended physical size, DPI is a perfectly cromulent thing to talk about again.

PNG files are pixel graphics. The requirement simply says that you need to include pictures in PNG format that have a sufficiently high resolution so that they don't look "pixely". In other words, you shouldn't include a 100x100 pixel picture and display it in a figure that covers half a page. (But if that is an inset that shows up in a 5x5 mm area, then 100x100 pixels are enough because that equates to ~500 DPI.)

Thank you for your answer; I guess that makes sense. So when they write DPI, it sounds like what they probably mean is the number of pixels per inch on a printed version of the paper (which wouldn't be a property of the image itself, but rather its resolution in combination with how it is displayed in the document)?

@HelloGoodbye Yes, that's exactly how this should be interpreted. It isn't a property of the image itself, but the number of pixels and how large you make that image on the page and how large the page is.

@HelloGoodbye DPI is an abbreviation that means 'dots per inch'. It is a measure telling you how detailed or grainy/ pixely a picture will be at a certain size.

@quarague I know what DPI stands for. So if it is a measure telling you how detailed or grainy/ pixely a picture will be at a certain size, then what is that size for a PNG image?

Note that PNGs do have a method to store DPI information. Of course, in reality the DPI depends on how the image is scaled. But it's possible that the publisher is checking this information in the PNG.

@HelloGoodbye It is the same thing.  If you need 300DPI then if you are adding a 1 inch by 1 inch picture to the page the PNG file needs to be at least 300 pixels by 300 pixels to meet that requirement.  If you want a 10 inch by 10 inch image then you need  at least 3000 x 3000 pixels.

@HelloGoodbye It's not the size of the png per se. It's the size of the png on the page. FYI 300 dpi is the general standard for magazines. Newspapers generally print at 150 dpi. 600 dpi is high quality art prints such as posters or glossy art magazines.

@HelloGoodbye FWIW A4 paper is 8.3 inches wide so a full-width image is roughly 2500 pixels wide (300 x 8.3 = 2490). That's just a bit wider than a 4k image. Basically a 4k image is just under half an A4 paper at 300 dpi.

Well, the instructions are a bit old-school. They have the underlying assumption that you submit your figure files in exactly the size they are printed and they want the resolution of any pixel content of the final paper to be 300 dpi. Of course, in modern typesetting, you can embed any figure at any size. So the instructions translate to: If your figures are embedded at a reasonable size in the final paper, any pixel content should be 300 dpi.
All of this in turn hardly ever matters if you follow the other guideline, which says:

The first choice should be vector graphics

… and for vector graphics, you do not have pixel content in the first place. So, if this is about plots, diagrams, etc., just deliver them in a vector format.
Now, depending on the subject of your research, you may have photographs and similar, but here you cannot control the resolution at this point and are stuck with what you have (see: Is increasing the DPI of a low resolution image a good idea?).
The only case in which you should worry about this guideline is if you have some content that needs to be rendered to pixels and you can control the resolution of the rendering.

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DPI matters for raster graphics, which encode individual pixels. This is to be contrasted with vector graphics file which contain information about shapes. Some formats, like PDF, can contain both raster and vector graphics. PNG is purely a raster graphics format. Too low DPI for raster graphics will look bad if the journal increases the size of the figure. 300 DPI  is a common minimum guideline for raster images, but it's generally better to create vector graphics whenever possible.

But is DPI even a property of a PNG file? Isn't what actually matters the resolution of it (i.e., its width and height in pixels)?

@HelloGoodbye - you do need to be sure there are enough pixels to be able to make a reasonably sized figure out of, assuming it will be printed at around 300 DPI. It needs to be a Figure, not a desktop icon.

@JonCuster Thanks. I figured that must have been the case.

@HelloGoodbye There are two parts to the answer. 1. The DPI that actually matters for publishing just depends on the number of pixels and the size in which the image is shown in the article, which has its own physical dimensions. 2. The technical part is that DPI is often, but not always, a property of PNG files. The file format specifies the data is organized into various chunks. The so called "pHYs" chunk has three fields. The first two specify the number of pixels per unit along the X and Y axes, giving the aspect ratio.

@HelloGoodbye The third field has value 1 if the unit used is one meter, or value 0 if the unit is unknown. So if the value is 1, there is a specified DPI, and thus the size of the figure is known. If the value is 0, there is no information about the physical size in the PNG file itself. Of course, as per the first part, this physical size is likely to be overridden when you include the PNG into your document.

Thanks for your elaborate explanation! I also figured out that even if the PNG file has a physical size and DPI, those are going to be overriden anyway when I include the PNG in my document. So what does it matter if I compile my PNG images with a resolution of 300 DPI, which the instructions tells me to do?

@HelloGoodbye What do you mean "going to be overridden"? The journal needs the figures in a certain resolution; if you're using software that's scaling them less than that resolution then this is your problem that you need to solve.

@BryanKrause what I mean by overridden is that once the image is inserted into the document, it is still going to get a new size, which will give it a DPI that depends on the figure size and the resolution of the PNG file, and not of the size and DPI of the PNG file. So the size and DPI file won’t have any effect at all on the final document.

@BryanKrause That the journal needs the figures in a certain resolution it’s not what they write; they write that they need them in a certain DPI, which is independent of  the resolution.

@HelloGoodbye You should be producing the figures at the size that they will appear as well as the minimum DPI required.

Prefer vector graphics. If you plot using e.g. Matplotlib, then simply outputting a PDF file does the trick in most cases:
fig, ax = plt.subplots()
...
fig.savefig("figure.pdf")

If it must be a PNG file, ensure that it has sufficient pixels to meet the target print DPI and print width. That is, given:
# An 8 inch wide figure at 300 DPI requires 2400 pixels.
print_dpi = 300
print_width_inches = 8
print_width_pixels = print_width_inches * print_dpi

# Your PNG file.
file_dpi = ...
file_width_inches = ...
file_width_pixels = file_width_inches * file_dpi

The following condition must be satisfied:
assert file_width_pixels >= print_width_pixels

What really matters is if you have the number of pixels needed, file_width_pixels (e.g. 2400 pixels wide).
As long as you create a figure with enough pixels, you are good.
The other settings (file_dpi, file_width_inches) can be chosen arbitrarily to match this constraint, and don't really matter.
In fact, you can easily convert an image with "1 DPI" in the metadata field into a "9000 DPI" image with the following command:
exiftool -XResolution=9000 -YResolution=9000 figure.png

This command neither adds nor removes the only things that are actually important: the pixels. For comparison:

Image A
Image B

file_dpi = 1
file_dpi = 9000

file_width_pixels = 234
file_width_pixels = 234

Clearly, both images are exactly 234 pixels wide, and thus have the exact  same quality. The DPI metadata is irrelevant. Look at the number of pixels, not the supposed DPI.
Regarding Matplotlib again, you may generate the appropriately sized PNG via:
fig, ax = plt.subplots(figsize=(file_width_inches, file_height_inches), dpi=file_dpi)
...
fig.savefig("figure.png")

Again, the file_dpi does not need to be 300 in order to produce a high quality image. (e.g., file_width_inches=10000000000000, dpi=1 is a perfectly valid choice from the publisher's point of view, though your fonts will be very, very tiny, and your computer may not have enough memory for an image with higher resolution than images from the Hubble Space Telescope. :))

In some way or other, you will not only submit your images (say, a PNG graphic that is 600 pixels wide and 200 pixels high), but also layout "instructions" where and how big to put that image on a page of (ultimately) paper. If these instructions amount to "as wide as two text columns", then those text columns better be no wider than one inch each, because at 300 dpi, your 600 pixels are good only for 2 inches.
If the numbers don't match, your options are to decide that the image shall appear smaller in size (and thereby less impressive, or perhaps your important 3-by-3 pixel markings become almost invisible to the naked eye) or you try to render it at higher pixel resolution.

Technically you can rescale.
I always understood this as a hint to check if your figures are going to look as you desire if they are scaled to the planned size and rendered at 300dpi.
That could affect:

rastered graphics (which you may like to represent in an integer multiple to avoid fuzzy edges)
minimum line widths/usage of different line widths to indicate data traces in plots
filling patterns
marker types which you use in plots
potentially fonts/sizes which you use (-> font hinting)
checking if gray values can be represented appropriately by the raster (300dpi is the B/W resolution, not the resolution in which you can represent a gray value)

The other point which I always understood implied was an polite version of "please don't send a 1200x1200 resolution rasterized graphics".

Thanks for you answer, Sasha! Do you mean that I should scale them in the document so that they get a size that equals the resolution of the PNG file divided by 300 DPI (or rather 300 PPI to get the correct unit) to see what they look like? That is, if I have a 900x600 pixel PNG, I should scale it so that it has a size of 900x600 pixels / (300 pixels/inch) = 3x2 inches in the document?

This sounds like more of a question for Google than for Academia Stack Exchange, but since preparing figures for the final manuscript and typesetting is always a hassle (would love to hear from anyone who thinks it isn't), here are my two cents:

Anything that is printed will need a higher resolution than something on screen. For example, powerpoint files generally work with a screen resolution of 72 dpi because all they are meant for is to be projected. If you don't want your images to look pixelated in print, you need to ensure they have a resolution of at least 300 dpi. That's just how printing works.

Familiarize yourself with this and differences between vector and raster images - there is lots of information out there online, also through printshops etc. because the same thing holds for printing posters for conferences.

In general, what helps is to prepare your figures at the real (i.e. actual, final) print size. That way you can ensure (by doing test prints yourself) that everything is up to par while preparing the figures.

Save .png for the web, .jpg for test prints/first submissions and always make sure you have high resolution files (.eps, .tif) for final submission. There will always be loss of data when you export as .png and .jpg because there is compression and loss of data (which is why the file sizes are smaller - and we like that until the publisher informs us that the resolution of our images is too low).

Of course any of this may become obsolete in a paperless world, but this is not the reality we live in so it helps to think of eventual printing at all times and to make sure that your raw files and source material do(es) have the right resolution and/or can be exported as such.

Hi BioBrains; thanks for your reply. I think my question was a bit unclear. I know what DPI is, and the difference between raster and vector images. My confusion is that I have never seen that PNG images or PDF documents can have DPI before, so I don't understand what it means to compile a PNG image or PDF file with 300 DPI. Furthermore, even if a PNG image would have a DPI on its own, the figure in the document that uses that PNG image will still have a DPI that only depends on the figure size and the resolution of the PNG file, and will therefore be independent of the DPI of the PNG file.

Hence my question how it is relevant what DPI I compile my PNG images with.


text:
How to ask what my salary will be for German PhD position
I am a biomedical engineer; I was offered a PhD position in Germany. They already prepared my contract but never mentioned my salary. I was wondering if there is a polite way to ask how much will be my salary.

So you were ready to sign without even knowing how much (or even IF) you were going to be paid? You are not asking whether you will make 50000€ or 65000€ a month! You are (basically) asking how much you can spend for a flat, for food, for the university... and everyone will understand that. I understand, because I am a really shy person, but you need to know.

@SteffX I don't know where you are from, but in job postings in the Netherlands (which is not Germany), this is not always listed, and especially for PhD positions it is a bit useless, because all first year PhD student will earn the same amount, there is a collective agreement.

People surprise me every day. How can you even consider any position without knowing what will you get in return? Phd is not a slavery (also in a lot of cases it is similar if to compare with a reasonable job), so you should start it only if it is beneficial for you.

I still did not sign any contract , obviously ! :) My supervisor was not able tell me how much my salary will be during the interview. That was the "problem " . Anyway I contacted the HR and they , hopefully , will give me some advice. Thank you again.

@Nick: If that's a university position, be aware that HR might not have the slightest idea - funding (i.e. the discovery and combination of funding sources) is something managed by individual institutes or groups thereof, and if the professor doesn't know yet, chances are that is because he or she is not yet sure about whether some upcoming funding opportunities will actually be available.

They will not tell you a number in Euro, as the person tasked with the hiring decision probably doesn't know the amount either. You need to ask for the paygrade and then use the calculator on this page to find the amount.
It is a very easy conversation, because neither you nor your supervisor can do anything about the payment. No negotiation, no decision making, nothing involved. You cannot say anything wrong (as in, reduce your chances for a good salary).
How to calculate the amount from the paygrade
First, you need to know which tarif applies to you. On the page I linked, choose "TVL West" if you will work in a state of former West Germany, and "TVL Ost" if you are in former East Germany. You will see a blue calculator form in the upper right corner.
The paygrade information your boss tells you will be of the form "E13, 50%". It may have been printed on the job ad, but not always.
E13 is the "level" determined by the job's difficulty, and is fixed for the position. The rules here are so firm, I don't know if it is even possible to have a PhD research job ("wissenschaftlicher Mitarbeiter") at any other level than E13.
The percentage is the worktime percentage paid. It is up to the institution to decide if they are going to pay you full time, half time, or some other fraction. Paid full time is practically unheard of outside of computer science departments, biomedical should be 50% or some more. The actual time your boss expects you to be present in the office may be different from what your contract says, and it is also up to your supervisor and work to know if you will need to put in some unpaid extra hours outside of fulltime work hours. If you have a cell culture which needs to be fed every day, you might find yourself coming back to the lab on weekends too. But the money you get depends on the number in the contract, and it is simply calculated as a proportion of the money for the full time.
The calculator also asks you for a Stufe. It refers to a pay "raise" based on years of work experience. If you are a freshly minted M. Sc., you start at "1", get upgraded to "2" after one year in 1, then to 3 after two years in 2, etc, up to 6. If you have worked somewhere else before and gained relevant experience (HR judges what is relevant), you can get into a higher group, but not higher than 3 when you are changing institutions, or when you are changing the E level within the same institution.
If you are single, your "Steuerklasse" (tax classification) is I. If you are married, try "IV" for the first calculation. If you are in a civil union with a same sex partner, that also counts, but I don't know how difficult it is to get a civil union or same sex marriage from another country recognized in Germany.
You will get some extra money if you have small children, but I don't know which ages count for how much.
If you are either Catholic or Protestant, you will also pay a church tax. For this, choose the link behind "Kirchensteuer" and enter the state in which you will be working.
Leave everything else at default for the calculation, and press "berechnen".
For example: in my institution, a biology PhD always gets a 50% E13 contract, and assuming he is single, atheist and has no previous work experience, this translates to 1758.68 €, of which he takes home 1208.12 €.
Beside the Stufe for years worked, your salary will rise a little bit every year, as the whole paygrade is adjusted in a negotiation on the highest level between the provinces' governments and the trade unions. You cannot get a pay raise by negotiating with your boss, nor could he give you one if he wanted. The exception would be giving you a few more percent worktime, if you are not already at 100% and if your institution's policy allows it. This is rare and will likely require you to take on one more project beside your PhD work.
An additional payment in December is customary, but it is not as high as a regular monthly salary.

It is incorrect to say that nothing can be done. If the candidate has previous work experience, or unique skills that are not generally available, a case can be made for increasing the "step" in the TV-L 13 scale. However, doing so comes at the cost of not getting salary step increases until the level is "naturally" reached. As an example, someone starting at TV-L 13 Step 3 would have to wait six years instead of three to bump up to TV-L 13 Step 4.

@aeismail This is not what I have experienced. First, the "step" in the cases I have seen is determined by HR, I have seen supervisors or Betriebsraete trying to influence it and failing. Second, the "waiting" for advance cannot be a firm rule, or maybe it has changed in the past. I personally started a job at 3 (taking my step from my old job) and advanced after 3 years. No negotiations involved, that's how HR and the accountants did it - and they never give more money if they are allowed to give less.

@aeismail I'm TV-L 13 and I get regular adjustments. Maybe these are not the big "scale" jumps, but simply inflation-related adjustments.

@Federico this is something very different from raises you can get (or not get) by talking to your advisor.

@rumtscho yes, I agree. I simply wanted to say that the salary is not completely "frozen" for 6 years, contrary to what I understood by reading aeismail's comment.

@Federico: You get annual cost-of-living adjustments, but not a jump in the "Step," which is a bit larger.

@Roland: If you have industrial research experience (particularly if you're coming from overseas, this is possible), then you can qualify to move upwards.

Just ask, there's nothing wrong about it. From what I know, PhD students in Germany are paid some fraction of the 'TV-L 13' bracket, about 50-70%.

100% correspond to 3438€ / month. See link (german).

I'd rather calculate with 50 % (almost all PhD students I know have 50 or 60 % contracts). That corresponds to 1200 - 1450 € net (= after compulsory social insurance and income tax are subtracted) per month.

1150€ is what I was told.

@cbeleites That also depends largely on the major. In Computer Science it's not unheard to get 80% as well. That's probably one of the best majors to be in though from a monetary point of view. In general I'd be surprised if that wasn't mentioned anywhere in the job offer though.

Engineers normally get 100% of the TV-L.

@aeismail♦ Really? Where? From what I've heard, in Kiel, electrical engineers get regularly 60% of TV-L.

Anecdotal: when I was a PhD 100% was the norm (computer science) for students directly employed by the university. Of course there were also different agreements in place (stipends, industry or private funding etc.). So a PhD's salary can depend on many factors. But some fraction of E13 is a good estimate. For what it's worth, I've heard of PhD students "sharing" a 50% E13 position, so essentially receiving a 25% E13 salary...

@AndrejaKo: E.g. University of Stuttgart, depending on the institute. I am an aerospace engineer and was offered a 100% position for a PhD (aircraft systems development), two friends of mine (mechanical engineering) have 100% PhD position (research on gearboxes).

True some get more, but: AFAIK, 60% is the current DFG recommendation. And personally, I prefer calculating with lower wage and then getting more rather than the other way round. (50 - 60% is what I know mostly for biologists, chemists, physicists and optical engineers for projects that are typically related to medical diagnostics).

@cbeleites: Engineers get more so that the offer can be moderately competitive with what they can make on the regular job market. The economics are different for students in the sciences.

@aeismail: I know - though I believe that would be better described as "do make on the regular job market": chemists have very good entry wages as well, but 90 % go for a PhD and do not even consider looking for a job directly whereas PhD is more rare for the engineers I know. However, a "biomedical engineer" may very well end up doing their PhD in a "part-time wage" institute. Anyways, let's not get too far off-topic: the usual range is well covered in answer and comments.

Just ask, using polite words. You're over-thinking this.

Some people have social anxiety or Autism Spectrum disorder, and to them it can be difficult to visualize people's reactions. They may be afraid of their polite words being nevertheless perceived as greedy or rude due to social norms they cannot see. It is likely that OP is not in Germany, and therefore not a native speaker of German (if he were, he would not have mentioned Germany), so there may be some language issues as well. I don't think you're being fair to him.

@EMBLEM You're over thinking it.

@EMBLEM Those are fair points. However, the asker doesn't mention language being an issue so it seems reasonable to assume that either they speak good enough German or they're happy to ask the question in English (German scientists speak impeccable English). Any reasonable boss will understand that somebody from another culture might make cultural mistakes and will see that the request was well-intended, even if it was accidentally rude. Also, knowing what your salary is going to be is a fundamental part of accepting any position: it would be crazy to object to somebody asking about that.

Congrats on the position!
I'm not sure how it works in Germany (and perhaps I am wrong since other answers talk about "grades"), but in the UK we often call it "funding" rather than "salary" since the money typically comes from research councils rather than the university itself. Some PhD positions come with funding, some do not, so for me it is completely acceptable to ask.
As a PhD student myself, I would feel much more comfortable asking "will there be any funding available for this position?" rather than "what's my salary?". Hope this helps.

This is not a valid answer in the context of Germany. PhD students are employees, and thus receive a salary.

I think this is a good answer for a different question. For what it's worth, in Germany, it is called "salary", and it does typically come from the university (or rather, from a state agency that pays salaries to all civil servants, including university employees on behalf of the university). It is possible that the money originally comes from some research funding (as in, funding for a specific grant), but it is also possible the money comes from the regular budget of the institute.

Ah, fair enough!

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text:
Is combinatorial novelty without insight useful? Who cares if we're the first to use tool T on problem P?
I'm a bioinformatician and former student of applied math. I want help to see if I should change my view).
Many academics, including all of my PI's on my major projects, justify their work by saying "We're the first ones to apply fashionable technique T on problem P". This is in situations where P is often well-studied and T was developed and established by other groups. I call it "combinatorial novelty" in the title because the novelty is not in new tools, nor new insights, but rather in new combinations. The justification is essentially "We're early adopters."
This would be fine if the studies produced valuable new insight about P. P is important and I'd be proud to make progress on it whether or not I'm using fancy new techniques like T. But usually, our progress on P is weak despite using T, so we need to turn to T's fanciness to justify our work. I see people using this "combinatorial novelty" to make their work seem like a big deal.
This seems flimsy, but if every PI I've worked under is doing it, then either it impresses grant reviewers, or it actually is valuable to science and I just don't understand why. Or both. Is this valuable to science? If so, why?

This feels to me more like a rant than a real question (though I think the answers are helpful to you), and I think it's also using a bit of a straw man argument. I often read papers that tout what you term 'combinatorial novelty' of their approach. I rarely, if ever, see papers where that is the primary or sole justification published in high-impact journals.

if you use the "CMV" tag, then perhaps you may want to replace "good point" and "+1" with "Δ" :P

@BryanKrause It feels like a rant to me too, but the answerers seem to have real answers. You comment is informative to me as well: "I rarely, if ever, see papers where that is the primary or sole justification published in high-impact journals." So would you say it's typically not enough except as a means to an end?

@burneraccount Not sure what you mean as a 'means to an end' but my personal opinion is that all science that is done should be publishable, regardless of impact. I felt like your question implied that you think people are using these justifications to make their work seem like a 'big deal' when they may be simply explaining what they did. Negative/uninteresting findings can still be very important, as the answers have noted.

Not enough for an answer, but I would imagine that if your team thought T might work on P, and decided to spend time and money finding that out, some other team might feel the same way in the future. If you publish your results, the other team(s) may decide to spend their time on money on different avenues of science, instead of carrying out the same experiment.

@BryanKrause When you say "I felt like your question implied that you think people are using these justifications to make their work seem like a 'big deal'", that hits the nail on the head. That is exactly what I see people doing. I will edit my rant to make this clear.

@burneraccount I think that's where you are wrong, then. Those parts of the papers aren't saying it's a big deal, they are just saying why the work was done. If I was going to tell you how I spent this morning, and I told you I took a shower and put on pants, and that I did those things to not smell bad and to have warm legs, I would be saying that to tell you what I did, not to claim it's a big deal. Incremental work is incremental work.

Is there some probability ex ante that applying T to P could generate new insights?

@henning In many cases that motivated this question, yes. In others, no.

Hmm, there is some nominal research value. If you publish results saying "This combination doesn't produce anything novel or unexpected", then you may be saving dozens, or even hundreds of other researchers the time to do it themselves. And that time they saved could possibly be used in other areas of research that are unexplored. Think of it as narrowing down many paths to fewer, increasing the odds that someone else might take the right path.

I suggest removing the words "without insight" from the question title.

Is this valuable to science? If so, why?

Because it leads us to understand if tool T works on problem B. How big the "insight" that we gain from this is depends a lot on how different T is to other tools that have already been used on B, or, conversely, how different B is from other problems that T has been applied to.
The range here goes from "it is mind-blowing that T could work on B" all the way to "meh, everybody knew that T would work because we use it all the time for B' anyway" - although I will grant that most works following this schema in practice end up more on the rather incremental side of things.

Good point. I think in my situation, the takeaway is usually "Too bad T didn't tell us as much about P as we hoped." But the paper is usually still pitched as "Look how cool T is!" So there is a disconnect between the rhetoric about T and the true value as seen by this question. Thanks for helping me think this through.

@CJ59, then maybe the tattoo should say something different.

@burneraccount are you saying that there's a disconnect between the motto of science and the way the motto should be presented to people?

No, I was just a joking.

I think this is difficult to answer in general. While it is probably not very valuable to apply random technique A to random problem B, it is also useful to find new ways to explore existing problems. Sometimes this "insight" is just that a tool from some other domain might be used to generate additional insight. For example, finding a new way to prove an old theorem in mathematics is often (not always) valuable as the new proof may, itself, offer insights.
So yes, valuable. So no, not so valuable. But it depends. It would be valuable if it helps other researchers get more insight into a field in general, not just into the problem at hand. But throwing stuff at the wall to see what sticks is just throwing stuff at the wall.

+1 For "It would be valuable if it helps other researchers get more insight into a field in general, not just into the problem at hand."

I’m more familiar with the final adage in the form “Throwing stuff at the wall to see what sticks is just feeding the paint industry”. :-þ

This would be fine if the studies produced valuable new insight about P

New insight isn't the only important thing in research. Many types of research have resource limits, including manpower, equipment costs, computing resources, etc. If applying technique T to problem P reduces resource requirements, despite giving the exact same results, that can have profound impacts on future research, whether it's being able to increase sample sizes, free up money for other equipment, or being able to do things at a larger scale or finer granularity. This in turn also makes it more feasible for smaller research groups to start tackling the same problem when they couldn't before because of limited resources.

I think your gut impression is likely correct.  If P and T are relatively well known than there is little value (other than for a new student in his learning) in applying one to the other.  For instance, using sophisticated crystallography software to solve a crystal structure that was already done correctly using direct methods.
I am very much a fan of datapoint science.  But ideally, you can do something new (make a new compound, find a boiling point, do a correlation of response to a medical treatment, etc.)  In other words I am fine with "stamp collecting".  But T on P sounds a bit weak.  There are so many cool things to look at, you would think these guys could get a little more novelty (even moderate things).
I'm even sympathetic to some "negative" results.  But it sounds like these guys are milking things.  And then the gushy wording...but don't get me started on hype scientists.
I think you have the right instinct.  I would just try to figure out something a little bit more interesting.  Doesn't need to be discovering gravity.  But in your own work, do a little more.

The wording you use is "Combinatorial Novelty". Interesting choice of words.
First, I guess you admit that there is indeed novelty to a work of such kind. And novelty is an important part of academic work. In order for knowledge to progress, new things need to be tried and invented. In academic medium, you also need to either make clear what your innovations are and if you aren't innovating you better cite the original source.
I myself look down on anyone who claims that he/she/his group/his company is "the first to...", since in the modern world you require lots of concessions and particularities before indeed you can make such claim. Example : "Our company is the first one to apply this research with only partial public funding into a product released in this region with academic and government advisers". If I hear this, I'm pretty sure someone else already did the thing, in slightly but not meaningfully different conditions. I'm also guessing whoever says this stuff is trying to trick a reporter into writing "this company is the first to develop a product with this technology".
Note that your combinatorial novelty is usually far from the scenario above. Also that unlike reporters and marketing departments, researchers usually report much more honestly and are reviewed with more care.
Of course, there is much less glory to applying a known technique from other fields into a new context than there is to developing a completely knew technique. Then again, what is more important? Glory and effort or results? If the technique you've borrowed heavily improves the state of the art in the field you work, how would you have found this out if not by trying to apply it? And since you've gone through the effort of learning, implementing and applying a new technique, then publish the damn results!
In mathematics it happens that a same technique is rediscovered over and over again by researchers in different fields because said researchers really needed these techniques in the fields they were working (Least Squares method is the example that comes to mind), usually this is restricted to simpler techniques, but imagine a method that would require thousands of lines of code to implement, do you really believe it becomes something so specific it has no further use? Do you think it would be efficient for everyone to develop their own brand of this procedure just to have authorship?
There are indeed cases when you might think in advance that a certain technique will surely not improve anything, but likewise, you sometimes have an idea that you think will be a great advancement, but you discover not to be an improvement at all. When that happens, you probably recognize that it was a worthy attempt. But if you were using a technique from other fields you might trick your self into believing that you knew the idea was bad all along.
Also, remember that people in academic careers don't always find new exciting results every day, maybe not every year, maybe not every decade. Yet, masters and PhDs and post docs need to be completed in their respective time constraints. It's good that we have methods that reliably generate novelty in a limited time frame. It's also good that something can be done to advance an academic career that could be otherwise halted. It's "better than nothing".
It is also important to compare between methods, such that a reference methodology can emerge in certain fields. Sometimes it is the "state of the art technique", sometime its just the "sanity check" one. For portfolio management theory, Markowitz model can be used as a broad reference for comparing against improvements, while constant ratio portfolio usually provides a good comparison. For a research team to develop these kinds of references it is also important to address the same problems with different tools and cultivate a tradition over it.
However, no research team should restrict itself to doing only that. And after the simple combinatoric is done and a novelty is published, some effort should be dedicated to tweak methods, adapt techniques such that some new meaningful advancement is at least consciously attempted, rather than stumbled upon by luck.

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text:
Importance of professional website to search committees
I am hitting the academic job market this fall and was advised to have a professional website, but I am completely lost in putting this advice into action.  I now am sitting here thinking about making a professional website in preparation of the job market. I am uncomfortable with this kind of self-promotion but, on the other hand, I do not want to ignore a low hanging piece of fruit because I don't like its shape.
My questions are:

Do professional websites enhance the application for potentials?
How much does the professional website matter at this stage in my career?
Is it worth time writing up my own webpage or is sticking to prefabs  like weebly good enough?

Background: My field is in the social sciences, though my area of research is computational within it.  My publication record upon finishing my postdoc will be strong. I am exceptionally uncomfortable with the idea of self-promotion or even engaging in social media conversations.
Any advice would be appreciated. My hope is that my research productivity will stand on its own and I will not have to create a professional website, but I hope you can advise me on how important it is.

A complete ORCID profile could matter more than a website.

As a social scientist I find it really irritating when people don’t have up to date websites. I don’t like relying on ResearchGate and similar...

For the record, I think social media presence and website presence are separate issues and should be separate questions. I see most of the answers are ignoring the social media side of your question.

Seconding @Dawn's comment, a professional web-site is an entirely different thing than social media stuff. Two wildly different images/facades, at least in this year...

The question How can I build reputation online as a programmer when my work is private? and its answers on The Workplace may be of interest -- addresses some aspects of whether an online presence is necessary (albeit for a different discipline).

OT, but of use for the OP: I am right now creating a place for Academic Researchers to easily create a website. It specifically addresses most of the concerns that I've seen on this page, and I'll update it with additional things that I see here. If the OP or anybody else from Academia.SE is interested, I can be contacted here.

Never mind that website, if you’re uncomfortable with self-promotion you’ll have a bad time on the academic job market.

There's very little self-promotion implied in a professional website, especially a spartan one. It's just a good way to unify your work output. For example, Google Scholar doesn't describe your research interests or list your current affiliation.

I look for and use the websites of job candidates all the time, and I know many of my colleagues do as well. The reason is that we can find things there that are not on your CV, for example:

Links to the PDF versions of your papers, if you put them there.
Links to pages that provide teaching materials for your students.
Numerous other things that belong on a website but can't be obtained through the CV itself.

So yes, do create a website. As @aeismail writes, take a look at the websites of your colleagues in the field, pick the ones you like best, then imitate that style.
What system you use -- handwritten HTML or a content management system -- is secondary to the content you put online.

I am a social scientist with a website and I definitely got hits ahead of interviews and especially ahead of campus visits. I also used it to keep copies of working paper and as a repository for replication data and code materials etc.

If you are somewhat technical, you can generate static HTML from Markdown. I do.

If the website is hosted on a university site (in the US), there may be constraints on the design as a result of the ADA. I know my university doesn't let arbitrary sites be posted without the use of their CMS.

@aeismail Which is a shame, because it presupposes that we as professors are incapable of generating §508/ADA-compliant websites, when it's actually not that hard (in fact, old-school webpages are more likely to be compliant than anything new-fangled).   I can do custom HTML at my university, but I have to jump through a lot of hoops and it still loves to still add a little link to jump into the CMS (which I combat with a little JS to kill that div haha)

True, but there’s a secondary reason for the CMS—consistency in branding. If people want to host their own personal website, though, there’s no reason they can’t do that. There’s also the fact that we as faculty tend to forget about mobile access, when that is part of the design and now how the majority of web access actually takes place.

+1 This answer matches how I use the web when  I need information about someone, whether for hiring or (for example) to prove to the higher administration that the person is a genuine expert whose letters of recommendation should be trusted. I prefer simple web sites where it's easy to find information. Make sure your site isn't a sloppy mess, and make sure it doesn't have so many fancy "features" that people can't find things.

The improvement in your chances is marginal but nonzero.
The reason for this is that the first thing people will do is read through your application package. If they can’t find a reason to advance your application further, they’re not going to bother checking out your website.
But if they do find something and want to learn more, having something available will be of some benefit.
It’s not hard to create a simple, functional website these days. Something that looks sufficiently “professional” for your discipline certainly cannot hurt your chances of getting further in the process. However, there should definitely be “meat” on the bones of the website—enough details to find your CV, your publications and other documents of interest in whatever discipline you’re working in.
But the standards are quite discipline-dependent. I’ve seen many math websites that are very simple text-driven pages, while others in the STEM disciplines might be more elaborate. What is needed in art may vary from history. So you should take your cue for the level of aesthetic needed from other recent successful applicants in your discipline.

Yes, mimic the standards of recently successful job market candidates with similar fields as you. Don’t spend extra time making it stand out, and consider just using the standard template for your university if available.  You can signal your computational chops by linking to your Git depositories or similar rather than by coding a website. The site should be a useful spot to have your info in one place.

I've been on hiring committees for both full time/tenure track and adjuncts as well as administrative/professional positions (CISO, server admins, etc)  at a community (no longer legally, we offer a few 4 year degrees) college.
I've never looked at social media of any type regarding a candidate, I don't recall any of my coworkers on the committees mentioning it, etc.  Only time I've even looked at websites has been when hiring a front end web developer, and that was more "samples of relevant work portfolio" than "what info can we find out about these candidates".
What "we" have looked at when hiring instructors is transcripts, "real world" work experience (for relevant fields like the health sciences and programming/networking/server admin AS degree tracks), teaching experience, experience with lesson planning and evaluation methods, etc.  Most departments ask potentials to give a demo lesson on some subject to evaluate teaching style, etc.  We have the usual questions vetted by HR about your greatest successes and failures, what type of cloth you would be covered in if you were a couch, etc. and for teaching staff there is always the one included about your teaching philosophy and what you do to promote learning, etc.
If you have time, work on related-to-teaching-in-general skills, like using a learning management system (I recommend Canvas) and other content creation/management tools.  Would give you a slight boost on any committee I (or probably any support staff) would be on and will let you hit the ground if not running then at least knowing you are in a race if you get hired at the last minute for Fall (the college I work at is madly hiring right now for both full time and adjunct, classes start Aug 22)

As others have stated more eloquently, a web presence will not make or break your application, but it can definitely help convey additional information, which could be an advantage, since people generally like what they (think they) know, and it helps anyone interested to know more about you.  As most of today's search committees probably don't have a majority of millennials, a prominent presence on Twitter (and likely most other primarily social media) is unlikely to help you there, so I would stick to some form of web page. (On the other hand, if you are later thinking about attracting students/postdocs, popular social media can improve the visibility of your research.)
Consider also the longer view – a website can enhance your reputation among the wider community if you put useful content on it, which could come in handy when it come to promotion or the next search.  You are in a computational field, so perhaps you have developed code that is worth sharing with others.  You could put that on a web site dedicated to your research, or (for more complex code) on a specialized place like GitHub, GitLab, Bitbucket, etc. (and link there from your web site).  Think about where you would look for information about your research, and consider if there are ways to contribute to those places.

My hope is that my research productivity will stand on its own and I will not have to engage in social media self-promotion.

The most important thing to keep in mind is that you are not promoting yourself, but your research!  Show that you are excited about it, and share what you have found out – both through your published papers and via less formal means, which also allow you to showcase research in progress, preliminary results, and ideas you are pursuing.  I would consider it in fact import to stick to your research, because too much focus on your good looks or personal rants will not help you to be taken seriously.
You may already be disseminating your working papers through sites such SSRN or arXiv.org (if not, consider it, if this is done at all in your field).  A website gives you the opportunity to point to all those sources from one place.
How you create the web site (prefab or DIY) is secondary to the content - something very basic is preferable to nothing at all.  Keep in mind that a site full of poor spelling and grammar that has not updated in 5+ years will do the opposite of what you want, so it is preferable to have something you can set up and maintain in minutes.  For a few dollars you can get your own domain name, but if something smacks of vanity (JohnDoe.com), it may earn as much ridicule as admiration.  Remember, if it is not important to the people you care about (how seriously would you take someone who judges people by the layout of their website or their domain name?), don't bother.

+1 for promoting your research. There is a ton of work out there, yours needs to be easy to find and appreciate

While the answer of aeismail is fine, let me add the following. If you are seeking a teaching position, and you have a web site that is especially useful to students of the subject, not just your own students, then it might be worth having. But if it is mostly just repetitious of what you have in your CV and supplied materials, then it will have little value at all.
Likewise if the positions are mostly research oriented then a web site that would be especially useful to budding researchers (future MS candidates, say) might also be useful to have.
And of course, once you are employed either or both of these are worth developing. I was once (no longer) the top google hit on my (not uncommon) family name, but this was in google's earlier days. I had an extensive and useful web site that was bookmarked by a lot of (mostly) teaching professors and students.

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Is a research thesis (report) with zero contribution to human knowledge acceptable?
An alternate title could be "Do we award a PhD for what the student became or for what he produced?". More precisely, if a research thesis report does not advance human knowledge, yet still shows that the student acquired the right skills of investigation, should the title of Philosophy Doctor be awarded?

I kind of always assumed that a thesis (PhD thesis in particular) should have some positive results (where positive means "advancing the state of human knowledge"), and that part of the art of finding a good topic of research was the art of asking the right kind of question, which would yield some positive result independently of the answer. Similar views are expressed in this other stackexchange question and the corresponding answers:

It is an expectation that the PhD would make an original contribution and/or advance knowledge in a given field. I understand this is a universal assumption for this level of study across all universities. (...) usually a PhD is measured on its contribution to expand knowledge.

Nevertheless, Justin Zobel defends convincingly the opposite view in "Writing for Computer Science", p.154 of the Second Edition:

even if good results are not achieved, the thesis should pass if you have shown the ability to undertake high-quality research. (...)
  A thesis with negative results can, if appropriately written, demonstrate the ability of the candidate just as well a a thesis with positive results. (...)
  it is you, not the research, that is the primary object of scrutiny

Is there an agreement across disciplines about this question?
I am not directly concerned (I reported positive results in my PhD thesis long ago, have many positive results to report in my "Habilitation" thesis, and I certainly aim for my students to report positive results in theirs), but I am curious about the real objective of the thesis:
- as an advisor, I could suggest a more risky topic if it had the potential to teach more to the student without risking the whole graduating thing; and
- as a referee or member of an evaluation committee, I have to judge students and/or their thesis...

Extreme Fictional Example
A student and advisor do the entire research work following the most rigorous scientific process for several years, only to find their efforts ruined near the end of the process either by a budget cut, the disappearance of the species they were studying, or the discovery that the problem is the consequence of an obscure results from year ago in another research community.
The student has followed and learned the scientific process, but did not contribute to human knowledge (apart from maybe improving the index of its bibliography).  If the student has showed the qualities required from a good researcher, should(n't) he/she be awarded the title of "Doctor in Philosophy", independently of the contribution made to human knowledge?
This is truly a rhetorical question, and I doubt this kind of situation happens often. Yet the idea is new to me and I kind of like it, albeit I doubt the whole community would agree...

Opposite Extreme Example
Imagine that a student, stroke by luck, makes an amazing scientific discovery which deeply impact human knowledge, and can be understood by all even though the student  poorly redacts it. It seems clear to me that the society would not benefit from awarding a PhD to such a student, who has not learned how to do research even though contributing to human knowledge.
On the other hand, setting two conditions for the awarding of a PhD, having learned how to do proper research AND having advanced human knowledge by using it, introduces trade-offs and compromises (which again do not serve society).

Do you mean only negative results? As in "this doesn't work"?

@Zimmerman: Yes. But it might also mean "I tried this and I failed."

My dissertation was a world class, high precision, extension of previous work into new territory null result. This kind of thing is not sexy, but they will still call you "doctor" after your defense.

Well, there's a difference in negative results if they can be proven or if they're result of empirical evidence (absence of evidence is not evidence of absence). I don't know which field you are referring to, but a negative result in mathematics is different from a negative result in, say, epidemiology.

Sorry for the misunderstanding about "positive/negative": it is supposed to relate to the improvement to human knowledge. I rephrased accordingly.

With the revised title, and in particular for the fictional example, the answer is clearly NO.  Zobel is simply wrong.  (Negative results also advance human knowledge.  No results at all do not.)

@JeffE: I used "positive" and "negative" in the sense used by Zobel, i.e. "advancing human knowledge" or not. Then the question boils down to: "Do we award a PhD for what the student became or for what he produced?"

@JeffE Did your "the answer is clearly NO" mean that we award a PhD for what the student produced rather than what he showed he could achieve?

@Jeremy: I wouldn't give a "Top Chef" award to someone simply for "showing the qualities" of being a good cook -- some actual output (tasty food) would be expected! Similarly, I can't imagine awarding a PhD simply for "showing the qualities" of a good researcher -- some actual research output would be expected.

In my view, if a negative result (failure) in a risky project is still acceptable for publication/thesis, then that project is not risky.

For what he produced.  (The only way to show what you can achieve is to actually achieve it.)

A PhD title (to some extent, and whether it should or not) acts as a gateway to the higher echelons of academia. Someone who has spent five years researching as part of a PhD but didn't get the title in the end might be a good candidate for a research assistant position, as he has research experience but I wouldn't expect him to start leading a research team and set a research agenda for the next decade until he's shown he can deliver. A PhD title is one such indicator, flawed as it may be for the types of extreme examples you cite above.

Disappearance of a studied species doen't influence the advance of knowledge they got from studying (mammoths or dinosaurs or even the very dead Ötzi are perfectly valid subjects for scientific studies). As for the budget cut, my PhD supervisor would have said that money in itself is not a scientific argument, and I agree to the extent that a budget cut doesn't invalidate the work that has been done so far. "the problem is the consequence of an obscure results from year ago in another research community" is a bit more tricky. Translation of results form another research community plus the...

... experimental demonstration that this translation did (or even did not!) work are valid advances. Finding that some has published exactly the same does hurt the thesis, though. Although I'd usually expect maybe strong similarities but the chance that it really was exactly the same is minute. I had a somewhat similar situation in my PhD thesis: a certain problem to be solved and I couldn't find anything in the literature. I asked around at conferences, but the answer was invariably that the solution would be important and please would I notify them once I have it. After developing a solution

... this solution suggested further search terms which did turn up literature in another field where the whole thing was discussed under a terminology that was totally different from my field's terminology. In addition, it turned out that the other field had settled to a default in some decisions that need to be taken in the solution that were not appropriate for my task, so I did contribute a) a critique of the existing research and b) a solution for defaults that are appropriate in my field. In terms of my solution-finding abilities, I know that I'm able on my own to find such solutions...

... since I did so. But in terms of the thesis or novelty for a publication there's no way for me to prove that I did it on my own rather than knowing the existing literature. So that part could not be considered as demonstration of my ability. Which is maybe tough luck, but also IMHO not something that should be entirely unexpected.

The question as currently asked is: Is a research thesis (report) with zero contribution to human knowledge acceptable?
And the answer to that question is no.
A thesis or portfolio submitted for a PhD or higher doctorate must make a novel contribution to human knowledge. It must also demonstrate that the applicant has acquired the appropriate level of research skills.
Some negative results do advance human knowledge. So a thesis with negative results and no positive results may make a novel contribution to human knowledge. e.g. demonstration of absence of an effect is a negative result, but can be a distinct and significant contribution to new knowledge (particularly if the effect was previously believed to exist).
However, just spending the time, putting in the effort, and churning out the right quantity of work, is not in and of itself sufficient.
Basis for this
This is based on a combination of my employer's guidelines, my experiences as a PhD supervisor, and advice from my colleagues. I hear that there are other (less well-respected) institutions that award doctorates just for putting in the effort and churning out the right quantity of work, regardless of novelty of contribution, or of demonstration of research skill
A quote from some official guidelines.
Here's a quote from the relevant part of the academic regulations for PhD examinations from UCL, University College London (pdf):

A thesis for the awards of EngD or PhD degree shall be examined in accordance with
  the criteria prescribed by UCL and the thesis shall demonstrate that it: ...
shows a student's capacity to pursue original research in the field of study
  based on a good understanding of the research techniques and concepts
  appropriate to the discipline; ...
represents a distinct and significant contribution to the subject, whether through
  the discovery of new knowledge, the connection of previously unrelated facts,
  the development of new theory, or the revision of older views;

Do you have references to any official policy expressing that, or is it just your opinion?

@Jeremy Thanks for the request for clarification. I've edited the answer accordingly.

I'd actually say that many negative results advance knowledge as long as the approach was sensible to attempt (that's where the judgment of the candiadate's ability comes in).

I assume that by negative results, you mean non-significant results.
"It is an expectation that the PhD would make an original contribution and/or advance knowledge in a given field." Yes, this is true. And "X doesn't work" is a contribution to the field.
(To use an example from my area). Health practitioners are constantly dreaming up things that might work to treat various ailments (illnessess), and using them. The job of health researchers is to find out which ones work - and most of them don't work.
We used to joke that our role as health care researchers was to say no. "Nope. That doesn't work. Don't do it. No, that one's not effective either. No, don't use that. No. No. No."
Pressure for positive results just means you tweak models and data until you find them - using 'researcher degrees of freedom' (see http://pss.sagepub.com/content/22/11/1359). Your results are therefore significant, but worthless.
My PhD thesis was trying to demonstrate the nature of the relationship between stress and psoriasis symptoms (many people say "stress worsens psoriasis" - it's taken as a given truth, but it's never been empirically demonstrated). I was trying to answer things like what kind of stress, how long does it take, does it differ between people? I never found any evidence that stress did worsen psoriasis. Nor that psoriasis worsened stress (or any other psychological symptom).
A PhD is an educational process. One should demonstrate that one has learned. The most important thing about a PhD is showing what you know, what you have learned, and what you understand. If anyone gets to the end of a PhD and says "Well, those results were all positive, just as I expected", they've learned little. At the end of your PhD (or any research project) you should want to start again, and this time do it properly.

Actually, a really non-significant result would be to do the entire research work (e.g. about psoriasis and stress) following the most rigorous scientific process, only to find at the end of the process that the result is a consequence of an obscure Russian (let's say) from the 50s. The student has followed and learned the scientific process, but did not contribute to human knowledge (apart from improving the index of its bibliography). Does he/she deserves a PhD for his learning? This idea is new to me but I kind of like it, albeit I doubt the whole community would agree.

@Jeremy Perhaps the point is that a PhD is supposed to have the wisdom to first ascertain whether he is researching a mere consequence of a Russian from the 50s before putting in an enormous amount of work.

@Superbest By obscure, I meant non-translated in English. A lot of results were obtained in parallel during the cold war, and some were obtained separaterly but "rediscovered" only later.

@Jeremy in that particular example the scientific advancement would certainly be weakend, but it probably wouldn't be zero (it's unlikely that the same set of stressors was tested, medical problems may differ between different populations, so even not finding any difference to back then would be some advancement). Also, as more replication is crucially needed in the life sciences, even "only" replicating the Russion results would IMHO be an advancement. Though (again IMHO) it would not be sufficient on its own for the thesis: the problem is that the candidate then cannot demonstrate they...

... designed the study on their own rather than just using the Russian design. And theses usually require novelty as that makes finding out what the candidate's work is (for which they are graded) is unambiguous/much easier.

This is a tough question. Just to abstain from the discussion of different standards in different fields, I'll talk of mathematics only. Also, I'll assume that it is a question about a PhD thesis, not about anything of lower level like Masters, etc.
The main thing is that the gap between "advancing human knowledge" and "mastering the subject" is huge and there is a lot of grades in-between. IMHO, the works that advance human knowledge are rare, be it PhD theses or papers in refereed journals. Most of us live off "doing what hasn't been done before", which is much less demanding. What I mean is that each work introducing a new idea is followed by 1000 ones applying this idea combined with already known stuff to all setups where it works. Each of those 1000 papers does what hasn't been done before but does not advance human knowledge because, once the new idea appears, every sufficiently high level professional can figure out how it may be applied elsewhere, though getting all details right may require patience and even some effort. I certainly would accept "doing what hasn't been done before" (a successful application of a well-known idea in a fairly straightforward way to a new setup) as a tolerable (but not brilliant) PhD thesis.
How much below that would I consider acceptable? Four out of every five projects I try end up in a miserable failure, when I cannot even claim that I have proved some partial result in the desired direction. I have never tried to write a detailed account of "mein kampf" for any of those (dead end moves with counterexamples at the end, chains of implications that never meet the goal, associations and studies of seemingly relevant things that failed to relate to the question at hand for some fundamental but hard to discern reason, etc.) but I would let a report like that pass as a PhD thesis if it really shows 3 years worth of high quality effort.
What I find not acceptable is a "literature survey" (understanding what is written elsewhere and relating things in a superficial way without deriving any new result or introducing any new twist into the story). In other words, my idea is that you should get your PhD after you show that you can "fight a mathematical battle on your own", not only study the battles fought by other people. The victory in a decent battle is sufficient, but not necessary. Sometimes you can be made a "general" even if you lose but show good fighting skills.
All this is my humble opinion only. As to the official point of view, in Russia we had the central committee that had to confirm every degree award before it became valid and there were written guidelines. In the USA it is way more relaxed, so 4-5 professors conspiring together can pass anyone (to the credit of them I should say that I cannot give an example of such conspiracy). Canada requires an external review to be positive (which, by the way, makes perfect sense as a simple safeguard against "local standard relaxation" to me), and so on.
As to "risky topics", the best ambitious projects are such where "something" can be done right away (not something that is worth talking about as "defendable" or "publishable", but something that shows that the student has the general grasp of the subject and decent problem-solving skills). If that something (or something equivalent) is not done within the first half a year, it is a sign of trouble and the ambitious project is better abandoned and replaced with an "apply a known idea in a straightforward way to a new setup" one. If it is, you have a chance and may consider taking the risk. Unfortunately, there is nothing that can guide you then except your gut feelings and your knowledge of the student. You and your student are in an uncharted territory all on your own, and no general advice can be given except "play by ear" and "act by circumstances".

"Each of those 1000 papers does what hasn't been done before but does not advance human knowledge because, once the new idea appears, every sufficiently high level professional can figure out how it may be applied elsewhere" But figuring out how to apply it and actually applying it are two completely different things, and only both together are probably what should get you the PhD (though some supervisors hand their students a topic on a platter). Also, I'd argue that finding out that X does (not) work in context Y does advance human knowledge.

@TomasH "figuring out how to apply it and actually applying it are two completely different things" That depends on the meaning of the words "to figure out". For me they mean that one can do the whole thing and write it down in reasonable time if forced, just doesn't consider it worth time and effort. In this case, there is little difference.  "finding out that X does (not) work in context Y does advance human knowledge" Yes for "does", if nobody else can see it. No, if (as it often happens) it is fairly clear to many. Again, the question is what the words "to find out" really mean.

Well, this negative result got a fair amount of press, this negative result is generally considered a big deal, and my guess is that a negative result about this problem would probably be considered an acceptable thesis. :-)  So the trivial answer to the original question is: "Yes."  On the other hand, it's easy to think of negative results that wouldn't pass muster for a thesis.  So perhaps the underlying question is: "How can we tell whether a negative result qualifies for a thesis?"
A thesis is expected to make an intellectual contribution.  If I prove a bunch of trivial negative results that surprise no one, then I don't make any intellectual contribution.  On the other hand, if a lot of good researchers believe X, and I show that X doesn't hold, then that changes the state of knowledge in the field and therefore is a contribution.  (In many cases, the real contribution from such negative results is the analysis explaining why X doesn't hold --- i.e., why the scientific intuitions of a bunch of good researchers are incorrect.)
So the questions I would ask would be: are the negative results unexpected?  Do they give us new insights into, or a better understanding of, the phenomena being studied?  If these questions can be answered positively then I think the negative results qualify for a thesis; if not, then IMHO they do not.  The bottom line is, simply: "Do the results (positive or negative) teach us anything?"

You assert "A thesis is expected to make an intellectual contribution", which clashes with Zobel's statement "even if good results are not achieved, the thesis should pass if you have shown the ability to undertake high-quality research". Sorry if my use of the term "negative" sent you off-track: I will rephrase.

@Jeremy It's Zobel's use of the word "negative" that's the problem, especially since he talks about "negative result". As JeffE remarked above, negative result and no result are two different things. If you prove X doesn't hold (negative result), you've advanced human knowledge and made an intellectual contribution. But if you haven't proven whether X holds or not (no result) then you haven't made a contribution. Yes, you might be unlucky and research down a dead end where it turns out you can't prove X either way, but if you show why you can't prove X, that is still a contribution

In fast moving fields one can get scooped fairly easily. For example a biology PhD thesis may be based around determining the structure of a protein. If someone else publishes the structure before the thesis is reviewed then there is not a contribution to human knowledge since the structure is already known. I think in these fast moving fields the student would be expected to do more. In my "slow" moving field I am aware of two theses (one PhD and one MSc) where the results where the key findings were published by someone else in the weeks before the thesis was finished.

An historical example: Alan Turing was named a fellow at Cambridge on the basis of work that Lindeberg published over a decade prior. Admittedly, this was not a PhD thesis, but I am under the impression the import is comparable. Keynes, for example, had no PhD but was named a fellow prior to becoming a professor.
There is no evidence to suggest that Turing was aware of Lindeberg's work, but I am under the impression that they're approaches were quite similar.
I have heard similar stories on a less grand scale. No doubt the results of many theses have been previously published. Should it matter whether this connection is discovered before or after the thesis is submitted, assuming the new results did not draw on the old?

For a research project to be successful, you need more than just

a good student

You also need

a good concept and
a good advisor

Of course these points are not independent: up to a certain point a good student will be able to make sure the concept is good. At least the good advisor will do that. But what if the advisor isn't that good, and in consequence the project concept is flawed and the student gets bad advise and that is the reason for only negative results?

IMHO, having only negative results likely hamper the project success in an indirect way:

do not underestimate the psychological effect on the student: not succeeding in something that the advisor told you to do can have serious effects on the student's self-esteem and that can in turn lead to giving up or anyways loose the "psychological bonus" that you can only get if you are convinced of your work.
Depending on what kind of negative results, and how the project was specified, "it doesn't work" could be either due to the student's inability/laziness or due to the fact that things just don't work that way. Therefore "it doesn't work" always has a danger of falling back onto the student, even if it was not the student's fault.
It is hard for the student to prove that it wasn't his fault in that case, which may mean wasting lots of effort just to make sure that noone can come and blame the student for the bad results.

All together, I'd be extremely cautious with

I could suggest a more risky topic if it had the potential to teach more to the student without risking the whole graduating thing

Whether I'd at all  consider this would also depend on other circumstances. Maybe it is because I've seen bad advisors putting students into risky projects and leaving them in the resulting difficulties.
So at the very least I think that entering a project that the advisor already before it ever began perceives as more risky, should be entered

by a clear understanding between advisor and student that this is risky (but much may come out of it)
only if the advisor knows the student well enough to be able to judge whether the student could stand this
only with a clear risk-management strategy: the risk the student takes must be made up by something else.
How about treating such risky projects as normal job and paying a full wage if you cannot guarantee that even a good student will be able to get a thesis out of the results?

Here's an example (heard of it by rumour only) of how such things go wrong:
project involves growing some plants over 2 years. If all goes well, student can finish thesis in 3 years.
Student is hired for the project on a fixed 3 year contract, and is told that the project really shouldn't take longer. (Apparently, there is no money for more than 3 years). In the 2nd year a minor flooding kills the experiment.
Floods are in no way an unheard of occurrence in the region.
IMHO in this project the student was left not only with a project that had the inherent risk that the work of 2 years may be lost just before it was "ripe". The really bad thing is that it was combined with no plan B (e.g. extending the project), and occured in a country where a failed thesis is a serious thing: it wouldn't work to say in the beginning: "let's try this, and if it fails after 3 years, why, then you can just go on and try another project".

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


text:
Do you list journals you have reviewed for on your CV?
Is it acceptable to list the journals you have reviewed papers for on your CV? Is it common? Do you think it’s recommended?
On the one hand, it shows that you are engaged in this necessary part of scientific research that is peer-review. On the other hand, it sounds a bit useless, because everyone actually reviews papers for journals, and it is actually an unverifiable information (reviewers are confidential).

Some journals publish reviewer lists, so it is sometimes verifiable.

CV's have different audiences - I have at least 5 versions (different organizations request different aspects). The journals reviewed winds up on the version of my CV I used for promotion and tenure evaluations, as it's a "service to the community" aspect. It won't be on my CV for my research web page.

Also: What about for non-academic resumes?

On the one hand, it shows that you are engaged in this necessary part
  of scientific research that is peer-review.

That's one of the major reasons why people list it. If your CV is being viewed as part of a performance review or hiring decision, or even for awards, this constitutes "service to the community" and indicates that you're a good citizen.
It's a noisy signal for the reasons you indicate, and so it doesn't carry a whole lot of weight compared to things like technical committee memberships and leadership roles, but it's part of the larger picture. Moreover, for more junior researchers who haven't yet had the chance to take on leadership roles, this is a good signal of service.

Let us not forget, that reviewing for a certain scientific field makes you officially the Boss. Confidence and reputation boost guaranteed.

I did this in the earlier stages of my career, when every little bit of CV weight helped. But as the number grew, and as I did other more significant things, I removed all traces of reviewing.
You need to show that you are involved in the community when you are young and starting out. Later other aspects will be more important.

I would say that being asked by a journal to review an article is an indicator of esteem and for that reason, suitable and useful for inclusion in your CV.
With regard to confirming - or otherwise - whether or not you did indeed act as a referee for a journal, I would expect that anyone who wanted to verify this could ask the journal in question. Which specific articles that you reviewed could, properly, remain confidential. However, disclosing the information that you acted as a referee for the journal would not, as far as I can see, break any confidentiality policy.

Probably, a journal can say without breaking confidentiality rules that X acted as reviewer for them… but I don't know if they actually would answer such requests.

@F'x: there could be potentially a problem with journal revealing its reviewers in a too liberal fashion. Most of the time, the idea is to have a blind review. Yet, many journals ask authors to list potential reviewers of their submission. Simply asking whether somebody reviewed for the journal could sometimes jeopardize the blind review process.

Many journals in fact publish a yearly list of people who did reviews for them in this or the previous year. Typically entitled "Acknowledgment of reviewers" or similar.

I will essentially repeat the answer I gave here. The context for what the CV is going to be used for is key. I have a single CV that includes "everything" since I started grad school and selected things from before then. Having a long CV makes it easier for me to create short CVs because it means I need to delete things instead of remember things.
When I would include this information depends on the purpose of the CV and your previous experience. If you are giving a talk and someone asks you for a CV, then I would leave it off. If you are applying for a job and it is your only evidence of service, I would leave it in.

It is accepted, common and, yes, recommended. Being asked to review means your knowledge in a field is acknowledged and sought by others. The details you provide is up to you. I list the journals I have reviewed for in impact factor order, but it can be any order. I do not provide the number of reviews for each but the total for all. There is no reason to provide more than number of reviews and names of journals.
You have a point in that this information is unverifiable in many circumstances. In my field it is, however, common to be known as a reviewer (you have a choice). How the information is used is up to those who read the CV but I think most people will assume you have not falsified your CV (and that assumption goes for the remainder of your CV as well).
In the end, the art of writing your CV is to add anything that can reflect positively on your (in this case) scientific merits and reviewing is such a task. I also include reviews I have done for large funding organisations (e.g. NERC, UK, and NSF, US) as well as evaluations for promotions etc.

I know I am late to this, but I want to add that "actually an unverifiable information" is no longer necessarily true. You can verify peer-review work via publons and apparently ORCID (https://academia.stackexchange.com/a/126707/134392).

https://academia.stackexchange.com/questions/94309/is-it-worth-creating-a-profile-with-publons  Is this answer advertising?

I am not sure what you mean by "answer advertising"

I just wanted to point out that the question and many of the answers saying that peer-review is unverifiable is no longer true.


text:
What is the difference between Abstract, conclusion and summary?
The above question is self explanatory, still I would like to break it into two parts.
Q1. What is the difference between abstract and summary/conclusion?
Q2. What is the difference between summary and conclusion ?
Thanks

See classic texts K. K. Landes. A scrutiny of the abstract. Bulletin of the American Meteorological Society, 50(9):1992, 1969. and J. F. Claerbout. A scrutiny of the introduction. Stanford Exploration Project, 59:287 – 291, 1988.

some links: http://www.ece.utep.edu/courses/ee3329/ee3329/abstract.html and http://www.aapg.org/bulletin/abstract_scrutiny.pdf

See How to write a paper.  Here's a choice excerpt: There are papers that may beneﬁt from a conclusion section, but they are
relatively few (say, less than 5% of the papers). Certainly, the inclusion of a conclusion section
should not be the default.

This page on Stackexchange may also help.

Tell them what you're going to tell them. Tell them. Tell them what you told them.

The abstract is written for the potentially interested reader. While writing it, keep in mind that most readers read the abstract before they read the paper (sounds obvious, but many abstracts read like the authors did not consider this). The abstract should give an impression of what the paper will be about. Do not use jargon or any abbreviations here. It should be understandable for non-specialists and even for people from fields somehow far away.
The conclusion should conclude the paper and is written for the reader who already has read the paper. In other words: most readers have read the paper when they read the conclusion. Again, this sounds obvious but, again, a lot of conclusions do not read like this. It does not make sense to write a conclusion like "we have shown this and that by using this and that method". Well, this is what the reader has just read (and what he may know since he has read the abstract). A proper conclusion should tell the reader what she can or he could do with the newly acquired knowledge. Answer the question "So what?".
A summary sums up the paper. I am not sure if a paper needs a summary.
I've written some tips for abstracts some time ago:

Avoid jargon. Although this sounds obvious, most abstracts contain jargon in one way or the other. Of course one can not avoid the use of specific terminology and technical terms but even then there is an easy check if a technical term is appropriate: Try to find a definition on the internet (if the term has a fairly stable wikipedia page, there it is not jargon) – if you do not succeed within a few minutes you should find a different word.
Use buzzwords. This may sound to contradict the previous point and in part it does. But note that you can use a buzzword together with its explanation. Again, the example from the previous point works: “Funk metric” may be a buzzword and the explanation using the name “Finsler” is supposed to ring a bell (as I learned, it is related to Hilbert’s 23rd problem). This helps the readers to find related work and to remember what was the field you were working in.
General to specific. In general, it’s good advice to work from general to specific. Start with a sentence which points in the direction of the field you are working in. So your potential audience will know from the beginning in which field your work is situated.
Answer questions. If you think that your work answers questions, why not pose the questions in the abstract? This may motivate the readers to think by themselves and draw their interest to the topic.
Don’t be afraid of layman’s terms. Although layman’s terms usually do not give exact description and sometimes even are ridiculously oversimplified, they still help to form a mental picture.

Just to add a little on this, abstracts should be short (1/2 page in my field) and should try to explain all of the key points of the paper, including the methodology, key findings, implications, etc. The goal of the abstract is to let the reader decide if there is any value in reading the entire paper.

For abstracts, I always follow the advice in Simon Peyton Jones's presentation "How to write a great research paper" (He credits Kent Beck, but I can't find the exact reference): 1. State the problem 2. Say why it's an interesting problem 3. Say what your solution achieves 4. Say what follows from your solution. It's amazing how much clearer and to the point your abstracts become.

I downvoted because, for technical fields, using jargon in the abstract is almost always a good thing.  Show me a mathematics abstract with no "jargon".  But you should avoid using terms that your intended audience will not already know.

@DavidKetcheson I stand by my recommendation. If you use a technical tern for you can find a good definition quickly on the internet (wikipedia, google books or sometimes the arxiv) then use something else.

@Dirk your last comment contains some grammar and spelling errors that prevent me from understanding it.

@DavidKetcheson Ooops - typing on a telephone in not my strength…  Here goes: I stand by my recommendation. If you use a technical term for which you can't find a good definition quickly on the internet (wikipedia, google books or sometimes the arxiv) then use something else. Because in this situation it may well be that the community has not agreed on the notion yet.

'It does not make sense to write a conclusion like "we have shown this and that by using this and that method"' - I somewhat disagree here. The reader has read the details of the paper before reading the conclusion (or maybe not even that, if they follow the abstract - conclusion - rest of text method of approaching an unknown paper), but especially due to this large amount of information, it is important that the conclusion briefly summarizes the key content in a few sentences. Cf. with presentation slides, where the last slide summarizes the key points.

Yes, this. The difference between the abstract and the conclusion is in the intended audience. A caveat is that many readers will skip to the conclusion to get an idea of what they will know after reading the paper. But you still write the conclusion as if to an audience that has already read it.

Also, there is somewhat called abrégé, which is placed right after the heading of each chapter. Abrégé is very (not longer then 1/2 of a page, i.e. it is shorter then typical abstract) and describes the chapter instead of document. (Feel free to correct me).

I would add: make sure the abstract isn't a "teaser".  It should actually summarize the findings, not leave them as a cliffhanger to draw in the reader.  Many people who encounter the paper will only ever read the abstract, and only ever want to.

A1: In the context of a journal article, thesis etc., the abstract should provide a brief summary of each of the main parts of the article: Introduction, Methods, Results and Discussion. In the words of Houghton (1975), "An abstract can be defined as a summary of the information in a document". The Conclusions (in some cases also called a Summary) chapter is a summary of the main ideas that come out from the discussion (e.g., Katz, 2009) and hence only a subset of the abstract. Usually, the Conclusions sum up the discussion whereas the abstract only reiterates the most important of the conclusions.
A2: The difference between a summary and the conclusions is less clear. First, it is not clear if the summary is to be compared with the Abstract or the Conclusions. A summary may also be more appropriate as Conclusions when writing an overview where the conclusions may not be easy to identify. As stated above some journals use the word Summary instead of Conclusions. Sometimes this can also be labelled Synthesis and cap off a lengthy discussion.
References:
Houghton, B., 1975. Scientific periodicals: their historical development, characteristics and control. Hamden CT, Shoe String Press.
Katz, M.J., 2009. From research to manuscript. A guide to scientific writing. Second edition. Berlin, Springer.

I don't think it's accurate to say that the conclusion is a subset of the abstract. Some elements from the abstract make it into the conclusion, but the conclusion will likely include information not in the abstract as well, such as a detailed discussion of "why it all matters".

@Peter Jansson Regarding the conclusions you mentioned ".... and hence only a subset of the abstract". Then you mentioned: ".... the abstract only reiterates the most important of the conclusions."   How it comes?

Abstract:
Author short story about what is in it (no matter good or bad, valuable or scrap)
Conclusion:
Authors statement about the findings justified by the detailed content  (findings/achievement/affirmation of a doubtful fact/negation of an established belief...etc) for a reader who has a guided-travel across by the author.

A conclusion section might for example include speculations about some patterns in the data, or proposals for future research.  It basically is really the only place to put your opinions. A summary I expect would not include any opinions and just re-iterate the findings and weaknesses in the study.
As the other answer mentioned, the abstract should include all the main aspects of the paper in an abbreviated form - the topic, the hypotheses, the participants and study design, and the results.

abstract = what is to be done by researcher in the given paper.
summary = what is accomplished in the paper under consideration.
conclusion = what are the limitations of study, what needs to be done by upcoming researchers.

I am not sure about the equal sign you use. I have seen papers using conclusion for summary and vice versa.

Summary: Its a brief note that gives us an overview, ideas and insight about major topics in a paper.
Abstract: Its a short note that express the contents of the work.
Conclusion: Its a statement/decision reached by the researcher based on findings in the research.

One basic difference: An abstract is always at the beginning of a academic paper.  A conclusion is always at the end.  A summary could be anywhere, even separate from the paper itself, so it's a bit more slippery.

The abstract is like a movie trailer.
The summary is an arrangement of actions/events of movie in a short way.
The conclusion is the objective of the movie in light of the evidence and arguments given in the movie.

The conclusion of one person can vary from that of another.

Now, the question becomes what is the difference between  movie trailor and summary?

You could go so much further with this analogy. E.g., summary and conclusion together are like a review of a movie, with the summary being, well, the summary and the conclusion the interpretation and rating.

The abstract might very well be completely separate.


text:
Nomenclatures and symbols expansion in text
In my thesis there are 7 chapters. Each chapter contains many nomenclatures and symbols.
Nomenclature:
Chapter 1
Monte Carlo (MC) simulation is wonderful. MC is a stochastic process.
Chapter 2/3/../7
Do I need to re-expand the nomenclatures at the beginning of each chapter or expanding at the chapter 1 (or first occurrence) is enough?
Symbol:
Same question applies for Symbols also. For example, Temperature (T)
(Note: Apart from the above, the list of abbreviations and symbols is a must in the thesis as per institute guideline.)

Related question, for expansion of acronyms in papers

Oh come on.  Is it really that hard to write out "Monte Carlo"?

In addition towhat has already been said, you should consider whether all abbreviations are necessary. Some are part of jargon but sometimes you see something abbreviated only to be used, say, twice. In such a case it is unnecessary. My advice is to be relatively restrictive with new abbreviations and ones that are not used very frequently.
I personally think there is a tendency to over-use abbreviations and making wise decisions of when to use them and when not to use them should be part of all scientific writing. In the days when papers were printed on paper there may have been an incentive to make the text shorter but with digital publications, this is no longer really the case.
Whether you should re-introduce some abbreviations in each chapter, I think it is a matter of style and taste. The purpose of reiterating the explanation would be if you think the use is rare so that the reader would forget about them between occurrences. Then it may be useful to drop them altogether.
Variables are slightly different. You should only need to provide an explanation once. That in addition with a list of variables should be sufficient.
As a comment, in your example you abbreviate Monte Carlo simulation as MC and in the second sentence you essentially say "Monte Carlo is a stochastic process" (it is a place). You should use MCS to make the abbreviation full and not subject to misinterpretation.

+1 but when weighing up whether to use an abbreviation, also consider the length - it's often not worth abbreviating 3 short words, except in figures.

Unlike papers, theses are longer and typically not read in full (someone is going to say “typically not read at all”, but if you are writing yours, please don't listen to these people!). So expanding acronyms for the first time in each chapter makes sense. On the other hand, people who will read chapter 4 of your thesis are probably experts, and so they already know the common acronyms in your field.
For example, Monte Carlo (MC) is trivially used by anyone who knows molecular simulations and wouldn't need to be expanded more than once. On the other hand, acronyms of less common methods might warrant expansion once per chapter: Transition Matrix Monte Carlo (TMMC), Self-Learning Adaptive Umbrella Sampling (SLAUS), etc.
Another solution is to include a list of abbreviations and symbols at the end of your thesis!

The list of abbreviations and symbols is a must in the thesis as per institute guideline.

@cosmicraga if you have a list of abbreviations and symbols, don't sweat it too much then!