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Offering admission and then no response!
I have applied in a US university (let say it is A) in a natural science department for PhD admission.
One weeks  ago I emailed the  graduate director to know my status because I received admission from another university. I wrote him that, I will not accept the other university's admission offer if I'm in their consideration pool. The graduate director emailed me back that I have received the admission. He also suggested me to sign the offer letter he sent me  and to complete the admission within two weeks.
After carefully checking the email, I saw that  I didn't  get  the offer letter in the attached file. I replied him back immediately regarding the missing attachment. I replied the professor two times regarding the missing attachment  in the consequent  two days but still I haven't received any reply. The admin and the graduate director are not responding whereas 7 days have been passed.
Somehow I had a connection with a current student who is studying (PhD) in that department and I requested  him  to talk with the admin.  He discussed my matter with the  admin who writes and handles the  documents. The admin told him that, She didn't process any document for me regarding admission offer.
What should I do in this case? I have another admission offer from another university but I'm very much interested to the program I'm taking about.
EDIT: I'm an International Student

I was on a waiting list (the professor wrote me in a precious email).  I showed my interest a lot to this department though i have another offer.

I think it's time to forget about the clowns at University A, and move forward with the sure bet.

To some extent it depends how much more you want project A (you say you are very interested in A but not how this compares to B). A PhD shouldn't be taken lightly; if you aren't as passionate about project B then this could be very important a year or two down the line. However, Mad Jack makes a very good point; if A is this hopeless before you've even started then I have to say it really doesn't inspire confidence.

Until/unless you get the formal acceptance letter, you do not have an offer of admission from A.  Do not attempt to accept an offer you do not have.

I expect by now you have had to decide - but
Often there is only one faculty member (grad advisor) responsible for day-to-day worrying about graduate admissions in a medium size department in the US. And it is only one of his/her responsibilities. The best assumptions are that email went astray or buried (make sure to check your spam filtered email also!), or he/she got distracted by other deadlines and cannot get back to it for a few days.
As you did, polite emails (not frantic or demanding) to request clarification are the way to go. Recognize that if there is a large time difference, that it will create slow motion exchanges. A distraction for the faculty member may put the response 2-3 days later. (Make it easy to find the email if it was buried after a day or two by putting your name and some concise note or something like 'grad admissions clarification question" in the subject line
Pay particular attention to the first email you received that said you were admitted but neglected to attach the form you expected. Were there several names cc'd on the email? Make sure to keep those people cc'd on your requests to keep them in the loop. It may be that they can pick up where the first person left off.
And finally, if you decide to accept another institutions offer, then after you have settled that, then send a polite letter to inform the department. It may be that you decide later to switch schools and want to try again with this department.

"A bird in the hand is worth two in the bush" - old proverb
In other words, a certain thing is worth much more than a possible thing. You have an opportunity to get into a school for certain. You should not waste that opportunity on a 'maybe'.


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What is background check for the graduate admission processing?
I received an unofficial offer  for graduate admission. The graduate director mentioned two steps which will be required to get the official admit.

Background Checking
Signing on the offer letter

As I'm an international student, is it for the security purpose? How long it could take to complete the  process?
One of my friends said that, It could be  aa check from USA gov. Could you please explain?

Which country are you from? The security check from the US government is called "administrative processing", which is initiated at the discretion of the visa officer when you apply for your visa. As far as I know acceptance from the university is decoupled from this type of government security check.

This is the type of thing that varies enough between departments, that you are going to have to ask the graduate director about it.

I'm from Bangladesh.

Could it be the department waiting to check the references? In other words, could this be a conditional offer on the condition that your references check out?

@Serafina, are you saying that, they could verify the reference letter?

I don't know if this is universal, but I do know that some programs require background checks for anyone interacting with individuals under 18. For example, from the University of Pittsburgh Department of Psychology webpage (bottom paragraph on that page):

Special note for Applicants to the Clinical Psychology Program: Consistent with Pennsylvania law, all students in the Clinical Psychology Program must satisfactorily pass required background checks that are intended to protect children from contact with individuals who have abused children or have been convicted of certain felonies (including drug felonies), as is further explained here: www.dhs.state.pa.us. FBI, Pennsylvania State Police, and Pennsylvania Child Abuse clearances must be completed during the fall term of the student’s first year. Students may not continue in the Clinical Psychology Program if they fail any of these background checks or decline to submit applications to obtain them. Cost of the clearances is to be covered by the student.

Given that many undergraduate students are underage, and given that as a graduate student you will almost certainly be interacting with undergrads as part of your teaching assistant and undergraduate mentorship duties, this is required for basically all grad students. I know that, at Pitt, this isn't just for the Psych program; lots of programs have a similar requirement.
Much of this is new since the Sandusky child abuse events.

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Are separate essays in a single volume (on a single subject) considered "the same source"?
This came up in a paper recently submitted and commented on by the tutor.  The paper relied on, and separately referenced, multiple separately authored essays in a single volume ("edited by...") on a single subject.
A comment returned was "... perhaps too much reliance on a single source (though the defining volume) ...".
This was a surprise as I thought that the separately authored (and credited) essays would each be considered a separate source (though published together).
Is this, perhaps, field dependent?
(This question is not in any way going to lead to any complaint about the remark whatsoever.  I just would like to know - as I said, I believed otherwise.)

In math, in the US, I'd say that you didn't exactly have a single source, but, well, somewhat. That is, there would possibly/likely have been a single editorial choice of contributors, and about viewpoints, context, and so on. Again, in math, in the US, conference proceedings and other edited volumes, even with many authors, generally do have a consistent viewpoint... for better or for worse.

We could argue whether this is technically this is or isn't "the same source".
More productive is to ask, "could relying strongly on multiple essays in the same edited volume be putting blinders on your thinking?", and the answer is yes.
The use of multiple, genuinely independent sources ensures you have had to engage with different perspectives on a topic: different framings of the issues; differences in terminology, methodology, conclusions/implications. If there are schisms, you are likely to have stumbled across them.
In a single edited volume, the editor is likely to have -- deliberately or accidentally -- chosen authors with the same perspectives, and therefore biases. In fact, the individual essays may well have been edited to uniformize their framing, teminology, etc. Now, in some cases the editor may have carefully reached out to include differing voices. But very often not, and you have no way of knowing unless you've looked elsewhere.
All of these things taken together mean that though it may look (from the number of different names) that you're taking a broad look at the topic, it is fairly likely to have been curated enough that this is not truly the case.

Your question suggests that this was for a tutorial. Perhaps the tutor wished you'd looked around more, even though you found many useful separate references bound together. You can ask.
I doubt that an editor would object that this was a "single source".

no sorry, it was for a class (undergraduate level) at an institution where the course, online, is run by a 'tutor' (their terminology)

I suggest you ask the tutor for clarification, just so you know for the future.

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How to satisfy the publisher when making an article available on a personal web page?
At least in computer science, many publishers allow authors to make their published articles (or final manuscripts of them) freely available on their personal web site. A usual condition is to provide a link to the publisher's web service, where the "official version" of the article can be found.
However, there is one peculiar point about this issue: in most cases, the copyright agreement contains a condition that the link has to be "accompanied by" some text, such as "The original article is available at XYZ". I would like to ask what does this mean, precisely. The main reasons I have for this are:

Many scientists make their articles freely available online and many of them provide links to publisher's web pages. However, I have never seen a personal web page containing text like "The original article is available at XYZ". For this reason, I do not have a slightest idea about how the accompanying text should (precisely) look like.
Suppose that you have a bilingual web page. In this case, taking the above condition literally would imply that you could make your articles available only from the English version of your site (or at least you should include an English text in the non-English version of your site).
The term "accompanied by" can be interpreted in a variety of ways. For instance, is it OK to follow the link by an HTML comment containing the requested text?

So my question is: What is the usual interpretation of this condition? And what is the most common legal way to satisfy the publisher in this respect? Thank you in advance.

"I have never seen a personal web page containing text like (...)" - maybe it's not the exact wording, but I have frequently come across papers stored on university websites whose authors placed a remark about where to find the original version on the publisher's site within the PDF file. Look at the to appear in note in this paper and the header in this one as examples of what I mean (both files are hosted by unis).

I believe the usual way is to ignore such strict requirements. People just give the usual citation information and a link to the published version. If a publisher can't accept that and raises an issue, it's their loss in the long term, as their business is almost entirely based on the goodwill from the academia and the volunteer work done by researchers.

Interesting question. I simply don't care what the publisher says. Sue me.

The main issue in these circumstances (to my knowledge) is mainly to ensure that the exact version which you can host (i.e. your original version, the version after edits and reviewer comments, and the final version that was actually published).  SAGE has a good overview on this issue here.
On this page it clearly mentions that when you host it anywhere, to "please make the following acknowledgement: 'The final, definitive version of this paper has been published in [Journal], Vol/Issue, Month/Year published by SAGE Publishing, All rights reserved'".
From my non-legal perspective, I notice to things.  Number 1, the Dos and Donts are clear spelled out with "you may" and "you may not".  This condition starts with "please".  So, for one, I find it hard to believe that such a condition is legally binding, and that if you change the wording slightly it should be fine.  In fact, looking at this closely, I don't even think I have a legal imperative to put any link whatsoever (at least according to this specific publisher and this speicific journal).
In the end, the matter rests in the kind of copyright agreement that you've signed with the publisher, but I cannot imagine a situation where you've followed all of the guidelines, put the link and the citation, but then get sued for slightly changing the wording on exactly how you refer to it. On the other hand, if this is the wording that they want, why not just put it using the words and terms and just clearly avoid these issues altogether?
Bottom line, ignore what other people do; just take the publisher's method, wording, and whatever else in order to make sure you don't run-afoul of any ethical, or legal issues now or in the future.

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To what extent can guessing part of a multiple choice test lower your average score?
Especially where there are numerous possible answers to each question, e.g. 5.
If one doesn’t know the answer to a question but puts forth a (somewhat) educated guess, at what point is the chance of getting >0 for this question secondary to lowering the value of ‘definitely’ correct answers?
In other words, by how much does ‘indiscriminate’ guessing devalue points accrued from confident answers?
Perhaps this is more of a statistical question, but hopefully there are some general opinions on this matter.

Depends on the the scoring/weighting system.

As @DanielR.Collins said. And in addition it also depends on what an "educated guess" means in statistical terms. Moreover, whether guessing is really advantageous for you also depends on your personal preference on how to weigh higher expected outcome against higher standard deviation of the outcome.

To the close voters - which "community-specific guideline" does this question violate?

@xLeitix: I voted to close because it's essentially a question about probability theory, not about academia.

I'm with Jochen, et al. In statistical terms, the Question is so trivial as to be almost irrelevant. Other than in statistical terms, how could the Question have a useful meaning?

@JochenGlueck Yes, it's about probability, but very much applied to a context that many students and some instructors care deeply about. I don't think a question is out of scope because it also happens to involve probability.

@RobbieGoodwin I would say that there are three answers which get debated quite hotly is a good indicator that the real-life interpretation isn't so straight-forward. I really do believe that question should not be closed.

I was afraid that this would become a discussion about statistics, but too late. In addition to the two issues that I pointed out in my comments below, all three answers so far contain the following statistical error (which I thus point out here instead of writing a separate comment for each answer): they apparently assume (explicitly or implicitly) that if, for each question, the expected value of guessing is 0 points, then guessing the answer to any number of questions will not change the expected value of your overall points. [...]

[...] For most exams this is not correct since you typically cannot get a negative total number of points (for the entire exam, and probably also for the multiple choice part of the exam only). [...]

[...] Example: Say the multiple choice part of the exam consists of 10 questions, each with just two possible answers, with precisely one of the two being correct. Assume you get 1 point for a correct answer, and -1 for a wrong answer, but at the lower end the score for the multiple choice part is cut off at 0. If you know the answer to 3 questions for sure and have no glue about the other 7 questions, guessing at all remaining seven answers will increase your expected points to a number strictly larger than 3.

@RobbieGoodwin: Hmm, admittedly, I would not consider the statistics to be completely trivial; please see my previous comments.

@xLeitix: Your previous two comments convinced me that I was wrong to vote to close. I thus voted to reopen the question.

I would add that the answers all assume that every wrong answer is given the same number of negative points.  I have set quizzes where one wrong answer is more reasonable and gets fewer negative points assigned to it.  I see no reason a quiz cannot result in negative overall points (see the show QI), other than it would be cruel and unusual.

@JochenGlueck I would be quite interested in your answer (not ironically, I would really like to read your analysis and learn something).

@xLeitix: In case that the question gets reopened, I'll try to write an answer (but the conclusion will probably be very boring, along the lines of "any serious optimization is not feasible in practice").

Before answering a question, calculate your expected value. If it's positive then you should guess, otherwise you should not.
Expected value is how much you expect to get from the guess. It's calculated by multiplying p(correct) * points for correct + p(incorrect) * points for incorrect. So for example:

If you are 100% sure an answer is correct, then p(correct) = 1 and p(incorrect) = 0. You should always guess.
If you are 25% sure an answer is correct (as is typical for a 4-option MCQ for which you have no idea), then you need the points for getting the right answer to be at least 3x that of the penalty of getting the incorrect answer. If there's no drawback for guessing wrong then the penalty is 0, and you should always guess.
Similarly, if you are able to eliminate two of the options then p(correct) becomes 0.5. You need the points for getting the right answer to be at least equal that of the penalty of getting the incorrect answer.

I once took an exam which awarded 8 points for getting the right answer and deducted 3 points for an incorrect guess. With these numbers, a 50-50 question is still worth a guess. You'll have to do the calculations for your specific case (and also estimate p(correct)).

I'm sorry for repeating myself, but as in the other answer the claim that one should guess if the expected value of the points that one gets from guessing is positive, ignores two important aspects: (1) the target function that one wants to optimise does typically not depend linearly on the number of points, and (2) one has to take the standard deviation into account in addition to the expected value. E.g., in financial applications of decision theory, it is typically assumed that higher standard deviation is only acceptable if it is rewarded by a sufficient increase of the expected value.

@JochenGlueck My suspicion is that we envision different types of MC questions, or different circumstances. In the exams I had in mind a typical student would in fact have a fairly linear target function - if they get it right they get a slightly better grade, if they get it wrong a slightly worse one. Of course extreme cases are imaginable, like if you think you have just enough points to pass without answering, then you should probably just skip guessing. But for a student somewhere in the middle of the scale I still feel EV is a fairly good heuristic.

@xLeitix: Yes, the strongest non-linearity certainly occurs close to the pass mark of the exam. But there might but other important non-linearities: When I was I student, it often happened that you got the best possible grade with, say, only 85% of the points (due to a version of "grading-on the curve", combined with low average scores in the course). So between, say, 80% and 100% of the points the grade depended very non-linearly on the number of points. (But admittedly that's all quite theoretical, because in practice you typically have insufficient information for any serious optimisation).

@xLeitix: Oh, for the sake of completeness I should add that my previous comment does, of course, not contradict your claim that "for a student somewhere in the middle of the scale I still feel EV is a fairly good heuristic".

As Daniel correctly observes, whether or not guessing is a good idea depends on the scoring system, more precisely whether you lose points for wrong answers or not. By doing a (very simple) assessment of your expected (point) value we can distinguish the following cases:
Case 1 - No point loss for wrong answers
This is the simple (and probably most common) case - a wrong answer is 0 points, same as not answering the question. It should be fairly obvious that in this exam design there is no reason not to guess. Quite frankly, if there is no punishment for guessing, you should always guess, independently of what else is going and or how much of an idea you have what the answer might be. You can only win.
Case 2 - Wrong answers lose some points
An alternative exam design is one where correct answers give a point, and wrong answers lead to some amount of point loss (either a full point or a fraction thereof). Not answering of course neither gains nor loses a point. Now it depends on how sure you are and how much you will lose if you are wrong:
Case 2.1 - Full point loss for wrong answers
In the most extreme (realistic) case, a right answer gives as many points as a wrong answer loses. Statistically speaking, it's ideal to guess here if and only if the probability to be right is >50%. So if you are quite, but not 100%, sure you should still take the answer. If you can narrow the options down to 2 but you don't know which it is, it's statistically speaking a wash if you pick one or skip the question. If you think three or more options could be right it's ideal not to pick any of them and move on.
Case 2.2 - Fractional point loss for wrong answers
If a wrong answer loses a fraction of the points that a right answer wins, you need to generalise a bit from Case 2.1. It's easiest to analyse this case by considering how many of the possible options you can exclude, and then assess your expected value of randomly guessing between the remaining options. For example, if there are four options, and you can exclude two of them, you really only have two plausible options left. When a wrong answer loses less points than a correct answer gives you, you should still guess. If you cannot exclude anything, you should probably not guess (under reasonable assumption about the test design).
Some further considerations:
If multiple answers are possible:
If multiple answers can be correct (e.g., answers A and C are correct, but not B and D) the same principles from above still apply, but the space of possible choices explodes to the product of all possible answers. If there is no point loss for wrong answers it's still always ideal to take a guess, but if a wrong answer loses points it becomes much more unlikely that guessing is ever really the right choice unless you are pretty sure you know what the right answer(s) are.
If "none of the above" is a possible option:
Statistically speaking "none of the above" is just one more option to consider in your analysis. That said, in practice this is a bit tricky since you will never be able to exclude this option unless you already know the correct answer for sure (and then you don't need this analysis). So if "none of the above" is a possible answer and wrong answers lose full points, you should only guess if you are pretty sure that one specific answer is right.

As a note for educators looking at this - you may read this answer and (correctly) conclude that the best way to disincentivize guessing is to subtract a full point for wrong answers, combined with having "none of the above" as a possible choice. While this is undoubtedly true, consider what the impact on students is who are not guessing, but who simply made an honest mistake. Essentially, this leads to the awkward situation where a partial but slightly incorrect solution is worse than knowing nothing and skipping a question entirely. If you intend your exam to be a measure of student knowledge, that's not what you want.
For exams with calculations, I found "none of the above" to be a particularly nefarious exam design - this prevents students who know in principle how to do the calculation from sanity-checking for simple calculation errors (did I arrive at one of the possible solutions?). In my opinion, the only reason to pick this design is if you want as many people as possible to do poorly, independently of how much they actually know.

I have two objections concerning your analysis in case 2.1: the conclusion about the optimal strategy is only correct if (1) the function that you would like to optimize depends linearly on the number of points you reach, and (2) you are indifferent with respect to risk. Assumption (1) is not correct in many exams, for instance since there is often a cut-off of points below which you fail the exam. Assumption (2) is only correct for students who are not risk averse, i.e. who are willing to accept higher standard deviation of the outcome even if it does not result in higher expected outcome.

IIRC many standardized exams used in admissions use the 2.2 method: partial deduction for wrong answers to account for pure guessing, but not for "educated" guessing.

I find this interesting in an even wider context - if, say, your exam is 50% of the grade for your course. A student going in with a failing grade may be incentivised to, say, pick more answers at random than a student trying to preserve a good grade - the failing student benefits from a larger standard deviation, if the dice come up in their favour!

@lupe: For related reasons, both of these existing answers are wrong. The objective is not to maximize the expected score but to minimize the likelihood of a bad grade. So if I know I have answered exactly enough to get an A, but I am uncertain about the answer to one other question, why on earth would I answer it just to reduce my likelihood of keeping an A?!?!

@user21820 In my experience, it's vanishingly rare to have that level of precision when estimating your own performance during an exam.  Unless you are a perfect student, there are always questions you're not sure about.  Exams also often have short answer or essay portions, about which you basically never have better than an educated guess.  In my academic career, this made it very rare to be able to strategize about individual questions beyond naively maximizing expected value of points received.

@A_S00: Firstly, what I said is true regardless of whether a student can or cannot achieve the objective. Secondly, if you are a perfect student, then you do not need to guess. Thirdly, when I was a student I frequently got close or equal to 100% and it was the case that I could sometimes know all the answers except to a few questions. Fourthly, it doesn't matter whether you have an open-ended portion, if you are a risk-averse person and want to keep likelihood of bad outcomes below some threshold. (I didn't mention this fourth point earlier because some people simply can't grasp it.)

If you are guessing completely at random from n choices, then you have 1/n chance of guessing correctly (if completely at random). That means with probability 1/n you get 1 point, and with probability (n-1)/n you get 0 points. Your expected value therefore is 1/n.
Typically, it's desired that a student with 0 knowledge should get a 0% on an exam, rather than 25% or 20% from guessing. This can be done by penalizing incorrect answers by 1/(n-1): Then your expected penalized value becomes 1*1/n - 1/(n-1) * (n-1)/n = 1/n - 1/n = 0 which is what we want. Another way to think of it is for say 5 choices, on average 1 out 5 guesses will make you gain a point, the other 4 will collectively make you lose a point to balance it out, therefore the penalty is 0.25 per wrong guess.
As soon as you can eliminate even one of the incorrect choices, the expected value of course goes above 0. For example, in the same 5 choice test, if you manage to narrow every question down to 4 possible answers and guess them all, you will end up guessing 1 correcly for every 3 wrong - you get 1 point for the correct, -0.75 for the incorrect, and come out with 0.25 points. The more you narrow it down the better it gets. But then again, if you were able to eliminate even one choice, then it cannot be said that you came in with 0 knowledge. You clearly knew at least enough to eliminate the one choice.

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Google scholar "cited by" redirects to a paper not citing the original
Consider the following google scholar search. Click on the first result "On integer solutions of quadratic equations" -> "cited by 20". You arrive to this page. This page should list the papers that cite the aformentionned. However, see the papers cited by the first result "Most Tensor Problems Are NP-Hard". I cannot find the original paper cited there. Is this expected behaviour?

Google Scholar's behavior with citations is sometimes rather weird when multiple indexed versions of a paper are involved. It certainly doesn't only count citations in the final and published version. Here I think Google Scholar may have picked up this citation link from the third arXiv version of "Most Tensor Problems are NP-Hard". As far as I can see, this version of the preprint doesn't actually cite "On integer solutions of quadratic equations" in the text, yet has it in the list of references, presumably included by mistake. (The earlier and subsequent versions listed on arXiv do not include the reference at all.)


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Can I trust web of science citation reports?
In viewing authors profile to try to assess their impact I notice that H-index is quite different in Google Scholar and Web of Science (citation report). In Web of Science there is also a search for Author which is marked as "Beta". Which of this reports should I pay more attention to?

It doesn't really matter, as long as you compare people within the same database.

Web of Science citations are based on a smaller pool of documents.  For one, their collection includes mostly peer-reviewed journal stuff, to the exclusion of theses, preprints and other non-journal sources which can be included by Google.
Because it relies on final journal versions, WoS also excludes citations in intermediate versions that may have been published on arXiv or elsewhere but are removed in the final manuscript.  WoS citation counts can also lag actual counts by several weeks, if not months.
On balance, WoS citation counts are subject to less fluctuations than Google’s.  I prefer Google because of the breadth of the search, but I would not compare WoS and Google numbers.


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Can I be in a leave of absence as a postdoc in Germany?
I am employed by a German university in an ERC grant. I was wondering if I can ask for a leave of absence from the position, while being able to come back.

Have you checked with the university yet? Folks there might have somewhat better insight into its policies.

I assume this would depend on the reasons. You might elaborate here (or not).

Can you clarify what exactly you intend to do? I find several meanings for "leave of absence" and Germany being Germany we do have rules for just about anything. Taking leave for family, research or work would all use different mechanisms, for example.

@MisterMiyagi taking time away to teach at a different university and then come back on my job, so in practice like a "teaching intership" of around 4 months

@user1868607 If it's not for a full-time teaching position (say, teaching one lecture only) that might actually be possible without leave. For example, one of my colleagues holds a lecture at another university about 1hr drive away from our main campus while still having her original position at our university.

@MisterMiyagi problem is these are places in two different countries (reachable by plane)

This will highly depend on the concrete circumstances for which you will have to talk to your superior and the people at your university - however there are some regulations that are likely to apply.
If you are employed under TV-L/TVöD, the following paragraphs may be relevant (highly paraphrased, not legal advice!):
§28 Sonderurlaub leave without pay may be granted under special circumstances
§29 Arbeitsbefreiung leave with pay may be granted for partner giving birth/death in family/illness in family/doctor visit, with a specified number of days for the different cases (mostly ranging from one to four days)

Leave without pay is mostly down to an agreement between employee and employer. One example I have seen was a postdoc leaving for three months for a private trip through India. The institute was fine with it so it was granted.

Yeah I guess my point was that in general it is possible to get approved for leave without pay - could've been said better


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What is the trade union for postdocs working in Germany?
I have an urgent matter to solve at my job as a postdoctoral researcher in a German university. However, I do not speak German and I do not know what is the relevant trade union to contact.

While there are unions that represent researchers in German academia (GEW, ver.di), depending on the kind of issue you are looking for something more local:
For general representation of employee interests this is often called "(Wissenschaftlicher) Personalrat" or "Personalrat der wissenschaftlichen Beschäftigten", for example at RWTH Aachen, TU Berlin or TU Dortmund.

can you elaborate on how do the two differ?

This explains it quite well: Basically the unions do not have local representations, but operate across companies (e.g. negotiating wages) - in practice the Personalrat is likely to contain union-affiliated members.

(In case you were asking about the different names: Just a matter of naming, although sometimes there may be separate councils for scientific employees versus everyone else)


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Subscribing to job announcement list
I was trying to get the feed from specialised jobs like this one: https://dmatheorynet.blogspot.com/
in my email.
However, there doesn't seem to be a standard method for this. A friend suggested to use an RSS feed application, but I'm not sure how these works. In fact, I had trouble getting jobs from Euraxess before but it seems that an RSS feed application is the right way to do it (see How to select a subset of Euraxess's RSS Feed? and Is there an RSS feed for academic jobs published on Euraxess?).
Perhaps with this simple site, it's easier to explain how this can be set up?

@Anyon Im trying to get the posts into my email. But there doesn't seem to be a simple way of doing so.

I don't think this site is the place to ask when you have technical problems with software.

The site you linked to seems to only post announcements submitted to the two mailing lists linked on the left, and (at least recently) predominantly those submitted to the DMANet list. Clicking those links brings you to instructions to directly sign up for the mailing lists. For that site, this seems like the most straightforward approach.
Now, if you prefer to use the blog's feed, look under "Subscribe To" and pick the "Atom" option. Atom is a feed standard developed as an alternative to RSS, but is functionally very similar and supported by most feed readers that can handle RSS. Now, you probably don't want to read the raw feed directly. The traditional approach is to use a feed reader, whether as software you run yourself that periodically downloads the feed, or as a web service. (Personally, I like The Old Reader.) But there are also services like Feedrabbit that will deliver new posts from a feed to you by email. Some alternative services are listed here.


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Asking for details of an accepted grant
I recently started a job as a postdoc in a University with a professor that received a large grant, the most prestigious in the continent. We had a match of interest between my later publications and the topic of the grant and that's why I decided to come to his group.
My question is whether it is standard to ask to read the text of the proposal. I know at least one colleague that read the proposal of his professor, but I'm not sure whether I should wait to be invited to do so.

Are you being funded by this grant?

@Buffy yes i am

Is the proposal not stored on the uni server where you have access? I have access to all proposals from our group because they are stored in network folders where all PhDs and Postdocs from our group have acces to. I don't know if that is normal or not (EU based).

@Sursula Good suggestion. I think that would be a detail specific to research groups (unless a research institute happened to have some standards, in which case it would be specific to an institute).

@Sursula I'll check that, thanks

It seems natural to me to want to see the details of such a grant in which you will be a participant. Reading it could help you align your own interests, where possible, with those of the larger group as expressed (partially, at least) in the grant. This would be especially true if it provides your own funding.
I would think it pretty standard for the PI to want to share details, assuming that there aren't required confidentiality concerns, such as national security requirements.
But you could express interest in the details and see what reaction you get, saying why you'd like a look at it.

Also, as a postdoc it would be valuable to you to read one or more successful grant applications as you probably need how to go about that for a successful career. Hopefully your PI will support that.

In my experience, it's standard for professors to ask new lab members to read their grant(s). It can be one of the quickest ways to orient someone to the field and project goals for the lab. A lot of effort goes into a typical grant, might as well get as much use out of it as possible!
So yes, if you haven't already been asked to read the grant that is funding your position I would definitely ask to see it.


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I will have a masters in accounting and I want to study the psychology of money, so I'm looking to get a phd in psychology, do I have to start over?
I want to learn how money impacts people's behavior and use that information to help people build wealth. I already have a working knowledge of the psychology of money and I am working towards my CPA license. I'm most interested in helping people reach their financial goals. I feel conducting clinic trials will help me accomplish that goal, but my background is accounting, not psychology. I could do both, but I don't know if that means I have to start over for a second masters or if I can use my experience and desired area of psychological study as a basis for a PHD.

What part of the world are you in? Where do you anticipate being employed to conduct these clinical trials (university, hospital, …)?

What would be a clinic trial in this case? I don't see a medical intervention.


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Professors and Postdocs - attending conferences throughout the semester
how do Professors, faculty and postdocs go about attending conferences during the semester/academic year? I understand there are conferences during the summer and winter break, what about conferences during the semester? Do professors habitually cancel their classes?

In my experience, the most common ways to handle this are (1) trade with a colleague, "I'll cover your class now if you'll cover my class sometime  in the future," and (2) miss some class meetings now and reschedule them for later (or earlier) in the semester. Of course, (1) works only if the course is not so specialized that no colleague can teach it, and (2) works only if the class is small enough to allow an off-schedule meeting time. Fortunately, the most specialized classes tend to be small, so that one of the two methods is likely to work.

The problems that can arise involve teaching schedules and lab management. The latter can usually be handled for some days by delegating to subordinates.
For teaching, however, there are a couple of strategies and they might differ depending on the course level. Conferences are seldom (in my field, anyway) as long as a week and often include week ends - non teaching days. Sometimes you can just give students a set of tasks to complete in your absence and catch up with them when you return - readings, projects, writing, whatever. If they miss a lecture or two it isn't normally a huge issue if the material is available to them in other formats.
But, we also tend to cover lectures for our colleagues when they need to be away. TAs can also do this to some extent. Having an occasional "work session" rather than a lecture might be a good thing. A substitute can answer questions if they are in the same field and familiar with the course. Some will even schedule an exam that is proctored by a substitute.
One way to think of it is that learning is something the students do and it is much more important what they do than what the professor does. If the instructor can make those activities continue in their absence then feeding out new information in a lecture may be unneeded.

"Conferences are seldom (in my field, anyway) as long as a week and often include week ends - non teaching days." Really? Weekend conferences? That sucks. Mine are almost the opposite pattern.

why waste a weekend by going to a conference when you can break an entire week by attending a Wednesday event?


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Finish paper or broaden question
I'm writing a paper on an extension of a topic I investigated in my PhD thesis. The paper refines what is known about a particular (automaton) model, call it A. I find the paper interesting. On the one hand, it uses classical results from a theoretical field (logic) and applies it to a practical problem. On the other hand, the solution is not completely straightforward. In fact, I came back to this problem after not having been able to solve it months ago.
Now, a researcher in my thesis committee sent me a paper discussing another model, let's call it B. It seems that B is more general than A but for that reason the properties one can prove about B are not as strong. Still there seems to be a transfer of ideas I used in my thesis in the study of B. The paper contains a number of "open problems". It also provides motivating material to continue developing my paper on model A.
I'm working alone on this problem and preparing simultaneously a grant application finishing in two months.
Question
Should I stop writing my paper on A and understand the unclear points in B? Should I keep writing my paper on A borrowing ideas from the development in B and stating that they motivated the questions I ask about A? In the latter case, should I defer the publication of A until I try the problems in B to avoid not solving the problems in B with the new techniques I come up for A?
I can work on a paper and a grant writing simultaneously but more would be difficult. Ideally, I should have the support of master students or my PhD advisor. But he left me on my own for the whole degree.

I don't think we can answer this without knowing your career position and your career ambitions.  Answers are going to be very different if you're in a teaching-oriented position which doesn't expect more research than for you to publish one mediocre paper before tenure or if you're a postdoc planning to leave mathematics for an unrelated industry job if you can't get a position at Berkeley.

I think your advisor would be the best person to advice you on this.
But also their answer should be filtered considering your own schedule and ambitions. I assume that if you extend the paper you could probably target a higher IF journal than with the current status, but you will need more time for that.


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Search engines for academic jobs
I have recently started looking for academic jobs search engines. There are some good ones for Math already listed in this site (for instance https://www.mathjobs.org/jobs?employers---m, https://euromathsoc.org/jobs). I have both a background in Math and Computer Science but a soon to be PhD in Computer Science. Are there any such search engines for academic jobs in Computer Science?
I'm trying google jobs but somehow I only get new jobs in my current country or its neighbour. So the second question would be, is there a way to tweak google jobs to show jobs in all the world and not just your current location?

Are academic jobs special in that sense? I would say that academic jobs that are publicly advertised are covered by the usual job search portals where you also look for other jobs, aren't they? You have to adjust your search parameters accordingly.

https://support.google.com/websearch/answer/7498276?hl=en-CH&visit_id=638156075739206809-964378889&p=job_search&rd=1

I found this support webpage to do so
https://support.google.com/websearch/answer/7498276?hl=en-CH&visit_id=638156075739206809-964378889&p=job_search&rd=1


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Submitting updates to a paper under submission
I have submitted a paper to a journal (computer science area,good journal). After some time I have realised that some results there could be improved and that some are not completely correct.
Is it viable to send the journal an update even if the paper is currently being reviewed?

@corey979 good comment worthy of an answer

It is, but it upsets editors and reviewers, who already invested time in the previous version, wonder why you did not do true diligence before submitting, and who are now wondering whether what you send is the final word.
That being said, alternative plans of actions that might be preferable are:

retract the paper because you realized it is not done yet, apologizing profusely about wasting people's time.
wait for the reviewer comments and then update, if the changes are small. (You also need to point this out in your answer to the reviewers.)*
put the new results into a new paper if they are really significant.

Added: If in option 2, you change your results, then it needs to be reviewed again for the changes. If you do not alert to this, then you could be engaging in academic misconduct. Thanks to semmyk-research for underlying the importance of this parenthetical remark.

+1 ... wait for the reviewer comments and then update, if the changes are small. (You also need to point this out in your answer to the reviewers.)


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Qualities to look for in a PhD Supervisor
I am currently looking for a suitable PhD advisor in a relatively new and uncommon discipline which is only offered by a limited number of institutes, thus supervisors are of limited choices too. While it's a no-brainer that the most important thing to consider when choosing a PhD supervisor would be his/her research interest, I would love to know what would you think could be some other really important considerations too. Specifically, I am interested to know if you would prefer a well experienced supervisor (e.g. over 20 years supervising PhDs) over a recent PhD graduate (within 5 years for instance) and why. Thanks!

Also see e.g. Choosing between an older, prominent supervisor and a younger, junior supervisor for PhD, What are the risks and benefits of being the first PhD of a new supervisor?, How should I take a potential PhD supervisor's age into account, when planning to follow PhD with habilitation?, How important is it that your PhD advisor is an experienced, well-known faculty member?

Thanks a lot @Anyon! Sorry for the duplication.


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Does stating "authors contributed equally" in a CV/interview/presentation imply that a similar statement is made in the article?
I contributed to a conference paper with a large collaboration where I another PhD student did a large part of the work. When time came to publish, all coauthors on the work verbally agreed that myself and the other PhD student contributed equally and the most, and should order and annotate the author list to reflect this fact.
However, the submission guidelines advised against annotating author contributions (maybe this is a field where author ordering does not matter as much) and so we decided to put the other PhD student as first author, myself as last author, and put contact information (i.e. email addresses) on only our two names.
In some of my professional documents (mostly CV and committee meeting reports), when I list this work as an entry in a bibliography I have started annotating my own name and the other PhD student's name with a footnote like "authors contributed equally to this work".
My question is, could this after-the-fact label cause professional issues since in the article it is never explicitly stated that authors contributed equally? Or in other words, would a researcher who read my CV and saw an "equal contributions" annotation there become suspicious or critical if they then looked at the article and found no explicit indication of equal contributions? Is there a better way I should be handling this?

I will have a formal pre-print version of the paper online that explicitly saying that the first and last authors contributed equally. This way, confusion or suspicion will be cleared.

I doubt that the question in your last paragraph will ever come up.

Did the submission guidelines say you cannot have two first authors, or did they just say you cannot add detailed annotations such as exactly who did what?

Working in a field where you need quite a lot of first author publications to get a job, I see no point in the "authors contributed equally" thing.  Having that listed on your CV will not make a difference when your competition has ten first author publications.

@Oliver882 somewhere in the guidelines there was some statement discouraging detailed annotations (I'm still looking for the explicit statement, this publication was written about a year ago). There were no instructions about indicating contributions of authors.

@AnonymousPhysicist I acknowledge that may be true for whichever job you have in mind, but I think there are many, many second-authors-who-contributed-equally who would disagree. I adopted this idea from one such second author's presentation on their highly influential work (where his equal contribution actually came as a surprise to me and therefore probably some of the other thousands citing his work)

In that case, I think you could have listed you and the other PhD student as joint first authors

I doubt that that such a statement external to the paper necessarily implies anything at all about what is in the paper. In some fields, in fact, author order itself is a strong indicator of the participation of the authors. In other fields, it is normal to assume equal participation unless otherwise explicitly stated.
If you are in one of the latter fields, such as CS, say, then such a statement in a CV would seem odd and unnecessary. If the authors are listed alphabetically in the paper, then, again, the natural assumption is equal participation, though not necessarily the same kind of contribution by each author.
While it seemed to be a good idea to "boost" the students a bit by listing authors out of alphabetical order, it was probably a mistake as it has implications of contribution to at least some readers. Stating otherwise in a CV could raise questions in someone's mind even if they aren't raised publicly. It seems like you are now trying to reverse a decision made earlier.
And, in fact, I question the need for such a statement. The paper speaks for itself and all authors get recognition for whatever contribution it makes.
I'm also surprised that the journal let you list the authors non-alphabetically and also didn't want a statement of participation. They seem, to me at least, to be assuming author order implies "level" of contribution, whatever that means.

"I doubt that that such a statement external to the paper necessarily implies anything at all" What do you mean? It says something. Do you mean it is not believable?

@Oliver882, in the best case it is an accurate statement of what all authors believe. But in the worst case it is a mischaracterization by an interested party that is difficult to disprove. There is no way to judge it. And, being unusual, it can be suspect. Hence the word "necessarily".

OK. I guess you meant that it doesn't prove anything or it isn't convincing.

I don't think people will be suspicious or critical. It is a minor issue.
However, to be safe, I think you should make it be documented somewhere public that you contributed equally. This is like the suggestion by High GPA. For example, list your publications on your website and put the annotation there. This makes your claim less suspicious, because it is public, so everyone knows that your co-authors can see it too.


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What good is my paper if the code is not open source?
I have been working with my research group on a number of papers in a certain area of Computer Science. These papers have a certain portion which is theoretical (i.e. contains theorems, formalisms, algorithms) and a certain portion which is experimental (i.e. describes implementations and empirical results).
With regard to the experimental/practical sections, my supervisor generally suggests prioritizing the information that illuminates the general thrust of the paper (i.e. the section in the paper itself along with figures, etc) over the actual implementation details like links to code. Even if he considers releasing some tangible material, he seems to prefer to release executables rather than source code.
I can understand the rationale behind this because:
(1) In Academia, the final paper is the most important and recognized artifact of research;
and
(2) Releasing unpolished source code could be embarrassing, because it might contain errors which could damage the reputation of our research group.
While I agree with this, I am conflicted over what this means for the value of the paper and the ethics of research.

The papers do have some theoretical content, but they do not seem to be particularly valuable. Anyone else could have come up with those ideas with a little bit of thought.

If there are no executable artifacts tied to my paper, I could just have been lying about my results. I am not, and the experiments are actually very rigorous, but this is fact is somewhat undermined

Considering that a large part of our experimental work depends on comparing with artifacts created by other research groups, it seems petty not to release our artifacts

The potential benefit of the transparency of having open source code seems to outweigh the potential harm from potential embarrassment that may result from bugs found by the community. In my experience, artifacts released by other research teams in similar areas do often contain bugs, but I still think positively of them.

Please help me understand if my argument has any merit, or if I am being irrational.

"Releasing unpolished source code could be embarrassing" — a lot of science code is more script-like and unpolished. Would you judge some other team based on their code or be grateful that they released the code at all and understand that we all have circumstances?  "embarrassing, because it might contain errors" — would you prefer to be wrong and non-embarrassed? Or to know the truth and have the chance to get your paper right? If someone finds an error in my code, I'm not embarrassed. I'm proud that someone found my code important enough to read it throroughly. And I'm grateful to them.

What good is a physics paper if every paper isn't also shipped with the unique equipment necessary to reproduce the results? Or a biology paper without samples of what's being studied? It's not a perfect analogy, and one can certainly make a good case that one should always release the source code, but lack of released code doesn't make a paper useless.

@NotThatGuy that seems very focussed on the title - the text of the question is clear about relative value and the pros and cons of releasing the code.

Won't you be just as embarrassed if the output of the released executable contains errors? With source code available, others may be able to track down the issues to report and or correct the code.

Your code has errors. My code has errors. Everyone's code has errors. If I can look at the source, I will be more convinced that you don't have too many errors. And if you hide it instead, personally, I think you know you have more errors than you should. Also, source can be re-built in the future; a compiled binary may depend on some runtimes that become hard to find in the future.

I don't think I can write a full answer, but there is a case to be made that releasing the source code makes it too easy for others to reproduce the experiment too exactly - including any mistakes in the source code would also be present when they tried to reproduce the results. In contrast, if the results are reproduced by someone who writes their own implementation based on their reading and understanding the paper, then their implementation is unlikely to have exactly the same mistakes as yours, so it is more useful for the pursuit of knowledge if their experiment has the same result.

I think you have this all correct. The world would be a better place if all papers released all of the software that is used to generate the results shown in it. And that's not just a personal thought of mine (and apparently of yours as well), it is empirically verifiable: Papers that release the software used get more citations than papers that do not -- in other words, others are also thinking that that is worthwhile.
People have all sorts of reasons not to release their software, including (i) they believe that they have a competitive advantage by keeping their software to themselves, (ii) they do not trust their own software, (iii) they do not comment their own software or otherwise use good software engineering practices, and don't want the world to see so, (iv) they do not want to provide support to others who would download the software and use it. I believe that (iv) is a legitimate reason. (i) is misguided in my opinion, because at least if the software is non-trivial, others trying to use it more likely than not will ask to collaborate with the authors of the software, rather than just use it themselves; in other words, the original authors would gain a competitive advantage rather than disadvantage from releasing the software. (On this point, I speak from many years of experience.) Finally, if someone does not want to release their software for reasons (ii) or (iii), I believe this is an ethically questionable approach: If you have no confidence in your work, you probably shouldn't publish it.
In any case, I believe that your arguments are correct. Whether they convince your adviser is, of course, a different question.

To your last comment point (iii); one can justifiably have confidence in their work being correct, even if it does not conform to good software engineering practices. Other than that, good answer.

Thanks for this answer. IMHO, (i) is also misguided because research is not supposed to be like sports or business, or some other zero sum game. The goal of researchers should be to advance the understanding of the subject, not to prove themselves the best

@Grzegorczyk I agree, but your output affects your livelihood in many systems of researchers' job evaluations, and so I'm tolerant of people wanting to have a competitive advantage.

@Thissitehasbecomeadump. Empirically, the software design and development community has found that code that is poorly written and poorly documented has a much higher density of bugs. If you know enough about software design to be embarrassed by your code, you should probably be also concerned about its correctness.

It's worth noting that well-regarded conferences (AAAI and ISWC come to mind) now have a "reproducibility" peer-review criterion, where reviewers are asked to assess the reproducibility of the work and the quality of provided supplementary materials. Just a few years ago, I didn't see this sort of thing. So the community is taking steps to push people to release code, data, etc. which supports the views expressed here (in both question and answer).

Regarding your point (iv) - do you mean "support" in the sense of technical support? I originally read it in the sense of "supporting them to carry out some aim" and got a very negative view of your answer, which I now think was incorrect.

See also: the Community Research and Academic Programming License.

@preferred_anon Maybe both. If you put code on the web for others to download, then others will ask you questions about it, will ask you for help to implement extensions, etc. They will, in other words, ask for your time. You may or may not be willing to offer that time.

Related to point (i), in some cases the software developed by the researchers is commercial software sold/licensed for profit.

@WolfgangBangerth I agree with you that sacrifice of time is a legitimate reason (although you can just say no). My mistaken interpretation was nothing to do with that - it was more like "I, the software author, don't think that you, the user, will use my software for the Greater Good (TM), so I won't provide you with it". It seems clear that wasn't your meaning.

Regarding supporting the code, I imagine one can fairly easily get away with adding a disclaimer saying e.g. the code is for illustrative purposes only and no support would be available to get the code running. If you post it on e.g. GitHub, it would be quite trivial for others to fork it and take over to fix any issues if they really feel so inclined. It doesn't seem like a particularly compelling reason to not release the code.

"They do not comment their own software". Poorly written code cannot be saved by comments, and clear, good code often doesn't need any comment at all. Did you mean "document" instead of "comment"?

I'd strongly add, also, things like releasing it with a docker file or similar can save a bunch of time - others can then figure out how to get it working outside a container, but making sure it runs when you install it will make the support process pretty trivial

I am searching for the reference to the claim "papers with code get cited more". Any graph, research, study I should be looking to?

While I won’t comment on your particular case, I think it’s useful to consider science as a whole. You should realize that computer scientists are uniquely able to distribute an entire exact experiment implementation broadly, instantly, to anywhere in the world by virtue of making the source code public. Anyone can reproduce such an experiment exactly, and this is (in principle) a benefit. However, for any physical or biological experiment, there is no way to transport the apparatus to everyone. The journal text, figures, published data, etc. are all anyone can hope to have to learn of an experiment and judge its conclusions and correctness. There is an implicit assumption (barring evidence otherwise) that the researchers competently performed the experiment and collected the data they describe. Trust is a fundamental component of scientific communication, and this is why malfeasance which abuses this trust (such as data fabrication) is taken so seriously.
All of this is to say that every other experimental science views journal articles, with all their flaws, as having value; they have been the primary vector for dissemination of cutting-edge results for well over a century. They work because there is generally a strong culture of scientific integrity among scientists, and people for the most part take the presented data at face value even though the experimenters could be incompetent or untrustworthy. So although you enumerate reasons why journal articles are to be mistrusted, they have served science through the period of the greatest expansion of knowledge in human history. And they shall serve you just as well.

Your first sentence might be a little toooo optimistic: the precise platform, network situation, and competing loads on the machine(s) running something can have significant effects.

@paulgarrett good point. It depends on the experiment, I suppose.

I was going to say the same as @paulgarrett: Have you ever tried to provide a replicable environment for anything other than a 200-line Python script? Have you ever tried to actually reproduce a computational experiment? What do you do if that was run on a 10,000 core cluster? What if the software uses a dialect of some language that is no longer easily supported by modern compilers?

@WolfgangBangerth I’m no computer scientist, so perhaps I have a too romantic notion of it’s work. I imagine that at least measurements of relative performance comparisons would be possible to replicate nearly if not exactly. But the more wrong my offending statement is, the truer the rest of my answer: Journal articles have value, as evidenced by the last century+ of experimental science as a whole, wherein experiments are impossible to replicate exactly, and so the community must trust the written word.

@WolfgangBangerth if it's run on a 10,000 core cluster the answer is simple - the ones interested in verifying it probably have access to a similar cluster too. as for your second point - a) if it was published recently, whatever compiler that was used is probably still available; b) if it's old a working compiler is probably archived somewhere; c) the vast majority of compilers are backwards compatible anyway

@Trunk yes, I agree with your assessment of experimental papers in engineering/physics research. My point is that despite all of the obvious flaws, journal articles are clearly regarded as valuable even for fields for which anything analogous to publishing source code is impossible.

Actually biological samples may not be available to everyone, but there's a big effort in many cases to make samples which are the same in specific, reproducible, standardized ways available to multiple teams, which has really enhanced research (although sometimes questionable in other ways). https://en.wikipedia.org/wiki/Henrietta_Lacks https://www.atcc.org/microbe-products/collections-and-projects/certified-reference-materials etc.

@Gilbert The field of computer and computational science has spawned a whole sub-field called "Reproducibility". Take my word for it: Reproducing computational experiments is not easy, even if you have the source code.

@WolfgangBangerth Thank you for introducing this to me! All the more reason to value a well-written manuscript whether or not source code is included. It sounds like computer science experiments are more akin to physical science experiments than I thought.

@Gilbert There is typically quite a bit of difficulty involved in building and deploying a non-trivial software project. I do commercial programming on projects expected to be useful in the long term, so we are generally a lot more concerned than academics are about long-term maintainability. Yet, even so, I've worked on plenty of projects where I've said only half-jokingly that the best thing to do to cripple a competitor would be to give them our source code and let them try to build it.

@WolfgangBangerth to be honest, the situation is particularly and un-representatively bad in numerics / HPC, both because of the large and nonstandardised computational platforms and the typically used languages (Python, C++, Fortran, Matlab) are all pretty horrible WRT versioning policies, backwards compatibility etc.. For a CS subdomain that can run its code on any CPU and using languages like Java, Haskell or Go (and making proper use of their dependency tools), the prospects are much better that you can relaunch a program from source 20 years later without major difficulties.

@leftaroundabout No, the programming language is not the problem. You can compile 20-year-old C++ with not very much trouble. The issue is that scientific software often builds on many other projects. Take for example my own project and the many other projects it can interface with: https://dealii.org/developer/readme.html#documentation If you wanted to build a 10-year-old version of deal.II, you'd also have to get 10-year-old versions of all of these dependencies. And you'd have to get a 10-year-old version of cmake. All of that is not likely going to be easy.

Just to be clear, it's the non-standardized parts of the process. The languages are well defined. The dependency libraries are not, and neither is cmake. They all change far faster and in non-backward compatible ways.

@WolfgangBangerth “The dependency libraries are not” yeah that's my point. If you have 10-year old project in Haskell, then stack build will fetch all those 10-year old dependencies no problem so you can just use the program exactly as-it-was; the .cabal files provide enough information to know how to go about updating the dependencies; and the strong type system ensures that any real conflicts will cause a compiler error immediately where the problem lies – instead of some cryptic TypeError exception somewhere else at runtime, or a 1000-line template instantiation failure mess·age.

@leftaroundabout Yea, well, that's good on the Haskell folks. Everyone else (that is, nearly everyone) is stuck without that benefit. That includes those who are building on Haskell stuff that isn't uploaded into whatever repository stack build draws from.

@WolfgangBangerth I used Haskell as the example because I'm most familiar with it, but I'm informed e.g. Java/Maven can do much the same thing. And apart from the language level, there's also NixOS and Docker, though these mostly just ensure you will be able to run the program as-it-was, not to move an outdated project forward. — My point was that solutions (at least partial ones) to the points you raised exist, and merely aren't used by the CompSci community as much as would be possible.

@leftaroundabout No no, they're all used alright. You make it sound like we're all uneducated hicks, but that's really not true. All of the big projects are offering docker images (and VMs, and debian packages, and ...). It's just that ten years ago, nobody used docker, so if you want to reproduce something from ten years ago, you have to do some actual work. And you can probably assume that ten years from now, people aren't going to be able to execute today's docker images, so you have to do some actual work. The same will be true for any other package manager you can probably name.

@WolfgangBangerth More could be said here but it has already veered of quite a bit from the question's topic. Just FTR, I did not write or imply anything about uneducated hicks, don't read ad-hominems where there are none.

Check out this famous paper (in AI) by Google. It described an algorithm by which an AI can learn to play a game - in this case chess and shogi - by playing against itself only, and how such an AI can beat the then-strongest engines at the games.
Google didn't release the source code. But! The papers contain enough information for someone else to duplicate their work. Using the same methodology, the chess engine community created Leela Chess Zero, and tuned it (Leela is likely stronger than AlphaZero at this point). The ripples of the new AI continue to be felt, since 1) it affected how Stockfish, the strongest traditional chess engine, was developed; Stockfish is likely stronger than Leela again, and 2) the gameplay ideas that Leela discovered (and continue to discover) have been incorporated into the repertoire the top human grandmasters.
Now we can ask the question: what good is this paper if the code is not open source? How do we know Google didn't fake their results? I'm sure you can see the answers in the second paragraph above.

I think this answer misses the point, though. You are considering a sample of one, written by a highly regarded collaboration, writing a paper for which it would have been clear to anyone who saw it as a draft that it will be a high-impact one. The question is whether you can generalize from this paper to recommendations for everyone -- and I don't think you can. Just because that one paper had enough information to be useful to others that they could reverse-engineer things doesn't mean that -- in general -- papers shouldn't bother with including their source code.

What good is your paper commercially if the code is open source? Even if it was the reverse and Google had released it, you can't take that as gospel either because you're not an internet monopoly poised to profit from its existence. Everybody's missing the point as usual : money. "The goal of researchers should be" to do what they're paid to do; to directly make us money, or make us look smart so other people pay us money to make them smart, which is stupid.

@WolfgangBangerth Just because that one paper had enough information to be useful to others that they could reverse-engineer things doesn't mean that -- in general -- papers shouldn't bother with including their source code. The question isn't "should papers include their source code?" however. It asks about the value of the paper given the source code isn't available.

@Allure But then the same question applies. Just because there is one paper without source code that has value does not mean that this is true on average. It also doesn't answer the question whether on average papers with code have a greater value than those without.

@WolfgangBangerth I feel like we're not reading the question the same way. From my perspective, the question doesn't ask if papers with code have a greater value than those without, either. It only asks if the paper can have value if the code is not available.

@Allure Ah, fair enough, thanks for pointing out the disconnect. I guess the answer to that is "yes", as that's how we've been operating for a long time and sciences has progressed with that mantra. Though I continue to think that a sample of one is not the way to make that point :-)

note that in this case the situation is completely different. afaict AI papers generally describe new architectures - so the paper is easily reproducible even without source code, and even if  it was released you'd still need to do the much more time intensive task of training the network anyway

@Mazura You might want to ask RedHat/SUSE, Oracle MySQL, Oracle Solaris, IBM Websphere, MongoDB, Docker, Chef, Puppet, etc. on how to succeed commercially with open source code.

Going point by point through your issues...

The papers do have some theoretical content, but they do not seem to be particularly valuable. Anyone else could have come up with those ideas with a little bit of thought.

This is undervaluing your contribution.  Your paper, by definition, does have some useful theoretical basis otherwise you wouldn't be publishing it.  It may seem obvious to you, but that's likely only because you've been living with this for a while.  And I'll remind you that Thomas Henry Huxley's response on first reading Origin of Species was "How extremely stupid not to have thought of that!"  What's apparently obvious after you know it may not be obvious before.

If there are no executable artifacts tied to my paper, I could just have been lying about my results. I am not, and the experiments are actually very rigorous, but this is fact is somewhat undermined

And this is why it's important for other places to independently check results.  It's not sexy, but it catches dodgy results produced through incompetence or malice.

Considering that a large part of our experimental work depends on comparing with artifacts created by other research groups, it seems petty not to release our artifacts

And if they've got the same dataset as you and the same code, they'd get the same results if they just ran your code.  The important thing isn't the code, it's the algorithm.  Which is NOT the same thing.

The potential benefit of the transparency of having open source code seems to outweigh the potential harm from potential embarrassment that may result from bugs found by the community. In my experience, artifacts released by other research teams in similar areas do often contain bugs, but I still think positively of them.

But what if the "interesting" feature of your results is actually a coding bug?  Someone simply blindly copying your code will get the same results.  Independently coding the algorithm though is unlikely to get the same bug twice, so they can report that they don't get the same results and your paper may be incorrect.  Having the source code would let them analyse why your results were dodgy, sure, but that's less important than discovering the fact of it being dodgy.  And more likely, they'll be in contact with you (or your supervisor) after they discover they can't reproduce it, and then you'll be checking your own source to find where it went wrong and publishing a correction.

@Trunk I don't understand your question.  It is only possible to reproduce results by carrying out the process yourself.  That's the definition, the only definition ever, of "reproducing".  At which point they've done an implementation themselves, right?

@Trunk Re the algorithm error, it's possible (I've seen it myself) that an feature in the results gets misinterpreted as supporting the algorithm as written, when in fact it turns out to just be a feature of the implementation.  If you were to implement the algorithm as written then it doesn't actually do what the paper says it does. :)  Whether this is a bug in the implementation or just bad writing-up in the paper, either way it's something that can only be spotted with a clean implementation.

The goal of a paper is to report a novel research finding. It can accomplish this perfectly well without providing source code.
It is not possible to describe empirical findings in full detail in a scientific paper (or arguably any format). It ultimately falls on the author to decide what detail is appropriate.
The scientific method is not about believing observations because they have been recorded in excellent detail, but about independently verifying them. So it doesn't particularly matter whether you provide the implementation code. What matters is whether it's clear to other researchers how they would implement their own version such that it confirms the results you obtained. Your code may or may not be an essential aid for that.
Some, including me, would argue that the virtue of a scientific finding is simplicity. In your paper, the more quirky things there are that have to be done "just right" - whether the algorithm works when implemented in a certain way with a certain language, or it only works on a certain kind of input - these all make the finding itself less interesting to begin with. So if your paper is really good enough to care about, the code is a moot point. I should be able to write my own code from scratch and verify your algorithms and theorems.
However, beyond the primary goal of justifying a claim, there are secondary goals that are well-served by making the implementation available:

If your implementation has a mistake, it will be found sooner
If your implementation turns out to be correct but hard to reproduce, you will not be accused of fabricating results
Researchers wishing to extend on your work will have a better starting point
Non-researchers (such as those in the industry) wishing to apply your findings will have a better starting point
People trying to learn about research in the field will have better resources
The code will serve as a public demonstration of your coding ability, if required by for example job applications

I personally would prefer if code was always published along with papers, but there have been many papers in CS and other fields that were published without code, and yet their claims are sufficiently credible and the findings they report are useful, so I would not consider it a hard requirement.
But to your concern about the code being unpolished: It doesn't really matter. No code is perfect. Either what you have works which means it's good enough, or it doesn't work which means your paper is wrong in the first place. You have little to lose from publishing code.
You also mention publishing binary executables, which is the really strange part to me. By publishing executables, you are asking people to trust that you did not introduce any malicious code (like viruses) into the binary, and also that your system and every other system in the chain (like the CDN actually hosting your binary) is secure from hackers and so forth. This is a very big claim, and completely unnecessary when you could just provide source code and completely obviate it. You are also training users to accept bad security practices. People who do publish binary code (eg. proprietary vendors) will at least take steps to mitigate the security hole, by employing security engineers and signing their binary builds. If your goal is to let people use your implementation without seeing the code, you should provide a web server so that the binary is executed on your system, or at least containerize it and provide something like a Docker image.

A comprehensive and well-balanced answer. The fourth paragraph is beautiful. However I can't quite agree with “the code being unpolished: It doesn't really matter”. In many cases, just spending one day to go over the code again, structuring it to be more reliable, putting it together with the right tests, and adding some comments, could save people trying to use it multiple days of work to get it to run, or even weeks of needing to re-do computations because they used the code wrong.

Unpublished papers, in and of themselves, are almost entirely worthless, like an unfilled lottery ticket, or a movie script that's never been filmed.
Once published, they get a little value, like a lottery ticket that has had the numbers filled in and registered at the checkout, or a movie that has been shot. They can all now be tested, and have a chance to win.
The community ascribes them value at this point, but this is equivalent to the face value of the scratchcard (a dollar, perhaps): there is a chance that the paper might turn out to be a "winner", based on correct data and correctly applied algorithms. Every panel on a scratchcard could be a winning number... or none of them may be. A movie could be a blockbuster... or a flop.
Reproducing the results is the proving ground: it's how they provide value to the consumer, like when lottery numbers are drawn, or a movie is released to the public.
The higher you make the bar of reproduction, the lower the chances of making any valuable contribution to the community.
The lower the bar, the more likely someone reading the journal you publish in is to say "hey, this is something I can give to my student to try" or "that's so easy to reproduce, I could try that out this lunchtime..."
There are those who argue "if you release the source, then people will run it and necessarily must reproduce your errors". This is very twisted logic: releasing the source saves them your entire development-cycle of time, meaning more people can work on what your paper gave. More eyes on your source can only mean more likelihood of finding errors: it is impossible for it to mean less.
There are cases (like the Google AI paper mentioned in another answer) where papers that involved software have been reproduced without source code, and managed to be beneficial anyway... but these are far less common. Even in the Google case, there was only one project which went through the trouble of rewriting that code. If they hadn't got lucky, if that project hadn't picked it up and run with it... what value would that paper have had to the community, then? How much MORE value would it have had if instead, they had released the source, data, and a docker file, so countless people could have just run one command and reproduced their results? How much bigger would the field of AI be now? Would we all be driving self-flying cars? We can't tell, but we can guess that the impact of the paper would not have been at all reduced by improving the reproducibility.
Assuming someone DOES reproduce your work, but gets different results... where is their error, if any? Even if you collaborate, and both use the same data to get different results... is it their error, or yours? How do they find out? What if you are not contactable? In software engineering it is considered a truism that you cannot easily identify or fix an error without reproducing it first, and if your output cannot be reproduced, it cannot be fixed.
TL;DR: All papers have some value, but without reproducibility, that value is minimal and aspirational, rather than real. Maximizing reproducibility maximizes real value and impact. Source code really helps these goals.

The potential benefit of the transparency of having open source code
seems to outweigh the potential harm from potential embarrassment that
may result from bugs found by the community. In my experience,
artifacts released by other research teams in similar areas do often
contain bugs, but I still think positively of them.

Let me answer from a software developer's point of view. Open sourcing the code has a huge benefits, even if it contains bugs or poorly written code. Point of being opensource is other people can contribute towards it. The code will be improved by others, bugs will be fixed and will be much more popular compared to a closed source one.
So, if you are confident on results, why hesitate?

In Natural Sciences we should put forward boldly our hypotheses and results inviting debate, correction and, ultimately, falsification. Do publish your code. Why not clean it up, while are you at it? If for no one else then for yourself, who is the one most likely to re-use the code in the future.