What's the meaning of "Topics in ... "? I have seen some courses in the university that start with "Topics in this ... Topics in that ...", I have also seen some books that use that word "Topics in Algebraic Graph Theory", "Topics in Structural Graph Theory", so, I've been thinking, what is the difference beetween a book of "Algebraic Graph Theory" and one of "Topics in Algebraic Graph Theory"?, What is the meaning of the word "Topic" in academia context?

"Topics in" usually means that the content is

not meant to be introductory (i.e., the reader is assumed to already have some prior familiarity with the subject), and

not meant to cover "all the important things", but can focus on a selection of (well) topics the author is interested in.

For example, a "Topics in Representation Theory" course can afford to skip both the basic properties of finite group representations (as it is not introductory) and focus (e.g.) on quiver and Hopf algebras. Often, books will have more precise titles than "Topics in...", but lecture notes often take their titles from those of the classes, which are often intentionally vague so they don't have to be changed every year depending on the lecturer. For a department, calling a class "Topics in Algebra" is an easy way to offer a course that will be on something different each year without the trouble of having to rename it every year.


The usage of a literally incorrect phrase to simplify the meaning in manuscripts I am writing a survey paper in computer science, where I frequently use the sentence:

  ....videos that are taken from uncalibrated monocular moving cameras....

In long sentences, the above phrase makes the meaning very complex. I found in some papers:

  ....uncalibrated monocular moving videos....

Although this phrase is not literally correct (because the adjectives should describe the camera, not the videos), it is widely used. I would like to know, whether this usage is OK in order to make the sentences shorter.

In mathematics we frequently create new meanings for phrases. The key point is that we explicitly state what we are doing (in theory at least).

So put somewhere early on put in something like

  For the sake of brevity, we will abuse terminology by referring to videos that are taken from uncalibrated monocular moving cameras as 'uncalibrated monocular moving videos'.


Do journals have legal copyrights to the online supplemental information? I have a paper in an biology journal (Wiley is the publisher) for which I would like to put a much more mathematical version on ArXiv, mainly the theorems and proofs located in the online supplement (that biologists will not read but that perhaps a mathematician who does not read this journal might be interested in), with no figures or sentences copied from the biology paper. I could perhaps call it a pre-submission version of the paper (which would be somewhat untrue, but Wiley does allow pre-peer-review preprint publishing on ArXiV; none of the peer-review process affected the supplement; so in spirit this would be sort of true).

Does Wiley’s copyright agreement prevent me from doing this? Wiley’s information on supplemental information doesn’t seem to address the issue.

Wiley
Taking a look at Wiley’s general copyright agreement, they only “the Contribution”, which in turn is defined in the header as “Manuscript entitled ___”.
Now it’s debatable whether the supplemental material is part of the manuscript. However Wiley needs at least some permission from you to publish the supplemental material and I could not find any information on separate copyright agreements for the supplemental material, so I assume that at least Wiley considers the supplemental material to be included in the manuscript.

Wiley-Blackwell licenses back the following rights to the Contributor in the version of the Contribution as originally submitted for publication:
a. The right to […] place in a subject matter archive, […]. This right extends to […] the Internet. The Contributor may not update the submission version or replace it with the published Contribution. […]

Again, it’s not specified whether this applies to parts of “the Contribution”, but given that a partial publication is less than a full publication in terms of copyright, I would argue that it’s included.
Finally note that all of the above only applies to the general copyright agreement. Wiley mentions that the individual journals may have differing agreements:

Author re-use rights vary between journals. Please refer to the copyright form you have signed or are required to sign to review the applicable re-use rights.

As there are some questions of exegesis in the above, the easiest safe way is to ask Wiley whether they agree with your endeavour.
In General
Addressing the titular question:

Do journals have legal copyrights to the online supplemental information?

As journals want to disseminate the supplemental material, they need you to at least transfer at least the rights needed for this. Since those rights are a subset of the usual copyright agreement, it makes sense from the journal’s perspective to plainly treat the supplementary materials the same as the main paper:

It’s less complicated for the reader.
It’s less work.
Most people do not care much about supplementary materials anyway.

Therefore I do not find it surprising that most journals (in my experience) do this. However, at the end of the day, you have to look into the journal’s copyright agreement to be sure, or even talk to the journal itself, if the copyright agreement is ambiguous and you do not want to risk any bad blood.
Finally a sidenote from personal experience: I once wrote a paper presenting a new method and wanted to attach the source code for an implementation. Since the journal’s copyright agreement did not allow me to disseminate the source code on GitHub with a free license simultaneously and the copyright department did not want to relax the copyright agreement in that respect, I ended up removing the supplementary material and posting it on GitHub alone.


How long should it take to read a paper? I am wondering how long it usually takes to read a research article. I can see that the answer depends on:

The knowledge and intelligence of the reader
The field of research
The journal
The individual paper

So I am particularly interested in how long a PhD student or a young post-doc would take to get through a molecular or systems biology paper published in journals like Nature, Science, Cell, PLoS and PNAS. We can focus on papers of typical complexity for the journal, and ignore those that are exceptionally easy to read or exceptionally complex.

On multiple occasions I have been able to skim an 8 page, 5 figure paper in as little as 10-15 minutes. From this I could glean enough information to follow and even participate in a class or journal club discussion (though there would be a lot of asking question like "how exactly did they do this/explain this in the paper? I didn't read very carefully").

However, when a paper is very important (for example I want to use a variation of their methods for my own project) I feel the need to read it much more carefully. It seems worthwhile to closely look at even very trivial things, such as description of standard procedures like cell culture in the methods, exactly how much of each chemical was used for simple, routine reactions, close examination of control experiments from the supplement, what papers the paper has referenced to justify their work, and even what commercial systems were used and from which company, and whether the paper actually followed the protocol in the manual and so on. After putting this much effort, I also feel like I should take notes. This produces a heavily highlighted and annotated paper plus about 4 pages of notes.

All of this takes a lot of time. Occasionally it could take me a few days to work my way through a very important paper (for instance, if their method is unfamiliar to me and I will be adapting it for my own research, or if I want to draw conclusions by reanalyzing their data).

My question is, is this typical? How long do you usually take to read a paper? Should I start putting effort into teaching myself to read faster, or should I just accept it and make time by scheduling other activities to accomodate paper reading?

I can imagine an "incremental" strategy for reading at arbitrary depth. For instance, you could read the paper several times, each time reading more carefully, like so:

Read only the title, parts of the abstract, and look at the pictures.
Read abstract more carefully, look at title headingfs of results section.
Quickly skim the results section to look for main point of the paper.
Scan introduction and discussion, read results carefully to understand obvious limitations of their conclusions.
Carefully read all sections, including methodology, to make a comprehensive list of all assumptions made and all potential issues with the research.
Carefully go through all supplements, look at raw data if any and consult the other papers cited as justification to contextualize the research.

Logically, I see the merit of something like this, but I haven't tried it for the "dense" readings I've talked about above. The reason is that I'm not sure how I can take notes effectively when I do something like the above. It may also be harder to motivate myself to re-read a paper I've already skimmed, because I've given away the punchline to myself and the novelty factor is gone.

I've also written an answer on Mathematics@SE, which I think might be relevant to this question.

Note that I am not asking how to read a paper. This has been addressed in several previous questions already. I am only asking how long it should take a typical junior scientist to read one, so I can benchmark myself and see if I am slower or faster than is usual.

As you have alluded to, there are many ways to "read" a paper and the first question to straighten out is for what type of information are you reading a specific paper. A beginner's mistake is to believe you need to read from the first to the last letter of everything you find. This way you never get anywhere and it is the beginning of the learning curve for undergraduates.

If you are surveying a field, you skim through by reading the titles first of all just to spot the ones of interest. This reading takes much less time than to actually find the title. Once you are past this you may take on the abstracts to weed out the ones that sounded right but were off topic. This will take a few minutes per paper. The next step is to read the parts that are of interest to you. Are your summarizing a field then conclusions and the discussion may be what you focus on. Maybe your summarizing methodology then those chapters are of interest. In any case, you will read parts of the paper that are mostly relevant to what you are trying to find out. This may take a on the order of hours, sometimes less per paper.

Sometimes you need to spend time on papers to understand the nitty gritty details. I spent 4 days reading three papers to understand the details. I needed to write down equations and try to see the missing steps between the paper equations to understand the derivations etc. This type of reading is not what you normally do but it certainly happens.

So in the end, what you will learn through your PhD is to hone your skills to be able to focus on the details you really need from any paper. It is rare that one reads one paper at a time, usually it is a collection of papers and one often has to return to check on details that you either have forgotten, or may have missed, or, in fact, the author has missed. The need to go into the smallest detail of a paper depends on what you need to get out of the paper and with time (continuing through your professional life) you will improve on these skills through additional reading.


How far back do Chinese universities go? Europe has many universities founded in the Medieval period; in the United States, universities started to be founded almost as soon as English settlers arrived. China, however, I know very little about.

I do know that China has a millenia-long history of scholarship and science, but that is not the same as organizing academia into universities or university-like institutions.

When did "universities" in the modern sense (i.e., permanent institutions that gather scholars to conduct research and provide higher education to students) begin to be founded in China?

Western Style Chinese Academy

From Tongwen Guan 同文舘 wiki page

  Tongwen Guan (Chinese: 同文舘), or the School of Combined Learning was a government school for teaching Western languages (and later scientific subjects), founded at Beijing, China in 1862 during the late-Qing dynasty ... It became part of the Imperial Capital University (now Peking University) since 1902. ...

Government sponsored Chinese style academy

From Shang Xiang 上庠 wiki page

  Shang Xiang (Chinese: 上庠; pinyin: shàng xiáng; Wade–Giles: Shang Hsiang), was a school founded in the Yu Shun (虞舜) era in China. Shun (2257 BCE–2208 BCE), the Emperor of the Kingdom of Yu (虞, or 有虞/Youyu), founded two schools. One was Shang Xiang (shang (上), means up, high), and the other one was Xia Xiang (下庠, xia (下) means down, low).[1][2] Shang Xiang was a place to educate noble youth. Teachers at Shang Xiang were generally erudite, elder and noble persons.

From Taixue (太学) wiki page

  Taixue ... was the highest rank of educational establishment in Ancient China between the Han Dynasty and Sui Dynasty ... The first nationwide government school system in China was established in 3 CE under Emperor Ping of Han ...

Privately owned Chinese style academy

From Academies (Shuyuan書院) wiki page

  ... shuyuan were usually private establishments, ... The shuyuan originated in 725 ...

From China’s Ancient Academies-Academies Tours to China

  ... According to “Records of All Officials,” a volume of The New Chronicles of the Tang Dynasty, “in the sixth year of the Kaiyan Reign (AD 718) Qianyuan Academy changed its name to Lizheng Academy (Academy of Elegance and Rectitude), the newly created positions of supervisors and copy editors were filled and compiler-academicians became auxiliary-academician ...


course vs module vs lecture vs seminar My question concerns terminology in computer-science MSc/BSc education in universities. Let us consider the following terms:

course
module
lecture
seminar

in the US and the UK.

Which terms denote one-off events, which events last a term, and which events denote a whole BSc/MSc program? (Yes, I've read the Wikipedia entry, but it seems controversial.)

US usage

A course is generally the main unit of instruction. You will register for a course that lasts a term--which could be a semester or a quarter depending on the university.

There could also be a year-long course but it would actually be two or three courses that you would have to register for each term.

It's also possible to hear the term "course of study" which is the set of courses that you need to take to fulfill requirements for a degree.

The term module is not very common in the US, but it would be a themed subset of a course. I've seen it used in professional development courses.

A lecture is one meeting of a course.

A seminar could be a one-off lecture or meeting but a seminar course or a course using a seminar format would be designed for a small group, likely under 20, with more discussion rather than lecture or every student presenting on various topics over the term.

UK usage

Course is used for the entire set of topics to be covered for the degree, so corresponding to course of study in the US.

A module is a themed subset of the course, what would be called a course in the US.

A lecture has the same meaning as does a seminar.


How does a recognized and well-known Professor help his students in getting an academic job? It is often told that working under big shots (I mean, recognized professors) in a particular research field is always beneficial on the way towards getting a faculty job in top research universities (perhaps in the US). My concern is how a big professor can help his students (PhDs, postdocs, project scientists who are badly seeking a faculty job) in addition to providing a recommendation letter? Is there anything else they could favor me on? Or, in other words, is there anything else I could ask them giving me a favor for? For instance, can I ask my professors to give a phone call to the faculty search committee (where I submitted my application) to consider my application with care? Such effort is nothing but highlighting one's application so that the search committee gives a special attention to it.

This doesn't usually work that way. When your prof gets involved on your behalf, besides writing the most stellar recommendation letter they can, they're just going to pick up the phone and call if they know someone at the place you are applying. A personal recommendation beyond the usual stellar letter from someone trustworthy that you know can go a long way towards differentiating between otherwise equally viable candidates.


Is it inappropriate for an author to copy and paste from his/her original paper in an extension paper? I am currently a referee for a paper.
One of the authors of the paper had written an earlier paper,
which I will refer to as the "original paper,"
and the paper which I am now refereeing is an extension of the original paper,
which I will refer to as the "extension paper."

I have found that large portions of the extension paper
are copied from his original paper.
In particular,
a whole section of definitions is copied from his original paper;
and some paragraphs in the introduction and literature review
are copied wholesale or with slight modifications from the original paper.

Is it acceptable for an academic paper to copy paragraphs and even a section from an earlier paper by the same author?

My intuition suggests that it is acceptable to copy the definition section,
with an acknowledgement that it came from the original paper,
since definitions are standard.
But it seems strange to me for the introduction and literature review
to be too similar to the original paper.

The answer depends on the relationship between the papers, and I'm not sure which applies based on the information in your question.  In computer science, at least, there are two general cases:

The extension paper is the "extended journal version" of a previously published short-form work such as a conference paper, workshop paper, or extended abstract.  In this case, the rule of thumb is typically at least 30% new material.  The extended paper will often contain large chunks verbatim, as it is expected to supersede the original paper, rather than existing as a separate work.
The extension paper is a separate work: in this case, extensive reuse of material is self-plagiarism.  Two exceptions: first, related work, methods, and definitional material may often be reused as long as it is appropriate to do so---the material should be appropriately customized to fit the new environment.  If the author would just be paraphrasing for the sake of paraphrasing, though, it's not necessary.  Second, introductory material may be partially shared, though it should be more heavily customized for the new context.

In all cases except for minor reuse of related work material, the extension paper must declare a clear and explicit relationship with the prior paper.

Note that many other fields do not have the notion of a "journal version" and thus have much stricter standards.


back assign DOI to extant dataset Would it be possible for researchers back-assign a DOI to an existing dataset they have rights to - where there is no permission to share it publically (privacy, organization proprietary, personnel to clean data...etc)?

This would at least allow tracking the same dataset usage across multiple papers (+ - stages of).

To clarify (30 Jan 2019): What I mean is for example there are many papers already published that use the same dataset. In some cases it is not possible to get rights to the dataset. However, at the very least I believe it is useful to back-assign a DOI to these datasets that would allow for people to track datasets across multiple papers, particularly in this age when we may inadvertently commit the sin of deriving effect sizes from the same population if the dataset being the same is not clear, as we sometimes may surmise where people who collected the data get authorship rights directly grandfathered in -  but in cases where they are not we may not be able to tell that we are using repeat datasets!

One way to get around the fact that these datasets are not available on open access is to have, under their DOI, descriptions of items that are included, waves of collection, and sample size - these can generally be derived and gradually pieced together from past studies. It then becomes clearer for replication or es. estimation whether the re-analysis actually used the same parts of the dataset. For example using only wave-2 instead of both waves, and using depression from Beck's inventory rather than depression based on interview.

I think is to a certain degree possible. One can potentially put the dataset in a data repository that assigns DOIs to the dataset. Then the dataset can includes the links to the published article as references. DataCite DOIs have a mechanism to do this. One can look at the documentation in  their website.
edit: Full disclosure, I work at DataCite as a developer.


What is the name of the style where research papers are written in a story-telling fashion? The following research paper is written in a story-telling manner:

One Mentor-teacher's Personal Narrative of Collaborative Research on the Teaching Practicum

What is the name of this style of research paper writing?

One word for this is an experience report. This search on Google scholar reveals that many experience reports seem to have a similar purpose to the paper you ask about.

An experience report relays some important, often qualitative information about the authors' experience in deploying a method, hypothesis, or idea in the real world, whereas a regular research paper focuses more on proposing a new method, hypothesis, or idea, and arguing why it advances the state of scientific knowledge.

Also, I think we should separate two things here: the style of the article, and the style of the writing. The style of the article is accurately described as an experience report. On the other hand, many experience reports (see the above Google scholar list) do not have such a flavor of a story -- unlike your article, which begins

  I remember sitting on the green couch...

I would say this is more a matter of writing style than actual content. I have read articles in my field (computer science) that are like this, but it is rare and unorthodox. It likely varies by field, and in some fields (I would guess that philosophy and comparative literature are examples), storytelling is a standard and accepted style of writing.


What to do when professor is not responding to emails or calls and his action is required to commence a masters thesis? I'm planning to start my master thesis soon. It will be an external thesis in a company.
As this kind of thesis is not very common in German universities, I need to find a professor to supervise me during the whole semester.

Luckily, a professor agreed to supervise me and advised me to meet with one of his PhD students to discuss my task, whether it meets all requirements.
We met the following day. Afterwards, I sent my professor an email to arrange a meeting to clarify some things and get his final OK.
As he hadn't responded for about a week, I sent a reminder. This time he answered, we met and discussed more things. He even suggested to call my boss to clarify my task, so that I don't need to "liaise" between them.
After talking to my boss, I sent my professor another email with times, my boss is available for the call.
This email remained unanswered for another week, as well. I wasn't sure how to react, so I contacted my professor's secretary. She told me to send him another reminder, also she would talk to him.
He answered me a few days later and promised, to call my boss during the following 2 days.
Unfortunately, he didn't.

I'm in an awkward situation now. My boss asks me regularly about my professor, because I can't officially start my thesis before we have his OK. I'm waiting for about a month now.
The phone call between them wouldn't last more than 10 minutes and all the little issues about my thesis would be resolved.
His secretary said, this behaviour is very uncommon. Also, I never had any problems with this particular professor.

You didn't really ask a clear question but I will answer the implied question: "How would you handle this situation?"

My experience with emails and professors is they are hit and miss. For some this is a very reliable method of communication, for others it is not. This isn't necessarily generational since quite often universities give young professors a new email address which becomes just one more thing they must monitor. In addition, as the semester progresses, most professors' inboxes become flooded with student email along with the usual administrative junk mail.

My suggestion would be find some way, phone call, in person visit or "ambush" outside of a regularly scheduled class. Once you find a way to make contact, explain your predicament with your boss and ask if there is a better method of communication. If the reply is "just use email" then explain this has proven unreliable and if you are to continue with the collaboration you need another method.

Bottom line is this is a collaboration.  If you are unable to collaborate, you will not finish your research and you will damage your reputation at your job.  If the professor is unable to communicate in a manner supporting the collaboration, you need to find a different professor of find a different thesis.


What to do when your research interest changes to a new topic half way into your PHD? I had started my PHD last year on a topic that has several interesting problem to solve in the area, however I don't find the problem interesting enough to spend 4 to 5 years. As time went by and I came across a new problem through my course work and interactions with various professors. I have started liking a different topic that is not very related to the original.

What would be a wise thing to do in such situation? Is it a common situation (changing topics midway)?

First, I've yet to meet a single PhD candidate that actually did what he proposed in his research plan in the first place. Unless he entered for some particular project (and still)

Is not uncommon to look for different topics and think that it may suit you better, and in all fairness, you should be doing something you like, not some topic other person imposed on you.

Now, switching topics, specially if they are unrelated, will have the consequence of delaying your PhD graduation considerably. I switched topics on my PhD 2 year, but I mostly changed the application, while the most fundamental part (in my case, it was the math) was pretty much the same, so I got to use most of the foundations I learned over the first couple of years.

I hope it helps.


When to assume the submitted paper has been withdrawn formally (Springer)? We had submitted a manuscript in September 2016. Till now the paper remains with the editor showing status "With Editor".

After several reminders, we could not get any reply from the journal side. Only it was the editorial manager (staff) who responded with standard reply "I have forwarded your mail to the editor...".

Finally (approx. 20 days back), we decided to withdraw the manuscript from the journal and hence we wrote a formal mail to the journal. Till now, we have sent 3 reminders regarding the same and have received no confirmation from the journal side. However, the status of the paper remains the same "With editor".

Question:
Now, should we consider the paper has been withdrawn from the journal and submit the paper elsewhere?

I am really inexperienced in such process and suggestions would help me learn it.

Your question does not contain information necessary to give you the best advice.

One possible interpretation is that you submitted the paper in Sept '16, waited a month and then started sending emails at the rate of about two a month asking about the process of its evaluation. Then not getting an answer, you got impatient and decided just before the winter holidays to withdraw your paper and try for a quicker response elsewhere. Whether or not that is a fair interpretation, I will try to make some comments about the submission and review process that you may find helpful.

First, from an author's point of view, the editorial process of reviewing a paper works at a regrettably slow pace. Often the paper is assigned to an associate editor for a preliminary review, and then sent to several reviewers for their opinion. Typically, associate editors and referees are donating their time and are juggling
several projects at once. Time delays vary widely by field, by individual journal, and by submissions to any one journal,
but several months is certainly not an unusual delay.

Second, withdrawing a paper after submission is unusual and should not be
done without compelling reasons, such as discovery of fatal errors in the
paper. As mentioned in @Kimball's link, withdrawal means wasted time by
editors and reviewers. It should be done rarely, for compelling reasons, and with abject apologies. Withdrawal because you think the review process
is too slow might be OK after very many months without any communication or signs of progress,
but not after the relatively short time you describe.

If this is mainly on target, I suggest you stop emailing the publication manager and wait for several months before submitting your paper elsewhere. Meanwhile,
spend your energy working on your next paper.

A couple of personal anecdotes may be helpful to understand the time scale. (a)
I have served several times on the publications board of a large professional society that publishes several well-known journals in my field.
Trying to speed the review process was always a major topic of discussion.
We used a high 'clearance rate' by six months as a sign of progress.
(b) Recently, by request, I submitted an article for an encyclopedia organized
by a major scientific publisher. I received periodic reminders from the
publisher's staff and the editor of the due date, which I barely met. Then I
heard nothing for four months until referees reports came in, along with
requests to make a few changes and additions within a week. None of this surprised me
because this was my third invited article for this publisher, and the first two had proceeded on a similar time scale.


Is it unethical writing two papers addressing two aspects of the same research project I am currently a master student working in robotics. In my research project, I have been building up a systematic framework for my research problem. I have collected a dataset in the process and experiment with it using some advanced algorithms I have proposed. And the paper 1 I have written address in detail how those advanced algorithms are defined and come in handy, while addressing the framework in less detail (more details are focused on the algorithms, making the scope of paper really large already. Only a simple logic procedure of the framework is briefly discussed). The first paper's already submitted in a computer science conference & is under review.

Then after some experiments, I have realized that, the systematic framework I have applied in Paper 1 can be a more generic framework, applying to different scenarios for different application purposes. In this case, the experiment I have in Paper 1 with the advanced algorithms can be seen as a complex extension to this generic framework & the advanced algorithms I have used for paper 1 experiments can be adapted or even simplified to better suit the framework. I have written another Paper 2 discussing this generality aspect of the framework, with the simplified yet slightly different experimental setting as a case study for the framework. A portion of the dataset originally proposed with Paper 1 is used. I have also discussed in depth how the generic framework can be applied for a completely different scenario, but I did not have the case study for it at the moment.

Also, while paper 1's still under review, I have submitted an ArXiv preprint. All similarities in paper 2 compared to paper 1 are properly cited, noted and discussed if necessary, when writing paper 2.

So the question is, is it OK to have this paper 2 submitted to another conference (an automation conference vs. the previous cs one) for review? Is paper 2 considered to be an "unethical salami slicing"?

Thank you!

Is it unethical writing two papers addressing two aspects of the same research project

Not necessarily. However, the following would be unethical:

Submitting two papers with any overlapping material without one citing the other and explaining the parts that overlap (giving credit to where the idea was originally proposed)
Submitting essentially the same idea to two different venues at the same time (farming to see if one of them gets accepted)
Submitting two papers with exactly the same idea, but making it sound like they are two different ideas

Basically the rule of thumb here is: be honest. It is fine to split an idea across multiple papers; my advisor has suggested doing this a few times, and it's worked out well. But if you are doing anything to mislead the reviewers, then you have a problem of misconduct.

  I have realized that, the systematic framework I have applied in Paper 1 can be a more generic framework, applying to different scenarios for different application purposes...I have written another Paper 2 discussing this generality aspect of the framework, with the simplified yet slightly different experimental setting as a case study for the framework.

Congratulations on this realization!
This sounds like a significant advancement over your Paper 1, and it constitutes research progress. So there is no ethical issue with writing the second paper.

  Also, while paper 1's still under review, I have submitted an ArXiv preprint. All similarities in paper 2 compared to paper 1 are properly cited, noted and discussed if necessary, when writing paper 2.

Great! It sounds like you have already taken the right steps here.

  So the question is, is it OK to have this paper 2 submitted to another conference (an automation conference vs. the previous cs one) for review? Is paper 2 considered to be an "unethical salami slicing"?

It is OK, since you have cited paper 1 and made it clear this builds on it. Now, it is up to the reviewers to decide whether it is too much overlap to be novel. You may find that the reviewers love the idea, or maybe they will say it's not clear to them how it is an advancement over paper 1. Either way, it is perfectly ethical to submit and see what they think, assuming you have been honest and professional.

For comparison, here are some examples of ethical behavior (roughly taken from experience):

We have a new idea, which we write up in Paper A and submit as an invited contribution. At the same time, we are working on an application that uses the idea from A, and in order to make the paper self-contained we need to explain some things about that idea. We write Paper B which applies the idea, and we put Paper A on arXiv and cite it in Paper B. Paper B has a section on the new idea and explains clearly that this idea was originally proposed in Paper A.
We have a paper which is too long and needs better motivation, so we decide to split it into two papers instead as part of the paper is not really helping the reviewers like it anyway. We write Paper A which basically contains the bulk of the original paper minus one section which studies a particular theoretical aspect. Then we write Paper B which studies the theoretical aspect. We rephrase Paper B to only contain the parts that were specific to that aspect, and remove parts related to Paper A, so that the papers no longer have any overlap.


If a PhD position I applied for and didn't hear back from is removed and relisted, does this mean I was rejected? I just wanted to know your opinion about an issue. I have contacted the supervisor of a research project for doctoral student position and I was encouraged to apply. By the way my experience really fits the position.

I applied through the online application platform and after that I didn't receive any mail regarding my status. Then the post for this research position was removed last week and I just saw that the position post is online again.
I think that they are not interested in me, because if they did I think they would have contacted me and not put the post again to search for applicants.
What do you think? Am I rejected?

Reposting a search does not mean that all the applications received so far are unsuitable. It often means that they have not received the volume of applications they expected/wanted and are worried, without having necessarily looked at the applications, that they might have missed qualified applicants.

Given you applied on line and never got a status update, it is always reasonable to ask if everything is okay. Given the position has been reposted, it is worth asking if you need to resubmit your application. Sometimes HR and electronic systems do not carry applications over from one position to the next.


Into which category of the CRediT contributor role taxonomy does an (extensive) literature review go? A large part of this one paper of ours is a literature review, that was carried out mostly by one person. In the statement on contributions (which is supposed to follow the CRediT taxonomy, Brand et al., 2015), we would like to honour this effort appropriately.
We however have a hard time figuring out which of the categories is appropriate (see Table 1 in Brand et al. 2015 and/or Allen et al. 2019). It seems to me that none fully encompass literature review. The following ones come closest IMHO:

Formal analysis: Application of statistical, mathematical, computational, or other formal techniques to analyse or synthesize study data
Investigation: Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection

To a lesser extent, also these might be appropriate:

Resources: Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools
Data curation: Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later re‐use

What are your opinions? Into which category of the CRediT taxonomy does literature review fit best?
-- edit: @Gaviota provided a link to a better accessible reference to the CRediT taxonomy: https://casrai.org/credit/

Literature review is "formal analysis."  In particular, it "synthesizes" data.
Exceptions apply for poorly executed literature review.


Could it hurt my grad school application if I state a secondary research interest in which I have little experience? Background: CS Undergrad applying to CS PhD USA.
I am interested in this 'X' domain but have not really invested time to do 'research' in it because it is computationally very demanding and I do not have access to such resources.
I have covered a good amount of theory in the topic but that doesn't add up to much unless I can show some form of practical implementation. That's just how the domain is.
I would really love to explore/research in the domain during my graduate tenure.
Question: Will it make me look bad if I say so while applying to graduate programs?
Note: I have good amount of research done in my current domain 'Y' and I will be mentioning that I will want to primarily want to work on that during my graduate tenure. But this domain 'X' has few avenues I would like to explore because it has shown to be beneficial when applied in 'Y'. Apart from that, the field on its own is extremely interesting to me.

I seriously doubt it will make much difference. You say you primarily want to work on Y, and your research experience is in Y, so you'll be judged as "a prospective Y student." If applying X to Y is a reasonable thing to do, then saying you're interested in X shouldn't hurt you at all. But without any real background in X, it also probably won't help you much.


How to cite a physical product? Assume I’m writing a paper about randomness.  I might have this text:

Traditional TRNGs are (typically) external add on devices of varying size, cost and complexity [11][12][13].

How would I cite three specific examples such as:

TrueRNG v3 – Hardware Random Number Generator selling for ≈£50

Quantis Random Number Generator selling for £1100

Dice-O-Matic accessed 25/11/2017 which is a DIY toy device

Are these even products, or are they just web sites?  What if the referenced product was a Ferrari sports car instead?  There are a few product citation-style questions showing up in the Similar Questions box, but they tend to be for software.  Is there a difference?
Note that I'm not asking a philosophical question, rather a formatting one.  What do I actually type for each reference?  What would you type if you were going to add a reference for say the TrueRNG?

In the example given, you are citing the specification, not the device.  The same is true of the Ferrari.  You are going to say something(s) about it, such as horsepower or wheelbase.  So, find and cite the specification(s) needed.

Edit: For the TrueRNG, you cite the web page, if that's the best information available, using whatever your discipline's citation style requires for a web page.  You're still citing the source of the information, not the physical object.

If you actually examine a physical product, e.g. reverse-engineer TrueRNG, then you describe the object you examined and the findings of your examination.  So, "I disassembled TrueRNG model ABC, serial number XYZ, and measured the actual entropy generated by the frammistan module using Someone's Method and determined..."  Similarly if you measure the acceleration of that Ferrari.  (I don't think that's the question you're asking, but that's what the title implies.)


How do you send personalized information to each student in Moodle? My institution uses Moodle as our learning management system. I want to send some information to each student that is personalized to each student but without any other students seeing it. This is a need I often have from time to time, but my most recent need is to give each student a  unique ID to access a separate online resource.

In the past, I have done things like posting a spreadsheet with all student information, except that I somewhat hid their student IDs by only showing the last four digits of their student IDs: that way, students could identify which row applied to them, but could not easily tell which one applied to any other student (unless they already know their student ID). This is an unsatisfying solution, since a student who knows another student's ID would then know their information.

Is there a way to send small bits of information customized to each student? I'm imagining some sort of interface where I could maybe upload a spreadsheet with each row having a student IDs and the custom information in other columns, and then students would only see own their uploaded information, not any other students' information.

Edit: In response to one of the answers: my point here is that I am looking for an automated solution where I can post feedback to many students (whether 20 or 200) at one time, yet personalized based on some criteria (in my example, giving them individual IDs for an external computer system). So, individual entry of feedback one-by-one is not an option for my need here.

Note: I've searched around other StackExchange sites, and Academia SE seems to be the one that would be best for responding to specific how-to questions for Moodle. I'm surprised there is no tag for "Moodle" or even for "learning-management-system".

Here's a quick thought based on a different learning management system (LMS). Sometimes in other LMSes you can upload feedback to assignments: you batch download each person's file, add comments, and upload each file with exactly the same name. Perhaps you could try a workaround like that, perhaps requiring you to create a fake or trivial assignment and get everyone to submit it.

For a more general approach that is LMS-independent, you could use a spreadsheet and your email program with mail merge. That is, one field would be email address and another field would be the comment, and optionally you could have the person's first name in another field. Then you could send a generic message with blanks filled in by the program. (You could send out a practice message to your own addresses or without any personalized content beyond student names and you can check your Sent log to see if it worked right.)

(The latter solution defeats the purpose of a LMS, but limited feature sets do, as well.)


Is the development of an original/novel method a contribution to knowledge worthy of a PhD? A PhD is supposed to be an "original contribution to knowledge". In my own PhD (in an area of Earth Science) I have so far focused on developing a particular method of computer modeling that will hopefully have a range of applications in my field. In short, my question is: Is the development of an original/novel method a contribution to knowledge worthy of a PhD?

The reason for asking this is that my supervisor seems concerned that I am drifting away from the 'pure science questions' in my field - i.e I ought to be trying to address a fundamental research question with this new model, rather than spending most of my time developing and testing a new method.

My instinct says that it should be just as valid, and browsing some of the other PhD theses from the department suggests that a few others have gone down this route. I also see plenty of papers published that are more 'methods-focused' than 'fundamental-question-addressing'. Personally, I get a lot more satisfaction from pursuing the former kind of research, even though I accept I am not directly answering 'the big questions' as my supervisor puts it.

On the other hand, I don't predict an entirely healthy academic relationship down the line if our opinion on what constitutes an interesting PhD thesis differs so fundamentally, and I also consider the possibility that I could just be plain wrong in my interpretation of "original contribution to knowledge".

While developing a method has merit within science, that method needs to be useful to answer a question which is not currently answerable. I too have worked primarily on methods development in graduate school, and got stung by this in some of my early work. So, while it's good to focus on the method, always have the question in front of you: "what do we want to know, that we can't know yet, that my method will be able to tell us?"


How can I ask a lecturer not to disclose (my) grades publicly? In my university, some, but not all, professors upload online the grades of every student and their names next to them a few days before making them final at the end of the semester. This is done to give a chance to the students to point out any mistakes the professor may have made.

No one other than the students can see the final grades. When the professor uploads the non-final grades, they are visible by everyone.

Since few people get good grades, others are jealous of them. Long story short, I really have an issue with this.

Is it unreasonable to ask my professors to email my grades instead of uploading them online, like they do with everyone else? What other alternatives do I have?

I live in a European country.

My experience from European universities is that even there, the grades should not be posted by name, but instead by a student ID number. However, in most cases, the university requires the public posting of the grades in such a manner (for exactly the reasons you suggest—to allow for petitioning of changes in grade).

Moreover, because of the large number of students who may be taking a course, it can be highly impractical for the teaching staff to respond to individual requests for reporting grades by email. (Imagine having to send out 1800 emails for a single course!)

Consequently, while you might ask for anonymizing of the grade reports by student ID instead of name, I think it would be unfair to ask the professors to report individual grades by email, unless you have an exceptional and demonstrated need for such an email (for instance, you are unable to get your grade because of extended absence without Internet access).


How do people who are not active in the academic community write papers? Is it possible? I've recently finished grad school.  I've written two papers since that time, and I think that I have more good ideas.  However, I find it difficult to write now for two reasons.  First of all, I don't have access to the ACM / IEEE literature databases anymore (they were gratis from my university.)  Second, I have no one to review or even proof-read my papers.

So, how do people who are not active in the academic community write papers?  Is it possible?

It is possible.  After years away from graduate school, I am working on several papers, covering both old and new topics.  However, it is very hard to do it completely alone.

I have the benefit of some contacts acquired through one of the forums on StackExchange.  They are willing to spend a limited amount of time (say half an hour a month, to within two orders of magnitude) in email correspondence with me, to receive drafts from me (but not necessarily referee: just skim for a few minutes and find more egregious aspects which they kindly point out), and most importantly, endorse me for posting on ArXiv.  Before the endorsement, I spent some time convincing them I wouldn't be a drain, that I could help them out, and that I might hope for a favor regarding endorsement, literature search, and so on.  Realize that from their perspective, I could be a potential crank or downside on their career, as I have given them almost no verifiable information on what little credentials I have.  I consider their involvement as a gift, and treat it with care.

With their minimum involvement, I now can post results on the ArXiv, and use that as a stepping stone to loftier research goals.  Since your express purpose is to produce research for others to read, you need three things: the research, the others, and a means to get the research to the others.  Getting to know one or two of the "others" is key.  Getting them by being helpful on this and other fora is one of the easiest routes I know for this step, but there are other methods.  There may be other repositories besides the ArXiv which may help you more, but I don't know of them; ask the "others" for advice, and respect their time.  The research part, that's your problem, again with which "others" might be able to help.


Are there statistics for the share of work which is put into planning, performing, analyzing and documenting of an experiment? I was wondering if there are statistics of the ratio of effort (or time) for the different steps in experimental research?

Given that all the work which needs to be done to start (literature review, funding etc.), I would assume the following steps are necessary:

Planning of the experiment (or simulation)
Performing the experiment
Analyzing the acquired data
Documenting the findings

Are there statistics or other research on how the overall work splits up into these steps? (Something like Planning: 10%, Performing: 5%, Analyzing: 50%, Documenting: 35%)

There is no widely applicable rule of thumb on how to allocate resources across sub-fields and individual researchers/labs, and often the percentage break-down will even vary by project. Its something you can note retrospectively, but the variance is so high as not really to be usefully communicable; if a rule of thumb existed, you should probably ignore it.

In practice, projects differ both due to the preferences, skills, and working styles of the individuals involved, and external factors of publication culture, funding, method used, and the problem itself.

As a simple example, some projects just need a lot of careful planning to make sure they come together. This is especially true when the subject of the study is difficult or otherwise expensive to work with such as expensive/rare equipment, costly chemicals, long-running computer calculations, and human beings (especially when they are from a "vulnerable population" or some risk is involved). On the other hand, sometimes planning just takes longer because the idea needs a lot of work - while on some projects it can feel like the idea is nearly fully formed from inception and things just proceed rather obviously from first principles.

Similarly, analysis sometimes can be wrapped up in a few days - in part because of how the project was planned, and in part by the method used. In an experiment using null hypothesis testing using p-values and decision rules, if you are doing things right your analysis should be incredibly straight-forward to run because you had better have decided long in advance of getting the data what you were going to be doing, because otherwise you are doing it all wrong. But if you are doing exploratory data analysis or using various flexible modelling methodologies, analysis could go on for quite a while ("what if you included X in your analysis? what about Y? What about an interaction between X and Y?").

Similarly, writing everything up into a publication could take a few days to a few weeks for some projects/venues - or could drag on for months and years (especially true in areas like Economics, and even more true when the authors target top journals). This also varies widely depending on the project (some are just easier to describe) and the methodology (the more complex and less familiar the methods, the more work may need to go in describing it and figuring how one even should describe it). In some paradigms the analysis stage is the writing stage, while in others the papers are more formulaic with established guidelines that feel more like filling out a template.

To restate the initial conclusion: no, there is no rule of thumb, and if there were you should ignore it. There is no Right and Proper Division of Research Labor. Instead, learn the factors of how the stages work together, what seems to work well and what doesn't, and adjust per-project, per-collaborator, and as you develop your style and skills. Importantly, learn the expectations of your mentors and collaborators on timeline and specific plans, specific to the exact project you have in mind. Often it is only when all the details are laid out clearly for someone else that you can discover that, oh my, there is no way that a certain task is going to be as fast or slow as you thought. This way you'll develop more "known knowns" and "known unknowns", and develop ways to account for them - and end up being shocked by the countless "unknown unknowns" that appear with every new experience no matter how expert you become.


How to proceed when graduate advisor treats me terribly, possibly because of age? I am an older graduate student who went back to school after getting laid off.

Going back to school wasn't easy and I had to take some classes to prepare myself for graduate work.

I had one professor who later became my graduate advisor and demonstrated a very harsh attitude toward me. For example, one class he taught was advanced statistical analysis which he did not use a book but used his own notes. I checked out a few books at the library to help with the subject. However, during a visit during his office hours I mentioned that I was using a book as well as his notes and he literally blew up at me. This wasn't the first nor the last time this happened. During one of the classes, he basically yelled at me and told me I was wrong when I answered one of his questions. But oddly, this seemed to back fire on him because another student rose his hand and asked why I was wrong? The professor then back-peddled immediately and stated that I was technically not wrong but would not explain further why I was wrong. As well, I have noticed that it seems that my grades are always marked down despite being equal work or better work than fellow students: I have compared solutions, quizzes, and exams numerous times. As well, to make things even harder during the second class I took with him, he called my cell phone at 10 PM on a Saturday and asked me, “What issues I had with him?” I felt this was completely unorthodox and weird. I replied I had none and even asked was there something in particular that I did to merit him calling me; he replied "no."

I know this sounds weird but I really think he is biased against anyone who is over 35 year old. I have had one other older graduate student tell me that he treated him in a similar fashion.

I currently have completed all required classes and electives but I need to finish my project to graduate. However, he is my project adviser. After much asking, he basically gave me the project late and I didn't finish on time. I am now into another semester. I now believe this is his weird attempt to play some final games with me in an effort to block me from graduating.

I have become so frustrated that I want to leave the university. It is hard to deal with him and he is very nasty at times.

I had a class with an associate professor who ended up leaving the university for full-time faculty position elsewhere. She never came out and said that this program wasn't good but she hinted that there are some better programs out there. I began to look around and actually found several that are about 35 minutes away that offer similar programs and may actually allow me to transfer with some credit.

I need to get a better job and that MS on my resume would open a lot of doors.

So I have several choices, I can either continue to try to work with a hostile university adviser or leave for another university. But if I transfer I will end up staying at the crummy job for about another 1.5 years. I would like to hear any suggestions.

Update October 2019

Not to drudge up the past but the short of it is, I graduated and the university awarded my MS degree.

That professor whom I had issues with or had issues with me, ended up leaving the university. What I ended up doing was taking some of the advice that JeffE posted. I contacted the former professor I had a good report with, and asked for a reference. She basically asked me why I would want to leave the program when I had basically finished, and I explained to her what had happened. Apparently, she is still on good terms with the department chair and actually called him on my behalf. I then contacted him per her advice and he assigned me a new adviser; within about three/four months of that, I had my final work submitted, presented the results to the graduate board, and it was approved. I graduated following semester.

Don't walk. Run.

Your advisor's behavior is unfair, disrespectful, abusive, and deeply unprofessional.  Their behavior reflects horribly on the department, and since it's unlikely that you are the only victim, may undermine the value of your degree.

The moment the professor blew up at you for using another textbook, it would have been appropriate for you to walk out of his office/classroom without another word, drop the class, report his behavior  (both in writing and in person) to the department head, and find (and/or demand) a new thesis advisor.  Even now, I would recommend all of those responses.

If the department has your back, break all contact with your old advisor, finish the degree, and get out.  Block his phone number (but save his voice mails).  Reroute his emails to a separate folder (but don't delete them).  If he shows up at your office, ask him to leave; if he refuses, leave yourself; if he follows you, walk to the department chair's office.  In any case, document everything.

If the the department is unwilling or unable to help you separate from your abusive advisor and finish your degree, then the department is being abusive and unprofessional.  Again, since you are unlikely to be the only victim of this abuse, it will get out, and it will undermine the value of your degree.  Then your best choice is to leave the degree program, apply elsewhere, and try to transfer as much credit and research progress as possible.

I recommend contacting the associate professor who left to act as a potential reference, and for more general advice about how to proceed.  She knows the local political landscape far better than any random stranger on the internet.

Your age and educational background is not relevant here.  Your advisor's behavior is completely unacceptable, and you should not have to put up with it for even a second longer.  Get. Out. Now.


What are the downsides of using arXiv references instead of conference/journal references for the same paper? I read in the instructions for one conference:

  Please do not use arXiv references for papers that have been published at official venues such as conferences, journals or workshops. Please make the effort to identify these at least for all papers prior to 2017.

What are the downsides of using arXiv references instead of conference/journal references?

Using arXiv instead of a conference / journal reference has an obvious disadvantage: readers won't know that the paper eventually passed peer review and was published.  This is important context, especially if they want to cite the paper themselves later.

Using arXiv in addition to a conference / journal reference might make sense, but some might consider it redundant.


How do you pick a major when you're interested in pursuing advanced study in an interdisciplinary field? I have a keen interest in some interdisciplinary fields such as neurophilosophy, neuroethics, and neuroaesthethics. In the end, I have chosen to study biology, but I am not entirely sure if it is the best possible choice, and maybe philosophy would have been a better choice.

What advice would you give to people in general who desire to be involved in interdisciplinary research? Do multiple bachelor's or master's degrees? Maybe even different PhD in the fields of interest?

If you're planning on doing advanced study in a truly interdisciplinary field such as the ones that you've listed, I'd recommend to look for what programs exist at the graduate level, and what their "home" departments are. If they're consistently in one department, I would then plan to do a major in the "home" area, and at least a minor in the other discipline (presuming there are only two fields overlapping). If you're planning to stop with master's degrees, you might consider two master's, but only if they were both funded. If you plan to do a PhD, there's probably no need for two separate PhD's—instead, try to look for a program where you can take coursework in both areas and do a project that combines them.


Applying to several faculty positions within the same institution The following question is not a general question about applying to many academic positions at different institutions, but rather about applying to several positions within the same institution (regardless of applying to multiple other institutions as well).

Starting questions are, as follows. What are the potential effects, following such strategy? Mostly negative, mostly positive, or neutral? A situation often occurs, such that an institution advertises several positions that I believe I would fit, however, among those, only one (usually) is the best fit (my preference), but others are also suitable (decent fit).

Therefore, my main question is twofold: 1) is it a good strategy to apply to all suitable positions within an institution; 2) if Yes, should I mention my preference, for example, in an application letter (if No, I assume that the optimal choice is to apply to the position with the best fit)?

I advise against applying to all open positions at once. Not only are you likely not to match the criteria for many of them, but you are also not interested in accepting the majority. By doing so, you just add to the workload of the hiring committee. Furthermore, you decrease in a way the value of your applications, as you should be striving to emphasize how you are the best fit for that particular position and that gets very hard when trying to apply to many different positions. You'll likely end up with mediocre applications everywhere or worse with the same application everywhere.

Typically, there is one position (as you say) which you would like to get and are probably best suited for (depending on your qualifications). So I suggest you narrow down the open positions to at most 2-3 where you would most enjoy working, if you insist on having a safe option, and try to write strong applications for all of them.

Finally, I don't see how a "preference statement" would help you. If you get your desired position, then it is not due to your preference, but because you had a stronger application than other applicants. If you, however, fail your best option, other options might see it as a negative thing, if you stated that you'd prefer to work at some other position and only failing there, apply to them. So, the idea is to apply everywhere with the same zeal. That's also why you have to narrow down your options, so you can accept any outcome, if needed. If you are not comfortable with that, it means that the options aside your primary are not attractive to you and I would refrain from applying to them (assuming of course you are not in a position that you have to accept any job). In the case you get accepted on more than one position, you are of course free to choose.


What does "research activities" mean? When a graduate/fellowship application asks about research activities, am I allowed to talk about expository pieces that I have written? For example, one semester I read a highly influential and important paper in my field. I then wrote a significant length expository piece based on it. Would this be considered a research activity? Or does this imply that it has to be original research? I looked everywhere through the application guidelines for the explicit definition of "research activity" but I couldn't seem to find it anywhere.

Writing an expository piece is not a research activity. Research activity in general refers to original research with things like systematic reviews and meta analysis falling into a gray area.


When should academic papers be censored due to public health/safety concerns? A recent paper in the Journal of Infectious Diseases, which reports the discovery of a new botulinum toxin, has been censored:

  Because no antitoxins as yet have been developed to counteract the novel C. Botulinum toxin, the authors had detailed consultations with representatives from numerous appropriate US government agencies.

The team sequenced the bacterial DNA corresponding to the toxin, but did not publish it.

I can understand the reasoning, and see how it might make sense, but I wondered: in such a case, what are guidelines that should be followed? The editors indicate consultations with governmental agencies, but I think the government might in some case be overly eager to censor data that should, from an ethical and moral point of view, be disseminated.

So, without turning this into a political question: what are guidelines (written rules as well as unwritten moral standards) that an author, reviewer or editor should follow concerning potential public safety issues? I looked for information on the COPE website, but could not really find anything relevant.

This is a rising bioethics topic in the area of biosecurity and dual-use developments. Last year there was controversy with H5N1 and censorship.

There is no set policy, though they are being devolped. Most recently there is the United States Government Policy for Institutional Oversight of Life Sciences Dual Use Research of Concern

The IAP Statement On Biosecurity principles state:

  Awareness. Scientists have an obligation to do no harm. They should
  always take into consideration the reasonably foreseeable consequences
  of their own activities. They should therefore:

  always bear in mind the potential consequences – possibly harmful – of their research and recognize that individual good conscience does
  not justify ignoring the possible misuse of their scientific
  endeavour;
  refuse to undertake research that has only harmful consequences for humankind.

The US NIH Office of Biotechnology Activities is often consulted for such research concerns and has a report Enhancing Responsible Science Considerations for the Development and Dissemination of Codes of Conduct for Dual Use Research and has a series of recommendations.

The WHO Laboratory Biosafety Manual does not address publication restrictions/censorship.


How can I decline the request to help a fellow student cheat? I have been helping a fellow student through a class I'm taking this semester - sending him homeworks, helping prepare him for tests, etc. The other day we had our final, which was online, and he asked me if he could pay me to send him the solutions after I turned in my test. (The program immediately gives you the answers when you finish.)

Somehow at the university I go to this is complete commonplace; people are consistently cheating on every test, every assignment, it's totally normal for me to be asked by a classmate I don't even know to help them cheat. Maybe it's something about me that broadcasts to people they aren't at risk of me snitching. I have recently decided I no longer wanted to participate in this, but I have had trouble telling people politely that I won't be helping them.

I have no desire to bring this up to any administration, because it's not a big enough deal to me, but it is a big enough deal that I don't want to be included. I also don't have any desire to moralize to these people, because first of all what do they care, and second of all what do I know. But I do need some help navigating this extreme culture of cheating. Thanks.

Edit: It is clear from the responses that I missed some important context. I have no problem with saying no to people I don't know and don't like, but I am coming from a position of formerly participating in this cheating. Thus it will be easy to misinterpret as self-righteous. "No, and leave me alone" is insufficient because it leaves my acquaintances wondering what changed. Not that I overly care whether they understand my decision, but I don't want them thinking I basically cheat when I need a grade and judge others for doing the same.


Value of recommendation letters from professors in a different field I will be a second year math under-grad in September, and currently doing an internship at applied math research institute. However, my interest after my bachelors is to do PhD in pure math at a top math school. My question is:

How important would the recommendation letters from my current supervisors be while I apply to grad school? (Note: My supervisors are very active in their field of research, but are not quite famous.)

Recommendation letters may also represent soft skills. Hence, they are important. Your personality is independent from your field of study, so there is little point in withholding recommendation letters - indicating what kind of person you are - to future supervisors (or committees) if you are going to spend the next 3-4 years working in a team or with a supervisor on a project.

Source: PhD Student


Reproducible science: do entities exist requiring Level 3 of TOP standards? Quite many (if not most of) scientific articles, related to computational sciences, which I have seen, do not share both data and code leading to the figures.

This may have of course good reasons due to other than scientific, still substantial aspects involving intellectual property and data protection laws.

Not widely known (less than 1000 Google results) is though this quote on reproducible science:

  An article about computational science in a scientific publication is
  not the scholarship itself, it is merely advertising of the
  scholarship. The actual scholarship is the complete ... set of
  instructions [and data] which generated the figures. (David Donoho, 1998)

Obviously, scientists trying to climb on the giants' shoulders should experience more and more difficulties with growing science complexity.

Then, there is also a set of "TOP" (Transparency and Openness
Promotion) guidelines to establish science reproducibility.

According to the CoS, Center for Open Science:

  Over 5,000 journals and organizations have already become signatories
  of the TOP Guidelines.

There are of course open access journals, but this is about free distribution of articles (very good but not necessarily reproducible).

Now my question is, is this TOP model elsewhere binding or is it sort of idealistic: specifically, are there journals/universities really inforcing the highest Level 3?

  Level 3: Data [and code] must be posted to a trusted repository, and reported
  analyses will be reproduced independently prior to publication.

Further sources:

"Building Research Integrity Through Reproducibility
University of Utah" Victoria Stodden, 2018 slides

There is at least one peer-reviewed journal, reScience, that explicitly publishes replication of existing work.

  ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible.

So as such it does require high TOP levels.

Overlay journals such as those in EpiSciences play well with reproducible science, since their model is that you put everything online first, and the "journal label" is applied by a review committee that agrees that the results are correct and worthy of inclusion in the journal


Is it unethical to supply a marked solution to a student who has shown intent to cheat, with the intent of identifying them for punishment? One of the adjuncts that I work with found that one of their students had posted their assignment on a freelancer website asking for the solution.  We joked that he should place a bid and fail them afterwards and had a lengthy discussion about sting operations and entrapment.  In the end we came to the conclusion we were not the police and to let the chair know, but ultimately take no action.

I was wondering about the ethics of anonymously giving this student a marked solution with the goal of identifying the student and removing them from the class.  In particular, I'm imagining we could write a correct solution with some additional embedded information that will be recognizable when the student turns in their answer (for example, extraneous steps that don't break the solution but don't add any value).

Some additional clarifications:

Assume that the student clearly only wants the solutions for a grade and is not asking for assistance in understanding the material
This would be done as a private transfer of documents (like email), not a public posting (like a Stack Overflow answer), to avoid issues of other students stumbling upon the personalized solution.
We would provide the correct solution, but with some additional embedded data that identifies the source; for example, metadata, comments, and, in the case of code, extraneous steps that don't break the solution, but don't add any value.

[We] had a lengthy discussion about sting operations and entrapment

The distinction between the two is important here. A sting catches someone doing something they were already going to do. Entrapment is where you trick them into doing it in the first place. If you posted an advertisement offering to solve the assignment for them, and some students took you up on that, that would be entrapment, and would definitely be unethical: they might not have cheated if you hadn't posted the advertisement.

But in this case, they have already chosen to cheat. The only thing you can change is whether the assignment that they cheated on is easily recognizable as such. In some cases, it's hard to distinguish between a sting and entrapment. That isn't the case here; there's a (presumably) timestamped job posting the proves they were planning to do this before you got involved.

You should still definitely run this by the department chair or someone first if you decide to do it, though. It looks similar to entrapment even though it isn't, and it's better to justify your actions before you do them, instead of letting it be an unpleasant surprise when the student complains later.

The fact that you'd be getting paid to do the assignment is an issue, as well. Returning the fee to the cheater after catching them probably addresses this issue. (And maybe turn it over to the department in the meantime, to avoid the appearance of potential impropriety.)

Edit: A comment requested an unambiguous conclusion, so:
My view is that it is ethical, but it's very close to the border, and either encouraging cheating that would not have otherwise happened or profiting from the actions you take would push it over to the unethical side. And because it's so close, it's important to have some amount of transparency (e.g. by telling the department chair in advance) so as to avoid both the potential for and appearance of impropriety.


What's the difference between a Doctor of Computer Science and a Ph.D. in Computer Science? I'll be starting a Masters of Science program soon and wanted to make plans for my professional/educational future.  I've been looking at different doctorate level degree programs and I'm having a bit of trouble in understanding the difference between a Doctor of Computer Science program and a Ph.D. in Computer Science program.  I've seen it listed both ways which leads me to believe that they're two separate programs.

Could someone explain what the differences between the two are and identify which is more appropriate for someone with a development and software engineering background?

Here are two programs that show the difference in titles. These are not necessarily the programs I have in mind but they're the first ones I could find that show the title difference.

Colorado Technical University - Doctor of Computer Science

Nova Southeastern University - Ph.D. in Computer Science

This is the first I've heard of a "Doctor of Computer Science" degree. Not having a dissertation requirement is a clear sign that this is different from a "standard" Ph.D program. A Ph.D (in any discipline) requires you to produce an original piece of research that you defend to a committee of experts. In addition, most Ph.D programs will have course requirements, residency requirements and so on.

Update: Some googling led me to the Wikipedia entry on 'professional doctorates', which most closely match the nature of the degree the OP describes. As Austin Henley points out, this is close in spirit to an MD (USA) and JD.


Will a research proposal of looking into applications of current technologies be frowned upon because it does not deal with fundamental research? I'm interested in robotics and particularly in how we can use autonomous systems to aid with environmental conservation. I'm currently looking into PhD programmes but it seems to me that the vast majority of research happening in the relevant departments covers pretty fundamental, low-level technologies which might sometime see an application if they get a whole lot of development.

So, what's the deal with research in technology? Are universities/institutes not a place for, say, researching "Using Reinforcement Learning in Methane Monitoring Drones?"

I can't say anything about your particular topic or area, but what you need to do is find the right advisor.  The right advisor will not necessarily be doing something very closely aligned with your idea, but will support you in pursuing your idea.  You need to be in an institution that will support interdisciplinary research.  Thus, your home department might not be a robotics program.  But you might take some coursework in robotics.  Make sure the institution you choose to study in has the key academic elements you need in order to learn what you need to learn, and will support your getting the lab space and time you'll need.

I think the key is to find the right advisor.  It might be someone with a joint appointment in two departments.  I imagine most large institutions would probably satisfy the other requirements.


How should students approach quals? Most universities (at least in North America) have the concept of "Qualification Exams" taken one year after the student joins the graduate course. These usually encompass all that was done in the first year and as undergraduate and might also test some other (research abilities) of the student by asking him to present papers (s)he found interesting.

Some universities treat this as a formality and everyone who attempts usually gets through while some universities take quals very seriously and won't allow the student to continue if he fails the quals (maybe, they will allow for 2nd attempt). These universities might subscribe reading lists consisting of textbooks and papers which comprises of "must-have" knowledge of the field. The latter might constitute as a difficult phase for students who find themselves ill prepared in a certain portion of the exam. (For instance, a pure math undergrad moving to a PhD in Fluid Mechanics and asked to attempt an exam on Fluid Machines)

Assuming STEM graduate course in a university which takes quals seriously (with a structure of written test >   oral exam > interview/research proposal presentation),

How should a student approach these exams?
Specifically,

How is it any different from the undergraduate exams that students give?
Do you really "prepare" for these exams or should you bank on the courses you have taken and the homework you solved to get you through?
Repeatedly, I have been told by students (across departments and across institutes) that the faculty is only interested in the oral section of quals as a sanity check and to see whether you have the urge for learning something new; is this universally true?

Qualifying exams are never to be taken lightly. Even when they prove to be a surreal experience, they need to be given the attention appropriate to their seriousness. These are exams that you typically only have one or at most two chances to pass. As a result, you want to get through them on the first try, so that you don't have to worry about them again.

How hard you have to work does depend a lot on the structure of the exam, and based on your previous preparation. If there are a lot of subjects to be tested with which you are not familiar, you may have to work harder than if you're getting A's in all of the classes that will be tested on the exam. (Even then, you'll still want to work through some practice problems to get a feel for the kinds of questions that are asked.)

As the original questioner mentions, in many cases the oral exam can be a "sanity check"—in more ways than one. If the decision has been largely made on the basis of the previous coursework and written quals, then the oral exam can act either to confirm the decisions of the panel, or as an effective psychological exam. One of my panels turned things into what amounted to a carnival side-show, with everybody acting completely out of character, and in a manner I found entirely unbefitting an oral qualifying exam. However, it made sense to see if there was any way to make me "snap" and lose my cool. (I was too busy trying to figure out why everybody was acting so oddly to really snap.)


What are the typical research requirements for an application to lecturer position to be successful? When someone is applying for a position as a lecture at an average ranking university in Sweden (equivalent to associate professor elsewhere), what is the typical number of research papers and of what quality he is expected to have in order to have a realistic chance of getting the position?

The skills to teach are more important than a list of papers...

Having had to suffer "good" researchers who proved to be poor at explaining things, then the focus should be on the lecturing ...


Conference submission while article under review for journal My co-authors and myself have recently submitted an article (in the wider field of economics) to a journal and we now wish to submit the same article to a conference while the article is still under review at the journal. There will be no such thing as published conference proceedings and as far as I can tell not even a working paper version of the article will be made available online.

Is there anything unethical about doing so, i.e. submitting an article to a conference while it is under review for a journal?

I am asking since for a journal submission one would write in the cover letter something along We confirm that the article has not been published or is under review elsewhere. Now I am drafting the cover email to the conference submission and I was wondering whether I should include a similar phrase and if so what to write?

For a conference submission, should (must/can) I indicate that the submitted article is currently under review in a journal, or would I not want to write any such sentence?

Currently I would not, since as long as the article has not been accepted it is still work-in-progress and as such perfect to submit at a conference (economics conferences also tend to attract fairly advanced-stage papers in my perception).

I have asked my co-authors, who are experienced and established professors in the field, on the first question and they both saw no problem. I am asking for a second opinion to calm my mind since I am likely over-thinking the issue and unsure how to address it.
I have found one related question:
Simultaneous submission short version to conference and full version to journal
but in that field it is common to have a conference paper first, often followed by a journal article. As I stressed above, the difference here is that there is no conference publication as well as the very different fields.
Submitting journal version of an article while conference version is still in review
asks about the other way round, submitting to a journal while the conference article is still under review. Here is different in that the article submitted is the same, there is no conference publication and the order is the other way round.

In economics it is entirely normal to submit a paper to a conference while it is under review at a journal. I write as an experienced academic economist who has done this (and seen it done) many times over the past 15 years. So it is fine for you to submit to the conference. No disclaimer/declaration is necessary.

The economics discipline has developed very permissive norms about this kind of thing out of necessity because acceptance rates at even mediocre journals are low (typically not much greater than 10%) and review times long. It's not unusual for a delay of 3 years to elapse between first submission to a journal and eventual acceptance at a (likely different) journal. Moreover, economics papers often take a long time to write and, as you correctly intuit, the most common practice is for economists to submit a paper to a conference only once a somewhat complete working paper is ready. If we were to insist on a strict sequencing of tasks then then result would be a decade-long cycle for a paper, whereas allowing overlap between writing, conference presentation, and submission allows work to be developed, circulated, and published somewhat more quickly.

All that said, you might like to consider why it is that you are submitting your work to the conference. An important reason, of course, is to disseminate your ideas. But another key purpose of conferences is that they provide an opportunity to obtain feedback that can improve your work. This is especially valuable the first few times you present, when you will quickly get an idea of what the controversial assumptions in your paper are, and what people perceive as the main weaknesses. It is often helpful to delay journal submission until at least after you have presented the paper a couple of times so you can figure out what issues the referees are most likely to raise and address these in advance. Given that you probably have to wait 4-6 months to get a set of referee reports back from a journal, it is often much more efficient to first get feedback on the conference circuit.


Why there is "Dr." before name of PhD degree holder? Is it right practice to place "Dr." before name of PhD degree holder? What is reason for placing such "Doctor" label before name?

Yes, this is the right practice.

A PhD degree is a "Doctor of Philosophy", and the appropriate formal title for that is "Doctor". That there are other professions that can be called "Doctor", for example holders of an MD degree, is simply because there are multiple fields one could be a Doctor of.


Is doing a Habilitation a financially viable decision? I know that a PhD is necessary to enter academia which means you can find a better job and get salary.

But, how about doing a Habilitation in Germany? Does it have any positive impact on the career or financial situation as an international student?

For instance, say someone is barely employed. So, he is supposed to get an "actual" job after the completion of his PhD. But, can he postpone getting the job until he completes his Habilitation? What would be the financial implication?

You have to distinguish things here:

A Habilitation is a qualification level (not quite like an academic
degree but you can think that it is sort of a grand PhD with additional teaching). This does not mean
that you have to be employed but in most cases you will be because
otherwise it is going to be a bit difficult to finance your life for the time it takes (normally 4-8 years)
A Habilitation used to be the formal qualification level to become
Associate Professor and Full Professor. The position of an Assistant
Professor (German "Universitätsassistent") was usually the time when you worked on obtaining the
Habilitation. Today most job advertisements do not ask for a
Habiliation anymore. Instead it states something like "Habilitation
or equivalent qualification". This allows hiring of international professors.
In daily university life a Habilitation might still be of
significant advantage. You (sadly) still hear phrases like "This guys does not even have a Habilitation - what does he even want". Also nobody will questions your ability to supervise students if you have the Habilitation (you can also without but it definitly still helps)
In non-academic jobs as in industry you will certainly not need a
Habilitation. It might rather be a disadvantage as you will be seen
as overqualified.


Different behaviour with wildcards on PubMed using quotation marks or not In my understanding of wildcards I would assume that this search string (in title & abstract fields) would give a result of 0.

telerobot*[tiab] NOT *robot*[tiab]

But it doesn't. The result is 49. For me this means that there are 49 publications which won't be found when just searching for *robot*[tiab].

Does this make sense for someone?

Ah PubMed made to much automatism of it. This isn't the exact search string PubMed will proceed. Try it and see the Search Details on the right to find out what PubMed made out of it.

The solution is to put the words in quotation marks. Only in that case PubMed take them as they are without doing something out of your control.

"telerobot*"[tiab] NOT "*robot*"[tiab]


Obstacles to collaborative research between experimentalists, theorists, and computational researchers As an undergrad, I'm just starting to get a feel for the academic world. Obviously, the ability to communicate well in a collaborative relationship is extremely important. As a fledgling STEM experimentalist, I'm beginning to learn like one.

However, can someone educate me on why there can sometimes be a communications barrier between researchers of different types (e.g., experimentalist versus computational versus theoretical researchers)? What types of obstacles cause these communication barriers? What are some examples of common sticking points?

I'm a computational chemist. I've worked with many experimental collaborators with wildly varying experiences. Barriers certainly can and most definitely exist. Here are some of the major ones.

Lack of understanding: This is perhaps the most common and basic reason and results from a lack of experience or knowledge in experimental techniques by computationalists and vice versa, quite simply because they were not trained in this area. As a computationalist, it is natural that I may misunderstand how certain experimental techniques work (especially the state of the art) because I do not spend most of my time working on it. This can result in me over-trusting a piece of experimental evidence, or underestimating the time and effort it can take to conduct the experiments. And believe me, this can definitely happen the other way round. One example, an experimental collaborator asked for some 'quick calculations' in a month and I had to point out that it would easily take a year and all of our computational budget to complete it.
Politics: In a collaboration, there is always the question of who is the major contributor. Yes, it is possible to have multiple corresponding and first authors, but it rarely changes this fact. In my field (chemistry), usually the experimental side is the major participant of a collaborative work. This means a computational PI will benefit significantly less, and his student will usually not be a first author. This generally leads to conflicts regarding contribution of time and resources given the unequal recognition of work. Sometimes, in order to solve this issue, particularly for long-term collaborations, the experimentalist and computationalist can 'take turns' directing the project and claiming the major contribution to a paper. (I am not supporting or criticizing this phenomenon, but it definitely exists in the field.)
Geographic/temporal reasons: Quite simply, many if not most collaborations occur between groups in different universities and different countries. The simple fact is you cannot communicate them like you would with your group members in the same room. This results in the need to schedule meetings and email communications which are often difficult (professors are busy!) and are invariably an inefficient way of transferring information. I've has cases where several weeks or even months of work were invalidated/or made irrelevant because I was only informed of new experimental findings many months later in a meeting.
Different objectives/expectations: There is some overlap with the political reason, but sometimes it can be purely due to academic reasons. It can be common for experimental and computational groups to have different expectations for what knowledge/results a work is supposed to yield. For example, a study can be trivially easy for a computationally group (and possibly uninteresting) to accomplish (via modelling), but can be extremely difficult for an experimental group to synthesize/characterize. Alternatively, a relatively simple reaction for an experimental group to conduct can be a multi-year effort by several researchers (or it can even be unsolvable/impractical by current techniques!).

Is it possible to overcome these barriers? Certainly. I've had collaborators who are well aware of these issues (from experience), and end up being very enjoyable to work with. As an aspiring researcher, the best thing you can do is keep these issues in mind when collaborating with other groups. If possible, try to learn the techniques which your collaborators are currently using, and maintain a steady stream of communication with whoever your direct counterpart is (usually, if you are a student, keep in contact with the corresponding student in the other group instead of the professor directly - though you can/should CC him and your advisor to keep them in the loop).

Edit: I decided to add some examples of hypothetical collaborators from the perspective of a computational researcher (for fun). Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

The Good: The well-informed and reasonable collaborator. Knows their stuff (even on your side), up to date on literature, knows what is practical and not. Knows if you are BSing them or not. Won't do you wrong if you do your job. Keep them well-fed with updates. Expect to do good science with them.

The Bad: The villain/antagonist. Only works with you because you are part of the same grant or project, and only sees you as a way of getting higher impact factor. Only interested in positive results, gives unreasonable deadlines, does not like to hear the phrase "but that can't be done!' Never satisfied, keep him well-fed, but for your own survival.

The Ugly: Nice guy, easy to work with. Okay with whatever you have. Takes anything you offer, but not terribly interested in the science behind the results. Likes you because you can help him get higher impact factor journals. Expect him to disappear one day when he has found someone better or no longer needs your help.