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Should I cite 200 webpages?
For my paper I am getting quarterly data on company earnings from their 10-Q reports. I am scraping together about 200 of these reports.
Am I supposed to cite all 200 of these, or I can just say the reports can be found using SEC EDGAR and cite it once? I'm using APA, if it matters.

Are the 200 reports all the reports on the site and does the site ever add new ones?

Its not all of them, and new ones are constantly added. @MisterMiyagi

I think this is a job for an appendix and not for citations.
Add an appendix (or in case this is too much for a journal paper, an online supplement) where you list the 200 companies/data sources in full.  Papers and theses are permanent publications, but websites change regularly, so listing to an external website is not good enough.  Although the internet archive should be permanent, it is better to keep the important information in one place.

An online only supplement is dangerous unless someone can guarantee its maintenance. And, being a website itself, it is subject to change.

@Buffy That's the publisher's job.  I think many journals are online-only anyway these days.  How the permanence of online resources is guaranteed is a different question.

The context here is a thesis, however.

@Buffy I didn't see that from the question.  For a thesis I would certainly include it in the thesis and not in an online supplement.  I've edited my answer to cover both cases.

Paper, it's a supplement or data file, but you put as much as you can in a thesis so it looks more impressive on the shelf.

If that single link points to a page/site that is updated, then it would be improper to link to only it. You might be required to link to each site that you refer to in the paper, especially for each company explicitly named, and, importantly, stating the date on which you accessed the data. If you can point to a completely stable site, like the internet archive, then the date isn't necessary, but otherwise it is, as content changes.
However, I can think of an exception. If you don't name the companies specifically, and if you have explicit permission from them to use their data "anonymously", then you can, perhaps, treat the data as a data-set and refer to it generically without citing each "data point". I'm using the analogy of a data set from a set of human subjects who aren't named in a work, but who have given permission for their data to be used. Permission can come from a general license, of course or an accepted understanding that the data is public. Note that I'm guessing a bit here and you could discuss this with your advisor, who knows the field and its conventions.
In a thesis, though, having a lot of references probably isn't a problem.

I think gerrit's answer is better; references/citation is the wrong tool for this use case. It's important that OP describe their methodology in a reproducible way, an appendix listing the data sources would be one good option, but I don't think intermingling with the other references is the way to go.

@BryanKrause, you said intermingling, I didn't.

Well, technically these are companies listed on the US stock market, so they are required by law to publish their quarterly earnings,  the SEC website collects these quarterly reports. None of the reports change after publication, I should have clarified it better, my bad. From getting the data from these sources, I will create the dataset which I explain and use in methodology. @BryanKrause I thought appendix is only for extra tables and charts, but I could put this there

@Buffy I think the presumption is that references all go together, and OP is talking about them with a citation style that they're presumably using for the rest of the paper; you haven't said anything about keeping them separate, you've discussed them as references and talked about things like "having a lot of references probably isn't a problem" in a thesis, which again seems to suggest that they're all together.

@JohnFire "Appendix" is a rather general term, you might not have them in a section actually labeled "Appendix". It's becoming more common in many fields to expect the entire data used to be provided in some form with the paper, perhaps deposited in some repository. If there's enough information in the data set to indicate where the data are coming from, that's likely also sufficient. The purpose of citations are to point people to where you've gotten your ideas. The purpose of telling people where you've gotten your data is a little different and likely need not follow a citation style.

@BryanKrause, note that APA style citation permits both footnotes and endnotes. It does not require "intermingling".

If you are proposing including this information in a footnote or endnote, I would suggest being explicit about that in your answer, but I would still downvote that suggestion because I do not think those are the best places or format for this information.

Does your institution have some kind of system for persistent research data storage? It sounds like you have compiled an original dataset by scraping this website. Your institution may have infrastructure in place for preserving research datasets. You would then cite your own dataset in your paper.

I found two sources offering advice on these types of citations:

Harvard Business School makes available on one of its resources web pages a document titled "HBS Citation Guide: 2024–25 Academic Year". This guide includes a section titled "Citations of Commercial Databases" (page 43). It includes the following guideline:

URLs are optional in database citations. If you include a URL, use only the briefest form which points to the main page of the database.

SEC EDGAR is a government database, not a commercial database, so I can't say for sure this guideline applies, but the distinction between commercial and non-commercial databases does not seem that important to me. The HBS Citation Guide also has a section titled "Government Documents" (pages 22-25). I don't see anything there pertaining to databases specifically, but I didn't look very carefully.
You may also want to consider whether the HBS citation guidelines, which I imagine are tailored to academics working in the areas of business, management and related disciplines, are relevant for the specific discipline you are publishing in.

By contrast, this library research guide from Washtenaw Community College does have guidance that suggests including the URLs for each downloaded report:

Annual Report or SEC Filing (10-K) from the SEC Edgar Database:
"Title of SEC Filing or Annual Report." Name of Website. Publisher, Date of Publication, URL, DOI,
or permalink.
"Nike, Inc. Form 10-K for Fiscal Year Ended May 31, 2016." EDGAR. Securities and Exchange
Commission, 2016, https://www.sec.gov/Archives/edgar/data/320187/000032018716000336/nke-5312016x10k.htm


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Entering Name With Accents on arXiv
I'm trying to submit a paper on arXiv, but I can't figure out how to enter my name with accents.
It's supposed to say "Dörr", but arXiv turns it into this:
Image
I tried to enter the UTF character directly and also tried to use the latex syntax for accented characters, as described here.
Using a different browser didn't help either.
On the same site it says "make sure [...] that any accented characters are properly represented", so it has to be possible to enter them.
Any ideas? It can't be that difficult.

Maybe that's just the site you're viewing that is misrepresenting it, while arXiv internally knows the right name? I wouldn't worry much about this; looks like a bug that will eventually be fixed even without your input.

Does the accent show up properly when you compile the LaTeX to pdf locally? Perhaps ask this at tex,stackexchange,com, with a minimal (non)working example.

@EthanBolker The question seems to be referring to the name in the arXiv web portal, not the paper itself.

I contacted the arXiv support, here's their reply:
"This is actually something we manually fix later in the process, after you submit. Our legacy perl based system does not handle utf gracefully. If you proceed with the submission as is or with Tom D\"orr we will change it to Tom D\"orr in the metadata so that it will be displayed correctly.
We are also in the process of updating our code base to python/flask that will support utf."

Weird but okay. This should be accepted answer.

Weird? Buggy, actually. I understand they may need to manually fix some instances, but if their normal process is to have that broken and manually change it, their code is at fault, sorry.

@Ángel That's exactly what arXiv support said.

Your image shows that it is being represented as if it was double-encoded in utf-8. I would first ensure your browser isn't misconfigured to show arXiv page as latin-1 despite being utf-8. Then, given that providing the utf-8 characters fail to render properly, I would configure the page as being in windows-1252 (which would show lots of those artifacts) and try sending the name in that encoding (your browser will use the encoding in which the page was served) which, hopefully, might end up correctly encoded (it would still be a serious bug, though)
Howover, since you claim that you tried to input it as D\"orr and it still showed as DÃ¶rr, that leads me to think that it may be a local representation problem on your end (maybe some other part of the page produces a stray latin-1 character, which makes the browser conclude the page cannot be in utf-8?).
Note you can always contact arXiv about the error you are receiving.

If you want to check your browser configuration, here is a page where it works for me: https://arxiv.org/search/math?searchtype=author&query=D%C3%B6rr%2C+P

Can you explain or link to how to do these things? I think most people would have no idea how to do that.

I got rid of accent marks. You'll always have problems with them.

source: Stack Exchange

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

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Can I show my co-author the final version of an accepted publication I reviewed?
I was reviewing a journal paper recently that is closely related to work a colleague and I are doing. We are currently in the process of writing up our paper, and I plan to cite the paper I reviewed in ours. The paper I reviewed has been accepted after two rounds, but has not appeared in print yet. The author does not have a preprint on their website (or other preprint services). We don't build on the reviewed paper I want to cite directly, but our work is closely related. In the future we will probably start building on the reviewed paper, so I want my co-author to be aware of it as soon as possible.
Can I send the last version of the reviewed paper (that I saw) to my colleague? Can I cite the paper as "(in press)" if I submit our paper before the reviewed paper finally appears in print?

@CharlesMorisset it is a journal paper and there was no earlier conference version (it is in a field that does not really use conferences in the way say CS does). I edited the post to clarify.

Incidentally, this makes a good argument why you should always post preprints of submitted papers.  If the paper is good, you'd like the reviewer to be able to share and publicize it.  But if it's not public, her hands are tied.

I would consider contacting the editor with this request, or asking the editor to put that request to the author on your behalf. A well-worded request could even make it past the editor to nudge the author into posting the preprint!

The answers, especially @Suresh 's answers, surprise me a bit with saying that they would not cite (or that you can't cite) an unpublished paper (even if they can get an "official" preprint manuscript) -- there was a question before where there's a consensus that it is perfectly acceptable to cite such papers.

No, you cannot. Until the paper is publicly available you cannot show it to anyone nor even acknowledge its existence. The only slight exception I would make is that if a colleague was going to pursue similar/identical research, I would tell them they might want to contact the author. I would assume that this would result in my identity as a reviewer being revealed.
Your options are:

Submit your manuscript as is, but add the citation as soon as it become public.
Reveal your identity as a reviewer and ask the author for a preprint.

Option 1 is reasonable since you believe the existence of the paper is not critical to your paper. Option 2 is okay, but remember the reviewers of you manuscript will not have easy access to the cited material.

another option is to ask the editor or author for permission.

While option 2 is nice for getting an "official" version of the reviewed manuscript, I wouldn't cite it in a submitted paper. As a reviewer, I automatically dislike papers containing references to publications I have no way to look at.

@silvado I did point out that reviewers won't have access. If you don't cite it, you run the risk of it becoming public during the review process which can also cause problems.

@DanielE.Shub, yes, I saw that, and just wanted to emphasize the problem in this situation. On the other hand, I don't think that it will be a problem if one doesn't cite and it becomes public in the mean time, since reviewers will see the submission date and must assume that you haven't seen the paper (if they are aware of it becoming public at all).

You can't cite the paper till it's in print or somewhere publicly available. As for sharing with your colleague, I think there are degrees. If the colleague is sitting in your office and you show them a hard copy, that's less of a problem than emailing a copy to a colleague (remote or local) which I would not do.

Showing someone a hard copy in your office is less serious in the sense that it's giving them less information then they could get by examining their own copy at length, and it leaves no proof behind.  However, it could still be problematic if the authors genuinely care about confidentiality, and it's certainly violating the journal's expectations.  At the very least, you should make sure your colleague aware that this information is confidential, so they don't accidentally think you were showing them a public preprint and go talk with other people about it.

That is absolutely correct. I should have clarified that the confidentiality should be emphasized even if you do show them a copy.

Since the paper will appear soon (it's been accepted!) and it is not essential for the present article, why not follow StrongBads' advice, namely submit your article as is, and add the citation in the proofs. Discussing the future with your collaborator can wait a couple of weeks.
Since you sound in a big hurry, I will elaborate in another direction now.
I have always been very adamant about keeping reviewer information confidential. Other people... not: in my discipline (theoretical physics) people have told me that they reviewed my paper or (worse) the paper of somebody else. In short, it is not unheard of to reveal such information, especially when the outcome is acceptance. Is it good for the trade? That's another topic for discussion.
So if you are in a HUGE hurry to discuss with your colleague you may follow Suresh above: if your colleague is not in a different city and you trust each other, you may decide to share the confidential information confidentially, which is to say, just show them the article which you in fact judged positively, discuss it, destroy the evidence. (I've seen that too, and without the destruction-of-the-evidence part.)
Though I personally would just wait...

It might not appear soon, depending on publication times. It is possible that the OP's paper would be accepted and fully proofed before the other paper appears.

I see. Then if it's cited (in press) the reviewer's identity will be revealed. I think the author and the editor may agree to this after simple consultation with them, depending how strict their policies are. BTW in my field this problem is rarely encountered, because most of us submit our preprints (before or just after acceptance) to the arXiv or personal websites so people can cite them before publication.

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


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Are books and professional society memberships allowable expenses under US federal grants?
Can you include books and/or professional society memberships in expenses under grants from agencies like NSF and NIH?
I've purchased lots of books under grants from DOE and the Air Force in the past, but I'm wondering if they're generally considered allowable.  My quick searching uncovered the following:

NIH seems to explicitly allow books at least
NSF: I'm guessing books are allowed under "materials and supplies"

I haven't found anything about allowability of professional society memberships.

The page you linked to in your question directly addresses memberships for NIH:

Dues or Membership Fees
Allowable as an F&A cost for organizational membership in business, professional, or technical organizations or societies.
Payment of dues or membership fees for an individual’s membership in a professional or technical organization is allowable as a fringe benefit or an employee development cost, if paid according to an established organizational policy consistently applied regardless of the source of funds.

I agree with your conclusion about books for NSF grants, and their page oddly doesn't list information about memberships. I would imagine that it could arguably fall under the section related to conference fees, but that's conjecture.

I have occasionally bought books with NSF grant money. At the time, I was told, by the staff in charge of grant accounting, that books are an allowable expense provided they directly relate to the research being done under the grant.

In my experience, professional society memberships generally fall into a gray area due to taxes. It is useful to split them into two categories. The first is where you get no financial benefits that offset the cost. The second is where membership gets you a discount for publication, meeting registration, or access to a journal.
In my experience the first cases is not allowable since it is considered a personal benefit and is tax deductible. I have never been able to charge these to any grant. The second case often results in fights with the finance office. They would prefer to charge the grant the extra money and not pay for membership. With a little bit of fighting, I have never had a problem getting them to pay the membership at the time of meeting registration when they can see the savings fully offsets the cost of membership (as it often does in my field). I have no idea what would happen if it was only a partial offset.

Well, obviously, if you get it paid by your employer, it would be fraud to deduct it from your taxes.

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


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Is there a good site for holding online discussions of scientific papers?
Many of my computational scientist colleagues used to use Google Reader to share and discuss new journal articles.  The loss of Reader's social features killed that, and we have subsequently tried Google+ and Reddit, but neither seems to work nearly as well as Reader did for holding this kind of discussion.  Does anyone have experience using a site they like for this purpose?
I'm aware of a few options, like http://annotatr.appspot.com/, that seem promising but appear not to be actually used much.

There was a site called Phygg that aimed to do this for papers on arXiv, but it shut down due to low participation.

There have been quite a few attempts at providing a comment/review system overlay on top of the arXiv, including scirate.com (defunct) and http://science-advisor.net.

You can hold a discussion in a Mendeley group, but not specifically about one paper, and the interface is lousy (think Facebook wall).

Hi academia.se mods, this was migrated over by op's request.

Why not use blogs? You can make them private if you want only group members to have access.

You can also use google+ communities...

What's wrong with Reddit?

Related discussion on MathOverflow: Are there any good websites for hosting discussions of mathematical papers?

There are now several mastodon instances for science people. Have a look at them. I cannot recommend one in particular, but they seem to form a nice community currently.

Might I suggest http://scicomp.stackexchange.com ?
While the StackExchange system isn't the best - and indeed isn't designed - for "discussion", I've found that many "What did you think of this paper" type questions can be phrased in SE-compatible formats. CrossValidated has a semi-periodic "Journal Club" bit, and questions and musings about scientific papers come up a fair amount there.
I think if framed correctly, they might find a useful home here.
Generally though, I think the online discussion of scientific papers suffers from a few problems. Generally, the two I find the most problematic:

Lack of a clear community to talk about papers in. Essentially, the problem your question is looking for an answer to. I haven't found a really good general purpose one, though I would love to if I did find it. There's blogs and the like, but even the ones talking about peer-reviewed papers are somewhat one sided in terms of their communication, and not great for anything but transient chatter.
A hesitation to talk about that online. Among colleagues, it seems somewhat easier to summarize things like "Bad paper is bad", or slice apart someone's methodology. I'd be somewhat more hesitant to do that anywhere where my identity is both traceable and the conversation is saved for eternity (the internet).

I think the lack of anonymity in a public forum will be a deal-breaker for many due to academic politics and fear of reprisal, even if a review is balanced, tactfully pointing out both strengths and weaknesses. People prefer closed fora to avoid this drawback.

@JackPoulson I think that it depends on what you mean by 'discussion'. SciComp is probably the right place for certain types of discussion. Things like 'In the paper by EpiGrad et al., can someone explain to me what they mean by X'?

@Epigrad: I think the idea is to do a serious discussion of the intellectual merits and disadvantages of individual papers, as well as areas of research. That's somewhat orthogonal to the goal of SE.

It is my impression that the Scicomp SE is for specific questions pertaining to (mostly) objective technical knowledge and not for subjective value judgments journal papers or research programs.

@Paul It should be noted that this was originally on Scicomp, so the response was a little cheeky. Though I don't think many of the reasons one might discuss a paper are inherently value judgements and inherently not based on objective technical knowledge.

I haven't used it personally so I can't vouch for its quality, but I know several people in another research group using a site called Journal Fire for this purpose.  Might want to check it out.
Also, I think the citation manager and social network Mendeley has some limited discussion capabilities, but I prefer to manage my references with BibDesk so I haven't use it much.

+1 for JournalFire -- it looks good, if I can just get my collaborators to use it too.  I use Mendeley, but not for discussions; its interface is not well-suited.

"After five years of connecting researchers at hundreds of universities and research institutes, we regret to announce that JournalFire has shutdown permanently as of October 15th, 2013." - from Journal Fire website

JournalFire is dead now.

For public discussions on arXiv preprints there is SciRate.
For general discussions, also private, there is PeerLibrary.

We have just extended BibBase.org to allow papers to be discussed in a fashion heavily inspired by StackExchange: http://bibbase.org/blog/stackoverflow-inspired-scientific-discourse
What's different about BibBase compared to several other sites is that authors integrate bibbase directly into their own homepage. It keeps links to collaborators up to date, and it links to pages on bibbase.org for keywords, and now also for discussion. We want it to be the unobtrusive research network that just helps scientists show their publications online on their own pages as they normally would, but with additional features that make it more than that. We think that it is important to facilitate discussion about one's own papers, and therefore this is now a feature provided by bibbase.
[This is an old question, but it seems that most listed solutions have been shutdown by now one way or another.]

The Selected Papers Network is a new effort in this direction that intends to federate content from all over the web.  To post something to it, just write a Google+ post with #spnetwork and the paper's arxiv ID or DOI in the body.  You can also post things directly at the site.  The developers are working on interfacing with other social tools like Twitter.
You can read more about the thinking behind it here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264905/.
Edit: The site was shut down several years ago.

This site isn't working for me. After clicking the link it just redirects to a page that says 'site is starting... click here' in the top left corner. Clicking that link just reloads that page.

Google+ being defunct, not sure this is useful now.

I use I, Librarian.  It is a reference manager, kind of like Mendeley but with a free option.
They have both a paid service option and a free self host option (like wordpress).
It can be private and they have per paper discussion capability.
For a private/semi-private group of collaborators I think it is quite decent.

Wikiversity has useful technical features (the same as Wikipedia): collaborative editing, referencing tools, discussion pages, version control, email alerts, etc. It can very well be used for discussing scientific papers.

For theoretical physics (or closely related) papers, one possibility is to use the Reviews section of PhysicsOverflow (note that it is possible for the registered PO users to submit there papers for review).

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


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What arXiv CC licenses are compatible with American Physical Society publishing?
I'm having some trouble understanding exactly what the American Physical Society (APS) transfer of copyright agreement does and does not permit, and I was wondering whether someone here may have a clearer insight or previous contact with them to know exactly what their position is. Their copyright FAQ is, I feel, not clear enough on the subject, and it's not quite clear how I should contact them about this.
The relevant paragraphs from the copyright transfer agreement are, I think

2. The nonexclusive right, after publication by APS, to give permission to third parties to republish print versions of the Article or a translation thereof, or excerpts therefrom, without obtaining permission from APS, provided the APS-prepared version is not used for this purpose, the Article is not republished in another journal, and the third party does not charge a fee. If the APS version is used, or the third party republishes in a publication or product charging a fee for use, permission from APS must be obtained.

and

4. The right to post and update the Article on free-access e-print servers as long as files prepared and/or formatted by APS or its vendors are not used for that purpose. Any such posting made or updated after acceptance of the Article for publication shall include a link to the online abstract in the APS journal or to the entry page of the journal. If the author wishes the APS-prepared version to be used for an online posting other than on the author(s)’ or employer’s website, APS permission is required; if permission is granted, APS will provide the Article as it was published in the journal, and use will be subject to APS terms and conditions.

The latter one looks pretty inclusive, but it does not specify whether all licensing schemes for e-print servers are acceptable or not. Similarly, in their  I am worried, in particular, by a note in the arXiv license help page:

The Creative Commons Attribution license in particular, permits commercial reuse and thus conflicts with many journal agreements.

In particular, posting to an e-print server may be construed as overstepping the rights granted by point 2 of the agreement, since it can be used by a third party as permission to republish it in print and charge a fee for it. If that is the only objection, then choosing a CC Noncommercial-ShareAlike license would be compatible with the above.
I have two specific questions about this.

Is my reading of these publicly available documents correct? Did I miss important information either on these or other publicly available resources?
Does anyone have specific experience with them regarding this question? Is there some specific contact for these matters?

(I believe the non-exclusive license to distribute, which is the most restrictive arXiv license, is definitely compatible. This question is mostly about the Creative Commons licenses.)

This is not a full answer, but at least the default Non-exclusive license to distribute sounds like it is compatible with the APS license.

Yes, that is definitely the case. That is the minimum for arXiv posting, and they make several references to the arXiv in particular throughout the process. This question is mostly about the CC licenses.

@E.P. Did you ever hear back from APS on this? I think you probably arrived at the answer here already (save for the case of open access publications in Physical Review journals), but it wouldn't hurt to have an authoritative answer.

I don't think you can use any CC license with such a publishing agreement.
Point 4 allows you to post the article to preprint servers. This covers the non-exclusive distribution license that Mangara mentioned in a comment: You permit arxiv.org to reproduce the article on their website. Importantly, you don't grant any rights to anybody else with this license.
If you put your article under a CC license, you would grant anybody the right to reproduce it (under conditions depending on the exact type of the license). Whether it is published on arxiv.org or not doesn't really matter here – arxiv.org will just be one of the potentially many users of this license. Looking at the arxiv help page, this is hidden behind the specific wording they use. For the non-exclusive distribution license:

grant arXiv.org a non-exclusive and irrevocable license to distribute the article

which makes it clear that you are giving permission to arxiv.org and not anybody else.
For the CC license, they say instead:

certify that the work is available under either the Creative Commons Attribution license, or the Creative Commons Attribution-Noncommercial-ShareAlike license

that is, you don't give a license to arxiv.org specifically, but to the general public, and arxiv.org is just going to make use of that general license.
I don't think your publishing agreement allows you to issue a CC license, even the noncommercial one. The publisher does not allow you to grant a license to anybody, just specifically to "free-access e-print servers".

I am unable to easily read the "grant arXiv.org a non-exclusive and irrevocable license to distribute the article" as "giving permission to arxiv.org" only. Rather, the "irrevocably" is that you can't change the license later to "revoke" it from arxiv.org.

@paulgarrett I didn't intent to discuss 'irrecovably', just that, in contrast to the second case, the first case states arxiv.org explicitly. Hope my edit clarifies this point.

Thank you for pointing this out. I'm waiting for further contact with APS before I accept this.

Ah, sorry, now I understand... Not denying anyone else the right, but not explicity granting anyone else, either. Just arXiv. Okey-dokey.

CC licences are incompatible with arbitrary restrictions
Paragraph 2. of the copyright transfer agreement that you cite allows you "... to give permission to third parties to republish [...] provided the APS-prepared version is not used for this purpose, the Article is not republished in another journal, and the third party does not charge a fee."
Offering the content under a CC licence means that you're giving permission to third parties to republish that content, period. Even if they would republish it in another journal or charge a fee.
You can't give those permissions according to the APS agreement; and you can't require those restrictions under a CC licence; so that doesn't work out.

I'm pretty sure that Creative Commons would disagree with "Even if they would [] charge a fee" if you have applied the non-commercial rider.


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Is it recommended not to have a figure on the first page of an IEEE journal article?
I have been browsing through a few IEEE journal articles and couldn't find an example where there was a figure on the first page of the article. So I tried to find the rule that explicitly forbids the authors from including a figure on the first page, but I couldn't find such a rule either. Here's one of the links where I looked: Preparation of Papers for IEEE Transactions and Journals
I am specifically seeking a clarification of such a rule (if it exists) for the IEEE journal article and not for conference publications (or even letters or tech notes).

There's no such general rule and I have published papers in IEEE journals with a figure on the first page, and I'm sure to have seen other papers too. In any case, the typesetter can take care of this if necessary.

Could you point me to an example? Since a figure can be published on the first page, my question seems to be pretty pointless. Would it be recommended to delete the question?

@ShashankSawant Please don't delete the question --- it's actually quite interesting, I think!

Could you point me to an example? — Three of the top 5 most "popular articles" in IEEE Journal of Solid State Circuits have a figure on the first page.

To the best of my knowledge, there is no such rule.  It would be problematic to put a figure in the first column (which is tightly compressed between abstract and copyright blurb), but a figure at the top of the second column is fine. However, due to the way that document preparation is done for IEEE publications, it is highly unlikely that an author will end up with a figure on the first page unless they specifically choose to do so.
The reason is that in the IEEE format (both conference and journal), one rarely gets much beyond the introduction in the first page, and certainly never in the first column.  IEEE papers are mostly prepared with LaTeX, and as such, if you use the normal figure placement, your figure will tend to end up at the top of the column after the column in which it is set and referenced in the source.  Since it's quite rare to have a figure at the beginning of a paper's introduction, then it will be correspondingly rare to have a figure on the first page. Complementarily, an author who chooses to have a figure on the first page will typically need to move the figure significantly forward of its reference in the text in order for LaTeX to choose to place it at the top of the 2nd column.  Most IEEE journals work directly from the author-submitted document rather than doing significant layout work by the journal, so if the author didn't put it on page 1, nobody else is likely to either.
Thus, we have the emergent phenomenon you've noticed: because the defaults and tendencies in article production for IEEE make it unlikely that a figure will appear on the first page without author intervention, most papers have no figure on the first page.
Personally, I like to intervene, because a figure on the first page really helps an article pop out to a reader and be more memorable and intelligible.  I suggest that unless you have good reason to do otherwise, you do the same.

To add on, with the bulk of matter within the Introduction and Related Work / Literature Review section, it would be naturally improbable to have a figure in the first page, unless few previously published articles connect with your research. Except for a few articles as pointed out by @MadJack.

While I agree with jakebeal's answer, there is actually a reference about not putting any floats on the first page in
M. Shell, "How to use the IEEEtran LaTeX class," LaTeX class files, Journal of, vol. 8, no. 1, Nov. 2012
Note that, as far as I'm aware, the "Journal of LaTeX class files" is not a real journal but was just the default header of the IEEE LaTeX templates at the time this article was released. On page 8, under section X. Floating Structures, is the following quote:

Furthermore, IEEE journals never place floats in the first column of the first page and rarely (if ever) do they do so in the second column of the first page.

Unfortunately no further qualification is given as to why this is the case. Depending on how you interpret this it's either a hard and fast rule, a strongly recommended style guideline, or just a statement of the way things are in IEEE journals.
Either way it's highly likely that this paper, which most people download and read when they download the IEEE LaTeX template, has caused some authors not to put a figure on the first page. Combine that with the way the IEEEtran LaTeX class works and the fact that the first page is usually a literature review with no figure references, has meant that figures on the first page are quite rare nowadays.
Given all this the best thing to do is to contact the editor of the journal you are submitting to, to see what their preference is and just go with that.

As an exception to the rule, I have 4 papers on IEEE transactions which have figures in the second column of the first page. It should be also noted that, in my experience, IEEE copy editors appear to be totally unaware of the IEEE style guides ;-)


text:
How to select a dissertation committee member wisely?
I'm currently developing my dissertation proposal, and am in the process of choosing my committee members.  I have heard that one should carefully choose their committee members, since they ultimately judge if and when your PhD work is done.  As far as I can tell, a committee member should at least have some expertise in my research topic.  I'm sure there's more to it than that, but I want to know what other qualities should I look out for?  What qualities in a committee member should I avoid?  I imagine these other qualities are subtle and difficult to judge at first.  Nonetheless, how do I know if they are a good fit for the success of my PhD?

First, ask your adviser.  He/she likely knows more about most of your department faculty than you do.  You don't have to do exactly as you're told.  But if you don't, this should be an intentional choice on your part, and you should have a good reason for that choice.  Beyond that, I see at least 3 broad areas to consider.  You want committee members who will

strengthen your professional network: introduce you to potential collaborators, and possibly help with your search for a postdoc or tenure track position; and/or write a letter of recommendation for you
give valuable feedback on your work: you think they'll actually read your thesis (you might be surprised how uncommon this is), and might have something constructive to say
be easy to work with in the defense process: likely to be flexible on the date of your defense, and likely to sign off on your dissertation without demanding lots of changes (fitting into 4 schedules besides your own can be a nightmare; it's nice to have a few committee members who are easy to work with on this)

Which of these contributions you value most will depend on what you're hoping to do after your PhD.  If you're looking to move into industry, many of your professors' contacts may be less valuable to you than if you hope to stay in academia.  Do you plan to stay research active? In the field of your dissertation?  Practically, you may have limited options.  At the very least, you should weigh 1, 2, and 3, and estimate how you think each candidate will contribute in each area.

Only one thing I would add to this excellent answer: At least one committee member should take you out of your professional comfort zone. Do not choose committee members only from your subfield, your department, or even your university.  Part of defending your research, especially if you are continuing into academia, is being able to explain your work and defend its importance to people outside your narrow academic circles.

@JeffE: One caveat to the suggestion of an external reviewer—try to avoid someone who's geographically far away. Scheduling committee meetings is hard enough as it is; it's exponentially more difficult if you have to organize them around the schedule of someone who needs to fly in for the meetings!

@aeismail: That's what Skype is for. I've been in more than one defense where the committee was spread across multiple continents.

This is certainly a very important question. Here is the make up of my committee and its pitfalls.
This isn't the best committee but has its advantages.
You could easily extrapolate from my experience:
Adviser/Committee member 1:

I have a fantastic adviser who always stands by me and supports my work. He essentially fights for me if things go awry.
He is also the PI of the project I am working on so my successful graduation and publications do interest him.

Co-adviser/Collaborator/Committee member 2/Extradepartmental committee member:

He champions the the ideas behind my research and has done so for the last 20+ years. So there is no problem that I'll get support from him

Committee member 3:

Doesn't have anything invested in my research but since it is generally related to his work, he is on board.
He is sitting on the fence as far as criticism goes.

Committee member 4:

He doesn't like my work since his adviser didn't like this work and its implications.
However, since I have a generally pro-me committee, I should be alright but not without breaking a sweat.
If I convince him of the merit of my work, I'll have no problem in the future convincing any other detractors See comment by JeffE.


text:
What are the main goals of administering preliminary / qualifying exams to graduate students?
If I were a Spanish Major as an undergraduate and decide to pursue a PhD in a completely unrelated field (like Theoretical Physics), it makes sense to give a qualifying exam to check that I had the necessary skills to begin the program.  But if I'm coming from a B.S in math to a PhD program also in math, it doesn't seem to make sense to give a qualifying exam, as if the knowledge I gained in my undergraduate was insufficient.  I presume that one is accepted into a PhD program because he/she has already demonstrated the "qualifying" skills.  Thus, I'm baffled by the notion of the qualifying / prelim exam.   I'm curious about the ultimate goals of these exams, and how they relate to the professional development of a graduate student.

This answer serves mainly to corroborate @Anonymous Mathematician's answer.
As she says, the most important thing to realize is that there are two different kinds of exams that go under the name "prelims / quals".  The first of these generally:
(i) tests undergraduate material
(ii) is administered soon after arrival in the graduate program
(iii) used to be used for preliminary weed-out purposes but is now -- at least, in most programs I know about -- used almost entirely for diagnostic purposes.
Probably (iii) is most important: once upon a time, many graduate programs -- even excellent ones, like Berkeley (in fact, especially Berkeley) -- admitted lots of students, as in up to 50% more than were expected to finish.  The idea was to give a large group of people, including those with less than sterling (or ivy) pedigrees, a fair shot.  Then after a small amount of time in the program -- maybe a year or less -- they would take a "prelim" exam, and a significant portion would fail and leave.
This is no longer the way graduate programs work (at least not in North America, which is what I am primarily familiar with, but to the best of my meager knowledge they don't work that way in other parts of the world either).  We pay much closer attention to each student we admit now than in the scenario above, and further our program is judged on retention and completion rates.  A graduate program in 2012 who dismissed a third or more of its incoming class every year would look disastrously bad by these sorts of metrics.  So this "weedout prelim" is, as far as I know, a thing of the past.
In the graduate program at UGA we still give a "prelim exam" to all entering graduate students, but as I said above we use it almost entirely for diagnostic purposes.  In fact we have a certain graduate course designed entirely for students who didn't do well on the prelim, whose purpose is to shore up their undergraduate knowledge ASAP.  Other than being encouraged to take this course, there are no direct consequences of failing the prelim (in fact, I'm not sure that one "passes" or "fails" the prelim in any technical sense).
In contrast, most of the "qualifying exams" that you hear graduate students talking about are something entirely different.  They:
(i) test graduate level material; in particular, most students do not enter equipped with the knowledge to pass most qualifying exams.
(ii) occupy students' attention for a while: in our program, students have up to three years to pass their qualifying exams.
(iii) really must be passed in order for students to advance in the program, in most cases.
I hope this answers your question.  Let me say though that the scores on the "prelim" exam -- i.e., the undergraduate level exam that I mentioned first -- are often all over the place.  All of our entering students have at least an undergraduate degree in mathematics.  So, unfortunately, no, an undergraduate degree in mathematics is not a guarantee of ability to do undergraduate level mathematics...at least not to the satisfaction of a decent mathematics graduate program.  (And a student who does poorly on this entering prelim may yet succeed in doing PhD level mathematics a little later on: that is, the fault often seems to lie with the undergraduate program more than the student.)

I've usually heard the terms prelim and qualifying exam being used interchangeably.  So then, in your opinion, a "prelim" is really a diagnostic tool, and a qualifying exam is a benchmarking "weed-out" tool.  I suppose that there is a minimum "quota" of advanced knowledge that is expected of a professional academic, which is the purpose of the qualifying exam.  Correct?

@Paul: unfortunately, yes, the terms "prelim" and "qual" are often used interchangeably, notwithstanding the fact that many departments do have two separate exams!  It's probably best to ignore this then and use "prelim" to refer to the preliminary exam and "qual" to refer to the more advanced exam.  Even this latter exam is not so much a weed-out tool nowadays: we really do want the vast majority of students we admit to complete the program.  But it does have that effect sometimes...

We had both type at my graduate program and call them the "qualifier" (tested undergrad material and was to be passed within a year of matriculating) and the "comprehensive" (covered the core graduate curriculum wand was to be passed between finishing the core course work and taking up full-time research). Both allowed one do-over, and both saw students dropped. That was in the mid 1990s.

Also, I would say "in my experience" rather than "in my opinion", while acknowledging that I can only have direct experience with a small number of graduate programs.

@dmckee: I think that academia has tightened up its game nontrivially even since the 1990s.  That said, things will certainly vary from department to department (and I am only talking about math departments!).  Some places have a reputation for kicking out students who do not pass their exams: the UGA math department, not so much.

@PeteL.Clark: As a general rule of thumb, is it safe to say that the more competitive it is to enter the graduate program, the more likely they are to use the qualifying exam as a weed-out tool?

@Paul: no, I wouldn't be willing to say that.  For instance, the Harvard math department (where I did my PhD) is famous for giving students as many chances as they need to pass the quals...and even, in at least one case, looking the other way when a student seemed constitutionally incapable of passing his quals but was able to write a very strong thesis anyway.

@PeteL.Clark: Wow.  That's amazing... I wouldn't have expected that to happen in such a prestigious school.

There are two types of qualifying/preliminary exams, those that cover graduate or undergraduate topics.  I assume you are just talking about the latter (while in my experience the former is a little more common).
As I see it, the big reason for preliminary exams on undergraduate material is that many students haven't actually mastered this material to a professional standard.  At less prestigious schools, many students won't have received straight A's (or the equivalent in other grading systems), so they had gaps or weaknesses in their understanding, and unless they took more advanced courses in the same area they may never have filled those gaps.  Furthermore, it's possible to get excellent grades without true mastery, and students sometimes forget things they knew while taking a course.
The preliminary exam sends a message of "OK, you're in grad school now, and as a professional you're expected to know this material cold.  If you have any doubts about your mastery of it, now is the time to study carefully."  The few students who don't need to study suffer little harm from the exams, and the students who do need to study benefit from the studying.

I concur with @Pete L. Clark and @Anonymous Mathematiciar. These exams have names that suggest the darker purposes in their pasts, but they are mainly used as administrative tools by graduate departments.
At the graduate school I attended for chemistry, we took "placement exams". These exams happen for three reasons:
1) to provide the department with a common baseline, as we all did not likely take exactly the same GRE on exactly the same day. Particularly, this provided a means to directly compare the national students with the foreign students.
2) to identify deficiencies, as @Pete L. Clark and @Anonymous Mathematiciar have indicated. In chemistry, the smaller schools occasionally cannot offer courses in all five disciplines: organic, inorganic, analytical, physical, and biochemistry. Rather than punish those students for attending small liberal arts schools, many Ph.D. programs accept them on the premise that they may have to take an undergraduate course to fill in the gaps.
3) to select TAs. All first year graduate students in my chemistry program had to TA undergraduate labs. The placement exams determined which course you got to TA. The highest scorers on the organic chemistry exam were TAs for organic chemistry labs. The highest scorers on the analytic chemistry exam were TAs for analytical chemistry labs, and so on.

I would argue that there is nothing "dark" about setting much-reach thresholds for students. Indeed, to encourage them to go on with work that they are ill-suited to may do them a bigger disfavor.

There are two main reasons:

as a "sanity check" to make sure that the student you've admitted is really the student you thought they were; and
as a weed-out tool, in case you've admitted more students than you have spots for in PhD projects in a given department.

Both purposes are significant. The second is the more unfortunate, and could generally be reduced through better selection processes and through better deployment of teaching and research resources and funding within a department. The former use is equally important, in that it makes sure that students don't try to "coast" their way through what the department believes is its core curriculum that students need to know.

As an addendum to the sanity check aspect, a Phd program at university X might attract students from many other not-X universities, and the qual process ensures that everyone has the same baseline skill set.

In places where it is given after, say, one semester or a year it is also a chance for students to self-remediate, which presumably filters out the students who are both deficient and unable to produce without supervision.

@Suresh: It makes a lot of sense for that particular situation.

@dmckee: Could you expand on what you mean by "self remediate"?

@aeismail: The way you phrase it, it mainly serves its purpose for the benefit of the academic department, and nothing for the student...  Does it serve any benefit purpose to the student as well?

To catch up on their troubled areas...many departments make the old exams available for students to study with. If they look at them the can identify for themselves where they are behind the expectation and take steps to fix it.

@Paul: The standard examination-based qualifier doesn't really do much for the student, in my opinion. Especially when the grades given are usually "pass" or "fail," with little guidance as to what might need to be improved (regardless of final score).


text:
How might changing topic affect a career in academia?
I am curious whether it is common working on different subjects in an area throughout an academic career. I think life is long enough to work on different and interesting subjects, but how common is that?
Particularly, let's consider area as computer science. Topic X and topic Y are not totally unrelated, but it is hard to find people who list X and Y together under his/her interests. For my situation X is software engineering and Y is computer architecture/embedded systems. How might pursuing masters on a topic X and applying to PhD on topic Y look like?

Possibly related: http://cstheory.stackexchange.com/questions/8339/how-easy-is-it-to-switch-the-area-of-research-in-cs-going-from-m-tech-to-phd

If you do a masters thesis on topic X and topic X is not too far from topic Y, for example, if they are both computer science topics, and your academic record is good and your background is sufficient to make the transition, then I don't see any problem making the change at this level. You can catch up on whatever knowledge you need while learning about and researching topic Y. This will not look bad. After you obtain your PhD, people will be less interested in your masters.
Changing later in your career is also possible, though a little more difficult.
One positive point about being able to demonstrate that you can change topic is to show that you are not a one trick pony, meaning that you are able to apply yourself at the highest level to different areas.
Indeed, as interests in the field shifts and technology changes and new topics open up, it will often actually be necessary to change topics in order to survive in the field. If people (or just you) lose interest in a topic, then you need to be able to change.
But it is also good to demonstrate that you can commit to one line of research. Apart from the advantages this gives you, such as having a deep knowledge of a specialised topic, it demonstrates your commitment and can be used to show that you have and follow a research strategy.


text:
What are the good indicators of fake conferences?
Here is the story:
After working on a project for a couple of months, I put the results in a scientific paper format in case I would submit it to a conference or yet this is a good practice for my writing. The paper is about a specific software engineering topic for which you could not find many scientific events to submit. However, I sometimes check www.wikicfp.com to not miss any deadline if there is one. Finally I found one, read the details about the conference and submit the paper.
This week I got the results. It was positive, I think this is great because I am warming up for academia. However, when I received the review (only one), I felt that this conference could be fake or bogus. They sent me a text file that has the following review (with the name and some strange gmail address as the reviewer email):

paper quality is good different models are explained but this is not a
  result oriented paper no comparison results for different models with
  graphical point of view

Thank God I had a paper rejected before. I had submitted it to a workshop on a different topic and the reviews were detailed, well explained without reviewer strange email addresses.
Besides the acceptance email has details more about the registration details, bank accounts, fees etc., than preparing final draft or other scientific related issues.
So right now I am a bit confused about what to do next or how to react. But my question is the following:
What are the good indicators of fake/bogus conferences?

can you share the name of the conference? usually folks could give you a reasonable estimate of it just by looking at the website, program committee etc. :)

What are the good indicators of fake/bogus conferences? Unknown Program Committee members.

+1. Or rather "unknown to your advisor", in case you are a grad student with little research experience.

seteropere: One-line answers aren't allowed per our FAQ's. You should either expand your answer, or it'll have to be converted to a comment.

Voting to reopen as the duplicate question as well as most of its answers are about evaluating about the quality of conferences under the assumption that they are not fake.

In your description, there are three telltale signs that the conference might not be very good:

The reviewer email is revealed: a conference that doesn't protect reviewer anonymity (blind review at least) is probably not very good.
The review itself is, frankly, not high quality. It may make sense for a standard conference scientific committee to appoint another reviewer if the first one didn't do his job properly.
Financial details appear very important to the organizer…

To make your final decision, see answers to this question: check out the organizers and invited speakers.

Seek the opinion of third parties.  Here are some suggestions.

See if the conference is indexed by a major digital library, like Scopus.
See if the conference is sponsored by a reputable professional organization, like the IEEE Computer Society or the ACM.  Although these societies sponsor all types of conferences, from top-rank to less illustrious ones, I haven't heard them sponsoring "fake" ones.
Have a look at the Conference Ranking Exercise that has been performed by the Computing Research and Education Association of Australasia (CORE).  If the conference is there, you'll get a (subjective) rank of its importance as a publication outlet.  Note that the list is somewhat dated.
Similarly, look at the conference ranking list compiled by Osmar R. Zaïane at the University of Alberta
Consult the automatically-compiled list created by Microsoft's Academic Search engine.

An alternative approach that a group of researchers followed back in 1995 is to submit silly or gibberish papers  (e.g.  "The Footprint Function for the Realistic Texturing of Public Room Walls", "Visualization and Intelligent Design in Engineering and Architecture ", "Distributed Multiprogramming System for Pen Selectors with Error Probability") and see whether they get accepted.

I have many years of academic experience and I have been a reviewer of submissions for conference presentations and scientific journals.  Speaking from experience, often times the reviews given in response to a conference abstract submission can be extremely brief.  This is often due to the reviewer having way more submissions to review than they ever dreamed of and a deadline of say "tomorrow" - YIKES!  Serious though... Okay so moving past that, to judge the merit of a conference, I would make most of my decision based on the conference hosts or sponsoring group.  If they have a large or influential membership within the discipline, who is directing the group of conference and their professional reputation, the size of the conference venue, etc... if that is all positive then I would go for it.
You might call up a colleague or two of yours in academia within your discipline and ask them if they would like to attend the conference with you. Sometimes those that are currently in the academia loop have insight about the "perceived impact" of a conference or sponsoring group.

I think there is no definitive way to understand if a conference is a fake or not, but here I can suggest  you a strategy.
Surely the most important indicator is the experts' and community's opinion: if you know someone expert in your field (maybe your supervisor?), you can ask him/her information about that conference.
Maybe you can ask also to online community members, too.


text:
Where do researchers get the money to replicate others' work?
I'd like to follow up on my previous question. If scientists have to replicate and validate/refute their peers' research, where do the "provers" find funding?
In my 2 years as an undergrad at a public American university, I've heard of a lot of professors researching new things and/or trying to find funding to research new things, but I've never heard of a professor dedicating resources to replicate his/her peer's work.
As I explore existing literature for my research project, I see that I could have used existing literature to guide the experiments I did for my project. Along the way, I could have replicated + validated* the existing "new" methods before trying my new method. Is this how researchers usually review their peers?
That is, professor X gets a grant to explore field A. Professor Y comes up with model 1 for field A. Using his own funding, Professor X designs and conducts an experiment to replicate model 1 before testing his own model, model 2.
Note: I assume my question depends on the field of study since a computer scientist can more easily verify a model + experiment than a chemist.
*Is there a better verb or phrase for replicate + validate/refute? Or does validation imply replication?

"Review" and "replicate" are not the same thing, but you seem to use them interchangeably in this post. Please [edit] your question to clarify.

I edited my question, but could you explain? Doesn't the process of peer-review include validation of your model?

Peer review generally involves a qualified expert in the field reading your manuscript (in which you describe your methodology and results) and commenting on its strengths and weaknesses. The reviewer does not generally attempt to replicate the work.

"Note: I assume my question depends on the field of study since a computer scientist can more easily verify a model + experiment than a chemist." Interestingly, Computer Science has a huge problem with non-reproducable research in basically all empirical areas of CS.

In my field (cognitive science), your supposition that replication is primarily pursued by investigators prior to extension is true. For example, I have an idea while reading a paper on learning targeted movements, I first implement the paradigm as described (either an exact replication or a conceptual replication) and then I extend it to test my idea. Thus, the money for this comes from the same places that the money for all research comes from: largely from government allocations and grants, partly from NGOs and industry.
However, there has a been a recent trend--again, in my field, but as part of a larger movement in the social sciences--to improve replicability of results. This has included fostering availability of code for software projects and pursuing independent replications purely for their own merit. Last semester I attended a talk on a set of these replications, which, along with similar attempts from other labs, were pushing to overturn the original result. In these cases, the money to pursue the replication is probably somewhat independent from other projects of the researcher. This means funding agencies would be unlikely to provide a grant entirely for this work, so it is probably going to be supported as a passenger on previously funded work or on University funds (e.g. start up and salaries).

I can only tell you from the field of chemistry (and for germany). Normaly you only get funding from institutions to produce/research new stuff. Additional you get money (resp. Lab space, chemicals, technicians, positions for phds/post docs) from the university/institute to work on your field.
So how to check others work. Thats easy. Lets say professor A finds a great new reagent to produce something. If you work in a similiar field or you (resp. a lot of times the phds get the ideas and the work) wants to get something different but you want to try his reagent/method you will redo his work to produce somthing new with it.
That means you use his work (and you will hoefull cite him for that) to try some of your ideas. And then you will also see, if what he published was correct.
For the down side (in my expreience),especially if the results were posted in a top tier journal it is very hard to reproduce others work at first. This may be because of the lack of your knowledge, but oftentimes authors "forget" to put in some details/knacks which will make the whole reproducing very hard. This will give Prof. A some more time to use his published method for his own research befor a colleague can copy his approach. Oftentimes something published in a mid or lower tier journal is better to reproduce since the experimental section is oftentimes more detailled or more "realistic".
Please note, that this paragraph is only from expreience and I don't want to blame someone special. It`s just what I experienced.
As an addition, I think there a (few) journals/book series where the reviewer should/must reproduce the result first befor he can accept your paper. but that's really rare and I right now can't list you one from my memory.

The same works in other areas, e.g. CS - if you want to say "Hey, I have a great approach Foo that is better than everything else, including current state of art approach Baz" then a reasonable way to support that claim includes a comparison of those approaches on an appropriate problem, showing that Foo gets a result of X, Bar gets Y, and X>Y. That involves a replication of previous work that shows what kind of results their approach achieves; and if it contradicts the original publication or indicates a limitation that they didn't publish, then this replication is a big part of conclusions.

In human (and non-human animal) behavioral fields such as Psychology and areas of Medicine, which often involve many paid participants and quite a lot of expense, replication is often NOT done "first" or separately from a new experiment! There just isn't the money and time to be so inefficient - so you need to dual-purpose and build upon existing literature instead.
Story Time
I'll use the example that was used to explain it to me, because I think it's a great example of the concept. In the study Effects of Marijuana on Memory (Weil, Zinberg, & Nelson, 1968), the researchers effectively wanted to know if pot screws with your memory - does it make you forgetful, etc? So they designed a simple experiment where they gave some people real pot, and other people fake pot - then they tested their memory. In that experiment, it turned out that the answer was "pot does screw up your memory" - people have a decreased ability to recall things while high on the ganja.
But that was just one small experiment - what if it was just wrong? As a part of the idea of building upon past work to answer more questions, they designed a new study that asked if the effect of pot on memory depended on prior experience with the drug. This is a new question, but note that it assumes that pot has an effect on memory in the first place! In the new experiment they split people into the previous two groups (pot and no pot), but then split those groups as well so they were testing experienced pot heads and people who hadn't smoke it before.
Part of analyzing the results includes comparing the memory to pot heads and non-drug users, as well as comparing the memory of people who did and did not smoke marijuana. This is, effectively, a replication of the previous study! However, the new study made it possible to look at interaction effects - and it happened to turn out that pot only made pot-naive people forget things they encountered while high. True pot heads, it would seem, have the same memory stoned as sober - and stoners sober had no notable difference in memory to non-stoners. Go figure!
If the first time was interesting...
The key in such experiment designs is that not only is a design testing something new (and thus making it worthy of funding and publication in most venues!), but it also serves to re-test (and replicate) previous studies. And this happens pretty darn often!
If the effect is interesting, it naturally raises a lot of other questions - questions that are closely linked to the original effect. In testing these other questions, it is often difficult to even avoid testing the original thing too, and so replication is a natural part of the scientific process in these fields. It also produces a lot of the "controversy" that some fields experience.
My favorite example of this is probably gender/sex differences in intelligence factors. Do men have better visual-spacial reasoning? Do women have better verbal abilities? For years it was thought that these were real, significant effects and gender differences - and more recently they appear to be very close to pure fantasy, with many studies indicating no difference in men and women whatsoever in such areas. At most, it is estimated that both of these effects have the respectively-weaker sex at average outperforming 30-40% of the opposite sex - rather than the 50% expected if the effect were nonexistent, with many studies indeed showing less, no, or reversed differences.
And none of these studies had to be pure replications - any study that looked at IQ needed only to collect basic demographic data from the participants to allow such replications, which they probably were going to get anyway. No extra funding, yet loads of replication!

I really wish explicit replication of high profile studies was a priority for funders. However, as was mentioned there isn't typically funding for this kind of research.
The state of social science research is pretty terrible. I have tried to replicate results of past studies en-route to designing/analyzing my own studies. At times it can be like seeing that the emperor wears no clothes.
I think a revolution is coming though. Open-source is becoming more common, and I think you'll see a younger generation of researchers, like myself, at the very least opening our code up to the public or other researchers. As an aside, if you want to see a researcher squirm, then ask for their code. Better researchers will probably move away from point and click stat programs, so that the entire process they went through en-route to results will be transparent. In addition, there's a slow revolt occurring against null-hypothesis testing, which has really been a major factor in the replicatability crisis, and you might see more funders putting money explicitly towards Bayesian models for common research problems where the evidence seems mixed or the policy results of the research-evidence are minimal.

For impact evaluation there is funding from the 3ie replication project. They list a number of studies they would like to see replicated. Expertise needed is development economics/epidemiology/medicine/development related political science or maybe statistics.


text:
Is it a good idea to connect data points on a plot to increase readability in slides?
I have a graph of points from a set of experiments,
that I want to present on a slide.
The important information in the graph is both the values of the points,
and more significantly, that the green points are above the blue points.

It is quick and easy to get my plotting framework (matplotlib)
to connect each point to the next:

It should be fairly clear to anyone that the relationship between points is not expected to be linear.
I thought I could want to put the line in to make it clear that one is below the other. It can be hard to see the point markers on the projector screen.
Is this a good idea?
Does showing plots this way enhance the visibility of the them for presenting, or does the fact the that lines themselves are fairly meaningless distract too much?
Audience  concerns:
The whole presentation is for graduating engineering students and must be simple. While they would normally have the capacity to deal with complicated plots, the content of the presentation is complicated enough that I don’t want to distract them with anything that might waste thought time. I have already rejected the box-and-whiskers plot as too complicated; this is a plot of the mean values of that data.

Looking at it now, perhaps the lines themselves enhance the presentation, it makes the gradient more clear.

This question appears to be off-topic because it is about data visualisation, not academia. This could be migrated to [stats.se]

It is about the best way to present slides to a audience. I believe it is on-topic for both. (Though I am fine if it is migrated). I suggest perhaps there are additional concerns that would be addressed on accadmia that would not be the focus on CV. Such as old dim projectors, colour-blindness of audience member etc. While these could be considered on CV, that are at the forefront of people minds here on academia

Having been through the "data visualization in academic context is on topic?" discussion already on this question (which is even less specific to academia, and where the "Leave Open" votes were a definite majority), it seems this is perfectly on topic.

If anything, the question would be a good fit for [graphicdesign.SE].

An FYI: At first glance, I didn't notice the color difference at all and thought I was looking at one data set.

Could you normalize the X axis (so that the horizontal distance is equidistant) and draw vertical lines at each of the x-values that have a point which crosses both the green and blue?

Actually not so much Graphic Design as User Experience.

How about showing the difference? That is, plot diff = green - blue.

If you do add lines, please do NOT spline-ify them.  I've  seen some horrible fitted curves generated, which misrepresent the underlying data.

Ad audience concerns: be aware that you always set an example when talking to students. Be careful not to send the message, "this simplified non-scientific plot is the real deal".

Concerning the general question
Yes, it is acceptable to connect points, even if only discrete data points exist in theory and there is no continuum. If there is reason to expect that somebody misinterpretes your visualisation due to this or if you can expect the audience to be picky about this, add the sentence (on the slide or spoken):

Lines are for eye guidance only.

Concerning your special diagram

As already remarked, the colours are not well chosen (and will probably look even worse when projected). I recommend to use colours with a strong contrast, for example a white or almost white background and for the data 1) black or almost black, 2) pure red. (Be careful about pure green though, since most projectors will screw it up – dark green is better.)
Depending on how important you consider certain things:

Use a logarithmic scale (or similar) for the abscissa (x-axis). This way points do not cluster that much for small x and will be easier to read.
Use a logarithmic scale for the ordinate (y-axis). This way, the exponential relationship you mentioned becomes apparent immediately. However, the points for small x will get even closer to each other.

If only discrete points can exist, a line is a bad idea because it implies that intermediate values would be valid. If only discrete points were measured it is a good idea. From your first line I can't quite tell which you meant, but I suspect that you and I disagree...

@Floris: Yes, we disagree on that. I do not deny the risk of misleading someone, though, and recommend  to take countermeasures, if it is any likely that this happens.

@Floris I disagree. In some contexts it may be valid (a line does not always represent extrapolation - sometimes it can represent connection). See for example Parallel Coordinates http://syntagmatic.github.io/parallel-coordinates/.

@PiotrMigdal - interesting tool. Note that in that case there is a clearly labeled categorical axis and the intention of the lines is explicitly to link - since the categories have independent Y axes (number of cylinders, displacement, etc.) which removes all possibility of interpreting the line as "intermediate points may exist and would have this most likely value".

Drawing the lines implies a continuous relationship between the parameters. So if you can expect continuity, then connecting the points is fine. A second point to make is to avoid colours that are as similar as the green and blue you have chosen. One reason the difference is hard to see in the first scatter plot is due to colour. Try to experiment with colours that contrast better and your problem may be solved by just altering colours for one or both of the data sets.

In addition to colours, you can also choose different markers (triangles and circles, for example). Assuming at some point the figure is published as black and white, the different markers will provide the distinguishing information.

@jayann The OP states clearly that this is about presentation slides which will be shown in color. So as long as you choose colors that most people can distinguish (think of color-blind folks!) I'd go with a single shape; plots with many shapes often look messy.

Perhaps it would be better to use different line styles (solid/dashed).

And test the colours on a projector before you give the presentation! The blue and green you've used here are not only likely to be hard to distinguish but also hard to see at all.

I do expect continuity and monotonicity (infact I guarantee monotonicity), i do not expect linear interpolation to be very accurate except towards the right end. Thus you are saying it is fine in this case.
Are you saying it is Good in this case, or just "not bad".
New colours are Blue and Red (and a third line in a  orange)

You say you want to compare your data sets qualitatively, that is make clear which is "better". Since they seem to follow similar functions and are close together, normalisation can be a good tool.
Consider, for instance, this plot:

Note how the defaults of Mathematica 10 end up creating a far clearer plot.
Knowing both functions are basically 1/√n you can multiply the value by, say, n:

Now the "winner" is more clear.
Similar effects can be achieved by (other) axis transformations, cut-offs, zooms, etc. You have to be transparent about what transformations you apply, though, because you can easily end up with a plot that says "A is thrice as good as B!" even though the real difference was miniscule.

I quote the question (bold by me): “The important information in the graph is both the values of the points, and more significantly, that the green points are below the blue points.”

@Wrzlprmft I read "more significantly". Since, when it comes down to it, a plot can not be expected to support more than one story at once, I decided to focus on the (apparently) more significant point the OP is trying to make. (Also, people tend to overestimate the importance of the values they get.)

Point taken, though using too many plots (and thus switching plots too often) is also negative.

@Wrzlprmft Definitely! My advice would be "skrew the numbers, nobody cares!" in most cases. (If your presentation is about the energy of the Higgs boson, everybody cares.) It all depends on the story you want to tell; I find most stories that focus on showing lists of (allegedly) impressive numbers or formulae quite boring. But ymmv, obviously.

Excellent point "let one picture make one point". I see that violated frequently.

I'm going to bring particle physics practice to the table and say never connect-the-dots. Nor should you run splines through data. Run meaningful fits through the data or nothing.
These rules reflect the understanding in that discipline that individual points can have significant error or uncertainty associated with them, and the reader needs to see the data in toto without focusing on individual anomalies. If you know the data can't have these issues then relaxing these rules probably doesn't cause a lot of harm.
So what can you do.

Use more visually distinguished markers. A combination of shape, fill and color (with as color-blind friendly a palette as you can of course) gives the reader several ways to hook into the difference.
Use a different plotting (normalized, anomaly from theory, linearization of power-laws, etc). This is what Raphael suggested. Finding these can be a bit of an art.
If you have a well justified theory (or even a good seat of the pants model), draw fit lines: those automatically reflect the whole data set (good!).

Some points on the basic drawing of your figure.

Ditch the gray background. It only makes the data harder to read and makes Tufte cry.
Using filled circles for both series is a way to cause maximum visual confusion.

For display purposes, a smooth curve is the most logical thing to use. There are some nice spline fitting routines that allow you to create a fit that can be constrained to minimize curvature (in the process missing points that don't quite lie on the smooth curve), or you can simply eyeball the data and come up with a reasonable fit (for display - not for analysis).
I spent just a couple of minutes on this, but came up with the following:

This is basically an overlay of an Excel plot that I created (making the axes invisible) - using a simple 4 parameter model:

For the blue and green curves, I found parameters
    blue  green
A    0.8    0.8
B    1.0    1.0
C    1.0    0.5
D    .05    .03

Obviously since you have the raw data and matplotlib, you must know how to do a better fit, but this works well.
In general, I like to show only as much information as is needed on a plot. If the point is "this is a rapidly decaying curve and green is above blue", then I would definitely leave off the grid, and maybe even most of the numbers (run the X axis from 0 to 100, with just two labels, and the Y axis from 0 to 1).
I think that your data probably doesn't go negative - so I would definitely want to fix that X axis.
If you want to further make the point "we measured this data", then leaving the points on the graph as well as the smooth fit is an OK thing to do. I would consider adding error bars to show that the fit is good - and that the points are bad.
Again - you want to make it so that the information is "only what you need". My personal preference would be like this:

So fewer ticks on the axes, but do add a legend (I call them "blue" and "green", but you should use a more meaningful name) and do label the axes - numbers alone are not enough.

To me not having points on the plot takes away the experimental nature and make it look like the results are far stronger than they are.

@Oxinabox - yet to me, a "doodle" with very few ticks on the graph (my bottom plot) suggests the opposite - namely that the results are weaker. But that's why I said - "It depends on the message you want to convey." Include experimental points (and error bars) if they add to the storyline - don't include them if they don't.

hmm yes, perhaps... http://tex.stackexchange.com/questions/74878/create-xkcd-style-diagram-in-tex

@Oxinabox - yes: xkcd is a great source of "approximate" plots, and the density of labels tells a story. My favorite example: Ballmer peak. High density of labels on X ("very narrow peak"), no labels at all on Y ("how do you measure programming skill")

MathJax is not supported in Academia.SE. You can write an equation in plain text, or use a web service like this to generate an image of your equation (as I did here)

Grids are an obsolete fossil that should no longer be used. Back in the days, they helped to make the plots, and also made easier to manually retrieve the data from the graph. Nowadays, it is no longer necessary, as tables with the data are available elsewhere.
And if and when they are necessary, they should be as little intrusive as possible. Your grey-blue background is just too heavy.

Once that is out of the way, you can try a log scale for the x axis, as many points are accumulated near 0:

I think here is pretty clear that blue is always greater than green. Whether adding a line or not is good, is a matter of taste:

In my opinion, and as a general rule, I would say that the lines are acceptable as long as the "wiggling" is due to the actual shape of the function, and not due to noise. That is, when adding more points (taking more measurements) will not change significantly the shape of said curve (or we don't expect it to).

"tables with the data are available elsewhere." If that only were true... I work a lot with grids (I  even produce reference plots on fine grid like a mm-sheet). IMHO the problem with grids is not the grid itself, but that many grids are distracting from the graph instead of helping to read it. E.g. in your example, the dotted grid lines are at the same time far to dark and obtrusive and due to being dotted don't to their job of helping to find intersections. Consider very light lines instead that are not distracting the impression of the graph, but if you concentrate on them help measuring.

@cbeleites if you need accurate values, you should not be manually reading from plots, but using digitalised tables, as there is too much chance of errors. A plot should help to show the order of magnitude, the general trend and the noise levels.

I do not think grids lines are obsolete, as they even approximate visual estimation is much more difficult without them. Going that way, one soon may say numbers on the axis are redundant.

An alternate approach to connecting the dots: insert a line between the datasets to illustrate that one set of points is above the line and the other below.  If it is not inappropriate to the data, use a log scale for X to gain some space between the packed points at the left to improve visibility.  Use of higher contrast colors and marker shapes was previously suggested.

Normally it is better to use some kind of curve fitting (splines, etc), as we are not assuming that the measurements are absolutely accurate and the connecting lines should go from point to point.
However measurement points must also be present and very clearly visible, as they are our results and the line is our hypothesis, interpretation. Ideally showing error bars (confidence intervals) would be a good idea.

I rejected showing error bars as too likely to confuse the audience.
Also the connection between the lines really isn't my hypothisis. it is A hypothisis, but it has almost nothing to do with the story I am telling. My story is about points being below other pounts

What kind is your audience to be confused by error bars? If they are scientists, they have seen such bars many times before.

As it says in the question post, they are engineers (well final year engineering students). Basically none of them have seen error bars since highschool.

Strange, biologists are taught statistics and engineers not. Well, then may be unusual audience for me.

Why would your average engineer need experimental statistics? Very very few engineers go on to do research, compared to biologists


text:
What papers can you ask someone to cite for using your software?
Ofen Software authors request that users cite a paper of theirs in publications resulting from projects using their software.
As such an software author, what logic governs what kind of papers you can reasonably ask to cite?
I see 5 cases:

The paper is about the software explicitly. Eg Theano
The software is a direct implementation of an algorithm described in the paper. Eg anything from here
They created the software to use in research presented in the paper. It has not changed since.
They originally created the software for use in the research paper, but since then it has undergone fairly large changes as it has continued to be developed.  Eg DeepLearnToolbox
The paper has nothing in common with the software, except sharing an author.

In which cases is it acceptable to request the paper be cited?
5 seems like an obvious no-no, but are the others in the clear?
This question is the flip-side of Should I cite Papers to acknowledge usage of tools?

All of 1-4 seem like fair game to me. Also, note that they can only "ask" you to cite their paper anyway. If you think it is not warranted in your specific case, it is essentially your ethical judgement call.

xLeitix, you seem to have reversed the point of view. You mean to say: "Also, note that you can only "ask" them to cite your paper anyway. If they think it is not warranted in their specific case, it is essentially their ethical judgement call."

Depends on who "you" and "they" are. I meant "you = the paper writer" and "they = the author of the software".

this question is asked from the point of view such that 'you=software writer' it would be clearer to keep to that convention (and topic), in comments and answers.

When I am using someone else's software and this software has been used in my paper, the least I can do is cite the related paper. In many cases when the tool is available online, authors typically include the citation along with the download (on the readMe or documentation file). In those kind of cases, I must admit I have never really checked the overlap between the paper and the software, because citing the author's paper seemed to me more a matter of professional courtesy and attribution of their contribution to my work than anything else. After all, if I trust the authors enough to assume their software gives correct results, not trusting them for which paper they tell me to cite seemed like an oxymoron.
On the other hand, if I was the software author I would be careful so that there is a significant overlap between the paper and the software (your cases 1-4). All of those cases seem to me like a reasonable reason to cite the related paper. There is also a case 6 when the tool was developed during the duration of a research project and there is a published paper that describes this project. In that case, citing this paper like this:

We use the 'tool' that was developed during the 'myName' project
  [citationToMyNamePaper]"

when using this tool, would also seem like a reasonable choice.
I must say that I have never met your case 5 ("The paper has nothing in common with the software"), when I actually checked for overlap. Do you have any concrete examples of such a case yourself? Because although something like that is possible, I do not think it is common.

The first paragraph describes how people (from a sample of one - me) react to using a tool provided by other researchers. After all, what you want to do with your software, depends on how other people will react to you doing so. In this case, it is relevant to the way you should present your work.

While all is relevant, I don't think this is a complete answer. Your first paragraph seems like it is answering a different question. and the second contains only 1 sentence answering the questions, and the rest is speculating about the question. Point 5 is mostly there to provide a baseline.

@Oxinabox In that case, I have provided the "baseline" of an "answer" as your case 5 provided the "baseline" for the question :-) Cheers

The section you added just now is what I feel was missing "All of those cases seem to me like a reasonable reason to cite the related paper"