• ## How to prepare an article for resubmission, Part II

In my previous post on how to prepare an article for resubmission, I failed to mention one important point: In your response to the reviewers, quote the entire referee report, even the introductory sentences. Don’t just quote the specific comments to which you are replying. This may seem unnecessary but it is in fact crucial, in particular if the introductory sentences were largely positive. (If they were highly critical, you may want to omit them, even though in this case you probably should provide a response.)

• ## Relationship between h index and total citations count

I came across an interesting paper [1] that derives a mathematical relationship between the total number of citations a scientist has received, $N_\text{tot}$, and the scientist’s $h$ index [2]. The paper, written by Alexander Yong, argues that for typical scientists, $h$ is given simply as 0.54 times the square-root of $N_\text{tot}$. The paper also derives confidence bounds on this estimate, and it shows that scientists who have written only a few highly-cited works will generally fall below this estimate. While the paper is set up as a critique of the $h$ index, I think it shows that the $h$ index works largely as intended. It measures the total amount of citations a researcher has received, but it adequately down-weighs the effect of a few extremely highly cited works in a researcher’s publication list.

• ## How Google Scholar discourages young scientists from posting preprints

I have previously blogged about the issues that preprints can cause on Google Scholar. Today I was reminded that these issues have real-world implication for junior scientists, and that they may discourage junior scientists from posting preprints.

• ## How to prepare an article for resubmission

So your latest scientific masterpiece has come back from review with the most likely outcome other than rejection: major revision. The reviewers and the editor think that your work has merit, but they also have a long list of comments and criticism that they expect you to address before the article is acceptable for publication. You read the reviews and you feel like they lay out two years worth of work. How do you best deal with this situation?

• ## The Google Scholar preprint bug

Google Scholar has a serious bug when it comes to preprints. If you have published a preprint of your paper, the later journal publication can be completely invisible to Google Scholar, seemingly absent from their entire database. Even a search for the exact article title will not find the article. And this condition remains for months. (It will eventually fix itself, though. After about a year.) I have now seen this bug in action for several of my papers, and I am confident it’s a reproducible flaw and not a one-off. I reported the issue to the Google Scholar team about a year ago (or at least, I filled in some web form that seemed to be designed to send them feedback) but I have received no response and the bug clearly still exists. I hope that with this blog-post I can draw some attention to this serious issue, so we can have it fixed. Thousands of scientists rely on Google Scholar every day. For many recent articles, this bug will steer these scientists towards outdated, early versions and make the authoritative article versions completely inaccessible.

• ## Should peer-review be double-blind?

As part of the recent discussion on anonymous peer review, several people spoke out in favor of double-blind peer review, where neither the authors nor the reviewers know who the others are. I have thought a lot about double-blind peer review, and I’m not entirely convinced, in particular when it comes to grant applications. While double-blind review might solve certain problems and remove certain biases, it would almost certainly amplify other issues, and whether the net effect would be good or bad is unclear. It would also give more power to people such as editors and program managers who operate outside the blinded process.

• ## How to schedule a committee meeting

One of the key challenges in obtaining a PhD is scheduling a committee meeting. In fact, I think that anybody who has managed to successfully schedule three or four committee meetings probably deserves a PhD just for that feat. After all, getting five professors into the same room at the same time is a tall order. Since scheduling committee meetings is such an integral part of graduate education, there should probably be a class on how to do this successfully. However, I don’t think any such class exists. So maybe this blog post can serve as a substitute.

• ## In defense of anonymous peer review

In a recent blog post, Mick Watson argued that anonymous peer review is bad for science. The post makes a number of insightful and valid points. However, the one point I cannot agree with is that junior scientists don’t need anonymity so they can freely speak their mind without fear of retaliation. Mick Watson argues that retaliation should be a non-issue, and that in the cases where it is not we just have to make it so. Frankly, I think this is simplistic black-and-white thinking. There are so many ways in which a senior person can make a junior person’s life more difficult; I would always recommend my graduate students and postdocs that they review anonymously unless they can write a very positive review.

• ## To grid or not to grid

I had a twitter discussion with ggplot2 author Hadley Wickham on whether or not to include a grid background in plots. He thinks the default should have a grid, I think the opposite. I believe we both agree that grids make sense for some plots and not for others, so this is just a question about defaults. On that issue, we remain in disagreement.

• ## R Markdown, the easiest and most elegant approach to writing about data analysis with R

This weekend, I finally spent some time learning R Markdown. I had been aware of its existence for a while, but I had never bothered to check it out. What a mistake. R Markdown rocks! It’s hands down the easiest and most elegant method to creating rich documents that contain data analysis, figures, mathematical formulas, and text. And it’s super easy to learn. I wager that anybody who has RStudio installed can create a useful document in 30 minutes or less. So if you use R, and you’ve never used R Markdown, give it a try.

It seems that Hadley Wickham, the author of the spectacular ggplot2 library for R, is not content with revolutionizing the world of computational data analysis just once. He keeps doing it. This spring, he released the dplyr package, a package that proposes a grammar of data manipulation. I predict that dplyr will become as important for large-scale data analysis and manipulation as ggplot2 has become for visualization. If you like ggplot2, you will love dplyr. [1]