As you might have noted, this blog was rather quiet in 2019, and for good reason: It was quite the exciting year for your two favorite bloggers, so exciting in fact that even our traditional review post is a month late. We’ve already shared the major changes on social media, and we’ll tell you here as well why our lives kept us from blogging:
A happy 2019, dear CogTales reader! The time around the change of years is, as is now tradition, a time to look back to 2018, which was an exciting and busy year for your two bloggers, Sho and Christina! (This might also explain the slightly less frequent occurrence of posts, please excuse us, but we’re planning to share what we are learning here, of course).
Good reviews are supportive, constructive, thoughtful and fair. They identify both strengths and weaknesses alike and offer concrete suggestions for improvement. Good reviewers acknowledge their own biases and knowledge limitations and justify their conclusions.
Bad reviews are superficial, petty, and arrogant. Bad reviewers are very opinionated but typically don’t justify their biases. Their reports focus on weaknesses only but don’t offer solutions or other form of helpful feedback.
In today’s session, I walked you through the review process and told you how I write review reports:
Here you can find a template for the review report.
https://authorservices.wiley.com/Reviewers/journal-reviewers/how-to-perform-a-peer-review/step-by-step-guide-to-reviewing-a-manuscript.html offers a detailed step by step guide.
https://editorresources.taylorandfrancisgroup.com/reviewers-guidelines-and-best-practice/ offer additional advice and concrete examples of how to express criticism diplomatically.
http://www.sciencemag.org/careers/2016/09/how-review-paper features a lot of personal strategies and experiences which are often different from what I do.
Where I stole the summary from (almost word by word): https://facultystaff.richmond.edu/~rterry/NECTFL/How_to_Review_a_Journal_Article_NECTFL.pdf
Dear CogTales reader, this post is about and made possible by you! You made this year the best yet in this little blog’s history. In today’s post, we want to take a moment and review which five posts you were most interested in. But first, we want to thank you. We’re hoping that you continue to come back and maybe even tell a friend or two about us. You can even contribute, if you have a story you would like to share, either with your name or anonymously, just get in touch.
So now let’s take a look back at the year as it is ending and review the top five posts according to our visitor statistics in 2017. Continue reading Looking back on 2017
Sometimes, things just fall into place: The evening before the most recent Academic Crisis Line on dealing with rejection and frustration, I got a pre-holiday manuscript rejection. As pointed out by the crisis liners, rejection in academia happens to everyone on a rather regular basis. So what we should really be concentrating on is to deal with it in the most self-preserving and productive ways possible. One thing that can really help is to talk through it, and to connect with others in similar situations.
So I thought it would be an interesting experiment to share my unfiltered thoughts while I deal with this rejected paper here on this blog.
A year (and a few days) ago Sho and I launched CogTales, and what a year it has been. Thanks to you all, be it as readers, guest posters, or in the comments, we’ve grown quite a bit in this short time. Posts covered research practices, personal experiences, an ongoing R course, and even a successful kickstarter campaign! Our most popular posts were actually those where we shared a personal story, be it about becoming an expeRt coder or standing up in a big room to ask the tough questions. This shows that those stories matter and are of interest, so we will continue to share the experiences and opinions of junior female researchers in cognitive science. If you would like to tell your story, just get in touch!
To a fantastic 2017!
Following up linear regression, in this lesson we’ll learn the math of logistic regression, and run a logistic regression in R. As always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 3.