Sho: I recently gave a talk on data visualization at the International Conference on Infant Studies (you can find my slides, along with the other wonderful talks on power, preregistration, and ethical data peeking here on the OSF). I also played the German Cats and Dogs Scientist in the barbarplots campaign on better data visualization (on the same topic, Article 1 and Article 2 on why bar and line plots hide differences in underlying distributions). In fact, being part of the barbarplots team was my entry point into thinking more about the importance of visualizing your data in a maximally informative and honest way. Informative means finding a good balance between simplifying/summarizing and showing the underlying data structure. Honest means not (accidentally) hiding important aspects of your data. Mahiko is my office mate and – this is something I discovered while preparing the above talk – an enthusiastic data visualizer. That’s why I asked him to put together our (mostly, his) favorite data viz resources.
This is part of (academic) life. The session was short, so I won’t bother to summarize. Just watch the video:
Some important points I had discussed in a previous session also apply here:
Naomi Havron is a postdoc at the Laboratoire de Sciences Cognitives et Psycholinguistique at Ecole Normale Supérieure.
She was recently interviewed for the departmental newsletter on her experiences as a woman and mother in science, and here’s a statement we found particularly on point:
Being a woman in science became difficult once I was a mom. Before, I believed that any inequality could be surmounted by working my ass off. But once I was a mom, I had to leave work early for my children. And I was judged differently from men. If it was me that was leaving early, that was judged as non-professional and proof I wasn’t invested enough in research. If it was a male colleague that was leaving for his children, he was complimented as being a devoted parent. (paraphrased and translated, see original French version here).
Naomi has been involved in organizing two amazing women-in-science events at our department recently, and we’re very excited she’s sharing her experiences and materials here with us.
If you decide to do a postdoc, do everything you can to do it right from the get-go! Watch what Sho (Cogtales) and Franziska (Ph_Dial) have to say ❤
Doing a postdoc can be a fantastic experience. In the last session of ACL, I talked with Sho Tsuji from Ecole Normale Supérieure de Paris who – just as me – is a very happy postdoc.
The most important thing is to find lab in which you can grow and have a PI that will be a great mentor not only for now, but for the rest of your career. Do a careful screening of whom you want to work with and try to get to know them and people who worked with them (or still do!). Be open-minded and use your network to find out about labs, job search specifics or grant opportunities in individual countries, and personal recommendations.
You will get the most out of your postdoc if you know what you want to get out of it. Make this guide you to what kind of project or lab…
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In our last post, Christina talked about academic networking on social media, specifically Twitter. There’s a reason that was her post, not mine: Even though I’ve been following most of her advice and this has improved my Twitter experience, I still feel awkward and out of place on Twitter, and I can’t get myself to create an account under my own name (instead, I’m tweeting as @cogtalestweet).
So today, I’m talking about my cup of tea: Live, in person networking. Specifically, the focus is on how to initiate conversation.
Within the span of two months, I’ve been asked to give essentially the same talk three times. The topic: how to network on Twitter (and other social media). How did this happen? Well, first a group of Parisian post docs organized a day-long workshop and apparently my tweeting is good enough to warrant inviting me back to my former home. Because I was invited, I took some care to prepare, and I think I did a decent job – decent enough, at least, to get some audience members to tweet about it and putting into practice what I just told them. Continue reading How to use Twitter for networking in academia
Here’s a (maybe not so well-kept) secret: I’ve got a PhD in modeling! No, not the posing kind, I constructed computational models of babies’ minds and behavior to better understand their early language acquisition. I learned a lot about cognition, babies, and data in that time. Next to that and two programming languages (Python and R) I also learned a bit about the modeling world. A key insight came to me after repeatedly trying to network with senior men and that being taken … the very wrong way. I must admit, I don’t know how much not being taken seriously as a modeler by some (no, not all) fellow modelers contributed to the fact that I took a step away from this field and am now an infant and a meta-science researcher most of the time. I am often thinking about what I’d recommend fellow women aspiring to a modeling career. So, at last, here’s the insight: build a support network of women modelers.* For those who watched a recent instalment of Academic Crisis Line, this might not be terribly earth shattering, but you have to realize that this is something that holds for your corner of science. I met a node in this support network soon thereafter, Olivia Guest, with whom I could talk forever about all those “fun” encounters. At some point, the idea to make a list of all fantastic, but probably vastly underappreciated women and nonbinary folks in modeling emerged, as she writes in her blog. There was some back and forth, questions about time investment, criteria, subcategories, so we effectively never got started, but such lists are super useful. For example, I suggested replacement speakers when asked to give a talk recently, and this list would have made my life much easier. So I am glad that Olivia turned to Twitter and simply asked others to make a list. The resulting thread is a goldmine. Continue reading Building a network of women and nonbinary cognitive modelers
My honest first answer before this session was ‘I don’t know’. Similar as Atsuko put it in this live session, I am not living in my home country for many years and it is hard to tell what is about me being a women and what is a cultural thing I don’t understand. But there is this almost painful awareness of having to justify my existence all the time. This constant feeling of having to proof that I deserve to be here.
The most important thing I learned in this session was that this will never stop. And accepting that this is something which will always be part of my professional life also brings some kind of relief with it. I am not alone and I am just as much part of the solution as everybody else.
I am very grateful for the insightful and genuine discussion with my colleagues…
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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
Papers are the currency in academia, they seem to determine our whole career. So, naturally, we try to publish as much as we can, while at the same time trying to produce good science. But sometimes authorship can become tricky, with hard decisions and disappointment. We share author-hard-ship stories here that cover a range of experiences, from being undeservedly excluded over the impression of getting too much credit to our own case that we consider ambiguous to this day.* All stories illustrate one key advice: Talk about authorship as early as possible in a project. This includes defining who is responsible for what, and discussing who is the lead of this project.**
Continue reading When authorship sails away – Stories of the intricacies of academic accreditation