 Hey folks, I'm Pat Schloss and this is Code Club. This is going to be a very different Code Club because I'm going to tell you how you can create your very own live Code Clubs at your local institution, maybe even with your own research group. The idea for Code Club here on YouTube was born out of a practice that my lab has had over the last few years where we get together every other week as part of our lab meeting for an hour in our lab meeting to go over different concepts in programming, different parts of reproducible research. And so we do that every other week. The other week we do a journal club and then for the other hour of our lab meetings, I know the two hours it seems long, but it goes fast. The other hour is talking about research. That's the way we've done it and it works pretty well. Be sure you check out this paper that my lab published last year in PLOS Computational Biology called 10 Simple Rules to Increase Computational Skills Among Biologists with Code Clubs. As I said, the YouTube version of Code Club was created at the beginning of the pandemic because I said, you know, my lab really likes these Code Clubs. Why don't we try to have Code Club virtually, right? Trying to do it here on YouTube. So again, being the beginning of the semester here in the United States, I'm releasing this on Labor Day, which at least in the United States kind of signifies the end of summer and the start of fall and kind of the start of the school year. I don't even know when the University of Michigan started. I think maybe it was last week. I don't know. Anyway, I'm getting emails from colleagues across the country asking for resources to try to strengthen the computational skills of their research group. This past week, I got an email from Dr. Nicole Petraziak. Nicole, I'm sorry if I'm butchering your last name. Nicole is an assistant professor at New Mexico State University in the plant and environmental science department. And she's thinking about her research group and she's recently started getting newsletters that I send out every week. She said, I like this so much that I want to integrate it into our lab meetings bi-weekly to help our students to advance their data management in analytical schools. Any recommendations where you would start with a crowd of students with zero to intermediate are experienced? What a wonderful question. It just warms my heart to have colleagues that take this kind of stuff seriously and really want to help their trainees to develop data science skills. I think that only improves everybody's ability to do science. I just love this type of question. So thank you, Nicole. I responded to her pretty quickly with my initial thoughts and I said, you know, I've gotten a couple other of these emails. I should share this with people here on YouTube. So the resources I'm going to share with you today are totally free. There are paid versions that you can get, but I'm focusing on the free versions. If you have other ideas for resources that our colleagues can use to help improve the data science skills of the people they're working with, let us know down below in the comments. The first thing that I would strongly, strongly recommend is go to rifamonas.org. At the bottom of that page, there is a little form that you can put your name and your email address into, and you will get a weekly email from me, a newsletter that will have, you know, my ruminations for a little bit, and then a section with practice problems engaging with R and perhaps eventually other reproducible research methods. So there's, you know, three to five questions in there that you could go through as a group. They're not questions that are just super involved or hard, but they, you know, require a little bit of thought. So that might be one idea. You could take those types of exercises and work through them together as a group, and then you could come back together and talk about the solutions to those exercises. The second resource that I strongly recommend is stay at rifamonas.org. And at the top of that page, there is a link for training modules. In there, you will see a link to, you know, a dozen or so videos and slide decks related to reproducible research practices, specifically within the microbiome research. The minimal R is my tutorial series for teaching people how to use the tidyverse to analyze microbiome data. The general R is the same thing, except it uses data that's not microbiome related. It uses epidemiological data. It uses weather data and it uses data about demographics of institutions of higher learning here in the United States. Both minimal R and general R give a very broad overview of using the tidyverse. In the reproducible research, minimal R and general R tutorial series, there are a number of problems scattered throughout that again would be great fodder for a lab meeting. You could go through the material on your own and then come together at lab meeting and go through those exercises together. When I say go through exercises, what I'd strongly encourage you to do is to pair people off and so that people are working in pairs going through these different exercises. A practice that we found to be really good is that one person types at the keyboard and the other person hands off and tells the person what to type. After about 20 minutes, you say switch and then you switch roles. The person that's typing doesn't know what to type unless the person that's sitting behind them or next to them tells them what to type. This has also worked pretty well using Zoom, using breakout rooms. You could put maybe two or three people into a breakout room and have them work together. It's really important that you switch those so that you don't have one person dominating the whole experience and that you then come back and share your experiences. Another great source of resources are books. And so over the years, my lab has experimented with this where we might take a semester or semester and a half to go through a book. And so two books that we've enjoyed going through are Our for Data Science and Fundamental of Data Visualization. So Our for Data Science was written by Hadley Wickham and Garrett Grolemund. Hadley is the original author of things like Gigi Plattu and D. Plyer. He coined the tidy verse. It was initially called the Hadley verse, but he's humble enough to say, no, no, no. It should be the tidy verse. Anyway, this book is great because it really steps you through the basics of the tidy verse and gives you a pretty broad overview of what's going on. Again, there's a free version up on his website. There's also a paid version over on Amazon. Down below in the description, I'll leave a link to Amazon. If you go through that link, I'll get a very small kickback that might help me buy a couple bags of M&Ms. As I mentioned, the second book that we've really enjoyed going through is Klaus Wilkie's Fundamentals of Data Visualization. This is not an our book, but it's a book that talks about different elements of data visualization, what things are good, what's not so good, and why are they good or not so good. One of the things that's really unique about this book is, again, although it's not an our book, it was written all in R. All of the figures were generated by R code. And so because Klaus did a good job of making this book open and has a free version like Hadley does of his book up online through his website, all the R code behind the book is also publicly available. So if you see a figure you like, you can go look at Klaus's GitHub repository and see how he did whatever he did in that figure that you like. Also, you can get a physical copy of the book. Again, link down below to Amazon. Get a small kickback more and more M&Ms. Maybe I can afford some peanut M&Ms. Who knows? Like I said, all these resources work great in a lab meeting. I understand that a lot of people perhaps don't have a big research group to work with. You might be the only person in your lab doing these types of computational practices. Do not despair. You can of course go through these things on your own, but it is really nice to be able to work with other people. Something I would strongly encourage you to do if you're in this situation, and even if you have a big group of people that you're working with, is to look for like-minded people in the community around you. At the University of Michigan, some grad students and postdocs got together and formed a group called the Data Analysis Networking Group. Dang, we're very Midwestern. Anyway, they meet monthly and they go over topics related to data visualization and data analysis, machine learning, things like that. I know another group at Penn State University has formed a similar type of organization. These would be great opportunities to come together and perhaps go through a book like one of these or any of the Riffamonis resources. Just like we do journal clubs as a department or across departments, to learn the latest greatest in our field, I think there's a great opportunity to do the same type of thing with our programming skills. I really hope that you use this semester to either within your own research group to start doing a local code club or to get friends together from outside of your lab to do your own local code club and feel free to steal that dang name. I didn't come up with it myself and I really have very little to do with the group, but I'm sure they wouldn't mind hearing that dang had been spreading across the world. Dig into these resources, get practicing for this semester, and I'm sure that your skills will only grow.