 Hello, my name is Elaine Rauschen, I'm one of the original developers of interactive tools way before they were called that. The new version is really hugely improved and we're so happy to share it with you. So we're going to cover a little bit about what interactive tools are and how you can use them to do interactive analyses in Galaxy. Okay, over in newsgalaxy.edu properly, there's a section down at the bottom called interactive tools and there you'll find all of these different interactive tools that you might want to use. In that section, we have a lot of different options, all of the same ones on live.usegalaxy.edu. But let's get started and let's look for it first at the Jupyter notebook. So the Jupyter notebook will let you import data directly into your environments. Once you start working in Jupyter, you'll have access to all of that data. Here, I've loaded in a CSV file that we'll be working with and some data on sleep of different organisms. You'll note that when the interactive tool launches, you get this blue box at the top. It says, oh, hey, there's an interactive tool starting. If you'd like to wait for it to be active, you can do that from here. And you can also always access them from the user menu. So under the user menu, you'll find them under active interactive tools or you can wait for them to become active in this view. You'll get a link once it's running. The interactive tool view looks like this. Here, it says stop. It's not actually stopped. It's starting. And we can refresh that occasionally to see if it starts. Oh, it's turned blue. It's running. It's ready to go. Let's open it in a new tab. Here, we can see the Jupyter interface load. We have access to a lot of different kernels that we can use if you'd like to use the Python 3 kernel to analyze your data or even an ARC kernel. All of that's available. Okay. In your home here, you have access to a default notebook. Let's go ahead and open that now. You'll note this little introduction at the top. You can use that to access all of the data in your Galaxy history. So if I look back on my Galaxy history, I have this ID 1 in front of the notebook or in front of the CSV file. You can use the get function that's automatically loaded. To process that or to download it from Galaxy. Let's try that down. So here, this get1 statement actually downloads a copy of that data to the notebook's temporary file system. And then you have access to it here. And you can rerun it. And it'll even use the cache version. And it's a comma-separated dataset. And we can access all of the data. It's fantastic. This makes a lot of this sort of interactive programming a lot simpler. Let's add a new cell. And you can even do things like plotting. So if you have some matplot lib or some numpy plots that you want to make, these are easier to do in the notebooks as well. Things that you just can't really do in Galaxy because you don't have access to all configuration parameters. But here in the notebooks, however, you can do whatever you'd like to in Python. I believe we can call plot.save and give it a name. Here it'll save the figure as out.png. And then you can use the put command to share that back to the Galaxy history. So I'll go ahead and run out.png. Of course, you should get the better filenames always. But if we go back to Galaxy, we'll see that that PNG file is getting uploaded to Galaxy. So this is what really makes these analyses reproducible. This is always the goal of Galaxy, right? Reproducible analyses. But unfortunately, when you move into the notebook world, a lot of that could be lost. So we've added a lot of features to the notebook to make it easy to make them a lot more reproducible. If you want to save the notebook, you can do that as well to make it further more reproducible just by saving the Python file. I'm going to go ahead and save the Python file inside the interactive tool. And then I'll run put, which will upload it also to Galaxy. When those are done, we'll be able to see them directly in our Galaxy history. Okay, now that our file is done, you can see the output and image stored right back in our Galaxy. We also have access to the notebook as well. We can see a preview of that notebook. And if we want, we can also run a new notebook, new Jupyter notebook using that existing notebook as a starting point. So here we can select load previous notebook instead and it will load in all of the code that we were running before. One of the amazing new things about interactive tools, as opposed to your older version, one of the things that makes them so much better for researchers, is that you can use interactive tools inside a workflow as well. So if you have a workflow, here we'll create a simple workflow from what we're doing right now. I'm going to call this testing IT workflow. You can add the notebook to your workflow. And then when you run the workflow, you'll notice that it has included data and it also has outputs of a notebook. So when you run a simple workflow, when you want to process your data as a final step for that, you can have it, you can send all of that data directly to the interactive notebook. Here I'll run it with the CSV file that I was using earlier. And whenever that workflow is done, it will let me know that there is a notebook running and then I can go look at that notebook and look at any of the analysis that's happening in there and, you know, change any visualization. So I said, summary step for your workloads, fantastic way to open up your tools to or open up your data to new visualizations. But Jupyter is not the only kind of notebook. Under interactive tools, there is also RStudio, another extremely popular notebook. RStudio works exactly the same way as the Jupyter notebook, except it does not come, it does not let you select data ahead of time. So once you launch the RStudio notebook, you'll be able to load data the same way that we did with Jupyter. When it's ready, again, you'll see click here to display and we can access RStudio. RStudio comes with a lot of most common packages that you'll need pre-installed. So things like ggplot2 and tidyR, dplyr, all of these come ahead of installed ahead of time, which makes it easy to get started with your analysis. As usual with RStudio, it's just like the desktop RStudio, you have access to all of your normal functions. But now you have access to a new gxget function which can be used to fetch data sets from your history in just the same way as the Jupyter notebook. So here we'll read the gxget1 into a data object and this is based on the first element in our Galaxy history. It'll go fetch all of that data from Galaxy and we'll be able to preview it, seeing all the same data that we saw in Galaxy. You can do your plotting as well when you're when you produce some data that you're happy with. Say you pull out one column and you maybe write that out to a file, just as an example. We'll save out a csv file and then we can use the gxput, just like Jupyter's put command, to save back the csv file when we're done. We can also use the gxsave command in RStudio to automatically save the R history of the commands we run and then all of the R data, any of the data objects we've loaded. And then we go back to use galaxy.eu. You'll see that you have access to all of those uploaded files as well once they finish processing. Here was the csv file we loaded and then here are the RStudio data sets. So with RStudio you can also install packages. If you're missing anything just use the install packages or you can use mamba if they need dependencies as well. But we hope you like it and give it a try. If you're looking for training on interactive tools there's a lot on the Galaxy Training Network just like for interactive tools. There's a couple on development and administration which may not be so interesting but if you want to see lots of examples look for everything type interactive tools. Here you'll see primaries. So how do you use RStudio in Galaxy? You're visualizing climate data. You can learn a lot from there. The Galaxy Training Network has also made a lot of R and Python data science tutorials easily available from within these notebooks. So here you'll find this data science survival kit. Foundations of data science. And a lot of these are tagged things like a Jupyter notebook or scroll down a little bit. R Markdown notebook. And if we look at any of these we'll see the section at the top which talks about how to access those files from within a notebook. And if you have an RStudio or an R Markdown document they'll give instructions on launching RStudio and then copying this code into the RStudio environment where you can then run the notebook or see the training materials directly in Galaxy. Here I can demonstrate that quickly. Under my RStudio active environment interactive environment I can paste in these two lines. It'll download this R Markdown and CSS file if it's needed. And here I can see a preview of the training materials. This training this is completely interactive as well. So all of these cells here can be run. So the same as the R Markdown documents are the Jupyter notebooks. So here for instance SQL with Python. You have a Jupyter notebook available. Actually I have two versions. I have one version with solutions and one version with that solutions. If you're teaching this of course. But simply running this command in your Jupyter lab terminal. You're the launcher you can find a terminal. And then I'll just paste in this. Download this notebook. And I'm not trying in right. Oh I am. It's just not showing up here yet. Then you can open up these notebooks directly inside Jupyter notebook. All of these are Galaxy training materials up to you know the normal Galaxy training material standards and you can play around with the code. You can follow the tutorial in full directly from within a notebook with interactive code rather than from within the training materials. Or you might have to copy and paste back and forth all of this code. So it makes it for a really nice experience. Really loving integration of training materials and notebooks inside Galaxy. We think this is a fantastic system. So we'd love for you to try them out. Please let us know if you have any issues.