 Hi, I'm Anne. I'm working at the University of Oslo in Norway and I will guide you through this galaxy tutorial on the functionally assembled terrestrial ecosystem simulator, FATES. This is part of the Galaxy Climate Workbench and I will be very happy to answer any questions on this tutorial or any questions related to the usage of climate data in Galaxy. So what we will learn in this tutorial is first how to run this FATES model using the Galaxy tool and how to upload input data for running the model. How to prepare the input data is out of scope of this tutorial but we will also learn how to customize your run or to analyze your model outputs and finally to create a workflow for making your research fully reproducible. A strong running before you start this tutorial, it can take quite a lot of time to run the model. We are running five years to have some scientifically significant results and it takes about three to four hours on the galaxy climate, the European instance. So to make sure you can also analyze the results and finish the tutorial in time on time. I have also prepared some pre-made simulations that you can upload in your history. I will show it in a separate video. Thank you. Let's start this tutorial. First, make sure you are logging on the galaxy climate instance. So make sure you have this galaxy climate. So this is a European galaxy instance usegalaxy.eu and in front you add climate. So the full address is HTTPS colon slash slash climate.usegalaxy.eu. Make sure you are logged in here. Here I'm logged in as my username and the tutorial we will do together is this one in the climate category here. And this functionally assembled terrestrial ecosystem simulator phase. So we are taking the hands on here. And we can start first to create a new history and then to create to upload the data. So as we will be using this galaxy climate instance in Europe, we have already the data available on this galaxy instance. So we will not upload the data from Zenodo, but we will directly import the data from a data library. And I will show you how to do that. But first, let's create a new history. So I click here on this plus to create a new history. And I will rename the history. I will call it fades for this tutorial. And then to upload the data, I will go to the shared data and data libraries. And you should see this whole system community modeling. Then the CTSM, which is for community terrestrial system model. And then this is where you can find the input data and the restart file. So I will explain that just after what what it is about but first let's select the input data. So this is really the forcing for the climate model for the face model and restart file. So here this is the restart story and the input data is here. Let's import it. So I will export it as a data sets to my history. And that's it. Let's go to analyze. And because they're already on the system, this is already green. So everything is all right. So let's first discuss these two input files because this is quite important. I will show you again the tutorial. We have to go back to here. So the input data. So for preparing this input data, this is completely out of scope of this tutorial and will probably in the future write a separate tutorial for explaining how to prepare the input data. What we provide is a table to the model where at least we have these four folders in in your table, which is the ATM, which is for the atmospheric component. So the forcing for the face model for the atmosphere, the CPL, which is for the coupling, which says how the atmosphere and the land can interact with each other. And the LND, which is for the land. So this is where the fate component is, is available. And the share, which is also a share folder where we have, for instance, topography and other information relative to non specific data. So the model we will be running is what we call a single point. So we have only one point, which is a location. So with a latitude and longitude, and this is a point in Norway. So it will represent represent some Norwegian Alpine Tundra ecosystem. So this is a latitude and longitude and the elevation. So why do we have these different locations? This is mostly because we can then compare with some observations, because this is a location where we make a measure measurements on a regular basis. So they have been prepared for you and they are ready to use. So this is a site included in the modeling platform, which is developed under the Emerald Project, which is a project at the University of Oslo in Norway. So we have uploaded this data set. So this input data, which is the input underscore version. So for each version of the model, you have to be careful to have a different version of the input data. And we have another file, which is a restart file. This is mostly because the model, any climate model, if you start from scratch, so without any restart file, the model will be very unstable. So what we usually do to have quite a stable starting point, we first run the model for quite a very long time. So here we run it for 200, 2300 years. So it can stabilize. It can be stabilized and we can make some further simulation. So this is usually what you need to do with climate model. This is the difference between a call start and a restart model. So we will now set up the CLM fate simulation. So we will be using this CTSM fate emerald galaxy tool, which is specific to the Norwegian ecosystem. And it is based on a general model CLM fate. And it has been adapted for this Norwegian location. You can find more information if you click for instance here. An easy way to find the tool is to click here, and then it will appear in the middle part. So what do we need to start the model? We first need to make sure we have the right input data. So this input input files at our ball. And second, we need to make sure we specify in the customized part with change. We don't want a startup. A startup is what I called before a call start. We want to start from this restart file where we have run this 2300 years to make sure the model and the results are scientifically meaningful. So I will change it from startup to hybrid. And I need to put a reference model. So here this is the name of of this experiment. So here for instance we put the name of the case for each experiment. So when I run this one, I gave a name which was called alp1 underscore riff case. I also need to specify where I want to start for this reference. So I will start from this 2300 years and this 0101, which is the first of January. And here this is a start date which we can use for storing the model output. And I will, I want to start from a very start date 001 to make sure it will go forward. So I will start from 001 and the first January of this year. So I mean, this is mostly for reference for yourself. It doesn't have any significance in terms of scientific results. What else do we need to do? So here we are, we can select different model resolutions or different location in Norway, but we will take this alp1 for this case. And here this is the name of your experiment. So, which is a string you can choose usually whatever you want, but it has to be meaningful. So for this one, I will call it alp1 XP, which is experiment. So this is very classical in climate modeling to call experiment with like here this is a model resolution and experiment for experiment. What else do we need to change? So make sure here for the restart, we don't want to take the input file, but we want to take this restart. Well, here we want to take the input data. We will run it for five years. So yes, we have five, but we need to change from N days to N years so you can type N years and then select. What else do we want to change? I think this is it for this one. Here this is some advanced customization we will see later on when we do some different simulations. So here you are ready to start. You can click on the execute. So you will have many different output file which I will explain once this is done. It usually takes a bit of time so you can make a break and we'll come back later when it is done. When it's first running, it will be orange and then when it's finished. So as you can see now it is running. It's all orange. It will still take some time before it finishes. Let's have a look at what we will get as output. So we have many what we call log files here with like the ATM log which is for the atmosphere component. And this is because this is some kind of couple model between the atmosphere and the land. So we'll have one for the atmosphere, one for the land is LND.log and a log file. This is mostly given, it will give you some information about the run and what has been performed for this component. So this is interesting to see if everything was all right. Then you have some log file for this CPL is a coupling because it's the land and the atmosphere they need to communicate some information between the surface and the atmosphere. And then you have a log file. This is a CSM log file, which is also a log file for the whole CLM fate component. What else do we have? We have this rough log file, which is for the runoff, which we need to have always when running a land and the atmosphere component. Usually I don't really look into detail for this component. This is not very important for us. Then we'll have some case info, information about the case. It's mostly like a text file, getting some very basic information about your experiment and recalling you what setting you have done. And you will create this restart info file, which is also a text file, which we will use, for instance, if you want to continue the run. So it's always have a restart info file and you should have somewhere the restart file, which is a tarboard. So these two files, here they go together. And we only use them if we want to continue the run after, for instance, here we are running five year if we want to run a longer simulation after the five year. We'll see later how to do that. I haven't explained this work here. This is where the model is running. And I look at this tarboard and I don't need it on my laptop. If I want to check something, especially when the run was not very successful or when the results are not as expected. Otherwise, the most important output for us is what we call the history file. And it will be in this one. So it's, it's a collection because we can have more than one output file, depending on how long you are running the model. So we'll wait for the simulation to finish and we'll look at this file for checking the model output. So let's go back to our simulation. It is completed now as we can see all the tasks are green and the run has successfully completed. We can look at some of the files, especially for instance, if you want to look at the logs file like here for atmospheric components, so it's quite large. So it will only show some part of it, but it's usually, I mean, this is successful. And we also have some other information like the restart file, restart info, which tells you how many years have run. So for instance, we know here we have started from year one, and we have run five years. So the next restart file will be at six year and January 1. Now what we want to do is to analyze the model output net CDF data format, and all the data will be in this history file collection. If we click on it here, we have only one file, because we have run a short simulation so sometimes we can have more. And if we click on it, we can see the format and see this is a binary format. And the first thing we will do is to change the format to net CDF because the tool for now didn't manage to successfully detect the net CDF format. So we click here on the edit attributes and in data type and here I will switch to net CDF and I will select and I will click on change data type. So it will spin for a few seconds normally not very long. And the next step will be to change the name of this file. So this file is also in this collection here. It should be done by now. Yes, no, not yet. We will change its name because the name contains some dots and some special characters that may not be correctly used for some tools in a galaxy so the best is always to change to some short name and meaningful name. So we can use for instance this net CDF file in in Panoply, which is an interactive tool for this to visualize net CDF data. So here I will again edit the attributes and I will call this file alp one underscore X dot NC. Yes, and I will save. Now we have both the format is net CDF and the name is alp one underscore X dot NC. Then let's go back to the tutorial in the climate. And this is this one, the function as a material ecosystem simulator. And so far what we have done is we have uploaded the data and setting up the CLM fate simulation this step here for creating a new case. And what we have done also is to change the type and renamed the data set. And now what we will use is this net CDF metadata info to get some information on the names of the variables and the dimensions of all the different variables. So then we can later extract some meaningful information for instance if we want to visualize some of the variables. So we will use this net CDF X sorry metadata info to generate two input files containing information on the metadata. And we will try to answer to this question which is what are the short names of the relevant variables. And which one will you pick if you want to do the result in a millimeter per second so this is to get some information about the canopy transpiration. Let's click on this tool here so we'll have presented here, we check that you are taking the right input file, which is one underscore expert and see, and we can execute. So it will generate two files. So one containing some short information about the different dimensions of the variable which we use for different tools. Exary tools. And the other one will be all the metadata contained in this net CDF files. And then we'll search for the canopy transpiration variable. So it's still running. Let's wait. Let's have a look at the two different files. So this one contains, oops, there is an error. All the different information about the variables but also the dimension so this is always a this is a net CDF data. So we have this dimension at the top. And we have the lens of each dimension in teacher here. And we have all the different variables and for each of them we know the different dimensions and we also know some, we get some more information about the metadata like the long name here and the unit so this is quite standard. We are following the climate convention, climate forecast convention CF convention for getting the net CDF output. And you can see we have lots of variables. And at the very bottom you will get all the global attributes, which usually you know where and who has created this file. We know which convention is used, which is a CF one dot zero and some other global information about this data set. So now let's look at the second metadata information. So this one is usually the one we use for some other tools and what we get is a list of variable names and dimension for each of the variables. So this is the info file you can use to get some metadata information about all the different variables and in particular the long names, which is usually where we find the variable names we we can relate to scientific variables. So, let's go back to the question. The question was, identify which variables would provide you some insights about the canopy transpiration. So usually what I do is I go here and I do control F to search for canopy transpiration. And here I found already one variable so here we have one variable FCTR, which is a function of time and LND grid. LND grid is a number of points in the grid and here this is a single location simulation. So this variable will be equal to one which we can check at the very top LND grid here you can see is one. So if we go back to this variable. And this is probably the first one again this FCTR this is the one we have seen. And the units are what per square meter so this is one of the variable and the time is mean so this mean that variable is average over each period of time. So here. So the time step of the model is different from the model output frequency. We average outputs over a month so each value is an average over the entire months. Let's look at the next one so we have another one which is QVE GT. And this time this is exactly the same variable but the units are different this is millimeter per second for the canopy transpiration. So, we have, we can see if we want to answer to the question which variables would provide you some insight about the canopy transpiration we can see. With the FATES model we have two variables one is called FCTR and the unit is what per square meter and the other variable is QVE GT which is in millimeter per second. So if you want to have a variable in millimeter per second you would need to take this variable QVE GT. The second question was what are the dimensions of this variable and this is what I briefly mentioned before this is over time. And for one single location so if we look at how many times do we have so if you remember we run five years so the time will be 60. So we have answered to the first question. Let's go back now to the tutorial. So we have done this metadata info and what we will do next is to quickly visualize the data with Panoply. So how do we start Panoply? Panoply is an interactive tool. And the easiest is to start it from here I will show and then to use this live galaxy. So if you look at it here we say that we suggest to use this live.usegalaxy.eu and this is mostly to avoid the problems when you are opening the application. So let's do that. I will here go to the interactive tool and you will find this Panoply here. And make sure you are taking the right input which is here this alp1 underscore x dot in C which is the netcdf history file exactly the same file we got some metadata info so far. And now we will really visualize the data so over the five years of simulation I execute. And here to have no problem when I start this Panoply interactive tool in the active interactive tool panel I will switch to the live.usegalaxy.eu. This is the same portal but this is a different view we mostly see the interactive tools and what I will do here I will first login so I'm logging I will make sure I'm in the right history. So I need to go in the history where my Panoply tool has been started so I can switch to this one and then analyze data and I will go to user active interactive tools and I will click here to get started. And Panoply just starts. So here this is a panel you will get initially and you have to select this input data alp1 and you can click on it and it will appear here and then open. As you can see here you have all the different variables similar to what we have seen previously with XR and metadata info tool. With here's a dimension so it says this is 1d variable 2d variable or 3d variable etc for instance we have mostly 1d and 2d variables because this is a single location simulation. And here this is all the metadata in a similar way than the metadata information of the Galaxy tool. So if we look at what is asked in the tutorial we want to search for some variables. So for instance we would like to find what is the long name of mortality. So in this tool what is quite nice is everything is in alphabetical order. So if I go to LM it will show up very quickly mortality so this mortality here. If I double click it will show you how to visualize but let's first look at some metadata. So when you click on the variable here on the left button of your mouse it will appear here and you will see the variable name and the different dimensions so we have 60 values for the time. We have a different level this left pft 12 so this is a different pft values we will see this plant functional types. And we still have one single point so we have lnd grid equals to one. So long name is rate of total mortality by pft plant functional type. So to answer to the question we have just answered to the first question if we go which is the rate of total mortality by pft the long name of the mortality variable. What is it's a physical unit so we can see the units are just below here and the units are. This endive per he and per year. So the next question is to plot the total carbon in the life plant leaves. So this variable is called the short name is leaf FC leaf. So we have h for L leaf SC which is the total carbon in the life plant leaves. Let's click here. And if you double click. We will show you how to make a plot and so we can we want to plot the variable as a function of the time so along the axis. And this is a plot we get here. So this time we get a plot and we want to save the plot in panoply. So you have here to go in file and save the image as so you can eventually change the name if you want to, and make sure you put this image in the output folder. We will close the panoply quit panoply it will be saved back into our galaxy history. So, do you observe any pattern in this plot and does it make sense from a scientific point of view. So what I can briefly say here is if we look at the time here and the different months and years. We can clearly see some seasonal cycle which is quite normal because the carbon in life plants leaves will will change as a function of the time depending if this is winter or summer. Okay, so we have saved or a plot in in the output folder. Now we can also plot the rate of total mortality per TFT, which is a second variable is mortality. So here what you can do once your plot is finished and you have saved the image you can go in file. And you can close so don't quit panoply because if you quit you will be you will leave completely panoply and you will not be able to to plot in any other variables. So, let's go to the mortality, which is here. And again, I left click twice to get this panel for plotting you can also get it in in the view here. And here we will select so this is a 2d variable as you can see so panoply will offer you the possibility to create a 2d plot. And then you have to choose if you want to have this fate this pft on the X axis or and the other time and usually what we do is we like to have the time on the X axis which is the evolution of the pft. And on on the vertical on the Y axis, we will see all the different pft. So I will create this plot. So by default it takes quite not very user friendly color scale so we'll first change the color to make sure we can see a bit better the pattern. And so that we can answer to the question, which is again can you observe any pattern and if this makes sense for you. So how to customize your plot in the panoply. First thing we can do is to change this color map. So you can choose any color map you you would find appropriate for for your visualizations. Sorry, it didn't select. There are so many different ones sometimes it's difficult to choose. So when you find yes this one are usually quite nice for making some plots. I don't know if there is much different let's take that one. For instance, and then the second thing we will do is to change the vertical so the grid here. So there are many things you can customize like the minimum and the maximum value which are this mean and max, but that's okay we will not change them. And the grid you can change the grid for for your plot so you can see a bit more. This plot correctly. So let's go to this solely. Don't remember exactly. For instance, no, that's not this one. Oh, this is not not the X we want to do why sorry. Yes, so here this is it will allow us to highlight a bit more this pattern which is at this level. One thing we can change now is to make sure it fits and fill all the plot so instead of starting at 05 here we can put one and here for instance, yes. Maybe. Well maybe this is okay here maybe five. It's maybe a bit too big. So we can maybe it was then. Yeah, we can still keep one. And here I can see it doesn't start at zero because we'll only get some output at the end of the first month. So instead of starting at zero, I will put one month which would be like 31 days to fill it. And then what we can see here is that we clearly have a seasonal cycle again if we look at the time here and this is always again this winter and summer values. So this is essentially for this pft which is this pft to again I will save this plot. So I can save the image as. And again I make sure I'm in the output folder. And for instance again and you can play a bit with this and you can change for instance different color scale, different values here. So you can see a bit more and adjust your plot for for your publication so usually I try a few things to see to make sure I can highlight very well the different. Seasonality here. So for instance it looks like this color scale really highlight more what I would like to highlight which is the seasonality so I will save again this image. And this folder. Yeah, I need to give a new name so I will probably call for color. So I have some plots, which I can save. And now you can make several plots and try different variables, I will close this plot here. Feel free to experiment with other variables that can be of interest for you and save all the outputs in the output folder. Once you are done, you can quit panoply. And then we can close this. And this one and go back here. And you will see this panoply output here will terminate in a few seconds and we'll have a plot which we can download on on our laptop. So here we have these plots here which I can look and here this is usually it tells you the version of the tool of the panoply tool. So we have done this three plots, and then you can choose the one that would be more appropriate for instance for your publication so you can download your file. So here on your laptop for later usage, but you have it in your history so then I go back to face here. So, we have done already the first two steps. And we'll now use not an interactive tool but another tool for making some visualization. We have used panoply for plotting and analyzing results. Why do we want to use a galaxy tool for for analyzing and plotting the results in state. Why not simply always use panoply. The thing is we want to make a workflow again we want to automate or research. So, running a panoply an attractive tool is not the best way to automate your research. It's really good for exploring your data for making new research and new discovery, but then the next step is usually to automate your process. So we'll go back to this training here. Where we have been so far we have run the model we have used panoply to analyze. We have expected the metadata and now what we will do is this far. So to use a galaxy tool for analyzing the seal and fake simulation so to create some visualization but directly from a galaxy tool so we can incorporate this step in in our workflow. So if you remember before we have used already some x-ray tool so this one now we will make some selection so we will select the values from this leaf FC variable. So we will have the value as text tabular data. So we can then use for instance a GG plot for making a plot in a galaxy. So let's take this one and as you can see we'll have to make sure we take the right input file always is history file. And we'll have this metadata info in for this variable as an input which is the result of some of the previous steps we have done already. And then we will run it at the end will always rename this so I can already copy it. We will rename the data set for future usage. It's always a good practice and we will extract the leaf FC variable so I can click here to get the tool. And I have run the alp underscore X dot NC as an input which is in a CDF file and immediately it already has it has chosen this tabular variable which is the list of variables and the dimensions and which is the results of the previous metadata x-ray tool we have used. For the variable name will select leaf FC which is here can select. If we want to select manual under the coordinate this not necessary we want everything and we have only one point so it's not really necessary to select and reduce the amount of data. And then we can execute. So it will take some time. Okay, so it starts running. So it's quite a small tool. So it shouldn't take too long because we don't have so much data. We have only five years of simulation. If you have looked at the introduction of climate data, you may remember that to be scientifically meaningful. In climate we usually take period of time about at least 20 to 30 years. So this is here a very short simulation we do, which is still interesting because we can see some seasonality. But it will not be something we can use for assessing the climate or the change in climate for this location. So if we look here and we click on this view data, we can see now we have extracted this leaf FC variable, which we have already done before with a panoply. So here we have one single location so it's this variable is not very meaningful. And here this is all the different times so this is for the first year at the beginning so the time will be zero two, because we have started the simulation on the first January, and then the output the model will output some results every month, and it makes an average over the previous months. So here we have up to January, your six, which is the average of the previous months. And here they are the values. And what we want now is to prepare this tabular file for scatterplots using GG plots. The first step would be to prepare this first column the time in a in a way that can be understood by GG plot and one of the problem we have here this is after the year, the months and the day we have a space for the time but the time is completely canceled for this climate model because we have only zero everywhere which corresponded to midnight. So what we will do is we will use a tool to remove this pattern to split and only keep this one. So back to the tutorial. We'll see we have done this first part which was to select this and get tabular values of the life FC. And now what we will do is we will clean the data clean the date using this replace part of the text will take the result of the previous step which will rename first the variables. The mode of output file to this. I forgot to do it so I do it again. And I go here and I will rename the file always very good practice I forgot. And I will control V. And I save. So it will change the name. I'm good. And then I can go back to my tutorial here. I will use this tool here where I will use this replace part of text so I will select the input this net CDF file which has been converted to tabular by extracting only one variable, and I will find this pattern which is the time. I will, what I will do is, I will replace all the occurrence of this pattern to have zero colon zero colon zero. And I will remove entirely these values. So that we have a clean tabular output. And we will find and replace text in the entire line. So let's do that. So what we do here, make sure I have the right tabular and the file to process in this net CDF XR selection. What pattern do we want to find this 00 colon 00 colon 00, which correspond to our minute and second. We want to replace with some empty because we want to remove this is not very meaningful for us. So find pattern is a regular expression. No, this is not a regular expression. This is only the pattern we want to remove. We place all occurrence of the pattern. Yes, we want to replace all the occurrence. The sensitive is doesn't really matter because this is numbers. And we want to find the whole words, we can say, yes, this is what we want to find we want to make sure we found all the zero colon 00 colon 00. And we want to replace it in the entire line, which is the default. As you can see, we can always get some email notification at the end of the tool, which is not here very necessary, but we can be useful when running the CLM fake models. I can execute writing for execution. Okay, so it starts running shouldn't be long. The only very long step is the first one when we run the model. So here, if we look at the results and we can click. So we have suppressed this, the time in the, in the dates here so we only have the year, the months and the day, and then the value of the leaf FC. So now this is quite clean and we can use for instance, GG plot the scatter plot with GG plot to plot the left leaf FC value as a function of time so here you can search for GG plot. It's usually coming very quickly so there is money. Yes, and you can see for instance this one would do very well. Yeah, so we haven't renamed since this is probably best before we do that to rename again, so I'm sorry, I go back I forgot to rename, and I will rename it to meaningful name which is a leaf FC. And this is a clean dot tabular so this is really the clean values of my leaf FC extracted from the net CDF file. And again now I can finally plot so it's not mandatory to change the name but it's usually a good practice for yourself so you remember exactly what you have done. So what do we want to do so we want to plot in the value in this leaf FC clean tabular with the first colon is the number the row number, then the second one is a time. And we have also this. LN grid, which is not what we want. So we have zero, we have the time is one. This is two to this one the time sorry now this is the row number is one the time is to this LN with these three as we can see here and the fourth is leaf FC. So what we want to plot is for instance, first colon as a function of the fourth leaf FC. We can give a meaningful title, which is here the total carbon in life plant leaves level for the x axis is time, and we can even put some more information but time would be sufficient we mostly want to see the evolution. And for the y level we can say this is leaf FC, and we can put the unit for instance which is kilo C h a minus one. And I made a typo, which is kilogram C. So we have this summer more advanced option which we can customize the plots, for instance, we can say here we want points and line. And in the output option so we can close the advanced option and in the output option. We can change the width and for instance of your plot and the height. Because this is a square by default but here we have five years of data what so what we would like to have is a plot which is larger than its height so we can really see the different years. So I will put for instance 19.0 here. And here this is five. And why 19 because I have five years so I mean I could put 20 if I want. It's approximately to have some larger some weights for each year. And the rest I don't really need to change you can change the format but you can keep it in PG if you want. So again, we here we have only plot plotted the if FC, you feel free to try to plot any other variables to try it out. Maybe there are some other tools in Galaxy for plotting. So if you make nice plots and you want to report it. You can share what you have done. And again this step shouldn't be too long. This is mostly taking your tabular and making a GG plot. Yes, this is done. And here you have your plot so it's quite large because it's good resolution so what you can do to visualize it better you can download it. You can for instance plot it. So for instance if we look at what it looks like. And this is what you should see. So this is really the same plot as before, but with GG plot. So this is fuller can be fully automated into your workflow. Now it starts to be quite exciting because we have run a simulation with fate, and we can automate some plots and create some plots automatically. So we will now create a workflow from the history we have. So we can rerun or simulation and plots so to make it fully reproducible. And it will allow us to be able to run new kind of simulation so for instance changing the inputs data, new data or some parameters of the simulation, etc. So it's quite interesting. So let's do this conversion. So what we will do is we will extract the workflow from the history. So we'll go in the history menu and will extract the workflow with a workflow extract workflow option. And we will have to remove any unwanted steps. For instance, if you remember we have used panoply an interactive tool. And then in a workflow, we don't really want to keep this interactive tool because we want to have a fully automated tasks tasks. So we will remove this step. And we will rename the workflow to something more meaningful. So for instance we'll take this CLM fate help one five years of simulation so we can copy here the string. And I will create a new workflow that we can edit before we run it. So let's do that. We go to the menu option. This is the history. Oops, where's my mouse can see it. Yeah, in this menu here. This will you click and you see all the different options you can do with your history and the first thing we will do is to extract the workflow. Here we can give a new name of the workflow. Before we create this workflow, we will check the different steps and we will remove, for instance, so here we see the inputs. The fades and all the different outputs generated. The XRI meta data. This one I don't really want. I will remove. This is a panoply not needed for automated workflows. I keep the net ID of XRI selection because this is where we select the leaf FC to create a plot and this replace and the scatter plot. So then I'm ready. I will create workflows. And I can click on the edit here and check the workflow. We need to do a few things before we can run. So if you can see some tasks are not connected. So we'll have to look at it a bit more. I can reorganize inputs. So I can see there is inputs. The input is here. And then this is a restart file. If you remember, then I can look at this, which is my CLM. Fade model. And here's the main issue if I see, I can reorganize a bit. So this is a different expression for the GG plot here, which is a final plot. But there is no connection here. So why what is happening. So here we are creating a workflow. So it's a generic to any kind of simulation you would do. And the thing if you remember, I briefly mentioned it, but when we are running the CLM fades and model. The output the history file is a collection. And in our case we have only one file, but sometimes when we are running long simulation, long simulations, the model will generate more than one history files. So one history file so we will definitely need to extract one of the file. So one we want to plot or we would need to merge or do something more complex but here because we have only one because we are running five year. What we will do is to add a new task in between to select which history file from the collection we want to use for plotting so and the tool we will be using is called extract. So you can do it for here. Yeah, this one. So you can select it from here or you can go back to your tutorial and go when we are making this. Look, extract. And here you can click on this, and it will automatically open the tools takes a bit of time to get the tool. If it doesn't work and always a bit too slow. I suggest you only take this from here. Yeah. So what we will select here will connect this one to that one. And when we click here will extract the first data set. Yes, this is fine because we have only one. We need to make sure. So here you click on the configure output. We want to make sure it has the right type. So we'll, we'll change the type and we'll put it to net CDF so this is mostly to make sure the output is net CDF. And for the rest, I think we can leave it this way. And here. This one, you have to click here. So you have to click here to make it. If you remember just before it was red, so I click on this connector so that now I can connect these two here. So we have a full workflow and you can rearrange your flow as you wish. So it is a bit more easy to understand the different steps. Okay. And now once this is done, you can save it here. So here we have created a workflow, but now what we want to do is to reuse this workflow. So you can share this workflow with others. You remember, if you look here, you can have the workflow options. You can download it if you want on your laptop and it will be a .ga file, just Galaxy workflow. Or you can run it. So all your workflow workflows will be in the workflow panel. And this is the latest will be at the top. It has been created one minute ago. And if you want to run it, so you click on this. And you have to customize the different steps of your workflow. So what we would like to do is to rerun a simulation, but instead of running the exact same simulation, we will change the CO2 values and the atmospheric CO2 so that we can assess the change in behavior for the plant, which is this fake model, when the CO2, the atmospheric CO2 is increased in the atmosphere. And this is really typical in climate change. We know that we have more and more CO2 in the atmosphere and the condition for the plants are changing. And they are impacting how they grow and how they die. So the fate of the plants. So this is quite an interesting simulation to do. So we have to edit this workflow. Make sure you have the right input and output. So make sure you are taking here the input data, which is this one. And the restart file because we want to do exactly the same simulation. But this time what we want to do is So this is input data. So this is the CDF, sorry. So we want to update some of the steps, which is, I can't see where is, sorry, this was this one. I need to customize the model. This is still five years. So this is not that one. I think this is in the advanced option where we can customize the CO2 values. Yeah. So here I can edit. And instead of 367, you more or more, I will credible these values. I will put 1468, which is a lot more so then we will really see a significant change in the behavior of the plants. So then the model output. And for the rest I can leave everything as is make sure you check everything and the different steps you extract. And then we can run the workflow. And again, it will take some time because as you remember it takes about three to four hours for running the CLM fate. So all the tasks here will appear. And once this is done, you will be able to compare and see whether the results are different or not. And what what we see in terms of response when we significantly increase the atmospheric CO2. We can really see some significant changes in after running five years. So if we look at the plot we had before, for instance, let's take go back to the plan of the output because I usually nicer to look at like this one. With, you know, this is not the right values here. This is this. Yes, this is a total carbon in life plant leaves. So there is no big increase when when we run this five years and with a quadruple CO2, the values will be shifted here on the y axis, because we have a lot more CO2. So if we look at the results here, because it will take far too long for running, you can look at it for instance tomorrow. But I can show you so here this is what we had before. And so it's more or less flat in terms of values. I mean, it's slightly changing but not that much after several years. But when you are looking at the same variable, but in under condition where we have credible to CO2, you will see that it's, it's quite significant how it will behave for for this variable which is totally expected. So it means the model works quite well. At the end of this tutorial we show you how to share your work so how to share your history, which I strongly encourage you to do so. You can know you can make your history share share share the with a link or you can even publish it and give the permission to anyone to access your history. Again, you can also use this workflow hub, which is a new way to exchange your workflow. And this is particularly interesting if you want to exchange your workflow and run it on a different galaxy instance. So you can of course download it but here it will be publicly available. You can copy this link here and I show you. We have already put this workflow for for this tutorial and you click on the galaxy climate. We have three workflows and you can. This is this one the CLM fades out once simulation five years. And what is nice is you can generate this workflow and this image where you can see all the different steps of your workflow. So for for this I strongly encourage you to look at documentation and there is some kind of few steps to fulfill to get this workflow image for instance. So we, it will convert the galaxy workflow to a common workflow language workflow. You can ask me on this on slack. So I hope you enjoy this tutorial. It's of course quite long if you do it in a day, but as you can see there are many stages where you have to wait for quite a while before getting the results. So for this I will make sure you can see the results in in my history. Thank you.