 Hi, I'm Anne. I'm working at the University of Oslo in Norway and together we will learn how to manipulate data from the Earth science domain. So here I'm logged in in the Galaxy Europe main server. You can use any server where the climate galaxy tools are installed. Also to start the tutorial I will click, left click on this hat here to get and see the training material. The training material is in the climate section and this is the first one, this Panjiroki system 101 for everyone. Introduction to X-ary Galaxy 2. I strongly suggest you watch a video for the slides. This is a very short video. It gives you an overview of what is Panjiro ecosystem and the X-ary. Now we will start really doing the tutorial itself. So we will really learn how to use these X-ary tools in Galaxy and what they can be useful for to select data, get metadata information about Earth science data, how to visualize on a map for instance and to filter data. Let's go through together. As a requirement make sure you went through an introduction to Galaxy analysis or Galaxy 101 for everyone depending on your background. I will not go again into detail for this Panjiroki system. You have an overview in the slides and in the video which is a separate video. First thing you need to understand is what data we will be using. So we will manipulate what we call net CDF data. So net CDF stands for network common data format. It's one of the most popular file format in climate science. It's not specific to the climate science domain but in the climate science domain in addition to this format we are using a set of convention which we call the CF convention which stands for climate and forecast metadata convention. So this is really a set of convention to name the data, to specify the units and all the metadata information we need to be able to analyze property of science data and climate data. The example we will be using is from the Copernicus atmosphere monitoring service. So I will strongly suggest you have a look to get some more information. You have many different data sets you can browse. This is very very rich. You have to log in and register but this is free of charge. Here we have put the data in the nodal tool and make it easier for everyone to analyze the data. We will be using the daily European analysis forecast over Europe. So the resolution is approximately 10 kilometers. And this is one of the best forecasting model over Europe. This is what we call an ensemble. So many different models running all together. And at the end we make the best estimate of the air quality forecast. And the variable we will analyze is a particle matter for particles lower than 2.5 micrometers of very small particles. And these particles at a very high concentration can really have an impact on the health. They can irritate eyes. They can provoke asthma and chronic bronchitis. So this is a very important variable for not only for climate but for weather forecast. We will be using what we call a four days forecast. So we have a date which is 22 December 2021. For this date we are taking the four days forecast. This is Howly. So every house we have a new forecast. And we will analyze the data. So today if you go to the Copernicus Atmosphere Monitoring Service you would be able to get the four days forecast but from the date, from the two days date. So let's create a new story and we will rename the history to Rangio 101 for everyone X-ray. So I'm copying this one and then I go back to my galaxy here. I click on the plus to create a new history. And then I rename my past and then I enter to make sure I'm getting the history renamed. Then I click again here on the hat to be able to go back to the training. And I will upload the data set we will be using. So here we are not getting the data directly from the Copernicus website. So this is a sample we have saved on Zenodo. I'm copying here. And I will paste it here with upload, past and fetch and I will paste. If you really want to make sure you can even select the format which sometimes we need for net CDF data. So this is why I'm showing it to you here and start. So it's green. So it will start uploading the data into my history and close. So it's still gray. So it means it's not running yet when it runs. This is orange like here. And when it's completed, it will be green like here. Let's see what are the next steps. Okay, so the next step is to add a tag. So this is very useful to add a tag into your data sets because they can be propagated into all the different tools you will be using later on. Okay, so it's running. Okay, good. So now I can add a tag. So here I click on edit data, data set tags. And I will use a hash tag and ADS. So don't forget this hash sign in front of it. This is to be able to propagate the tag to all the tools that will be using this dataset. Let's go back to the training material. We have done this part. Now we want to really understand the dataset. So let's have a look a bit more. This is already what I said. This is net CDF data. You remember this is particle matter for particle lower than 2.5 degrees, 2.5 micrometer. And this is from the CAMS European Air Quality. And this is a four days forecast. Now we want to get metadata. So this is a net CDF data. It's binary. You cannot really inspect the data without a specific tool. So we will be using this net CDF X-ray metadata info tool. So here if you click on it, it will present you the tool here, which is very handy. And we can start to execute. And you see all the tags here, ADS is propagated to the tool and to the output of the tools. I'm using because they are using this dataset as an example. Okay, well, it's not starting again. Let's try to see what is the next step. We will look at the outputs generated by the tool and then we will answer to the question. What is the name of the variable for particle matter for particles lower than 2.5 micrometers and the physical units? Check it's running. So this metadata tool will return two files, two datasets. Okay, so now it's green. So the first dataset is a tabular. I can click here on the eye to see the content because it's a text file tabular. And here we see all the different variables containing the binary net CDF file. So we have this variable, which is the variable we are interested in this particle matter. It's a four dimensional variable. We have time, level, latitude and longitude. And here the numbers are the number of levels is one level 97 times 400 longitude latitudes and 700 longitudes. And that's the same. We have all the information for the longitude, latitude, level and time. If we look at this info file here, we will get additional metadata information. So we'll get all the variables and types and also their names and their units. So here if we want to answer to the question, which was what was the name. So this is really a particle matter for particle lower than 2.5 micrometers. And this is a long name is mass concentration of particle matter 2.5 ambient aerosol in air. And the unit is microgram per cubic meter. Let's go back here, click on the hat and you could check that this is exactly what we got. So it can be slightly different depending on the version of the tool, but it shouldn't be very much different. Let's go to the next step, which is to get the information on the coordinates. So the coordinates is like the latitude, longitude and all the levels and the times, etc. So for this, we will use another tool from the x-ray tool sets, which is netcdf x-ray coordinate info. I will click on it. And this tool takes again a netcdf file as an input. So here I can immediately click on the execute and it will return a list. So here I will have a list with all the different variables and coordinates. So here, if you remember when I clicked here, all the coordinates were longitude, latitude, level and time. And this is what we will find out here. Let's wait. Maybe we can check if there is any other questions. Yes. Okay. So we'll have a question on what is the unit of the time coordinate? What is the frequency of the particle matter for particles lower than 2.5 micrometers? What is the range of values for latitudes and longitude? So we have three questions to answer. It's running. For this question, we'll have to look at the files in this coordinate list. Okay, now we have five items in the list. We draw this. So the version is just to give you the version of the tool of the x-ray tool, which can be very useful. Especially if there are any issues, you can tell us when you send a bug report. Otherwise, we have the time, longitude, level, latitude. So if we want to answer to the different questions, which was like for instance, let's go back here. If you remember the unit of the time, what's this? What do we do? We look at the time. Okay, and here we see the units of the times. This is in Dyson and Howly. We have Howly data, as we can see, and we have effectively four days of forecast of Howly data. And if you really want to see exactly the units of the variable, you can check here. Here, the variable is a time delta unit. So this is, as we can also see in the time file, this is not an absolute time. As you can see here, the first one starts at zero. So we have a four days forecast, but we don't start on the 22th of December. We start from zero and we go up to four days. Okay, so this is why we call it time delta. Then we were looking at the number, I think. The frequency of the forecast. So we said this is Howly data. This is what we could see. And the range of values for latitude and longitude. So if we look first for the longitude. So they go from 335.5 and 44.95. So this is not covering the entire clock, as I said earlier. This is only over Europe and you have green which in between, which is on the zero degree and the western longitude are on the scale of over 190. We'll see how to shift the longitude, no longitude later. And the latitudes are from 69.95. So this is quite up north. This is northern of Norway. And the longitude in the south is 30 degrees, which is also quite low. Okay, so we have answered to the question and you can also here check that indeed you have answered to the questions. So now let's have a look at the data itself. So we got information about the metadata. And these are the two tools you have used so far, which are the metadata information and this coordinates x-ray tool. Now we want to plot the data on a map. And for this we will be using this x-ray map plotting. And it will take a netcdf file as an input. We'll also use the metadata information and we have to select the variables we want to plot and also the time. And then here we can let's use this. So you click on this one and you will have it here. So we'll take this netcdf data here you have to change and make sure you are using this metadata information. You need to select the variable. So the variables we want to select is this particle matter. We have to select the variable for the latitude which is latitude and the variable for the longitude. And we want to select the time because we have a four days forecast and here we only want to plot one time. And I don't remember which one we can probably take the first one. So this is the first time we have in the data set. So here we will select time for the variables and here you can select and we'll click only on the first one to start. So the longitude values. This is if you want to make another selection for the smaller geographical area but we won't do that. Shift longitude. So here we will shift the longitude. Why? Because we want to plot over Europe. And for now the data is over 0 and 360 and if we leave it this way we'll have a one line blank line in the middle at zero degrees. And this is because the last, so the resolution we have is approximately 10 kilometers. So we have a gap in the data. So the range of values to plot. This is the minimum and maximum value. So I think we can probably select some like 0 and 35 as here. So this is to make sure we can see on the plot. We can add the country border. So I don't remember what we specify here for the country border. We can add 0.2, 0.5. So here I can do 2. This is to have the borders, the countries and the cross line. We don't really need the land. This I don't need a plot title. We can specify a plot title but this is otherwise not necessary. It will specify a default title and the color map. So here we will use Romar and underscore R. Which is a nice color scale for this type of data. And for the projection I think we don't specify. Oh yeah, okay. We specify your projection and here I will copy this first and I will explain. So this is to specify on how to project the data on a map because the data is on latitude, longitude and to be able to see properly. Here we will use a mercator projection and centered on a 12 degrees longitude. So you can check all the different projections. For instance, I'm searching for carto pie projection. And you will have all the lists you can use. So you see this will show you the data but from a different projection. And I will execute. Again, it will provide me a map plot list. And you will have depending on how many times you are plotting you will have one or more plots. So here we are, we have chosen to plot one time only. So we'll have only one in this list. We go back here. So we should have something like that. We'll see. And again, why we shift the languages, this is because the languages were coded between 0 and 360 degrees. I mean, not exactly between 0 and 360 because we are only over Europe, but using this scale. And we want only to see what is over Europe and the zero degree is over here at Greenwich. So we will have one part between 0 and 180 and the other part with negative longitude. So we need to shift and make sure the plotting is correct. Then we'll have to answer a question, which is to make the same plot, but this time to visualize the forecast for the 24th December. At 12 UTC. Okay, so let's have a look. Let's try. And again, every time you always have the solution to explain you how to do. Okay, let's go back here and see the plots. So here we have one plot. And here it is. You have your plot. I forgot. And this is what you can see. I forgot to put the country borders, which we could check at country borders, but they are not really visible. So it should probably put them a bit darker. Anyway, we'll do the same plot, but this time will change the time. So instead of taking the time, which is here on the first day, we'll take on. So what do we want to take? We have the 22 of December, which is the first time. And what we want is the 24th December. So how do you go from 22 of December to 24? And the first time is 00. So we need to take two days later because we are on the 22nd on the first at 00. And we need to take 12 UTC. So let's go back here. So we use this smaller row here to rerun the job, but I will modify. I will take exactly the same here, but I will choose another time here, which is two days. Okay, two days and 12. Okay, and let's run. And I will put countries border a bit more. Or maybe I put 00.2 for the countries. We don't need to have the countries themselves too strong, but the cost line would be better if we can see them a bit more. So I take here and I execute. Then I need to go back to the pendulum here to my history to get my tools. I have to wait. It's probably not running yet. We have to wait. Okay, so it's running. Okay, so it's done. So now let's compare if we look at here. So you see we see a lot more the cost line, which is very handy. So here we have quite high concentration. So it's a bit higher than 35 microgram per meter cubic meters. And if you wanted to compare with the other one, which is the one we have here, if we go up. So they are quite changing a lot of changes. So the units are the same because we, if you remember, in the tools we specify, we wanted to have the values between zero and 35. And this is to make sure every time we plot we have the same scale. So here we see this is cleaner in some areas, but this is very strongly over. I mean, over the same region actually, which is the Alps here and the north of Italy. So it's always very high concentration here. And they are stuck on the Monday. Okay, let's go back here. So we see some differences. And this now getting to the next one. So here what we have learned we have learned to get some metadata information. So we then have learned how to get to plot on a map and to select a different time for plotting but now we will make some more advanced selection. So subset from the coordinates. So what we call coordinates, if you remember, we, in our case, we have the time, we have the level and the latitude and the longitude. So what we will be using here is another tool, which is a net CDF x array operation tool, which is a very powerful tool to be able to select slices. So for instance, we can select different times, but we can also select different geographical area, etc. So the first thing we'll be using here is very similar to what we did before. But this time we will add a filter. So we are using this x array operation and we will slice the time to get only the first 24 hours. And we will then rename the data set. So let's do that. Okay, here you can take several data set. I'm taking only one metadata information. You always select your metadata, select the variables. I want to select a concentration. Don't choose to select anything else, but I want to subset by coordinates. So I will click here and I to make it visible. I will select values from the list. So I will make a selection. So what did we say here? We need to select the time. Okay, between zero and base. So let's take the time from the list. And we'll select time. And we select the time values here. And we want to have slice. And zero and one. So when we take zero and one day, the first one will be included. So we'll have all the values between zero and one day. But this last one will not be included. So this is something to remember. So here we'll have, because we have hourly data, we'll have the forecast from the first day, which is on the 22 December 2021. And we'll have hourly data until the 22 December at 23 hours UTC. And then I can execute. Then we'll, once this is done, we will use this coordinate info. And what we want to verify is how many data times we have in the data set. So if you remember when we did initially with this info file, if I click on it, or if I click on the time, let's click here. This is easier. You remember, I had 97 data rows. So from zero to 96. So now we have selected data only times between zero and 23 hours. This is hourly data. So we should have much less. But we have to check. So this is what we will do afterwards. So this is returning another net CDF data. Which means we can use also the different tools we have used earlier to get metadata, but also to visualize. Okay, so here it is. Let's go back here. And we will use this net CDF XR coordinate info tools. We have used it before. And this one takes a net CDF data. I think maybe we want to rename the data set beforehand. It will be much easier later on. So let's rename this. Don't forget to rename this here. So I go back here and I want to rename. So for this, click on the edit attributes. And I change the name. And I save. Okay, so now it is. Then I can go back here and I will use this XR tool, coordinate tool. Okay, and this is the right file I can execute. Here is a nice citizen science project. So while it is running, we can try to answer here and help. Sometimes here I cannot see. You can identify if this is likely a mail or likely for mail. I don't know. So I would say likely a mail. Okay, I cannot see. This is likely. Okay, so now it's done. And we have all the different coordinates values. So if I click on the time, here it is. I don't have all the values I had before because I made a selection. I only selected 24 hours. So I have only 24 rows. So from 0, 0. And 2, if you remember, the slice was up to one day. And the last value is not included. So this is why we have only the 23rd hour here. Good. So this is perfect. Good. So now what we want to do is to use exactly the same tool. This time we want to select data but over only one region. So we will be using a selection and making plots of the particle matter for this particle lower than 2.5 micrometer over Italy for latitudes 43 and 40. So this is over Italy and a longitude 11 est east and 15 est. So and the question is, can you tell us if the forecasted particle matter will increase or decrease during the next 24 hours? So here, what does it mean? It means we will make a lot of different selection. Let's try to start from this one. We will use this X array and we can select which file we want to select. So we can, we can take the first one here if we want. So the metadata in four, which is this one, as usual, we want to select variable. Yes, because we want to select this particle matter. Then we want to subset their coordinate. Yes. And we will take the data from the list. Yes, and we'll select the variable. So there are different things we want to select. So we want to have the date between 10 UTC and 5 PM UTC. So we need to select the time. And here we select the times and we need to select a slice. So we want 10 remember 18. Because if you remember the last one will not be included and 4340. So we'll add another filter. But this time it will be the latitude. And for, so for latitudes, it's a bit tricky. Because this is what we have here for the slice. The slice where we select needs to be exactly in the same order we have in the data. So in the data, if you remember, if we go back here, if we take the latitudes, they go from 69 to 30. So it's decreasing. So we need to provide the slide exactly in the same order. So let's just go back here. Okay, so maybe we need to, again, take the tool, sorry, get it here. Okay. And we take this one. You remember, we take metadata here and we select. Then we add a sub-save per coordinate and we start this time. Slice between 10, 18. Then we add another filter because we want to select on the latitudes. And we take the latitudes. And the latitudes will take a selection between 43. So slice three. Okay. Yes. We can even type 43.05 and 40.05. And now we filter on the longitude. We take the slides and here we take between 11.05 and 15.05. That's it. So we have selected over the time and the geographical region. So we can click on execute. So we are at this question. So we do exactly the same. And to be able to see something, we will make a map. So we'll have to see all the different steps we need to do to make a map. So it's a bit like a summary exercise. It's running now. Good. A good habit will rename much easier. And here I will rename to this camps particle matter. And I put the date, which is the date of the start. So the 22 December 2021. And this is over Italy. And this is between 10 and 17 UTC. I can say. Okay. So now I will do all the different steps to be able to, to plot. So I can use. Sorry. So here I also show you how to search. You can search for the X sorry, and we can do the metadata information. So metadata. It will give us all the metadata. But at a very high level. So all the dimensions of the variable and not the variables. So I can click here. And then the second one, we will use coordinate. So this is also to get metadata, but it also gives a content. So the values of the coordinates. We can start to search. We have it here. Coordinates. Yeah. Actually, we can already run it because if you remember, it only takes. As an input, the net CDF five. So now the next step will be to make a visualization. But here we want to see all the evolution of between the. Zero zero. And five. UTC. So we'll make the plots for all the times. You can select here the plot. And we'll take here this input file to always check you have the right one. The metadata information. We want to always plot the same variable. Actually, we have only one plot. Viable we can plot. The name of the latitude. The name of the longitude. Time selection. Yes. Time. So we need to take the times. And here we will select all the times because we want to plot everything between 10 and five. 10 a.m. and five UTC. In the afternoon. Okay. So always good to put orange if you remember. Zero and 35. So then we always have the plots with being the same scale. And we can add some country border. We never add a lot. But the cost line, this is between the one one. So we can add six to have the cost line quite strong. And the rest we don't really need. You can always choose a title plot for your plot. Again, you want may want to use projection. So I copy the same one as we did. And we can add the same color map, which is nice and useful for this variable. And I execute. Okay. So now it's done. So we have eight items. So we have eight plots. So it's not very handy to visualize a plot one by one. So this is the region of Italy. We are interested in. And this is here. Napple or Napple. It is nowhere. Napples. But to make it easier to be able to compare all the different plots on one single plot what we will do. I go back here. We'll make what we call a montage. So we will put and aggregate all the plots all together. And here I will enter the name of the tool. Image. Okay. So we have image correct image. And I will take the last one. So this is 32. Yeah. How many images we want to have? How many columns. So here we'll take four. I think four. We can leave it as is. We'll have only one image. With all the different images we created in the Napple plots. Much easier to analyze. Okay. So this is it. And I can already check. This is PNG format. Okay. So here you see you have all of them. So you can even download it and visualize it. This on your laptop. So you can use. All the different plots. We can. Much larger. We can zoom a bit. We want to see. So let's maybe have a look also to the part of Italy we are looking at. So here this is Italy. But we are looking at this region here. Remember. Make it larger. This is a small. You have an island here. This is here. You see. So this is really the region of Napples we are looking at. And we can see all the different times. So. You can also zoom to see the different times here we are. At 11. And here we have 12, et cetera. So. Zoom out. So this is a 10. And this is 11. 12, et cetera. And the last one. Is this one here. So there are some changes. If we look here, this is the beginning of the day at 10 o'clock. And this is the last time at 5. UTC in the afternoon. We. We can see. That is changing slightly. So here it's always very high. And relatively high concentration here. Around the region of Napples. And it tends to spread. So here we. We had the concentration all along here. And here. And this is the same here. Now it's slightly decreasing in this area. But it's going a bit more south. And this is moving in the south. And slightly moving and going again very high. So we can't really see there is a specific pattern. You can see there are some changes. And some spread in the southeast direction. By the end of the day. But I mean, this is not very big changes. We can see here higher concentration. Okay. So let's go back. Kind of so close here, which I was showing. And so here we see we have done the last step. We will be learning is what we call where statement. So so far. The filters we made was over. The coordinate value. So we filtered over time. We can filter over latitude, longitude and labels. If we had different levels, but here we have concentration at the surface. So we have only one level. But now we want also to filter but directly on the values we have here. So for instance, we would like to mask some value. So for instance, those that are either too low or too high to be able to highlight a bit more some information. So we'll be using the same. Let's go back to the tutorial. We will be using the same plot as we did before. But we will mask values that are too small, or we will only highlight values that are greater than 30 micrometer per cubic meters. Micrometer. Yes. And for this, we are using exactly the same as we did before, but we will add one filtering. This is to only plot value greater than 30. So here do not plot value below this threshold. So let's do that. We are taking the same. Here with it, which is this plot here. So for being able to rerun it, you can, for instance, get this tool version and click here on run this job again. We'll have all the parameters. So this is easier for us. We don't have to rerun it completely. Actually, that's here. And here what we want is to do not plot value. Okay, below this threshold and we put 30. And the rest will keep as is and rerun. So now if we go back to the training material here. So here, this is what an example will look at the values for zero. So the first time. But in fact, we will run all the time between 10 and 5 UTC. We are able to answer to the question using the same geographical area region over Italy. Can you tell us if forecast PM 2.5 will exceed 30 micrometer cubic meters between 10 and 5 UTC and December 2022 and 2021. And for this, we go back here. We could look at all the different values one by one. So you see, we have some values or some areas around apples that have concentration greater than 30. We can do the same one page than before. So we can rerun this task here, for instance, but instead will change will take the latest one. It will be easier. We'll have all the plots all together. And this is where we'll actually see a lot more than before on the spread of a different region because it was not very obvious before. It's running. Okay. So now we have it. That's earlier. We can download it. I mean, we can also see and that this may not be very easy to spot. Okay. Here we see they were some point, no values. And again, we have more values. So when you say this selection over the values to mask some values, this allows to identify region, a car, for instance, quitters and some values or lower. So here we see a lot more where we have values quitters and 30. So let's close it, go back to the training material. So we did this one. So then we can see exactly where the values are quitters and 30 micrometer cubic meters. The last thing we will see is to create some time series. So here we are selected over a region. Now we will make sure we can select only one value. So we'll use exactly the same tool initially. It is a x array tool. And we'll make this very same selection with it before. But we'll select only one value. So we'll select data over napalm. We will latitude north of 40. 85 approximately and 1426 East. In terms of time, we can select all the time. So here we only make a selection with the x array operation over the entire period of time. Let's do that. So we make sure we take as an input the first file. We select the metadata that we have to go back. We select variables. We want to select by coordinates. So we want to select from this. And we want to select latitudes. We take latitudes and we slice. So we want to take a slice between 40, 45. So there are different ways to do it. Either you can take a slice or you can take a value. It really depends. So here if we take between 40, 95 and 40, 85, we have only one value. Because this one, if you remember when we slice, it's not included. And the resolution is 0.1 degree. We can do the same for the longitude. And you could instead using this slice, you could take this value. Longitude. Let's make sure we take the right longitude, the first one. And we can slice. And we can take a longitude between 14.25 and 0.35. And we want to take all the time. So we select. So here we will have again an hcdf file. We will rename it. So it's easier to identify. So we have the date and the location. And we'll even add a tag. So if we use it later on, we can identify the file. And all the tools that have used this input as an input. Okay, that's good. Let's rename it. Instead here we'll use the name of the location, the date, which is the date and the name of the variable. And we'll add a tag. So we'll have two tags. We'll add the numbers. Okay, so now I have the two tags. Then we'll use a new tool, which is a selection tool. So this netcdf xre selection. And this is really to convert from xre to tabular. Because tabular is a text file. And then we can use other tools in the Galaxy ecosystem. For instance, to make some visualization. So let's do that. And what we will do is to take the latest netcdf. We'll select a variable. So we can take some metadata. Actually we can take some metadata here. We take the concentration. And that says we'll take everything we have in the file. So this time the output will be a tabular. So it will be a text file. We'll be able to see it with this file. And I will again rename it. It's the same name, but this time with the tabular. The tabular. And I can visualize it. I click on the array of view data. You see you have the time, the level. It's always zero because we have data on the surface. It's latitude, longitude. And the value for the concentration. For each forecasted values. So last thing we can check. So here we are from a qualitative point of view. Can you say if the particle matter, for particle lowers than 2.5 centimeters, increase or decrease over the four forecasted days. So for this there are different ways. I mean it's a bit difficult to see the values here from a quick inspection because there are too many values. So what we can do, for instance, is to make a plot. So we can use a scatter plot. It's great to do a scatter plot with ggplot. But here what we want to select is to have like the time on the first, on the x-axis and the values, the concentration. So we want to have column one and column six. We can put a title if we want, but this is not necessary. Let's put some information on the x-axis. And some information on the y-axis. So here I put on the x-axis. This is a forecast time. So this is an hour. This is a unit from December 2022 from 2021. And on the x-axis, this is a particular matter for particles lower than 2.5 micrometers per cubic meters. I can add some additional options here. For instance, I can want to have points and lines and I can change an option, use of defined points. I can add some transparency if I want. Like 0.7. I mean, you can try it out and make your own plot if you want. The rest I can leave it as is and then I will execute. So with a plot, we will see if it's increasing or decreasing. So we will see two ways. So this is the first one, which is the simplest one to make a curve. So here you can see already with this. And we can see the curve. So it looks like this is decreasing. I can even download it if I want to see it here. Yeah. So clearly it was increasing over an hour and then it's decreasing. It's maybe increasing a bit at the very end. So there is another way to look at it is to use what we call climate stripes. But for this we need to make some filtering on the data to prepare them for the climate stripe tool. So let's do that. I will show you. So we are taking this tabular and what we will do is we need what we would like is to have the right date. And not starting from zero. So this we will change. So we'll use a regex tool, which is a standard Galaxy tool. And we will replace zero day by the 22 December and the day one, one days, it will be 23 days, 24, et cetera. So then it will be easier to visualize the data. We can do that. So we'll use regex tool, which we can search here. And this will be colon regex and replace because we will search in the columns and we'll replace. And we can add different columns. So what we will be using this, this is the second colon, which is where we have the time to change this here. And we will check regex. So we will change here. So we have to select this zero days. And we will replace by 2021 1222. And I will copy this. And then here I will 23. And here it will be not zero days, but one. And I will add another one. And here we have two, add another one, five. And this is three days. And we have a forecast on these four days. So we can put four days. And here we will have 26. Okay, so we are all right. So we will replace every occurrence of zero days. So the string zero days by 2021 1222 and et cetera. So we'll replace by the date and we can exit it. And we will return another tabular, which we will be ready for using climate stripes, which are visual way to see this increase or decrease. So if we look, this is still a tabular, but let's look at it now. Now we have time, which is from 2022, 2026. So we have level, latitude, longitude and the particle. So now we will search for another tool, which is stripes. This is a climate stripes from time series. And this is a time series here. So we can take this one. And the variable we want to plot here is this is this variable we have, which we can copy past. From the title, we can PM 2.5. This is four days forecast from December. Oops. From December 2022 2021. We can have some advanced option. So the X axis will be the time. It will be the date actually time and date. And we can put a format. So then we'll not see this 2021, 12, et cetera, but we will really see the date. So here we will put a format, which is say the year. So this is why we are using this percentage. Center months, the day and the time. So we'll have all the different values for the time. Oops. I think here I made a mistake. This is an M, B, H, and format for putting the date. So here this is the format of the input column. So this is exactly the format we have here, which is the year, the months and the day, a space and then the hour. And we have a column, then the minutes, column, second, and then we have a dot. And then this is also. And now we can choose how to format when we plot. So here we will only plot the day. So the months, but in a short name and the hours. This is the units. For the color map will select winter and why, because it's a nearly a binary color map. So we will really see if clearly if it is increasing or decreasing. Okay, so it's green. And now here it is. So you see this is much easier to see that before like 24th of December, they were some high values in terms of concentration of particle matters or particles lower than 2.5 millimeters. So after this date, and we see this is quite blue here. So this nearly no concentration, no particle matters, at least not at a concentration that is high enough to be visible. So let's see here we are we did the last question here. And I show you do different ways to get this information and there are many others you could use other tools because we are converting we since we have converted the next year data, which was binary to a tabular you can really do and use all the tools, you know, from the galaxy ecosystem. So this is it. I hope you enjoy the training and really recommend you to use your own data trial with maybe different days and look at the climate trainings. We have a few others that may be of interest.