 This session will be an overview of creating data elements. Specifically, we will go over the creation of data elements, data element groups, and data element group sets. We will also provide a description of the various fields within the data element maintenance. In particular, we will focus on those fields that are mandatory, which we must fill in every time we create a data element. We will also link these concepts with analysis outputs, showing how their creation in DHIS2 links with our eventual outputs in our analysis tools. Let's go ahead and get started with the session. In this demonstration, we will show you how to create data elements within DHIS2. In order to create data elements, we'll go to our apps and search for the data element application. You can see in this example, we do not actually have any data elements created within this system. We want to go through the process of creating data elements, groups, and group sets in this demonstration. Let's refer back to training land in order to see what this looks like after it's been implemented. In training land, we can see that there are a number of different examples of data elements that have been created within training land. These have been further classified into data element groups. These groups have then been categorized into data element group sets. Let's go through this process of creating these within DHIS2. Before we create the data elements within DHIS2, we need some examples to work with. Let's have a look at this syndromic surveillance data collection form. Before we actually create the data elements, we have to identify what the data elements are. This form has a number of characteristics and we will discuss all of them throughout various examples. Let's just focus on the data elements for now. In this particular example, there are six data elements that we can identify from this particular form. This includes acute fever and rash, prolonged fever, influenza-like illness, suspected dengue, watery diarrhea, and bloody diarrhea. Underneath each data element, underneath each data element is its case definition. We can refer to these later on. We can take some of these data elements and create them in DHIS2. Let's go through that process now. We are now back in the blank DHIS2 system. You can see there are no data elements currently created. Let's go through an example where we create a couple of the data elements from that example form. In order to create a data element, we'll click on the plus sign located in the corner. We can see that there are a variety of different fields that are associated with the data element in DHIS2. Let's describe a couple of these now. Just like before, mandatory fields are indicated with a star next to their name. We can see, for example, that name, short name, domain type, value type, and aggregation type are all mandatory fields. This means we must fill them in whenever we're creating a data element. The name and short name of our data elements have been described in our data element principles discussion. This is where we apply those naming standards to the data elements and then enter them into DHIS2. We can also provide the data element with a code. This code can be used for various purposes that will be discussed in other academies. We can also apply a description to the data element. As an example, we can take the case definition from one of the data elements on our example form and place that in the description. We also have another name type, form name. In the form name, we can take the name as it appears on the form and place it here. This includes any types of codes or any other text that you want the user to see when they are entering data. Next we see domain type, value type, and aggregation type. All of these three fields are mandatory and must be filled every time we create a data element. We will discuss these a little bit more when we actually create the data element. We also have a field for our URL. In the URL field, we can add in an external link. As an example, we can put in the URL for a data dictionary that might have a little bit more description on this particular data element that we are creating. Next we have the category combination field. By default, it is selected as none. Don't worry too much about this particular field as it will be discussed in other academies. During our examples, we will just leave it as none. We then have additional fields, option set, option set for comments, legend, and aggregation levels. These will also be discussed in other academies, so we will skip over them for now. Underneath we have two additional fields, category option combination for aggregate data export, and attribute option combination for data export. These fields apply to data export, so let's leave their description for other sessions. Let's go back to the top and create our data element. We can select two data elements to create as an example. We have already identified the six different data elements that exist within this form. Let's work with two examples, prolonged fever and watery diarrhea. Back in DHRs2, I want to start by giving the data element a name and a short name. Let's start with the prolonged fever data element. In this example, I'm going to start by giving the initials of the program, syndromic surveillance, and then I give the name of the actual data element. Next I give it a short name. In this case I'm just using the acronyms of the program and the data element. We can of course give it a bit of a longer data element name, but if this makes sense, we can use this in analysis later on. I am going to fill in one of the non-mandatory fields, form name. This is because when the user enters data, I don't want them to see the SS portion of the name that I've created. In the form name, I will just put the same name as it appears on the actual data collection form. This can be longer or shorter than the name we've given the data element depending on the data element we are defining. Next I choose a domain type. You can see that there are two domains, aggregate and tracker. In our examples, we will be using the aggregate domain type. The tracker domain is for event and program-based data elements. Those are discussed more in the Tracker Academy. Next I have to assign the data element a value type and an aggregation type. If I click on value type, we can see that there are a number of different value types available. Let's look at some of the different number types that are available to denote the difference between these different value types. By default, number is selected as the type. We also have other number types such as integer, positive integer, negative integer, and positive or zero integer. All of those types will allow me to add number values, but their definition is a bit different. For example, if I look at positive or zero integer, this means that I can only enter values that are greater or equal to zero. If I look at positive integer, this means that I can only enter values greater than or equal to one. I cannot actually enter any zero values in this data element field during data entry. Depending on the restrictions that we want for this data element, we choose the appropriate type to correspond with that description. In this example, I will just select positive or zero integer. This will allow the user to enter any value that is greater than or equal to zero. As there are quite a variety of value types, please refer to the readings to learn a bit more or ask us any questions you might have about the different value types that are available. Next, we select the aggregation type. We can see that there are a couple of different aggregation types available. The aggregation types correspond to how the data element will roll up through the organization unit hierarchy. By default, sum is selected. What this means is that if I am collecting this data element by health facility and I want to see the output by health network. In order to create the value for the health networks, it will sum all of the values in the health facility to provide me for my output for the health network. This is true of the other types as well. If I choose average as my type, then it will average those values for the health facility when we select our health network rather than sum them. So we should be a bit careful when we select this type. We will use sum as our type in this example as we want the value summed through our organization unit hierarchy. The next field we have is store zero data values. The value type is positive or zero integer. This means we are allowing the user to enter in values that are zero. But whether or not these are stored in the database is indicated by this selection. The implications of storing zeros in the database will be discussed in other sessions. For now, we are going to leave this deselected. The next mandatory field is the category combination. As mentioned earlier, we are just going to leave this as none. This will be discussed in other sessions during other academies. So don't worry about it for now. After we have defined all the mandatory fields, we can go ahead and save the data element. We will go ahead and click on save. You can see when I do that the data element appears in the data element management screen. Let's go ahead and create the other data element that we referred to. Watery diarrhea. We will click on the plus icon and we will provide it with a name. This is the name on the form, but we are actually going to change this up a little bit. Note that I have swapped diarrhea and watery. This is because we have defined multiple types of diarrhea on our data collection form, including bloody and watery diarrhea. By naming the data element this way, I can find the different types of diarrhea grouped together when I perform my analysis. Next we assign the data element a short name. We are just using the acronym of the data element and program as before. I am also going to assign this a form name. Remember this is what the end user sees when they are entering data. So we want to make sure it accurately reflects what is on our data collection tool. Next let's select a domain type. We are going to select aggregate as before and we are going to use aggregate in all of our examples. We will have the value type correspond to the previous value type that we selected. Positive or zero integer. We will leave the aggregation type as some. The category combination is still none and we will leave it like that in our examples. Once we have defined the fields, once we have defined all the mandatory fields, we can go ahead and save the data element. Now we can see that we have these two data elements available in our data element management. I am going to go ahead and add in the remaining data elements from that form. And then we can move on to create the data element group and group sets. So I have gone ahead and added in the data elements from that form using the same steps that were shown in the demonstration.