 We also have made improvements when it comes to data quality. So data quality is an important aspect for us. So in 236, we made huge improvements when it comes to outlier detection in the data quality application. So we had this new approach for finding the most significant outliers in the desert, basically. So far, we only support the set score. In 237, we support something called modified set score. And this is kind of a tweak of the original set score. It's based on the median instead of the mean, which makes it less sensitive to outliers in the dataset, basically. So this is actually a very useful feature that you hope people can start to use more to find bad data in their databases. So you can use it by going to data quality. We go to outlier detection. You can pick a data set or many data sets if you want. We can go and pick a district or facility, whatever. We can find a time span. So we can go to back a couple of months. Here we can choose between the algorithms that we have. We have set score, modified set score and also min max values, which are pre-generated in the database. So we can pick modified set score. We can select threshold, max results. We can also have other things like start and date for the data periods and the candidate to sort by. So we run this. It takes a few seconds. And now it shows us the outliers for the data for this particular district. So here we can see that there's obvious outliers. For instance, like 245, while the median is 23, for instance, which gives you a modified set score of 358, which is very high. So this is obvious and outlier in this dataset. So this is something we encourage people to use more to basically fix issues with the database. You should also be aware that some data sets or some data elements are sometimes cyclical or periodical. Things like malaria and so on will follow the rainy season and so on. So this should be used with a little bit of care. You should know what you're looking at. But for things which are more stable, like, you know, and mental care and child health, family planning and so on, it's a great tool for for finding outliers in your data.