Okay, so we're coming back together now, after putting it all together. We reviewed a study from the literature, we took that study, looked at the study design, looked at the different permutations and all of the different parameters involved in conducting a study, of that type. And we thought about the data collection. We really thought about it hard. What fields we would be collecting and how, how those fields would be coupled together on in how we were asking questions of the patients and at what time points, Then we, we sort of took those lessons and we put it together in an electronic data capture. System, Redcap. We created the project, we created the data collection instruments for that particular project, and finally we did a lot of testing on that on that platform. And that testing is really important. it, it, it's very important that, that in the testing phase that we provide an opportunity for input from everyone on the study, all the way down to the person at the floor with the patients, sort of entering the data to the PI or the principal investigator for the study. To, to the biostatisticians. We want to make sure before we launch that study that everybody's comfortable that we're collecting the data right, we're collecting it at the right time points, that when we collect it the the information is going to be there for us and it's going to be understandable. We, we did not look a lot at finalizing the project design in Redcap because we're working on a sample Redcap install just for this Coursera platform and we're not allowing projects to be finalized there, but, but really we were almost there. After doing the testing if we just sort of push the last button or two, and said push this project into production mode, then we'd be we'd be live, and we'd be collecting data. So I'd say there again, you know, the trick is doing lots of testing, get a lot of input before we start the the actual study procedures. including the data export. One of the great things about having a good solid data capture system is, if you practice with it, your, your practice data that you're putting in, to sort of make sure that all the branching logic works, that the, the validation code works for the different fields, and all the dot, data quality rules work, et cetera. If you're doing that right, then you got a fairly decent practice set up data that you can, you can then actually sort of export. And then your bio-stats group can, can look at it and can make sure the data are being collected exactly as they are needed in order to run the analysis scripts later on. In fact, a lot of times what we'll do is recommend that since you know the target, you know, you know that the data are going to be collected in this way. And we can just click this button and get your data out into SAS or STADA or whatever whatever package that you're using. We can go ahead and start pre-creating some of the analysis scripts to automate the process downstream. So along the way, we've we've done all of that, but I think we've also reviewed a lot of the best practice principles that we talked about early on in the course. And so I hope you can see how these all come together, in one place, when we're, when we're actually thinking and, and, and brainstorming about actually setting up a study and taking it into the implementation phase.