 Welcome to the data management video series. I'm Kristen Briny, the data services librarian here at the University of Wisconsin, Milwaukee. In this video we're going to talk about data management plans and this is a really important part of data management because a lot of people get their first exposure to data management through writing a data management plan. And if you don't already have to write a data management plan a lot of funders really love this idea or are embracing this idea so you might have to start writing data management plans. So it's an important technique to know how to do well. So I'm going to talk about a general data management plan format but always be sure to refer to actual specifics of the plan that you're required to write and look for the details that you have to have in your plan. But generally a lot of plans include five major details which I'm going to go through in more depth in this video. So to start out with a data management plan is usually a fairly short document usually less than two pages that describes what you're going to do with your data during and after a project. So it's a balance between what you're realistically going to do but also what you're ideally going to do so what you really like to do to take care of your data at its best. So you want to describe five major things about your data in data management plan. The first is how what kind of data are you going to create. The second is how are you going to document that data. The third is how are you going to take care of any sensitive data. The fourth is what are you going to do with your data after the end of a project. And the fifth where applicable is how are you going to share your data. So let's look at those five different components in more detail. So every plan should start with a general description of your data to kind of get context for why you're making certain decisions about what to do with your data. So you should talk about you know I'm planning to generate this type of data. It's in this file format. It's going to be this big. It's going to grow at this rate. And this is where I'm going to store it. So that kind of information gives an understanding of what your data is. The second part builds on this description by talking about how you're going to document your data because your data does not exist without documentation. So you should talk about if you're just using a research notebook, if you're using protocols, if you have a survey instrument, those kind of things talk about your documentation, what you're going to do. Now a lot of funders really like people to use metadata schemas and metadata is a very rigid formalized method of documentation. It's really helpful to use. But if you don't use metadata, if you don't need metadata, it's not absolutely required. Just talk about how you're going to document your data. The third part of data management plan is data security. And this applies to data that falls under HIPAA and FISMA and FERPA and data that needs to go through IRB approval data that might result in a patent. Those kind of things because you want to prevent disclosure or early disclosure of that data to unauthorize people. So in this section of your plan, you need to say what regulations your data falls under. And more importantly, how are you going to comply with those regulations? What systems are you going to put into place to protect your data? The fourth part of a data management plan is what are you going to do with your data after the end of a project? And for most people with federal funding in the United States, the minimum retention period is three years after the end of a project. So you want to hold on to your data for at least three years. Ideally, actually, you want to hold on to your data for five to 10. And the reason is that sometimes questions pop up about your research later after a project. And one of the first things people are going to ask to see is your data to understand your research better. So really opting to hold on to your data for a little longer is just safer. So in this part of your plan, you want to say I'm going to keep my data for X number of years. How are you going to keep it? Who's going to be responsible for that kind of thing? Do you know that you can actually outsource some of this retention to a data repository and have them make sure your data is retained and available for many years after the end of a project? The idea of a data repository is actually pertinent to the fifth and final portion of data management plan talking about data sharing. And this is the new requirement, just like data management plans in research, people are going to be talking a lot more about sharing, we're going to be sharing a lot more data. And so in your data management plan, you're going to have to talk about kind of the who, what, when, where, why of how are you going to share your data? So when are you going to share it? What form of your data are you going to share? Who's going to be responsible for that? Where are you going to put your data? And again, a recommended data repository, it's very hands off, you put your data there, they make sure it's stable, they make sure it's maintained, they make sure it has a citation, things like that, going to be a lot easier for you to share your data using a data repository. So that gives the full picture of a data management plan, you have those five components, they talk about how you really ideally like to take care of your data, but also something that's realistic, something that you can't take care of. And if you follow these five general pieces of data management plan, and you follow your funder guidance, and you write something that gives a good description of your data, that should result in a very good data management plan and help your funding application.