 Welcome everybody to the fourth in the series of webinars about the Fair Data Principles. This is the webinar on R for Reusable. I'll just briefly introduce myself. My name's Keith Russell. I work for the Australian National Data Service. I'm the host for today and thank you to my colleague, Susanna Sabine, who's in the background, co-hosting this webinar and organizing things in the background. Just as a general introduction, the Australian National Data Service works with research organizations from around Australia to establish trusted partnerships, reliable services, and to add value to research data and enhance the capability in the research sector. We're working together with two other increased funded projects, research data services, RDS and NECTA, to create an aligned set of joint investments to deliver transformation in the research sector. This webinar is part of a larger series of ANZ activities which aim to support the Australian research community in increasing our ability to manage research data as a national asset. This is the fourth and final of the four webinars in the series about the Fair Data Principles. We've had webinars on Findable, Accessible, Interoperable, and now we're up to the fourth one, Reusable. Please note, this is one that comes up every now and then, the R stands for reusable, it does not stand for replicable or reproducible. So, reusable is actually broader than those other terms and means that it can be used for more purposes than just purely to replicate or reproduce the original research. Today, I'll give a very brief introduction on what Force 11 says about reusable under the Fair Data Principle. Now, first of all, I'll give a brief introduction to what Force 11 agreed on as part of the Fair Data Principles under the heading of reusable. So, first of all, I'd like to emphasize that to actually make your data reusable you will also need to incorporate elements under Findable, Accessible and Interoperable. So, if your data is not going to be findable or not going to be accessible it will ultimately not be reusable anyway. So, this is, you best to see this as on top of making your data findable, accessible, interoperable. These are extra elements that you need to think about to make it reusable. The way they've talked about it, well, first of all, there's this first high-level heading saying that the data and the metadata should have a plurality of accurate and relevant attributes. Well, that's pretty general. And they then drill down into three specific attributes that are required. Now, the first of those attributes is that the data and the metadata are released with a clear and accessible data usage license. If you make your data available without any license at all it makes it very hard for a user or a potential re-user to actually use it because it's just completely unclear what the agreement is, if there's any copyright over the data, if there's any restrictions, things like that. So, that's why it's very important to have a license. So, it's clear what you can do with it. And if you do assign a license, please make sure you use a standard license. Definitely preferred. And ideally in a machine-readable format because that way machines can actually interpret whether the data can be used by that machine to do an analysis to pull in the data and to actually incorporate it in analysis. Or whether they need to skip it because it's not licensed for that purpose. A narrator will talk in much more detail about a possible framework to use to assign a license to your data. The second point they make under these attributes is that the data and the metadata should be associated with information about the provenance. Now, this provides clarity on the steps that were taken in collecting, selecting, analyzing the data. So, all the steps that have been taken to turn it from raw data into derived data and into that final data set that is made available under FAIR through using the FAIR data principles. So, this is for a potential re-user. This is extremely useful information because it gives you much more information about the context and the background in which the data was created and whether the data will also be suitable for the purposes that the re-user wants to use it for. Attaching provenance information is easier said than done and I'm really grateful that Margie is going to be able to talk a little bit more about what's happened in practice and how GA has tackled this and how GA is incorporating provenance information. Now, the third and final point they make about these relevant attributes is that the data and the metadata should meet domain-relevant community standards. Under Findable, they're talked about more in general about having a metadata that allows the data to be findable. And under Interoperable, they talked a little bit about the data and using standards. The point they're making here is that it's very useful to make sure that the data and the metadata is in a data format and a file format that is commonly used in the discipline. So, that means another researcher in that same discipline can easily pick it up and use it. And if you use a metadata format, think about using one that is common in the discipline too so that it contains specific fields that are relevant to that discipline so that a researcher in that discipline can easily understand more of the detail what columns are in that data set, what the context is around the date in which the data was collected, etc. So, that makes it much more useful for a potential re-user from that community to pick up the data and reuse it.