 I'm Gisle Paulson, and I'm going to talk a little bit about the kind of complexities that arise when we try and move beyond our particular datasets into these kind of multidisciplinary, multiproxy projects. And I'm basing my experience from this particular project, DataArc, which aims to do just that in the North Atlantic, where really the data sources are only partly archaeological. We're dealing with historical sources, we're dealing with literary sources, we're dealing with paleo-ecological sources, a lot of different things. And what we've chosen to do as a kind of exploration and connectivity layer for all these different data sources is to take SIDOC, the SIDOC CRM Concepts model, but we've chosen to kind of use it a little bit creatively as a map, as a concept map to get us from these general concepts about the past onto the very data themselves. And so I'll talk a little bit about SIDOC. I'm not going to talk about DataArc, but our dear leader, Colleen, is sitting in the back of the room who will give a, hopefully, a comprehensive talk about it on Saturday. So please see her talk for more information about DataArc. I want to say that the reason why we chose SIDOC is it's been a central standard for data integration in kind of broad applications through archaeological, cultural heritage, historical communities. There are a bunch of extensions dealing with specific kind of domain-specific issues when it comes to integration. And it has a very active community support group. So I'm going to show you a couple of slides about how SIDOC works, but if you're interested, please look it up. It's impossible to cover in 15 minutes. But simply put, it's a tool set for clarifying and describing the character of elements in a data structure and the relationships between these elements. We talk about classes or E for kind of properties to do with the elements themselves, tables, that kind of thing, and then P for properties when talking about the relationships between these data elements. And so when you try and do this, you realize that this is quite complicated. Just saying that a site has a place or a person has a place or an object has a place will run you through lots of classes, lots of properties. You really have to kind of, when you think about just every single kind of aspect to emplacement, it becomes quite complicated. So SIDOC intends or sort of attempts to really capture that complexity on all the kind of different variabilities that may be expressed either sort of implicitly or explicitly in data structures. Same with time, you've got a couple of different ways to articulate time, durations, events, sequence, that kind of thing. Just touching on a couple of the extensions when you look at the kind of core tools and you start trying to use them, you realize immediately that there are some shortcomings in the original plan, but all these extensions allow you to take it a little bit further. So if you're interested in kind of really getting specific about certain events, you can look at CRM Geo. If you're interested in kind of talking about not necessarily just what happened, but the sources detailing what happened, you can also look at the kind of declarative extensions in CRM Geo and so on. Again, let's not spend too much time on SIDOC, except to say that it is quite a powerful toolkit to do the kind of integration work necessary when it comes to multiproxy projects. So the dataset I'm involved with in DataArc is called eSlave. It's a site database for Isent. There are about 100,000 sites in it, concentrated in certain areas. Only about a third of them have a place, have a kind of geolocation, but the other two thirds have been kind of derived from historical sources and so on. So it's a sort of interesting mix between archaeological and kind of historical places. It has certain problems. The main issue I have with it is that it sort of focuses quite heavily on the site as the primary unit. The schema itself does not necessarily allow for a lot of historical contextualization. And for that reason, a lot of things we know about the way sites interacted between each other, the connections between places simply does not fit in the data layer. So I addressed this issue a few years ago, and that's my PhD project, which essentially takes that kind of core settlement structure of Isent, eSlave, and a little bit beyond that. And it kind of explores the way places are connected and the way these kind of collectivities kind of build up. So I went to historical land census documents, reading through them. You can see quite a lot of information has to do with rights on to other places or owners who live somewhere else. So kind of taking that into a model, you can say that every place has kind of subsidiary units, both where people live and environmental resources. You can scale that up and say that all these places are connected in some way through reading historical documents. And so what you end up with is kind of a series of layers of connectivities, networks of ownership, resource access, social obligations, transfer of goods, all these kinds of things, which I want to stress don't necessarily only kind of show connections as incidental to the way sites operate it, but in my opinion is kind of core to the ontology in which the sites kind of behave in the past. So for that reason, the database layer that I've constructed on top of the kind of national survey database is really a database of connectivity. And the research, the ongoing research used in that database is to do with kind of understanding how these interactions lead to emergent properties and to social complexity. So the question is, how well does CIDAC handle a database with this kind of golden mind, social complexity, connection between places and so on? And so just to show you the kind of work I've been doing in order to map my stuff to other databases through Datark is the example I want to take is Driftwood. Driftwood is quite an interesting resource for Iceland. Generally speaking, it's coming from Siberia, northern Norway, places like that, Washington to the shores of northern Iceland. And because Iceland isn't forested to any big degree, I mean, we have four bridges and that kind of thing, it's actually a crucial resource for construction across the country. So the management claim and transport of Driftwood from northern shores to the south play a vital part in Icelandic history. Just to give you a sense of what these places look like. I wouldn't expect this to be what that shore looked like in medieval times, but it's probably an aggregation of about a century. But these beaches were full of Driftwood. It was reliable. It came very reliably and like I said, played a key part in construction across the country. But what I want to stress is that we can't really look at kind of Driftwood claims and Driftwood transport as just a question of kind of material transference because first and foremost, it's a map of power. Certain farms in the south, very rich, very powerful farms, almost always church farms have claims on these tiny, very minor places up here. Not only do they have claims on the Driftwood there, but the fact that they have these claims essentially means that these have to be inhabited because they are, if you want Driftwood, you sort of have to stay there by the shore and collect it. This is the exception. Generally speaking, Driftwood comes here and then just kind of leaves. So these places have to be inhabited by tenant farmers in order to ensure that the Driftwood is collected, aggregated and brought south. So it's kind of a map of power structures, map of inequality, also a map of production chains because we can also assume that this Driftwood was actually worked in the north and brought south. So in other words, it's not really enough to talk about claims between X and Y, which would be easy inside out, but not so much if we have to take it into all these kind of dimensions of power, dimensions of inequality and so on. So what we found when we started doing Psyduck mappings of this was we had to expand a little bit on the way in which Psyduck should be used. And primarily what we had to do was to kind of apply multiple classes onto this object because just for my purposes, you can think of Driftwood as being like one of several classes in the ontology. If you think about what other people in the project are doing, a paleocollegist might be interested in the provenance or the kind of the age of the wood. Some are looking at climate change, might be interested in the way kind of Driftwood and sea ice have that dynamic, and they would be interested in other kind of property classes and other relations suiting their purposes. So when you try to use an ontology like Psyduck, it's almost impossible to keep it sort of neat and tidy. Instead, what happens is that you make this assemblage of concepts. The Driftwood keeps appearing again and again and again in slightly different forms and in a way kind of to use assemblage theory. We can talk about sort of relations of interiority where Driftwood keeps appearing but keeps sort of having relations within each other and then all these connections to other parts of the data structures. And that is a major kind of element of complexity when it comes to using Psyduck. But what I want to end on talking about is this. Because of the way in which you have to really work through every single element to which kind of your data relates to an object like Driftwood, we've had some very good discussions about what the data sources are saying about a certain object. We've had very sort of generative, very productive discussions between informatics experts and domain specific knowledge producers because of this highly articulated language that you have to go through in order to get to some sort of concept map using Psyduck. Thanks.