 Everyone, well Kent from Wiki Education here talking about making data more collaborative. I'm presenting with my colleague Ian Gill from San Francisco MoMA. And I want to talk to you about setting up a project on wiki data, specifically opening up data to the rest of the community to allow everyone to do it. So Ian is going to talk to you about his project specifically and I'll be using some examples from all of his MoMA work but the main idea is that if you represent a set of data or you come from an institution and you want to share that with wiki data it's a kind of complicated process to share all of that data on wiki data. And so I teach these wiki data classes that orient anyone who attends to the fundamentals of wiki data, how to use specific tools, and how to query wiki data and get the data that you want out of it, so that you can share your data, understand what's going on with it, and kind of fill in gaps from there. And so Ian took this course in mind with sharing data from SF MoMA's collection, and specifically he wanted to look at traveling exhibits that SF MoMA had hosted over the years which was a set of data that was kind of sparse and had a lot of gaps. And so the easiest way to deal with these gaps was to kind of build a query. And just to give you an idea of how complicated queries can be I'll share my screen. And there's a lot to parse here, and it's not always clear what you're looking at what you want to know so this is a query that Ian wrote. In the course we spend time unpacking a lot of what these variables mean how to construct these queries what happens when one breaks. It's wonderful when they work, but sometimes they don't and it's not always clear why they don't so we explore which kind of items to use how to phrase the querying. And then when you run the query which I can do by pushing this blue play button you can see a set of results, and you can understand what they mean, and what to do with them after that. What I want to focus on is wiki data is very large and it's not always clear what we're talking about so, for example, I want to focus on this little bit of phrasing from this query about location. I've pulled up this property page on wiki data and I want to share it with you. I'd like to point out that it's kind of confusing because there are a lot of different location properties on wiki data and this is the one that specifically speaks to the location of art and artworks. But there's also P 131 which has to do with geographical location, among many other properties that describe location, and it's not immediately clear but if you click on this discussion tab up at the top. So you can read all about how this property works and so what we do is we spend time building out these queries, running them to understand how all of these different properties relate to different items on wiki data. And this is useful because it helps frame a project really really well. And so Ian was able to do was build out this query, apply it to his set of data from SF mama, and then come to some concrete conclusions about that data. Which, if we go back to the query tab, is that you can see all of these traveling exhibits when they came to SF mama, when they left. And if there were any gaps he was able to fill them in with their local data. And additionally what this means is that other museums now can take this data and apply it to their traveling exhibits so you could actually track one piece of art as it traveled across the country from in this case it looks like we're looking at the 30s, but you can imagine if more museums add this kind of data we could have a much fuller picture. You could have a collection data, but you can apply this to other aspects of a collection to so you can run queries about artists, their works where they're located. You can analyze gender balance, you can analyze time period. So there are all these different variables that you can take a look at, based on very similar queries. And so once you have that one kind of query down and you understand the way that the parts work. You can really hit the ground running if you want to do a deeper analysis of your collection. And a lot of this comes from just spending time in a course, kind of setting the stage understanding the fundamentals, and then being able to apply those fundamentals to the specific research questions that your institution might have. So that's one way that you can help make data a little bit more collaborative and I'll now turn it over to my colleague Ian who will talk about more specifics about this particular project. Thanks for listening. Here to the introduction will. My name is Ian Gill, and I'm the documentation associate at S of mama. My responsibilities at the museum include working with the museum's collections management system, conducting research to augment the database and working to share our data in the online collection. In 2019 I took wills introduction to wiki data course hoping to learn more about the platform and how we could contribute to it. So here are a few things that I've worked on since then. I've illustrated the first project I worked on related to the museum's exhibition history, which at that point was available as a PDF on the website. It looked like this. I took the exported data from our CMS and use the quick statements tool to make it suitable for upload, ensuring that each entry pointed back to the museum website as a reference. And, as you can see here, we now have over 3200 entries on wiki data. As of uploading, I noticed that other wiki data users had already started adding to them, which I thought was super cool. So moving beyond exhibitions, this is the S of mama online collection, which we frequently add new works to. You'll see that there are a lot of different records for just various artworks in the collection. And then there are also corresponding artist records. So one of the things that we wanted to do was really just see how we could translate this information to wiki data. Currently, my primary focus is indicating the artists in the S of mama collection. Once an artist is listed as in the collection, I can query them and gather artist information from wiki data. Using data exported from our CMS in conjunction with the program open refine. I had the following statements to artist records has artworks in the collection of used in conjunction with the S of mama entry and the S of mama artist ID, which draws from the unique identifier used by the online collection. For all of this information, I include references that point back to the museum website. So for example, here's the record for Ruth Asawa, the Japanese American sculptor. You'll see there's a whole lot of information in this one. This is a particularly robust article. When you scroll through, you'll see there it is. So Ruth Asawa has works in the S of mama collection, and there's the references. And then if you scroll even further down, you'll see the S of mama artist ID. Let's see. There it is. So you can open up that and then see the references. And then if you click on the name, it'll take you back to the online collection page. So this is how we are indicating artists on wiki data. Once artists are indicated on wiki data, they will show up in the query that we've built, which if you look at it right now, we have a little bit over 2200 artists in the museum collection reflected on wiki data. Once an artist is listed as in the collection, you can then amend your query to include other sort of information that you're looking for. So for example, here's a query that will help me create, which includes the artists in the S of mama collection, as well as any associated ethnicity information and references. So if we run this query, you'll see here the results. So it's a list of all of the artists in the collection that have associated ethnicity information. So here let's click on the record for Alma Thomas, the expressionist painter. Let's see if you scroll down here. So here's the ethnic group statement as well as the value African Americans. And then you'll see that so there's a reference to the Getty Union list of artists names. There's a reference to the African American visual arts database and then a publication notable black American women by amending the query. We are able to search wiki data for specific information, which we can then use to augment our own records and aid with research projects. One such project is the artist identity project where staff are looking to research identity information about the artists collected and acquired by the museum. Things such as ethnicity, gender or national identity. By tracking this information, SF mama hopes to be better able to interpret the art we acquire or exhibit and also generate metrics with the aim of becoming a more inclusive institution. Anyway, thank you for joining us today at wiki mania 2021. For anyone interested in learning more, I started a wiki project SF mama page that lists the specific statements and data that we're working with. Please feel free to reach out if you'd like to collaborate and thank you again.