 100 people register, but I know how things go these days and we are recording this session. So this will be available in the cloud afterward. I'd like to thank Sylvia Gutierrez for joining us today from the Wikimedia Foundation. We will be discussing Wikidata and books. And did you want to introduce yourself, Sylvia? Do you want to wait? I'll do in a while, but I think now we can, ooh, that. First you, and then I'll go. Okay, so my name is Lori Bridges and I'm a librarian at Oregon State University. I am one of the seven co-organizers of every book It's Reader campaign. And you can find more information about the campaign on bit.ly forward slash every book It's. The campaign was initiated a couple of months ago and we started planning. So we haven't had a lot of time but we're excited to see that so many folks are interested. The overall goal is to improve the coverage of books but also literary works and oral stories. And we've chosen the month of April because it is World Book Day on April 23rd. And so we're hoping to get more folks involved. Just yesterday we learned that Romanian libraries are on board and Wiki Conference India is involved as well. Book coverage in Wikipedia is extremely important. I am coming to you from the United States and you may have seen on the news that we have many book challenges. And for those challenges, I have looked at the stats in Wikipedia and those pages are viewed hundreds, if not thousands of times every day. So it's very important that we improve book coverage on Wikipedia. And we will have more events throughout the month, including tomorrow. Mexico is hosting a kickoff event. And so do go to the every book. It's media meta page, excuse me, it's there on the screen and then you can see what events are coming up. And so now it is my pleasure to welcome Sylvia and I look forward to answering any questions that you have in the chat. Thank you so much, Luria. And thank you to all the seven co-hosts that have organized this amazing international campaign that considers also oral knowledge as obviously part of our literary heritage. My name is Sylvia Gutierrez. I am Senior Program Officer for Libraries at the Wikimedia Foundation. Before joining the foundation, I was a Digital Humanities Librarian for seven years and I'm passionate about connecting, enriching and visualizing data. Why? Because I feel this is a way of critical thinking and let's see if you agree with me when we finish this workshop. If I'm looking like this way is because this side I have the chat. So I'm also seeing if anyone has questions or wants to share something, we will have a time to share a little bit about ourselves like who are we? So we'll get to that. But anyways, I got bitten by the Wikidata mosquito in 2014 and ever since I'm a huge fan of this project and I hope that at the end of this talk you're also a big fan because you will see the wonderful things that it can do. So just so you have a little overview of what we'll be looking at today, we will talk about obviously what's Wikidata, the power of linked data through Sparkle queries and also how we can use Wikidata to search the other projects like Wikipedia and Wikisource which are two very relevant projects when we're talking about books and also of course how we can add missing information, how we can add that book we love and we feel that everyone should know about. So getting to know you, would you mind going into that link? I'm gonna write it in this chat as well and it's an anonymous survey and it's just to get to know you a little bit better. So this is like this, I think I wrote it correctly and if you did enter that link, now you can write where you come from and so we have a participant from the United States and there are three other participants typing and I'm excited about where you all come from. I myself and I'm in Mexico City. This is the country where I was born and yeah, let's see who else is joining. What's one nice in Poland? That's great, that's great. Brazil, yay. Wow, Malaysia, that's amazing. I'm so happy to have you all here. I hope, yeah, my English is understandable for everyone and if not, please just ask and I'll be happy to come back to anything that it's not, hey, Brazil is getting bigger. Nice, nice. So we have some more participants. You can still use the poll and but now I would like to know if not in the Slido, if it's too complicated but I would like to know what do you think when you hear the concept of linked open data? You can also write it on the chat, but interesting. Yeah, we see network, relationships, semantic web. I think this is very nice. We see how connections is something that people tend to think about. Interesting that also people are talking about unique identifiers, connecting silhouette knowledge. That's nice. Connecting things that were before separated and now get connected triples. So I see that there are some people joining that do know the lingo, the words of the semantic web. I like also how people are talking about discoverability. So yes, this is why we wanna join this amazing project with Data because it does allow us to discover things as we said, as someone said, I'm sorry, in the survey, the knowledge that was separated, it allows us to discover that sharing. I really like that that work comes up sharing knowledge and metadata is also pretty important for our workshop today because it is a lot about using that metadata that we know from libraries and now connected. Yeah, Wikibase, oh, we won't touch Wikibase today, but do check it out if you are planning to host like your own Wikidata, so to say. If you need to control your data, and that's really interesting, Wikibase is really interesting. So yeah, metadata is getting bigger. Yes, we do think about metadata and we will talk about that. So I'm glad that many things that you also feel like that are connected with Link Open Data appear here. Okay, so when we talk about the Wikimedia projects, many of us think about Wikipedia because I mean, it's the most popular one, the one that has been longer out there and so on, but you might know that there are a lot of other Wikimedia projects that just comments for images and video and so on and that would be great for oral knowledge. Also, there are very nice initiatives from Nigeria and other countries that are looking at oral knowledge doing some recordings and sharing them there, but what we will talk about today is Wikidata and also a little bit about Wikisource and what is Wikidata? So I think many of you know, but just so we have like a sense of how big it is, it's more than 100 million items. We will talk what items are, but it's huge. It's a lot of data, a lot of open data, which is amazing and there are more than 20,000 active editors and more than 300 languages and all of that is why we love Wikidata also because it's collaborative. And if you join, like if you people in this call today join this is gonna get better because Wikidata is going to be as good as its contributors are and as diverse as their contributors are. Also, one thing we love about Wikidata is how multilingual it is. Of course, not all languages are there, but I hope we will get there and this is very nice and it's free to reuse, which means people doing amazing stuff with it. If you don't know about wikitrivia.tomjewatson.com do check it out. It's a very fun game where you get to organize events in time. So you have three lives, those three hearts and it's Wikidata powered. So there are a lot of games, not just this one, but I feel like this could be a great game about books for instance that you just organize which ones were published first. You see there's the great Gatsby. And yeah, that's a pretty, oh, thank you for sharing the link. And yeah, also there's istropedia or highstropedia, I don't know. And this is another very cool tool also powered by Wikidata and Wikidata. And this allows you to create interactive timelines. So here we're looking at all the books from Dr. Seuss, which is one of my favorite authors. And you see his books in a timeline and you can go to the wikipedia page and learn more about these books. And it's a very nice tool for schools and in general, I think it's, yeah. And it's also thanks to what we said before that it's a huge database that it's collaborative and this is what makes people get so creative. And of course we also have that the CervantesVirtual.com, that's a digital library in Spanish. And this is a project created by Blanca Sanchez and she did this amazing interactive data visualization where you can look at all the female writers in their digital library. So you can filter by their country of origin or the language in which they publish and so on. So these are things that we don't have yet in our digital libraries, but we'll get there. And if we collaborate and if we create better data in Wikidata, we can do all these amazing stuff. So finally, I will say that Wikidata is big, Wikidata is collaborative, it's multilingual and as you said in that survey, it is semantic. And what that means is that elements are connected with meaning, it's not longer the hyperlink that was just one thing connected to another without any specific meaning, just the blue link. Now it is Carolina Maria de Jesus, a Brazilian author connected to the diary she wrote from the favela and this connection has meaning. She is the author of this book. Also, she has a country of citizenship, which is Brazil, right? So again, a link that has meaning, it is semantic and this very resomatic relationships can go anyway, any direction. So Carolina Maria de Jesus is connected to Brazil, but also her book is connected to Brazil because the narrative location of this diary slash novel is Brazil, right? So this is the semantic road. And as you also mentioned, those can be sent as triples and why they are triples? Because it's one element connected to another through a link of meaning. And the way this looks, the way that this looks in Wigideta is like this. Every single one of these elements has a QIT, like a unique identifier. You also mentioned Eurus when we were talking about things that come up to your mind when you think about linked open data. So we have elements and we have properties that connect these elements. This is the way that, you know, knowledge is connected in Wigideta. So let's have a look at it live. I'm guessing you're still looking at my screen and you're, I'm typing Wigideta and that's something you're also seeing, right? Yes, we can see it. Yes, that's great. So I'm gonna write here, Carolina, Maria. And, or I mean, I could also search for Cuarto de Despejo which I think in English it has another name. So this is a good exercise because now we see that there are two things that are called Cuarto de Despejo. So this one here that is actually an album she recorded with music and this other one here which is the one we're looking at. And this is, this is nice because we get a little bit of description here so we can know which one of those two is the one that we're looking for. So we'll click on this one. And as we mentioned, there are QIDs. So the QID for Child of Dark is this. This is the unique identifier that distinguishes this element from all the others. And we also have properties that connect this element with other things. So for instance, this property you will see very often which is P31 and it's instance of. So this is to describe what is this thing? Child of Dark, it's an instance of literary work. We can, this gets very deep, very philosophical. I was never a cataloger. So I am a librarian, a digital humanities librarian but I know defining things, it's very profound and we won't have the chance to talk a lot about this but for now it's sufficient to say that this is the way that you will model most likely the work that you're going to upload to the data. You will call it, if it's a literary work of course you will model it this way. So can anyone write in the chat, what is the QID of literary work? I'm gonna go there and can anyone see? I mean, it's a huge number but at least the two last numbers of that identifier can you write it on the chat? Just that we all know that we are on the same page. Yes, Violet Fox, that's absolutely right. That's perfect, Arden, that's the right QID. Yes, Lori. Yeah, so that's the unique identifier of literary work. And as you can see here there is another property called subclass of. Can you tell me what is the PID of subclass of? Can you write it on the chat? That's correct. Anila, Lori, Arden, it is P279. Yeah, so you see we have QIDs for elements and PIDs for properties for the ones that are connecting those elements. And we can see that literary work is a subclass of written work. So written work is anything, a master thesis, a letter that there are many types of written works and literary work is one subclass of this. That's great, Tebogo, nice, yes. So yeah, I think you all got it. You all know now that Wikidita is created with different elements that are connected to other elements through properties. And this is the way we do those connections we were talking about, great. So there are a list of common properties and you can look at them here. And as you can see, one of the most common properties is actually sites. Why this is? Well, because there's a huge movement inside Wikidita that it's really interested in academic publications. So they are connecting which articles are citing other articles. And that's a huge thing in Wikidita. So there was even this conference called Wikisite in which people were talking about this. So that's one of the most common properties. And as I told you, instance of is something that you will be looking at very often. And if anyone remembers, this instance of will be filled with one element if we're talking about a literary, like a novel for instance. So can anyone type what would be, how would instance of be filled if the thing we're talking about it's a novel? What would it be? Do you remember? I mean, you do have the QID above but it was something that it's a subclass of written work. So what would it be? I think it was kind of a confusing question but we can look at it here. Yes, yes, Janet. That's correct, literary work. So as we see here, this novel slash diary is an instance of literary work. Yes. And as you typed before, the QID of literary work is this one, the one that ends with 34. So great. Well, I mean, this is the first WikiData class. So it doesn't matter if we're wrong now, we're learning and we'll get there. So these are common properties. And what is even better? There's this amazing group of people that this project, WikiProject, is linked in the meta page of the campaign, of the Every Book It's Reader campaign. And it's very good. This is gold. This here will be your guide for whatever you wanna do with books because they wrote all the properties that are fairly relevant for literary works. So here, they even did some nice examples. So here you see, for instance, they tell you one property you will be using is instance of, this is P31. And they add in an example, for instance, the autobiography of Alice B. Tocles would be an instance of written work. Thank you, Lillian, there's the link. So that's the same goes for title. That would be a very common property you will be using. And it shows what the name of the property is and also an example of how this is filled. So we all know the diary of Anne Frank and the title here is in Dutch. So that's the way this element, I mean, this property is filled, right? So we will take a look at this at the end when we will add an element to WikiData. But just so you have like a general idea of what are the most common properties you will be using when you model a literary work in WikiData, there you go, go there. And also they are very helpful people. And if you have questions, you can see who are part of this project and you can ask them questions. So do that. Another thing that you will be, let's say confronted with are external IDs. So we already talked about elements. I didn't talk about strings, but we will get there. And the third thing that it's really relevant when we're modeling books are external IDs. So you can go at this link. And I think I can, oops, that didn't go as I planned here. So I'm gonna write the link. Oh, it's already there. Thank you Violet. And this is a query. We will talk about what queries are. Don't get frightened by all this select item, item label. It's easier, I promise. I'll show you how it can be easier. But one of the wonderful things about WikiData is that we can ask questions to it. And unlike chat GPT, WikiData is not making up stuff. Like WikiData is just telling you whatever it's in its database and it's clear where that data comes from unlike chat GPT where we don't know where they scrape their information from. So what I'm asking here is I wanna see all instance of WikiData properties to identify books. And this is why I was talking about external IDs. So this is a way to look at those external IDs. And here we go. So we have for instance, ISBN, that's an external ID. So it's another like a QID from another project. So we know ISBN is one of those identifiers, OCLC control number, DOI and so on, like Google box, Google books ID and et cetera. And we have also from libraries. So for instance, we have an ID from the Deutsche National Bibliotheque from Germany and the National Library of Germany, the Project Gutenberg eBook ID and so on. So there are a lot of IDs that we can connect with WikiData. So going back, we know that there are elements, there are properties, they are connected and this elements can be either information such as the name of the title of the diary of Anne Frank in Dutch for instance, they can be another element as we saw that Guarto de Despejo, it's an instance of literary work which is another element. And we also saw that elements can be connected with external IDs. I think those are the things that are, yeah, the most common things you need to know if you want to model books. So there's a comment there that you can use Chattypity to write a WikiData query and that's true. Yes, you can do that. It doesn't always work and it's not always the right query but it's amazingly done, yeah. Although we would have to also talk about the ethical implications of using something like Chattypity which was trained by paying very low, very low, what is the word, income? No, like, yes, I mean... Wages. Wages, that's the word, thank you. Very low wages, which is very sad. Anyways, let's go do happy thoughts about people contributing, not because they're paid very low wages but because they want to contribute to free knowledge and here we have, now we're gonna talk about the power of linked data. We're gonna squeeze what linked data can do for us and as I said, I promise that we will look at something that makes those queries easier. So if you go here to queryweekdata.org slash query builder or even if you just go to queryweekdata.org and then click on the query builder which is here, this one. Then you go to this easy to use interface and what we can do here is that we can ask for instance of literary work for instance and we can say, because we know that the property we're looking at what we want is instance of, we already learned that. So here you write the property and here you say if it's not literary work or if it doesn't matter what it is like you can add it there. For instance, I could say like regardless of value I wanna see country of origin which I know that that's another property that could be also done. But let's say we want to get instance of matching literary work. So you start typing and it already suggests some elements in wiki data. So I'm going to say I want an instance of literary work and you can always look at the element you're interested. So for instance, here we were with Huarto. Oh no, I think that's the album. I'll go to the book here. So we can see for instance this element it's an instance of literary work and also it says country of origin that's another property Brazil. So let's say I wanna query other literary works that have country of origin Brazil. So here I start writing country of origin of origin. This one, yes. And here I say I write Brazil and I check that I'm selecting the right one soon. So not Indiana, Brazil, Indiana but Brazil, a country in South America, like this. And then I run the query and here I get many literary works that are, that whose country of origin is Brazil. So I can go to the QID, I can click on it and there I see it. I see this book by Monteiro Lobato which is as we asked the literary work and the country of origin is Brazil. Right? So that was pretty easy, don't you think? I mean, now how could I ask for literary work from Botswana for instance, what should I do here? Can anyone tell? Like where should I change the value? Yeah, someone should definitely write a dissertation about the different book titles across languages. I feel like that I've seen that about movies like how different they can be and it's hilarious. Like Elma Street is the way it's called the movie in English and then in Mexico it's something like the nightmare of blah, blah, blah. It's like 10 words. I don't know, it's a huge phrase. So yes, it's really interesting. Great, Arden. Yes, that's correct. Good, Alan. So there's the answer to my question. Here I would need to write Botswana and only with that little change I can do the same query here, run it and now instead of getting literary works from Brazil I'll get literary works from whose country of origin is Botswana and none come. So this is why it's important that you're here. Like we want to make this database more diverse and get more of those works that we would like to see here. Anyways, you know how it works. So it doesn't mean that the query broke. It means that there's no data on literary works from Botswana. Maybe they're modeled in a different way because sometimes people write instance of books. So it doesn't mean that necessarily that there's no information. Maybe it's not modeled in the way that we're asking. But if you know about a literary work from Botswana you can search like a very famous one. You can search for it and we can do that and see how it was modeled. So yes, perfect. Now we know how to build our first query. And yes, this is one of the ways to find the gaps. We will do a little bit more complicated queries. So you know that that's a way of doing a query but you can get a lot of inspiration from the examples that are already available in the Wikidata query service. So if you go there, I hope you all saw I clicked on examples and I can search for other queries that are already available here. So this one is a very famous one which is searching for house cats in Wikidata and getting the picture that is associated with those cats. So here we're asking for an item that has the property instance of house cats. So Q146 is house cat and we're getting the pictures like that that are related to Wikidata. And can anyone say what should I change here in line six? I mean, I can change it also here to get dogs for instance. So what should we change here to get dogs? Actually there's a, I don't know that there's horses but here what we could do is delete this QID then press control space and start typing here dog like this and we see that the QID is Q144. So if we only do that change, I can actually change the metadata here of this. So if I do that change and I click on run, now I will get instances of dogs that have an image. And obviously I could also search for instances of books. I can change that here as well that have an image, right? So you don't need to create the whole query. You can look at examples and just look at an example that might get the things that you need. Just you can learn how to change it and you can change it here as well. So we can see if there are any literary words here, I'm sorry, like that, that have an image associated to them and you can change that also here in the information. So there you go, that's the way you do it. So you now know that you hear, here's the thing you're asking for. So you can write banana here, it doesn't matter but here's the thing you're asking for and you're telling what conditions you want to be fulfilled. So here is the property that you want. So P31 that we already learned that it's instances of and here you say instance of what and you fill this with the QID of the thing you're looking for. So, great. Now let's look, if you wanna learn more about this and you're like, oh no Sylvia didn't explain this in a way that works for me or anyways or you wanna just do it again, you can go to this tutorial, it's really good and you will go through all the elements of a Sparkle query and so please take a look at that and I think it's in different languages. So feel free to look around and also do look at the example queries that are out there. There are so many good ones. So people have done the craziest queries. One of them is most famous sons and daughters of librarians for instance. And you wouldn't guess who the most famous one is. I did it yesterday and it's Superman. So Superman is the son of, yeah, well, she's a extraterrestrial being but she was a librarian in her other world. So it's very cool, all the queries that you can find there. So go there, look at the example queries and modify, just play around and you'll find many, many interesting things. Now, let's go to queries that could make sense for us, for the campaign of every book it's reader. So what I did here in this query is that I asked for instance of literary work and I ordered them by site link count. What that means is like how many Wikipedia's they have or how many Wiki source elements. So they're ordered by the instance of literary work that has more links to other Wikipedia projects. And this is like a proxy for being famous, right? Why am I doing this is because I feel like if you look at these famous works, you can get inspiration to do, to model your own book. So here you can see Hamlet or Romeo and Juliet and if you look at those elements that are so famous and have so many links to so many Wikipedia's and so on, you can see like all the things that you could model. So here you see, oh, I could add images, of course they need to be in comments or you can see, oh, I could add this property which is inception, title, derivative work. So things that are based on Romeo and Juliet like movies or any other book and so on. So yeah, this query can help you see the most famous literary works like The Hobbit or many other Harry Potter. And when you look at those, you can get many ideas of all the things you could say about a literary work. So like main subject for instance and a form of creative work. So we don't say instance of novel but we say instance of literary work and with this other property form of creative work, we say what type of literary work it is. It's a novel, a diary and so on. And as you can see, they can be filled with different elements. So genre does not to be just one of them. It can be young adult fiction, but also fantasy but also adventure fiction and so on. And one thing we will talk about later is the references. So yes, you can say many things but they will be better if you add a reference to those claims. So here we have for instance, the idea that the Harry Potter and the Philosopher's Stone was created except thought about in 1990s. And here we have references that support this claim. So that is about famous literary works. So there you have the query. No questions until now. I'm looking at the chat. I don't think so. No, good. Now we're gonna look at books with external identifier which in this case is the Gutenberg ID. So this is another thing that it's very nice. Once books, I mean once literary works are connected with this and the intervarious we can do things that we cannot do at the Gutenberg page, right? So here in there are a lot of books, very amazing books, free books in the project Gutenberg. It's an amazing project. But I can't ask right now, give me all the books that you have that were written by women authors, right? We can't do that right now. But because the Gutenberg ID is connected to Wikidata, we can do that. So here I'm asking all items that have a project Gutenberg author ID and that person with the Gutenberg author ID is a female person, right? So I'm saying sex or gender, that's the property, is female, like that. And also I added here optional, tell me the country where they're from. So you can take this query I'm giving to you and you can change whatever you want. Like you can say, I am not interested in people who have a project Gutenberg author ID but I'm actually, actually I could change this so it can be more general, just ID, I'm gonna change here and I'll share the query with you. So instead of being the Gutenberg ID with a specific ID, I can say, oh, I'm actually interested in all the female authors who have a Google, no, actually think VF would be a good one right, with a VF ID and who are female and I can run the query. So you can play around, as I said, you don't need to learn how to do the whole query because you can find one that makes sense to you and use it. I'm gonna share the link here, so in case you wanna change the IDs. And as I shared before here, I think I gave you this other query with the external IDs. So here you can see all the external IDs you could be looking at here. So this is gonna take a while because VF is pretty big. There are a lot of elements with a VF ID, but here you could see other, yeah, IDs you could be looking at. So if you're interested at, I don't know, there's, oh, but this is for books actually, but for authors, there are other IDs and you wanna see all of them that are female, you could look at that. So as I have here, this is Wikidata property to identify books and there's Wikidata properties to, Wikidata property to identify people, right? So I'm gonna give you that. Here we can search for QIDs for people and you can use a query I shared and yeah. So VF is one of them. The ID of the German National Library is another one, the Library of Congress. So you can search for all female writers, women writers in the Library of Congress or the National Library of France, for instance, right? You can just change that p-value that we have here to that. And I'm sorry, this is taking a lot of time because VF is huge. You could search for authors with the most number of identifiers. Yes, there's a way of counting that. So what I normally do is, I Google. So I say something like Wikidata query authors by number of identifiers and maybe you don't find that. So Sparkle, sorry, I should have written that, but maybe someone already created something like that. So here you see this is the language to count. So here you have select and you say count. So it would be something along these lines. So you will count the properties and you will say what properties you're interested in. And if you get stuck, like if you can't find the way of, I think it was, if you can't find the way to do it, you can always, there's a place in Wikidata where you can ask for a query. So it would be something along these lines. Like you would count properties. And you can try it, you can try to do it. But if you can't, there's also in the page I shared before, so where I found the query of most famous daughters and sons of librarians, this one. Here you can see that there's another button that it's called request a query. So here you could ask, oh, I would like to do this and this and they will help you create the query you're looking for. Normally it's already there, but in case you don't find it and you can ask for one. So now I'm looking at a question in the chat and it says, hi Sylvia, I'm looking at this. Things fall apart, a great book by Chinua Tseve. And I am curious about all the identifiers. Yes, let's go to the identifier section. So it's this one. So we have VF and so on. And none is routes one to an African library. There's even an ID for a core topic. What would be the problem? How can we get African libraries in there? Yes, that's a great question. So in order to get African libraries here, you would need to create a proposal for a property. So you would need to make a case of there is this library that has this consistent identifier and we want this property to be in Wikidata. So that's the way the National Library did it. So you can see, for instance in here, let me see if I can show you very quickly. They did a case, like they said, yes, we want to create a property that is the identifier for the subject issued by the Bibliotech National Difference. And they said the form of this identifier, so it's an eight digits identifier. And they gave some examples and so on. So no, you don't need to be a National Library. You just need to make a case. Like we have this persistent identifier, that is a requirement. So it doesn't need to be a persistent identifier. So for instance, I did the case for LM which is the encyclopedia of Mexican literature. So this encyclopedia has persistent identifiers for writers. And I made the case. I said, look, this is a very important encyclopedia and it has relevant information about authors from Mexico and now it is a property. So I think, for instance, this writer who I really like I think she has an LM ID. It doesn't. And then I do let me see if I can find it. Yes, this is the identifier. So I made the case for this identifier. And now, yeah, we can add information about them. We can add those properties. Did I answer the question? Was it clear? I think maybe I should tell you where you do the property thing. So create a property data. There's a page where you suggest a here property proposal. So I'm going to add this link here. Wait, why is there like a smiley face? I don't know. Well, I don't know, but I think it's converting one part of the URL. Yes. That part. Yeah. Because it has like two points and a P and it's converting that to an emoji. Anyways, but you see the page here. And that's where you do the. The property. Proposal. So here it says like before proposing a property, please look at the ones that already exist. And it has to be discussed. There. And how can, so I see another question persistent identifiers. How can we get that? So normally. Libraries, especially national libraries. Have persistent identifiers for their authority control. So if they do. Then it's easy. If they do have those persistent identifiers, but if they don't, then it's a little bit difficult. And it doesn't have to be a number. It can be a word. As long as it is persistent. So yeah, that would be the thing that we would need. So for instance, just to spread some good news, there is. This database. The African journals online. And they have persistent identifiers. So that is a property. If they don't, then it's a little bit more difficult. And it doesn't have to be a number. So that is a property. And with the data. And actually, in my example, at the end. We're pretty. Are we ending in three minutes? No. Technically we advertised for an hour, but I set the zoom up for 90 minutes. So it's fine to go over and if folks have to leave, then that's fine as well. Okay. Okay. Okay. So let's go to the other things. Yeah. So I'm going to go quickly through this other query, which is nice. This is a query to look at the narrative. That is not. So I had another query to look at the narrative place. A map that shows the narrative place of. I don't know if it worked, but I have it somewhere here. So just like, let me take a quick look. And what is, what that means is that I'm with this query, I'm looking at the place where. The book is taking place. So to say, I got it. So I'm sharing my screen again. Here. So with this query, which I'm sharing in the chat as well. You can see. Items that have a narrative location that is property 840. And I'm getting the coordinates of that place. So narrative location. And it's an instance of famous P 31 instance of literary work. And here you can see literary works that happen in different parts of the world. Here you can find literary works that occur in a place in Ecuador or Colombia, Nigeria, Congo, Angola, and so on. And if you click on those dots, you'll see the elements. So here, for instance, we have forests of the pickings. That's a book that takes place in Kenya. Right. So that's a thing we can also model and it's cute. I mean, so that's, that's, that's another query. I'm going to, yeah, sorry about that mistake that this should have been the link. And finally, let's look at quickly just other. I'm going to just post them in the chat. We don't have time now to go in detail. It suffices to say that this is a way in which we can use WikiData to search Wikipedia. So for instance, here I'm looking at authors who are Polish citizen, for instance, and they have an article, Wikipedia article in another language, but not in English. So we do a translation if we know the other language in which this book is in. So here you can, as I said, we can change the country. So we can say, oh, I want to see all authors who are Ghanaian citizens. Right. So I would only need to change line six or here. And instead of writing Poland, I would have to write Ghana. Right. And, and I would get all of them who have an article in another language, but not in English. But you could also be interested in, for instance, maybe she wants to do articles in Portuguese. So she could change this line and just add PT to Portuguese and get all Polish or Ghanaian. So here I could change to Ghana. Ghanaian citizens. And see all of them that do not have an article in Portuguese, but in another language. Right. So this is a way that this is a query that can be used for the campaign. We can look at authors from other countries that don't have an article in our language. So here is the query I did before. And this is for Polish author. So here we have a Stanisław Lim and so on. And of course we could also add who an author who is a woman. Right. So we could add here author. That with the property gender, sex or gender, which is P 21. And then female like that. Like this. Who is a woman. And, and so on. So I'm going to share this link in the chat as well. So here you have that query that can help. Perfect. That's, that's, that's just to say that we can use it to query we did that, but we can also use it to search books in a weekly source, or, you know, literary works. So here I did another query that I'm also sharing in the chat. And this is to look for all female authors who were born. And it's commented so this line won't run, but I can take that hashtag out. And here is to look at all women that were born in India, for instance, that have a work in wiki source. So if I take that hashtag out, you take the hashtag out of the query I just shared. There you would find women born in India who are female and have a work in wiki source in English wiki source. Right. Oh, sorry. This should say wiki source like that. Anyways, so that's that. And let's go to the final part, which is how do we add what's missing. So how do I add the book I want to we did that I first wanted to show the power why we would want to do it, but now let's say how can I do it. And I'm going to live at a wiki data element of a book I really like, which is Amanda written in 1980. And there's a very complicated way of stating that this book was banned. So we won't get there. There's a whole discussion. We can link the links to all the discussion that it's going on. But yes, I'm going to give you some tips of how you can add the book you want. So these are my three recommendations. If you want to add a book to the data, you should have number one. Link to a famous work. So I showed you already a query how to find famous words. You should have a link to a related work. So for instance, it could be another book from the author. Or it can be a book from an author in the region. So here I have a book from another South African author. And just to have an idea of how this book was modeled. Or I can have a very famous book from the region, so to say. So the book I want to model is from a South African author. And the thing I can do is search for another book that was like that. Just take a look at all the properties that I could fill. So here I see this. So here I see a book from another South African author. So here I see a book from another South African author. So here I see this book and I see whatever they have about this book. I'm sorry. So that's one thing. And then you should find references for the properties you'll use. And once I say, okay, this is. Oh, and also, you should look that the author of the book is already in Wikidata because if not you would need to create an element about her. Or him, right? So fortunately, my author is already in Wikidata. We won't have time to cover, but I will give you a link on how to learn more about adding authors. So in this website, learnwikidata.net, you can find more information about how to add an author. But let's say your author is already in Wikidata. So here you have it in hand. This South African novelist. And that's that. That's the first. You have your model ready. You have a famous work you can take a look at. You have a related work and you have your author. Great. Now you will need to search for references. So publication date, main subject, and so on. I have my references. So I have this. Newspaper article in which they talk about Miriam Palli. And they talk also about the book I'm interested in. So they do say that Amanda was published in 1980. So I have a source, right? I also have a source that this book is about the Soweto massacre. So this article, which is from the African journals online database, talks about this book and it says that it's part of the four novels that are about this tragedy, this uprising in Soweto, where they killed 350 children who were hoping for a different type of education, right? So I have my reference here. I also have a reference to the fact that it is a novel. And that the narrative location is Soweto. So I have all these journal articles I already looked for. And so I have my sources. I have my model. And I could even look for some external identifiers. It's not needed, but it's useful because it proves that the book that you're talking about exists, right? It's there. It has a persistent identifier somewhere, right? So now I have that. So let's do this. Let's do it. Let's search for, so I'm going to search for Amandla first. And I already did this because there are many things that are called Amandla, because it's a word that means power in Shosa, in Sulu. So there are many things, but none of this is the book, right? I already did my research. I know none of this is about Amandla. So I'm going to look at it. It's because here I have my author, and I can ask to what links here. And I can see that. I mean, there's this, there's this redirection, which was because someone else created a page about Amandla. And there's this link to this talk page. So there's no book connected to that literary work that we can. So I now I know that Amandla is not there, and I can create it, right? So when you search for it, and it's you have to log in to your account. This is my, yeah, my WeeData, WeeData account. And since it's not there, I can create a new item here. I can click here or sometimes it even appears here. So I'm creating it in English. That's why you can select another language. And I'm writing the label in English. So it's called Amandla. And I can say 1980 novel written by South African author and medium plan like that. Oh, sorry to see you go and sorry to longer than expected. But yes, so we click on create and now we can add statements. So first thing is instance of and I can write literary work. I can add another statement. And as you can see, it already suggests once I write here instance of literary work, it already suggests properties that are coming to a literary work. So it says, oh, who is the author? And I, as I said, it's useful to already have handy the QID of your author. So here I can write the QID just to be sure that it's exactly this medium plally and not another medium plally. Like that. And I can add the publication date, which is 1980. And the reference because here I have a reference that states that truly it was written in that year. So here. I added that but I can add a reference and say a reference URL. It's here and publish. Like that. So I have references to say that it is true that it was written in 1980. And I can also say that the main subject is so what the so what the uprising, so I can add here main subject. I have the QID this so what the rising like that. And I can add my reference, which was this article. So there we go. We add the reference. So on. So are there any questions until now. Okay. So I'm going to add only the, the ID. So I have, I search for this book in library thing. And I can add everything ID. Sorry, work ID. That's that one. That's perfect. And I will also add the Goodreads ID. And I just wanted to show how it works. So when you search for a book in Goodreads, for instance, a month, you see one edition, right? But then you have to go here to show all editions. And when you click on that, you get the right ID. That's the right ID of the book. This one, which shows all different editions. So that's if you want to add a Goodreads ID work ID. That would be this one. And you can check that when you click, it should show the right. The right information. The same goes for this other external ID. When you click on it, it should show library thing. It should show the right book. So. Yes, there are a lot of lists of books that need to be created. One of them, I mean, you could see, you could use one of the queries we looked at today. And if you see, for instance, like there is no literary works from authors from Botswana, you can make that your, you know, your thing, right? So you can search in WPF for authors from Botswana and add their works there. And in the Weki Projects book, I think there's another list. And the meta page, I think, was I wrong, Lillian? You were going to do some lists of the books that people could edit. And Laurie, I think you were creating also Listeria lists, right? Of books that people could edit. So there's a lot of work to be done. One of them, as I said, are queries. You can search for books that are not sufficiently described. So, for instance, as you could see, even Bessie Head, even a book from Bessie Head, which is a very famous South African author, it's not really well described. So we saw Maru, I think that that was the name of the book. Or I can search here and it doesn't have enough information. So here you see it's very poor. So you can almost give for granted that books that are from Latin America or bi women are not, or Africa are not really well covered. So yes. Oh, you created for Wikipedia, right? But I mean, if they're not in Wikipedia, most likely they're not in Wikipedia data either. So you can create both at the same time and then link them. Yeah, lots of work to be done, but I'm so happy you bear with me and I'm sorry I came 15 minutes after the time. But if you have any questions to connect, I'm here. Yes, my email and let me know if you have any questions. But also, thank you so much, Flori Lilian. Oh, thank you for coming, Sylvia. Great. This is an amazing presentation and I mean, Wikidata is not my area of expertise and I'm still going to go play with it and it was very fascinating. And I've also unmuted Lilian, so she could speak as well. I know she knows a lot about Wikidata. Thank you, Sylvia. Thanks a lot. It was a wonderful presentation and I'd like to invite everyone would like to create some kind of event during our campaign about Wikidata. You can work individually, but if someone are planning to do some kind of specific event, please join us. As you see, there are lots of work to do. It's like a simple label about a title book and Wikidata. It's like a huge work. It's not something small. It's huge. So please let's connect with us and improve the information about books and writers on Wikidata. Yes. Yay. Thank you everyone for coming for everyone that contributes to the campaign and to Wikimedia projects. Thank you.