 So it's 3.30, so let me just dive right into it. Yeah, so the agenda for today's brief little session is a general understanding of link open data, as well as where wiki data stands and where wiki data is being positioned in this link open data space. I also run through very, very quickly about editing and querying wiki data. I'm not going too much into the details of how to do it because I think in the earlier sessions across the hall, our staff has done a fantastic job, you know, and if you're interested, there are many, many more materials online that you can actually look into, but for the benefit of some of our friends here who may or may not know what wiki data is, I will just quickly run through this part and then I will talk a bit deeper about wiki data partnerships specifically from a wikimedia Germany perspective because I'm from wikimedia Deutschland. And for the second half of this session, I'd like to do a quick check-in with the audience to really think about, you know, how do you think wiki data is relevant for different institutions of the world and how do you think that the community will be able to, you know, see the value of wiki data. So it's more of interactive session, so don't be shy. If you have any questions, we can always talk about it at the last half of this session. So a quick introduction of myself. My name is Alan Ang. I'm a partner manager at wikimedia Deutschland. Wikimedia Deutschland essentially means wikimedia Germany. There is my user name, as you can see, and I can reach via the email there, alan.ang at wikimedia.de. And I was very happy because I'm at Jackie just now. Jackie is from Smithsonian. She got away here at the wikimania. So it is really exciting to see old friends and familiar faces. So link open data. Let me just do a quick overview of what link open data is. In wikimedia Deutschland, we have about three main products that the link open data ecosystem is trying to focus on. The first two are wikibase, wikibase suite and wikibase cloud. So what wikibase essentially is, they are open source database application that allows for collaborative editing and management of structured data. It sounds a bit confusing, but don't worry, you will get a clearer picture of what it is soon. So wikibase comes in two particular forms, wikibase suite and wikibase cloud. Essentially what it means is that wikibase is a software that is actually powering wikidata. And if you want to have your own little mini wikidata in your own repository, feel free to download wikibase or even to use the wikibase software in order to have your own wikidata and be connected with the link open data web. Why are there two products? Wikibase suite is actually a software that you can download via Docker install. So you will require some technical information and understanding of how to implement this. And wikibase cloud is actually our new, wikimedia Deutschland hosted service that is accessible to smaller, less tech savvy users. So what this means is that if you have, I mean, if you do not have the kind of technical competency on how to install wikibase suite into your own database and then what you can do is upload your data set into the cloud service that we provide and it is actually a wikibase software that's powering it. So at the same time, you can also be connected with the link open data web. So you heard of wikibase and of course the highlight of today's conversation is wikidata. So at wikimedia Deutschland, you may or may not know, we are very, very enthusiastic to support and promote wikidata as part of the whole link open data ecosystem. So wikidata essentially is a free and open knowledge graph giving users, giving you access to high quality up to date machine readable data and powers different projects such as Wikipedia. Now I'm just reading from this because we as in the team at wikimedia Deutschland recently spent a lot of effort, rethinking about how we can position wikidata in the bigger scheme of things, right? Especially in the current context of machine learning, generative AI, yeah. So a lot of words here. I'm not going to bore you a lot of words, but let me just give you the gist and essence of it. What is link open data and why is it important? We hear this term running around very often. So what link open data, it means essentially is you see open data and you see link data, right? So if you break down this link open data concept into firstly open data, open data means that data that is freely available for anyone to access and to use it means that it is copyright free, anyone can just use the data for their own little projects or for whatever use they intend to. And open data is important because it can be used by different individuals, businesses, corporations or even NGOs or governments in order to make better decision and to create new products and services and to help the whole world to grow as a whole, right? I mean the whole knowledge work that we're trying to do here. And link data is a way of publishing the data on the web so that the data can be easily connected to other data and other information is available all over the world as well. So what link data does is it works by using standardized formats and protocols to link data together, thereby making it possible for the data to be discovered and retrieved in a more efficient and effective way. So we think about, okay, open data, link data. First you must have data that is open and in order for the world to benefit, to get more, to get understanding to be able to access the open data, we should make sure and support the concept that all open data should be linked together. So link open data therefore is a set of principles for publishing and sharing data on the web in a way that makes it easy for data to be connected and to reuse. So the principles of LOD, which is link open data, include using standardized formats and protocols so that data is openly and publicly available in the whole interconnected work of information. Now I spend some time talking about this because this is very important. In our experience when we talk to different institutions to tell them say, hey, you know, you know, Wicked Data, do you know Wicked Pedia, you know, Wicked Projects, many institutions, organizations, they heard of Wicked Pedia, less so of Wicked Data and even lesser so of Wicked Base, but they understand this as projects. They do not understand why it is important for the organizations to become part of this project and the fundamental reason behind that is link open data, right? So at Wicked Media Germany, we have a vision of a link open data ecosystem where as you can see in the middle here, we see Wicked Data, right, it stands right at the heart of the whole link open data web and you see many, many small Wicked Bases here. These Wicked Bases actually, what it stands for is that different institutions, they use the Wicked Base software in order for them to share their database with the whole ecosystem. And at the heart of this Wicked Data and Wicked Data, as you can see, it's connected to different multiple Wicked Media projects as well. So this is the vision that we have at Wicked Media Deutschland and Wicked Media Germany and this is why we are working hard to not only make sure that the products, namely Wicked Data and Wicked Base, are constantly being updated with the support of the community, but also at the same time, we're doing outreach to different institutions of the world to inform them of link open data, inform them of the different projects and products that we have, and also more importantly, to develop the capacity so that they will be able to onboard these projects successfully. So let me just run through this very quickly. I think it has been discussed several times about what Wicked Data is, so I just want to just put it loud and clear that Wicked Data is a knowledge graph. It is, if you think it's a database, you're not wrong, but a knowledge graph is a higher level of a database because a database can be just a database that different data sets are not connected to each other. For knowledge graph, it means that different items are connected to each other. And it is a very integral part of the Wicked Data projects that contains structured data. And what is interesting is that it is linked to many, many, many, many, many databases, right? Catalogs and websites, et cetera. Like most Wicked Data projects, it is multilingual, which is extremely, extremely important because we believe in inclusivity and we want as many, many communities to come on board as possible. And it's collaborative. So what is important for us to understand is that the data that is on Wicked Data are released under public domain license, CC0, Creative Commons Zero license. And it is based on statements and references, which you more or less have some concept. If you don't, that's fine. I will just give a quick overview of it later. And of course, more importantly, if you have a data in a knowledge graph that is only for humans to interpret, and to this day and age and time, it is insufficient, the data must also be able to be interpreted and manipulated by machines as well, right? In order to create value to the world. A few quick numbers. Wicked Data was launched in 2019, sorry, on the 29th of October in 2012. Last year we celebrated the 10th anniversary birthday of Wicked Data. So today it is, sorry, not today, this year it is actually the 11th year of Wicked Data. And every, sorry, and on 28th, 29th of October this year, we are celebrating the Wicked Data Conference that Wicked Data Germany is co-organizing with Wicked Data Taiwan. So if you have not seen anything about Wicked DataCon, just go online and just type Wicked DataCon 2023, you will see more information to learn more about Wicked Data. As of January this year, there were more than 100 million data items. And there are about 23,000 active editors with more than one edit per month on Wicked Data. And it is arguably the most edited Wicked Media project. Why do I put these numbers here? Because I believe that some of you here from different organizations and institutions, and you have also have experiences in dealing with data. And one of the most critical thing about data is whether the data can be up capped and can be updated frequently. And if you try to update your database within your own means, you find that it's not sustainable. And on Wicked Data, we have a very active community, voluntary community who has been doing edits and updating the data on Wicked Data every month. So this heat graph here is very interesting. And I think it would actually serves as the foundation of why I'm doing what I'm doing here. The heat graph here shows the number, the brighter it is shows the origin of where the data comes from in the world. So as you can see in Europe and North America is very, very bright and other parts of the world, it is not so bright. So when it's not so bright, what this means is that the items of Wicked Data are not in terms of numbers, it's just not reflected there. And what this means is that more majority of the 100 plus million data items come from the Europe and North America. So therefore, we have to do a better job in trying to have more of our presence on Wicked Data so that those projects that are reusing Wicked Data's data will be able to see and understand the kind of projects that we have. So our goal, said Wicked Data, is to give more people more access to more knowledge. It's a very important goal. So we are, I was aggressive but we're actively trying to reach out to different organizations, institutions about Wicked Data. And it's also a free and open knowledge base that can be read and edited by both machines and humans and this is important because we do not want to just build a product that is accessible only for humans or only for machines but actually for both humans and machines. And we also want to provide support inside and outside the Wicked Data projects. We have been working with different institutions as well to provide support for them, not only capacity-wise but also fund some of the projects. We also want to provide free, usable repository and a hub for a link open data. And this underpins a lot of why we do what we do here in Wicked Data, Boiseland. So this is an example of a use of Wicked Data on Wicked Media projects, for example Wikipedia. That's one of our friends we are having this conversation earlier on. I just put Lee Kuan Yew here because we're in Singapore. We could also use other examples as well. So the info box that you see here is actually from Wicked Data. And an example of a Wicked Data item you see every Wicked Data item has an item label. In this case is Lee Kuan Yew and every item label has its own unique identifier, a queue number that is assigned to it with its own unique identifier. And we have gone through this little tutorial earlier on this morning that there's an item description and you also can create some alias and it's a term box that shows you the different languages where the item can be described or can be shown. So we have learned earlier on and I should not dwell too deep but just a quick run through that the items that are structured on Wicked Data takes the form of a triple structure meaning to say it has item property and a corresponding value to it. So how the items are being modded is according to this structure. I put a durian there. I mean, for those of you who do not like durian, you should try durian when you're in Singapore. Yeah, one of my favorite food. So for example, we see here Lee Kuan Yew, right? Has a property instance of and the corresponding value of the instance is a human. This is important because Lee Kuan Yew is not a book. Lee Kuan Yew is not a river. Lee Kuan Yew is a human for this particular item and so therefore we tag attribute human to the instance of and not a durian for sure, yeah. So it's not only instance of, you can also add different properties as well like spouse and then there's a corresponding value of the name of the spouse and you can also add certain qualifiers to qualify the value that you have selected, right? When was the person a spouse and when did this relationship ended, right? And you can also put a reference to it as well. So having all these 100 million items on Wikidata, as you could see from Lee Kuan Yew's example with a Q number of Q131248 and you also saw earlier on that Lee Kuan Yew has a spouse by the name of Koa Gyochoo with a Q number of 1076140 and et cetera, et cetera. So what this means is that every items on Wikidata can therefore be linked and interconnected to each other in this way and this is how in a way this different interesting knowledge graph is being developed before your very eyes. I'm just a very quick one. So Wikidata, we have learned over the past, I think 10, 15 minutes or so is made up of items. For example, Lee Kuan Yew and I've talked to some of our lovely friends earlier on, whichever local community you are from and if you know of some notable people, sportsmen, celebrity or even some author, some writer, some books, some reverse that has not found Wikidata. So you can also create these items. I will give you a quick run through of how you can easily create an item, okay? And every item has a random and unique Q number. For example, Lee Kuan Yew has been assigned a Q131248 and a triple structure is used to model the data on Wikidata, namely item property and value. Now this is important because when you are creating an item or editing an item on Wikidata, you have to think in this way, right? So additional details can also be added to the statements through qualifier and references. Yeah, it's also therefore important to know that items are there for link to each other, as well as different places in other databases and catalogs, hence the name link data. Big web, link open data, right? So editing Wikidata. So there's two ways we can edit items on Wikidata. You can do it manually, right? Or you can do it not manually. So you can create a new item or you can edit an existing item. I'm not sure if it's, let me see this time, okay? Maybe not, but I just run through this very quickly. Instead of doing a demo, I just run through the slides but we can do a quick demo later on. You know, just sit down in front of all of you. I can show you how to create and how to link, how to edit. So if you go to Wikidata homepage, you will see this beautiful site. I mean, for people who are familiar with the Wikipages, we think it's beautiful, but for most of my friends, who are not familiar, they're like, wow, this one is very, very different from the usual websites that they go to. So on the left, you can see a create a new item. So if you click on that, then you will be asked to say, okay, what is the labor that you want to create? What's the description? And then what's the aliases? And then once that's done, you can just click the blue button create and the new item will be created, right? Alternatively, you can also do it in a sandbox, right? A sandbox is like a safe space for you to make changes or to test around certain things without actually publishing what you're doing. So apart from manually creating an item or making edits on an item, you can also do automated edits as well. We have different tools on the Wikidata but the more commonly used ones are quick statements to open refine. I think we have earlier on heard from one of our friends who shared about open refine for data reconciliation as well as, I would say mass import, but yeah, in a way for you to upload a list of data into Wikidata effectively instead of just creating item by item, right? There are also other tools as well that you can use on Wikidata. Terminator, nice name, makes a match, cradle, Wikidata fist. Most of these tools were actually created and developed by members of the community and it is not developed by Wikimedia Deutschland, right? So we actually work very, very closely with the community members to come together to understand how we can build a stronger and better ecosystem for Wikidata. So having more than 100 million items of Wikidata, what is the point if you're not able to query the items and data on Wikidata? Just now, Asaf has done a very fantastic a session of Query and Wikidata and I absolutely love it, I learn a lot from him but essentially we know that there's two ways to query Wikidata. The first is through the Wikidata query service where you will need some basic sparkle language. Just copy and paste and make some changes, explore if you have got issues or questions. You want to do some advanced querying, there's always various tutorial guides, videos available on the internet but otherwise send me an email, I can point you to certain resources, or we can also run a query, sparkle query workshop, one of the sessions in the future, right? So if you do not have any query, sparkle query language background, no fear, there's also the Wikidata query builder where as you can see the interface over there, it looks fairly easy and straightforward to navigate. You just type in whatever properties, items and then you will be able to run the query, sorry, just properties, yeah, some of the inputs that you can have. So these are just some of the example queries that you can actually do with a Wikidata, statues measuring more than 30 meters in height all over the world. You can also display them in different ways, in different visualization, different ways to visualize them. You can do a timeline, you can do a map, you can do a bubble graph, just to give you an idea of how the different data can be represented on Wikidata's query service. So if you are interested in querying Wikidata, there's, I just put two courses here, the first was this morning session and the next one is actually 16th of August, 1115, room 309, the session name is called First Steps with Sparkle. So you can feel free to join this particular session if you are very interested to learn more about how to query a Wikidata with Sparkle. Now comes the interesting part. Yes, I see your hand being raised up. This is exactly what I'm gonna talk about in the next slides, ta-da, you know, Wikidata partnerships, so very good question. Let me just pause that and see if I answer your question. Excuse me, excuse me. So, yes. English Wikipedia or taken from Chinese Wikipedia, as simple as that, in which it cannot be, I mean, we cannot verify, I mean, where this thing comes from because if they say, they take it from English Wikipedia and then someone edits in that English Wikipedia, then we cannot trace back, I mean, from where that statement was taken from. Secondly, just now you were talking about Lee Kuan Yew. Of course, Lee Kuan Yew is like a single entity. It has its name Hyde Alma Mater, his favorite food, his spouse name, his previous job discretion, Prime Minister, Minister. It's so easy, but how about going to more outline kind of like topic or in Wikidata, we call it like high importance. Let's say like for Singapore, it's the article of culture of Singapore, crime of Singapore, history of Singapore. I don't see there is enough statement in the Wikidata of that particular article. I mean, let's say like culture of Singapore, it's just probably like subclass of sociology of Singapore or country Singapore facet of culture. That's all, only like a couple of statement. I mean, first of all, that one is a really high importance, high importance level of article in any Wikiproject. Let's say like culture of Singapore, history of Singapore, but in Wikidata itself, there are only like three, four statement. I mean, so what's the point? I mean, I mean, what to extract from that? Okay, I mean, to answer your observation, right? It's not exactly a question per se. For someone to enrich the data on Singapore, you will realize that we need a more stronger Wikimedia Singapore community, which unfortunately in Singapore, we do not have a Wikimedia Singapore community. There's only a handful of us, right? I think five people that we know of who are Singaporeans and active on the Wikiprojects. And most of them, or most of us are actually on Wikipedia, right, and less on Wikidata. And this is not something that's unique to Singapore, but actually also unique to this part of the world, the ECEAP region, as well as in Africa and even Latin America as well, where Wikipedia is a more well-known project and the Wikimedia community is actually more vibrant. And not many members of the Wikicommunity really hears about Wikidata. And that's this, I don't know, you can call it a psychological barrier about trying to get involved in Wikidata. But therein lies that little conundrum that we are facing, right? So if the community is focusing a lot on Wikipedia and then English, for example, in Singapore, but the same article is also in different languages, and if one wants to make a change on this particular information on this article, that means this community member has to make a change on the different languages. But the way to bypass this is actually Wikidata. So that's why we are here trying, we from Wikimedia Germany, as well as the Wikidata community says, hey, you know, there's such a big Wikidata and we want to grow the community to raise awareness so that more members of the Wikicommunity will hear about Wikidata, not only Wikipedia, Wikimedia Commons, but also where the essence of it all, the importance of Wikidata in the whole link open data web, right? So you just need to make sure that we have a very robust, I would say make sure, but we try to have a robust, good quality data of Wikidata. And then it would actually have very tremendous value for all the different projects. So yes, Jackie. Wiki platform in essence is a collaborative tool. So Wikidata is trying to communicate and using a structural data called RDF. It is based on RDF, but it's not pure RDF. So it is trying to convey the triple concept into the community. And to your question, is that some items only have two statements or three statements, a very bare minimum because oftentimes they were generated by bot. And all they have is they're just want to get in there and occupy a space. Now case in point was I was watching a TV drama that was talking about the antiquity of Chinese cultural civilization. And came to, they're talking about the dance, that called Dunhuang Dance. That is all the school in Beijing, dancing school in Beijing basically created this particular dance based on the mural on Dunhuang caves. So I was surprised that I find an entity in the system and it was generated in 2013. And for 10 years, there's only three statements. And because I was, you know, I watched this series and then there's that two episode describing this dance. Then I went on to research because I was just interested. Then I just beef it up into, you know, maybe 10 or 20 statements. So the moral of this is that don't get discouraged if you have come across an item and you just think there's only two, three statement. What's the point? Well, you never know because someone else may come along behind you and beef it up. And the next thing you know, they got ingested, retrieved and then more people come behind you and add the statement. I think the goal here for wiki environment is that get the word out and get the interest infused in people and so they can pick up the ball and that's run together. Thank you. Jackie is from Spinksonian libraries. So we have been working with Spinksonian for a long time whether wiki data, wiki base or also other wiki media projects as well. So I mean, as an institution. So we understand, thank you for the question observation and thank you Jackie for elaborating because it really touches us because we really want to raise awareness about wiki data and to get a community to be more active on it, right? Given the significance of the role that wiki data is playing in the link open data web. Are you okay with that? Yeah? Yeah. I mean, after this session, I'll be around. Feel free to come to me. We can have more conversation. I'm very, very happy to do that. So wiki data partnerships. So I just want to run through very quickly about this. Who are the data partners for wiki data, right? Essentially, it's organizations, yeah? We have colleagues who are working with the community and they are the community communication managers. And for me as a partner manager, we are really reaching out to different organizations and institutions all over the world about link open data, wiki data, wiki base. Yeah. See you later, Jackie. So in what way do organizations interact with wiki data? I just list down here in two main ways. The first is to reuse, right? To reuse the data or wiki data, right? For the different projects that they have. The projects can be small, individual. I'm a university student. I do a project or it can be a large company like Google, Amazon, Apple, you know, for their whatever commercial projects, yeah? This is one way where institutions and organizations are actually interacting with wiki data. The second way is actually contributing, right? Contributing in three big ways. The first is to support us in finding mismatches on wiki data versus the database. For example, if in the library's database, the birthday of Lee Kuan Yew is, you know, 2005, obviously it's wrong. And then on wiki data, it is actually, you know, 19-something. Then there's a discrepancy. And what is useful is that, you know, they can actually surface this mismatch that they have found and then we can make some changes to it and to correct the data. Or they can even contribute data, right? Let's say, for example, in different, some museums and archives in the rural area of India, which is far, far away, and they have an open collection that has a very good, wonderful collection of the indigenous culture. But it's not shared with the world, right? They have a museum that people pay tickets to walk in and nobody really have, the world doesn't have much access to it. But if they are able to open up and contribute, they are open data to wiki data or to be connected link open data web, then more people in the world, more institutions, more organizations will be able to have access and learn more about, you know, the local indigenous culture, right? And also, of course, there are many, many organizations in the process of wiki data. They realized that there's a lack of some tools and we want to build certain tools for wiki data. And so they built tools for wiki data. So who are the wiki data re-users? You know, we have wiki data editors, the 21,000 per month, you know, wiki media projects, have you seen earlier on wikipedia, wiki media commons, you know, they reuse wiki data as data, companies, organizations and communities, such as OpenStreetMap, big OpenStreetMap community, and they're using data for wiki data, MapKata, GovDirectory, Amazon, you have mentioned Google, Microsoft, IBM, Orange, Telco from France. I wrote them down here because I know for a fact that they have been different, they have different projects working on wiki data and also public institutions, government agencies, government projects and even a glam institutions, libraries, galleries, archives, museums as well as we have seen earlier on from Spiffsonian, they have been reusing wiki data as data and working with wiki data for a long time. We have newspapers and data journalists, I've interacted with different news agencies as well, they have been reusing wiki data as data in the kind of media report that they put up, you know, like for example, something happens in this, that part of the, in some parts of the world and they need to know, you know, what is the capital city of the country, for example, and all this data can actually come from wiki data. So we have got scientists and researchers, you know, like Jay Storlabs opened up browser, for example, they have been reusing wiki data as data and of course, there's many, many, many more and I've put a link here to last year's conference event that we have organized called Data Reuse Days. So at Data Reuse Days, we have a lot of Data Reusers companies and organizations that gave a presentation on how they have been reusing wiki data as data for the different projects. So the link is here. I will share the presentation deck with everyone and there's so many, so many more organizations and companies that we have not known of yet because there's no need to tell us if you are using wiki data's data for your project, right? And there's no way we will know. What we do know and how we know is when some of these companies and organizations, they run to certain issues and they write to us, write to me and they say, hey, you know, we've got some questions and issues, how do we solve this? And that's when, ah, we realized it. So you are using wiki data's data, right? So that's how we know that. If you're interested in Data Reuse and the topic of the Reusers, I've put in two sessions here. One is the Data Reuse and Reuse session and the other one is on 19 of August, is Data Partnerships and Future of League Open Data that is taking place on 19 of August. So have I answered some of the questions on, you know, is Google using us? Yeah? I mean, as to how they are using us, how which of these companies are using us, we may not know for a fact, clearly how they are using. But one of those examples is like, you know, Amazon Alexa, you know, you ask some questions and the answers that they gave, it's being put from wiki data or Microsoft Bing, for example, you know, or even the Microsoft Maps, they are also using wiki data's data and yeah, many, many other, you know, exclusive projects that they have, you know, that we have unheard of, yeah. Yes, you have a question there? Ah, okay. So this might be related to the privacy of wiki data. A few months ago, I was editing some articles about Indonesian legislative candidates and I found out that someone in wiki data has made all the item pages for the Indonesian legislative candidates and what's interesting is in the data page, they put the location of the legislative candidate and what, and more interesting is that they infer the location of the legislative candidates based on the identity number that the editor has obtained through the dataset. They've, apparently they've got the data to go GitHub and that they, what do you say? Use the GitHub file for the wiki data. Is this kind of thing acceptable in wiki data and should we remove it from the data or should we just keep it because the location is not too, it's still broad but it used the identity number. That's my question. Wow, I mean, I mean, it doesn't sound very ethical, right? To expose the location using the identity number of anybody else in this world and putting on wiki data. So I think if we work based on common sense and pure, you know, ethics value, the value of ethics that we have as a community, if we see this, I think we should just remove it, yeah? Yeah, I think this is, I mean, whether you are a politician, celebrity or you're just an average person, I don't think we want our identity number or location to be on open platform for the world to see or to use. So yeah, but good, thank you for raising up and good work in finding it out because this is what the community is for, right? We are here to check and balance vandalism across different wiki projects, they are always there, but we believe in the power of community, yeah? Yeah, so that kind of took me longer than I have a plan for, but we had about 45 minutes or so of discussing what wiki data is, what wiki data partnerships is about. I just want to do a quick check-in with everyone here. Now you have understood a bit deeper about wiki data's role in the whole link open data web. You have also understood that, you know, there can be 100 million items, but if nobody's reusing these items, what's the point of having 100 million items, right? You also learned and understood that, hey, there are many, many big corporations, you know, even projects out there that are using wiki data's data. That means that your contribution or the community's contribution of wiki data is actually, has a value, right? Someone is using your data that we have put in together. But as a partner manager myself, I would like to check in with the community, not only yourself, but also with a wider audience next time, is that should we engage and attract more organizational users to wiki data, yeah? This is one of the questions that I want to ask to think about just if you raise your hands and shout or something, you know, this is nothing formal, I'm not going to do a survey, yeah? Second question I want us to think about is, you know, should organizations contribute more to wiki data, right? Because there's always this contention of, okay, you know, there are big companies who use the data of wiki data because it's free, open source, but they use it commercially for their own projects. They rip the people off, you know, for whatever. We can say yes or no, doesn't matter. But should they contribute back to wiki data, you know? If so, how, right? Because some of the biggest issues and challenges that we have faced where we talk to big companies of corporations is, you know, wow, I mean, it's open source, right? It's there, so we just use it, anybody can use it, you know? And then what do you want us to do? You want us to give you $10 million in exchange for that? So yeah, that's this little kind of struggle. So what ways do you think, you know, as a community ourselves here, do we like for these re-users to contribute to wiki data? And, you know, what do you think organizational users are concerned with when they use wiki data? Because when we ask questions like this, it will help us to reflect a bit more about what kind of data we want to put on wiki data, right? And how would this data, even the quality of data would enable different projects to rip the benefits of the knowledge that we have put inside there, yeah? So I don't know, just, you know, if you have any comments or feelings, you know, just feel free to give a shout out, you know? Otherwise, I'll be very happy to have this conversation offline if you're a bit shy. I understand that, not a problem. Yeah, I'm aware that we have friends from the Philippines community here, right? Yay! And Prim is from Cambodia, right? Yeah, and Indonesia, right? And where else are y'all represented if I may just go by the room? In India? Okay, nice to meet you. Ghana, oh, Ghana, yes, nice to meet you too. In India, and my friend over the corner. Philippines, oh, that's wonderful. Philippines, and gentlemen at the back. Malaysia, oh, yes, this is wonderful. I mean, I'm a Singaporean myself, right? So I think we are all very neighborliness. I can just give you some examples of the projects that I'm currently working on. As my friends from Indonesia, you may or may not know that Wikimedia Germany has been over the past one year putting a lot of funds into Wikimedia Indonesia to develop the technical capacity of lexims, lexical graphical data on Wikidata. If you're not aware of it, yeah, please be aware of it. We are very dedicated into raising the awareness of Wikidata in this region, yeah? And I've mentioned earlier on that we are working with Wikimedia Taiwan as well, this year for WikidataCon and Ghana. I'm currently working on a project with the Takabani user group, right? To work with the Ghana parliament, right? To open up the data of Ghana parliamentarians since independence until today. Yeah, so in India, we have a local colleague who's in India from the Bodhi, I'm not sure if you've heard of him Bodhi, yeah? So yeah, and he's also supporting us in reaching out to different institutions as well about Wikidata, Wikibase, and also we are developing a capacity building as well. Yeah, so Cambodia, I'm also now in conversation with Prim, right? Yeah, Prim is from a cultural institution, am I right to say that? Is it culture? Is it right to ask a cultural institution as well? And so Malaysia is one that I want to really talk a bit closer to. Are you the only person from Malaysia? Wow, okay. And coming in Singapore and Wikimedia this time, are you the only one? 20 in Singapore, wow, fantastic. So we should really have a conversation sometime, yeah. So we as a Wikimedia Deutschland, we are actively trying to work closer with the different communities to raise awareness about link open data, Wikidata as well as Wikibase and to develop the capacity for this as well, yeah. So as you can see, Glam institutions are some of the biggest users and re-users of Wikidata. And we really want to, you know, we need your support in helping us to reach out to the different Glam institutions as well to see how they can share their data on Wikidata or the Wikimedia projects, you know, so that more parts of the world can benefit from it and we can, where's the hit map? We can have, you know, brighter sports, right? In our parts of the world, yeah? And not just dominated by, you know, North American Europe, the data of Wikidata, yeah. Anybody has any questions or issues that you want to discuss or follow up with? Yes? So I think I just want to answer the question in the slide that you've presented, like in Indonesia we also have really a lot of Wikidata competition because one of the biggest question that people ask in Indonesia like other Wikimedia projects is how do we get paid? We work, we make the data, and so what's the return for us? So a lot of Indonesians doesn't understand it. So it's really hard to attract people from Indonesia to edit Wikidata. And so the way to attract people is to do competitions, do rewards, give rewards to people who had work. So yeah, it's really not a sustainable way to attract editors but so far it's going good. We've had increases from Wikimedia editors interested in joining Wikidata as well as Wikimedia Indonesia. Well, this is really innovative idea of having competitions. And do you give rewards, one of the three rewards for the competition? Do you give prizes of like, 100 million rupiah or something? I think it's more of a certificate. Yeah, okay, that's nice. And yeah, the flies are limited. I've never seen the ones where they give money but probably more of like stuff such as books or immunizations or mobile data packets. Like last time I've joined one and I was given like 100,000 rupiahs of mobile data to use. There you go, see? So it's a very innovative and creative way to try to engage the community. And this is one of the common issues that we have been facing as well. For myself, it's really talking to the different government officials, right? It was like, oh, you know, why should we do that? Oh, you know, why do we want to do that? What's the benefits? So, but one of the ways that we managed to overcome and achieve some of these challenges is if let's say, for example, a museum in Malaysia, right? Some parts of Malaysia, Sabah, for example, right? KK in Sabah is they've got indigenous culture and we want to actually encourage them to open up their archives, you know, if assuming that they're all open data and to share the legal open data web and we need some local support, we can always support them in applying for grants from other foundation or even apply for funds and grants from other international organizations that will help to meet this course. We have achieved this in one project in Singapore. It's called the Community Archives in Singapore from my community. We've supported one from Nigeria as well. In fact, the Nigerian Libraries project, we are ongoing now to develop the capacity for WikiData in order to upload bibliographic data onto WikiBase and WikiData and also the Ghana Parliament Project as well. So it's also funded by the Wikimedia Foundation and Wikimedia Deutschland as a technical supporter. We are actually supporting them in getting the grants. So these are some of the ways where we can help to address the lack of financial resource issues and questions. So I think in Philippines, it's going to be quite exciting and yeah, I mean, I look forward to work with the friends from Philippines as well. Any questions, ideas you want to share? Yes, no? We have about five more minutes. Otherwise, no, I can always conclude early on, sorry, conclude early and then we can all have a cup of coffee and talk over tea break. All right, so if there's no further questions or ideas, thank you all for the attention and thank you for sitting through this. My name is Alan once again and I'm sorry, my email is at the front. Please reach out to me. Let me show again my email and I would love to keep in touch with everyone if you have whatever ideas or if you have other opportunities or if you think that there's several ways we can work closer together. Oh, by the way, Wikimedia Deutschland is the largest Wikimedia chapter outside of Wikimedia Foundation. And we have about 150 people on Wikimedia Deutschland and Wikimdata is actually, we are managing and running Wikidata as well as Wikibase and of course German Wikipedia as well. So, yeah, thank you, thank you.