Wikidata for (Data) Journalists
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Giesemann, Elisabeth
Data journalists work similarly to scientists: they formulate (research) hypotheses, analyze data and sometimes even collect data themselves. For this, open data ressources are crucial. They can can reveal unfamiliar correlations, lead to new questions and stories. Wikidata is the largest linked open data ressource on the internet. But what exactly is stored in it and how can journalists use it for their work? We will have a look at the way Wikidata works and how it can be used a.k.a. queried for stories and research.
 So our agenda for today is that we will have a look on key points of data journalism. We will quickly explain what wiki data is, what tools you can use inside of wiki data for data visualization, what other third party tools are there for your research, then we have a look at critical research done with wiki data, and finally we have a critical look on the data of wiki data itself. Key points of data journalism are that you want to interview a data set. So you want to find connections, correlations and causalities behind the data. Also you want to visualize the data in a compelling way. And you want to write your own story. You want to find a new spin and a new look at the facts. And all of these things you can do with wiki data. At wiki media Deutschland we want to support evidence based reporting. That's why we want to support you in using wiki data. Also data journalism helps you to tailor your story to the users or your readers. Data journalism helps you to create visual storytelling instead of walls of text. And this again helps you to convey facts faster and way more easy and that makes your story way more inclusive. So how do you get to a story with wiki data? You want to find and recognize patterns in a data set. You can search for geographical data. You can search for similarities and differences in the data. And you can also search for missing data. Because that also exists in wiki data. You can visualize your findings with the tools that you find in the wiki data query service. And what's most important is you can connect to the wiki data community and find people who are working on a similar subject or have a similar research question to the one that you have. So I included this visualization to show you that data is only the beginning of your story and the path that you will take. You want you to use the data in wiki data to create a compelling story and therefore contribute value and your idea about what's in the data. Because data is a lot but it's not everything. As we've seen in the last months, many people aren't convinced by facts. Also there's a lack of time and there's a lack of data literacy in our society. It's not always easy to understand the complexity of historical events and developments, to understand the complexity of medical data or demographic changes. So it is important to have a storytelling aspect to your data, have good visualizations and an easy to understand approach to convey the significance of your data and your story. And finally, it is important to remain transparent and clear about the use and analysis of the data. So what is wiki data? Wiki data is a free linked database that can be read and edited by both humans and machines. So it is a database of linked open data. That means that the data doesn't just sit there in tables, it can be connected and combined with other data, find on wiki data. As such, it is a realization of the semantic web as dreamed by Tim Berners-Lee and also wiki data won a prize for its realization of the semantic web. We just celebrated wiki data's 8th birthday. It currently holds 90 million items and has 44,000 active users and contributors, which makes it the most edited wiki media project. It was initially used to or thought of to support the projects of the other projects of the wiki media ecosystem and seen as a central storage for the structured data of the sister projects like wiki voyage, wiki source and the most famous wiki media project wiki pedia. But it also has another function, which means which is to provide free and open data to the internet. And that became really huge. As already said, we now have more than 90 million data items on wiki data. A colleague of mine created this map and you can see here the geolocation data that is in wiki data. And we are very proud that it's distributed all over the world. But we also take it with a grain of salt because as you can see, it's very bright in Europe and on the east and west coast of the US, but there are very dark spots where we can't record the knowledge in the same way as we do in our western societies. And that brings us to the question of what is knowledge equity and how can we actually best serve everybody in our global society. So how does it work? Wiki data holds items which are real things or concepts in the real world like Berlin, Barack Obama, helium, and these items are identified with an ID, the QID. So Q76 or Q... I can't read the number now. So these items have labels, descriptions, aliases, and side links. Labels, that means it's described in all of the languages that wiki data holds currently. Those are around 300. Descriptions are forms to describe what the item holds. And aliases sometimes, one item has several names, etc., etc. An item also has properties. Those are used to label the data like a person is born somewhere, its date of birth or death or the location of a specific building. Statements hold information in properties. So P47 shares the border with another country or the population. Statements also have qualifiers to expand the information and then also they have references, which is very important because for scientific research you want to have those references. So here we see again our item, Berlin, Q64. The property is the population of 3.7 million. So what's new about research with wiki data is that you can ask your own questions. Before you would go to a library and some librarians are awesome, but they would give you books with specific facts and you would consume them and try to use them for your research. At wiki data you can ask very specific questions that nobody else came up with before. So for your research, you want to do your own wiki data queries. That's what we have the wiki data query service for. The good news is that you don't have to learn Python or R or become a data scientist, but you want to learn a bit of sparkle. We included a few resources here in this presentation and there's also going to be a talk given by my colleague Lukas on the 29th on how to query wiki data with sparkle. We also have a guided tour on wiki data on our website, which I can recommend. So as said, once you query your data, you can visualize your results for more compelling storytelling. And there are several ways of doing this and I'm going to show you some of this just to give you an idea. You could, for instance, ask the query service to show you airports that are named after a person and color code them according to their gender. Gender of the person, not the airport, obviously. You can ask the query service, show me everything connected to the item Berlin. You can ask it to show you the population of the countries that are bordering Germany and how it developed. You can also ask the query service to show you the most common cause of death among noble people. Or here it shows you an historical overview of space probes. For all of the children and grandchildren of Genghis Khan. So we had a look on the visualizations inside of wiki data's query service, but there are also tools that use wiki data's data for their own visualizations and I'm going to show you some of them now. So here is histropedia, which makes time beams of historical events using data from wiki data. This is en van der. Basically it lets you create your own private library and then uses the data from wiki data to describe the publications. Here is ask me anything that's done by different researchers in Europe and it lets you pose questions in natural language to wiki data. So you don't have to use the query service. That's a way to use wiki data that's also used by a lot of voice assistants like Siri and Alexa. And here you have skaldia, which is basically a platform for scientific publications that are published under open access and collected and it can answer you questions like who published, what paper, with whom, who and when, or who wrote the first paper on COVID, when was it published, etc. And here we have some of all paintings. Basically it's a database that creates all of the paintings in the world and lists their metadata so you can combine it in your own specific way. So I showed you a couple of examples what you could do and I want to hint at other researchers who did great stuff with wiki data and used it for very cool storytelling. If my slides work. Okay. Here we go. So women's representation and voice and media coverage of the coronavirus crisis. That's a study done by a researcher called Laura Jones regarding the representation of female experts within the coverage of coronavirus. It uses evaluations of wikipedia and wiki data to show how much representation was there of female experts. And as we see it's not a lot. Finally there's another great example I want to tell you about. It's the project called enslave.org. It's a linked open data platform based on wiki base which is the software behind wiki data and it basically shows or it collects and connects data related to the transatlantic slave trade. So people who suffered under the slave trade and the records that were done by the people active in this slave trade, those data is collected. It has been collected in several databases and enslaved, built one large database to connect them and rebuild the stories. Which I think it's a really great idea to or a really great way to humanize people who have been dehumanized with data. Like you can see here, they collect data from newspapers and from the slave holders to recount the story of individuals. So finally I also want to talk to you about one thing at wiki data that is always on our minds which is that wiki data is not perfect. I highly recommend the talk by Ozkeys, question in wiki data in which it is explained that all classification systems are inherently dangerous. Wiki data is a large encyclopedic classification system which makes choices, ethical and political choices about what is notable, about how to categorize information. And these choices, they reduce complexity and they reduce also specific forms of history, like oral history. This reduction has consequences. We know wiki data is used by many programs, apps, voice assistants and what and how we store information and wiki data really matters. So we ask ourselves what is encyclopedic knowledge and how can we organize it in a more inclusive way. Encyclopedic knowledge is a Western concept and we can and must do better than just use our own Western view to organize the world. But then also the wiki principle applies, we have a huge community behind wiki data that helps us to make these decisions and you can also become a part of this by researching wiki data, using it for your work and also contributing your research. So once again I want to tell you, you can use wiki data as a tool for your storytelling. Wiki data can help you find connections between data, wiki data can help you build visualization in its query service. You can ask questions about historical data, correlations more critically than you could before. But there are also downsides to wiki data because it is an encyclopedic way of organizing Western knowledge. So this was only a start. I'm looking forward to our Q&A session now and if you have further questions, concerns or have ideas, you can contact me and my colleagues and you can also contact me individually. Thank you. Hello and welcome to Elisabeth. Thank you very much for your interesting talk. That was a very great introduction. Hi, thanks for having me. I'm happy that I was able to talk a bit about wiki data and how you could do storytelling with it. I wanted to add that obviously you can ask me questions now, but also I want to hint at the great introduction of wiki data that one of my colleagues gave yesterday, two of my colleagues, which is already online and tomorrow there will be a query service workshop where you can learn a bit more in depth how to query wiki data. Yeah, that's a very good hint. There's actually two questions in the chat right now. The first one is, are your slides going to be published because people are interested in your links to the tutorials, obviously? Yes. That was, I asked before, I think the talk will be published and the slides. Is there a wiki packer board where I can put it otherwise? I can also put a link on our Twitter account, wikimedia.deutschland. I think Twitter for now would probably be the best idea actually to check on the wiki packer board, but we will let you know where you can find everything. I put it on wikimedia.deutschland.twitter. It's at WMDE, I think. We will also read it obviously, you will find it, I promise. There's another question, what resources would you recommend for self-studying the writing of queries for query wiki.wiki.org? I put some links in the slides. There is, yeah, we have like a few tutorials on wiki data. I was also a couple of months ago a very nice and very easy tutorial published by wikimedia.israel. We didn't do it, but I can recommend it. It's a very low-key introduction to your first queries. We will also publish that somehow. I have a question for you as well. You mentioned that wiki data is like a great way for meeting other people that are working on similar topics. Is there some kind of greater community of journalists using wiki data? So far, the community is mostly research-based. That's also why we wanted to reach out here. I would recommend getting in touch with the community on there regarding the research topics that you have. You can also get in touch with us and we connect you. I have a noise in my ear, but I hope it's only me. I don't have it. It might just be you, but I feel like there might be also an echo on the stream that let's us know what people on the chat are saying. So I don't have any other questions in the chat. Since there seems to be an echo on the stream, I don't want to annoy people any further. So I would suggest for everyone who has further questions to you that you can meet in our big blue button meetup room that I will be posting in the chat right now. We will continue our program here at 2.20 with another talk about Flutter by the one with the bright. So I am saying bye for now. Thanks. Bye. Bye.
 So our agenda for today is that we will have a look on key points of data journalism.  We will quickly explain what wiki data is, what tools you can use inside of wiki data  for data visualization, what other third party tools are there for your research, then we  have a look at critical research done with wiki data, and finally we have a critical  look on the data of wiki data itself.  Key points of data journalism are that you want to interview a data set.  So you want to find connections, correlations and causalities behind the data.  Also you want to visualize the data in a compelling way.  And you want to write your own story.  You want to find a new spin and a new look at the facts.  And all of these things you can do with wiki data.  At wiki media Deutschland we want to support evidence based reporting.  That's why we want to support you in using wiki data.  Also data journalism helps you to tailor your story to the users or your readers.  Data journalism helps you to create visual storytelling instead of walls of text.  And this again helps you to convey facts faster and way more easy and that makes your story  way more inclusive.  So how do you get to a story with wiki data?  You want to find and recognize patterns in a data set.  You can search for geographical data.  You can search for similarities and differences in the data.  And you can also search for missing data.  Because that also exists in wiki data.  You can visualize your findings with the tools that you find in the wiki data query service.  And what's most important is you can connect to the wiki data community and find people  who are working on a similar subject or have a similar research question to the one that  you have.  So I included this visualization to show you that data is only the beginning of your story  and the path that you will take.  You want you to use the data in wiki data to create a compelling story and therefore  contribute value and your idea about what's in the data.  Because data is a lot but it's not everything.  As we've seen in the last months, many people aren't convinced by facts.  Also there's a lack of time and there's a lack of data literacy in our society.  It's not always easy to understand the complexity of historical events and developments, to  understand the complexity of medical data or demographic changes.  So it is important to have a storytelling aspect to your data, have good visualizations  and an easy to understand approach to convey the significance of your data and your story.  And finally, it is important to remain transparent and clear about the use and analysis of the  data.  So what is wiki data?  Wiki data is a free linked database that can be read and edited by both humans and machines.  So it is a database of linked open data.  That means that the data doesn't just sit there in tables, it can be connected and combined  with other data, find on wiki data.  As such, it is a realization of the semantic web as dreamed by Tim Berners-Lee and also  wiki data won a prize for its realization of the semantic web.  We just celebrated wiki data's 8th birthday.  It currently holds 90 million items and has 44,000 active users and contributors, which  makes it the most edited wiki media project.  It was initially used to or thought of to support the projects of the other projects  of the wiki media ecosystem and seen as a central storage for the structured data of  the sister projects like wiki voyage, wiki source and the most famous wiki media project  wiki pedia.  But it also has another function, which means which is to provide free and open data to  the internet.  And that became really huge.  As already said, we now have more than 90 million data items on wiki data.  A colleague of mine created this map and you can see here the geolocation data that is  in wiki data.  And we are very proud that it's distributed all over the world.  But we also take it with a grain of salt because as you can see, it's very bright in Europe  and on the east and west coast of the US, but there are very dark spots where we can't  record the knowledge in the same way as we do in our western societies.  And that brings us to the question of what is knowledge equity and how can we actually  best serve everybody in our global society.  So how does it work?  Wiki data holds items which are real things or concepts in the real world like Berlin,  Barack Obama, helium, and these items are identified with an ID, the QID.  So Q76 or Q...  I can't read the number now.  So these items have labels, descriptions, aliases, and side links.  Labels, that means it's described in all of the languages that wiki data holds currently.  Those are around 300.  Descriptions are forms to describe what the item holds.  And aliases sometimes, one item has several names, etc., etc.  An item also has properties.  Those are used to label the data like a person is born somewhere, its date of birth or death  or the location of a specific building.  Statements hold information in properties.  So P47 shares the border with another country or the population.  Statements also have qualifiers to expand the information and then also they have references,  which is very important because for scientific research you want to have those references.  So here we see again our item, Berlin, Q64.  The property is the population of 3.7 million.  So what's new about research with wiki data is that you can ask your own questions.  Before you would go to a library and some librarians are awesome, but they would give  you books with specific facts and you would consume them and try to use them for your  research.  At wiki data you can ask very specific questions that nobody else came up with before.  So for your research, you want to do your own wiki data queries.  That's what we have the wiki data query service for.  The good news is that you don't have to learn Python or R or become a data scientist, but  you want to learn a bit of sparkle.  We included a few resources here in this presentation and there's also going to be a talk given  by my colleague Lukas on the 29th on how to query wiki data with sparkle.  We also have a guided tour on wiki data on our website, which I can recommend.  So as said, once you query your data, you can visualize your results for more compelling  storytelling.  And there are several ways of doing this and I'm going to show you some of this just to  give you an idea.  You could, for instance, ask the query service to show you airports that are named after  a person and color code them according to their gender.  Gender of the person, not the airport, obviously.  You can ask the query service, show me everything connected to the item Berlin.  You can ask it to show you the population of the countries that are bordering Germany  and how it developed.  You can also ask the query service to show you the most common cause of death among noble  people.  Or here it shows you an historical overview of space probes.  For all of the children and grandchildren of Genghis Khan.  So we had a look on the visualizations inside of wiki data's query service, but there are  also tools that use wiki data's data for their own visualizations and I'm going to show  you some of them now.  So here is histropedia, which makes time beams of historical events using data from  wiki data.  This is en van der.  Basically it lets you create your own private library and then uses the data from wiki data  to describe the publications.  Here is ask me anything that's done by different researchers in Europe and it lets you pose  questions in natural language to wiki data.  So you don't have to use the query service.  That's a way to use wiki data that's also used by a lot of voice assistants like Siri  and Alexa.  And here you have skaldia, which is basically a platform for scientific publications that  are published under open access and collected and it can answer you questions like who published,  what paper, with whom, who and when, or who wrote the first paper on COVID, when was it  published, etc.  And here we have some of all paintings.  Basically it's a database that creates all of the paintings in the world and lists their  metadata so you can combine it in your own specific way.  So I showed you a couple of examples what you could do and I want to hint at other researchers  who did great stuff with wiki data and used it for very cool storytelling.  If my slides work.  Okay.  Here we go.  So women's representation and voice and media coverage of the coronavirus crisis.  That's a study done by a researcher called Laura Jones regarding the representation of  female experts within the coverage of coronavirus.  It uses evaluations of wikipedia and wiki data to show how much representation was there  of female experts.  And as we see it's not a lot.  Finally there's another great example I want to tell you about.  It's the project called enslave.org.  It's a linked open data platform based on wiki base which is the software behind wiki  data and it basically shows or it collects and connects data related to the transatlantic  slave trade.  So people who suffered under the slave trade and the records that were done by the people  active in this slave trade, those data is collected.  It has been collected in several databases and enslaved, built one large database to  connect them and rebuild the stories.  Which I think it's a really great idea to or a really great way to humanize people who  have been dehumanized with data.  Like you can see here, they collect data from newspapers and from the slave holders to recount  the story of individuals.  So finally I also want to talk to you about one thing at wiki data that is always on our  minds which is that wiki data is not perfect.  I highly recommend the talk by Ozkeys, question in wiki data in which it is explained that  all classification systems are inherently dangerous.  Wiki data is a large encyclopedic classification system which makes choices, ethical and political  choices about what is notable, about how to categorize information.  And these choices, they reduce complexity and they reduce also specific forms of history,  like oral history.  This reduction has consequences.  We know wiki data is used by many programs, apps, voice assistants and what and how we  store information and wiki data really matters.  So we ask ourselves what is encyclopedic knowledge and how can we organize it in a  more inclusive way.  Encyclopedic knowledge is a Western concept and we can and must do better than just use  our own Western view to organize the world.  But then also the wiki principle applies, we have a huge community behind wiki data that  helps us to make these decisions and you can also become a part of this by researching  wiki data, using it for your work and also contributing your research.  So once again I want to tell you, you can use wiki data as a tool for your storytelling.  Wiki data can help you find connections between data, wiki data can help you build visualization  in its query service.  You can ask questions about historical data, correlations more critically than you could  before.  But there are also downsides to wiki data because it is an encyclopedic way of organizing  Western knowledge.  So this was only a start.  I'm looking forward to our Q&A session now and if you have further questions, concerns  or have ideas, you can contact me and my colleagues and you can also contact me individually.  Thank you.  Hello and welcome to Elisabeth.  Thank you very much for your interesting talk.  That was a very great introduction.  Hi, thanks for having me.  I'm happy that I was able to talk a bit about wiki data and how you could do storytelling  with it.  I wanted to add that obviously you can ask me questions now, but also I want to hint  at the great introduction of wiki data that one of my colleagues gave yesterday, two of  my colleagues, which is already online and tomorrow there will be a query service workshop  where you can learn a bit more in depth how to query wiki data.  Yeah, that's a very good hint.  There's actually two questions in the chat right now.  The first one is, are your slides going to be published because people are interested  in your links to the tutorials, obviously?  Yes.  That was, I asked before, I think the talk will be published and the slides.  Is there a wiki packer board where I can put it otherwise?  I can also put a link on our Twitter account, wikimedia.deutschland.  I think Twitter for now would probably be the best idea actually to check on the wiki  packer board, but we will let you know where you can find everything.  I put it on wikimedia.deutschland.twitter.  It's at WMDE, I think.  We will also read it obviously, you will find it, I promise.  There's another question, what resources would you recommend for self-studying the writing  of queries for query wiki.wiki.org?  I put some links in the slides.  There is, yeah, we have like a few tutorials on wiki data.  I was also a couple of months ago a very nice and very easy tutorial published by wikimedia.israel.  We didn't do it, but I can recommend it.  It's a very low-key introduction to your first queries.  We will also publish that somehow.  I have a question for you as well.  You mentioned that wiki data is like a great way for meeting other people that are working  on similar topics.  Is there some kind of greater community of journalists using wiki data?  So far, the community is mostly research-based.  That's also why we wanted to reach out here.  I would recommend getting in touch with the community on there regarding the research  topics that you have.  You can also get in touch with us and we connect you.  I have a noise in my ear, but I hope it's only me.  I don't have it.  It might just be you, but I feel like there might be also an echo on the stream that let's  us know what people on the chat are saying.  So I don't have any other questions in the chat.  Since there seems to be an echo on the stream, I don't want to annoy people any further.  So I would suggest for everyone who has further questions to you that you can meet in our  big blue button meetup room that I will be posting in the chat right now.  We will continue our program here at 2.20 with another talk about Flutter by the one  with the bright.  So I am saying bye for now.  Thanks.  Bye.  Bye.
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