Collectively, the course participants contributed more than 900 total edits in more than 100 Wikipedia articles, adding almost 50,000 words and 718 references. The course not only yielded a substantial number of article edits, but it also resulted in a marked increase in the quality of articles. The work from the course raised the scores of 31 articles by at least five ORES points, a measurement Wikipedia uses to help rank the completeness of an article. The ORES score is determined by several variables, including article size, number of sections, references, and images. 

While Wikipedia encourages anyone to make edits to pages, regardless of background or experience, professional expertise – including knowledge of and access to high-quality sources – proves invaluable in enhancing and expanding the content of articles. 

“Contributing to Wikipedia aligns with a core professional goal I have: to democratize knowledge,” said participant Anne McClanan, art history professor at Portland State University. “The Wiki Education course empowered art history professors to contribute, ensuring that scholarly expertise is accessible to a wider audience, breaking down barriers to information.” 

McClanan, a Byzantine art historian, improved several articles including Byzantine silver (explore her changes inspired by a specific thesis), as well as the Wikipedia article about the Byzantine Empire. McClanan’s improvements of the Byzantine Empire article are particularly noteworthy, as the text was already considered one of the highest quality articles on Wikipedia, indicating the comprehensive and robust nature of its information and sources. Using her deep understanding of the subject area and related sources, McClanan was able to add small yet key pieces of information to the article, filling in content gaps previously unaddressed by editors. This article has been viewed 250,000 times in the past month alone and continues to be regarded as one of Wikipedia’s best articles.

Like McClanan, the other subject-area experts in the course sought to improve articles related to their own unique interests and professional backgrounds. James Clifton, Director of the Sarah Campbell Blaffer Foundation at the Museum of Fine Arts, Houston, focused on the article about Bernardo de’ Dominici, an Italian art historian and minor landscape and genre painter. James not only crafted a detailed section about one of his works, but he also improved the article lead, cleaned up a long list of works, and polished the article’s overview of his life. Take a look at the Bernado de’ Dominici article in “the visual editor” mode – this view shows Clifton’s additions (in green) and text he removed (in red), edits which enhanced the overall quality of the article. 

For Clifton, the importance and impact of Wikipedia for both scholars and the general public cannot be underestimated.

“I use Wikipedia frequently,” said Clifton. “It is the quickest path to at least superficial – and often profound – information on countless subjects.  As such, its importance as a widespread source of information is incalculable, and it behooves those who contribute to it to make it as accurate and accessible as possible. The Wikipedia editing course taught me to do that in my own small corner of the world.”

As the scale and detail of Wikipedia more than eclipse that of every encyclopedia which preceded it, Wikipedia can often feel expansive and even complete. However, as these courses demonstrate, no knowledge system is immune to content gaps and systemic bias. Wiki Education courses provide experts with support to leverage a global platform and share their knowledge, research, and passion with the world. And in the process, they make this vast source of information a little more complete for all. 

This year, our courses will bring together groups including medical professionals, political scientists, and climate change scholars (just to name a few!), creating a bridge between their professional expertise and the information accessible to everyone through Wikipedia – making a great thing even better. 

Interested in learning more about the work of this course and its reach on Wikipedia? Visit our open-access Course Dashboard, and be sure to explore our upcoming courses for subject-area experts provided by Wiki Education.

Wiki Education thanks the Samuel H. Kress Foundation for their generous support of the fall 2023 Art History Wiki Scholars course.

Course participants:

• Paul Albert, Scholar, George Mason University
• Anne McClanan, PhD, Art History Professor, Portland State University
• James Clifton, Director, Sarah Campbell Blaffer Foundation, Curator, Renaissance and Baroque Painting, Museum of Fine Arts, Houston
• Margaret Ann Zaho, PhD, Associate Professor of Art History, University of Central Florida
• Kate Dimitrova, PhD, Lecturer, Department of Art, Architecture + Art History, University of San Diego
• Maria Ketcham, Director, Research Library, Archives & Collections Information, Detroit Museum of Art
• Jessica Allison, Collections Database Manager, Detroit Museum of Art
• Maura Wilson, Department Assistant, University of San Francisco
• Elizabeth Macaulay, DPhil, Associate Professor, Graduate Center, CUNY
• Anne Betty Weinshenker, PhD, Professor Emerita of Art History, Montclair State University
• John Hagood, Librarian, National Gallery of Art
• Susanna Caroselli, PhD, Professor Emerita of Art HIstory, Messiah University
• Casey Long, Head of Research & Instruction, Agnes Scott College
• Lalaine Bangilan, PhD, Gallery Director and Adjunct Professor of Fine Arts, Misericordia University
• Zoe Kobs, Student, University of San Diego
• Daniel Maze, PhD, Associate Professor, Head of Art History, University of Iowa
• Shirley Schwarz, PhD, Professor Emerita, Assistant Teaching Professor, University of Evansville
• Lindsay Cook, PhD, Assistant Teaching Professor, Penn State University
• Joy Kearney, PhD Candidate, Royal Netherlands Military Academy
• Eelco Nagelsmit, PhD, Lecturer, Leiden University
• Emily Everhart, PhD, Assistant Professor, Chair of Liberal Arts, Art Academy of Cincinnati
• Daniella Berman, PhD, Project Manager & Researcher, The Drawing Foundation
• Christina Tatum, Instruction & Outreach Librarian, Agnes Scott College

This approach was inspired by the successful Women in Red project on Wikipedia that has generated thousands of articles about women over the past few years to help close one gender gap on Wikipedia. Similar to Women in Red, the Equity Portal pages query Wikidata to generate lists of articles from other languages that exclude English. The idea here is that since the articles exist in other languages, they already pass notability and should have some references associated with them. Our hope was to continue sharing this resource in the community, courses, and beyond to have English language Wikipedians write articles about people from these lists, increasing the available diversity and representation of content on Wikipedia. It’s been a few months, so what progress have we made?

A lot!

First, as of January 1st 2024, there have been over 3,600 page views for all of the pages associated with this project. This is a lot of eyes! If we take the ethnicity page as an example, we can see that there has been sustained interest in this page over time, with a few spikes here and there. Those spikes often reflect our efforts to promote the pages and demonstrate how they work.

Second, we have been working hard to promote these pages to the community by presenting at conferences, like Wiki Conference North America and Wikimania, announcing the resource during webinars, and promoting the resource in classes that we teach. Many of the days with higher page views correspond to a presentation or a webinar. We also published an article to the Signpost, Wikipedia’s community newspaper. Anecdotally, the feedback we received was very positive about the page’s creation and purpose. We hope the community continues to spread this resource beyond those we have already shared it with so more Wikipedians can contribute to improving the representation of these communities on Wikipedia.

Third, other editors are interacting with the pages. Continuing with the ethnicity page as an example, we can see that there have been some edits to the page. Edits can represent maintenance of a page or of a table being altered, which may mean an article has been created. Unfortunately, it is difficult to tell when an article from these lists has been created since the tables update regularly (an update can indicate a new name is added through Wikidata or removed as a result of an article being published). There are several reasons for this. Wikipedia is a large community and anyone can write an article whenever they want. They may find it from this list or they may find it and write it from a different source. Another reason is that these lists are generated from Wikidata. They are based on queries, which are where the tables come from. These queries pull in several thousands of results and there’s no simple way on Wikipedia to keep track of all of those changes.

Over a few short months with the equity portal, there have been more than three thousand visits to these pages, as well as over 1,300 page views for just one presentation about the lists. The high interest in this tool is encouraging, and we expect more community members to continue to visit these pages. Most importantly, in the long run the Equity Portal can help to make Wikipedia more representative of the world we live in.

With support from the Nielsen Foundation’s Data for Good grants program, we have been developing a free and open Wikipedia resource that encourages editors to create articles to improve representation of diverse groups and topics on Wikipedia. There are some amazing projects that are working to address this issue on Wikipedia that have been around for a few years — Women in Red, Art + Feminism, Black Lunch Table — to name a few. It’s our hope that this tool can complement the work of these projects.

For example, Women in Red, uses Wikidata, a linked data knowledge base that connects all language versions of Wikipedia, to generate lists of articles that could exist in English Wikipedia, but don’t yet. Taking a page out of their book, we are creating a resource that allows community members to do the same thing, but with a broader set of demographic variables. In addition to individuals who identify as women, we have constructed pages that list thousands of potential articles based around sexual orientation, nationality, disability status, and ethnicity.

These lists query the other language versions of Wikipedia and pull only the results that don’t have English language articles. From there, community members can select individuals and generate English language versions of the articles. Since these articles exist in other language versions of Wikipedia, the idea is they already pass notability – a major requirement for articles to exist – and have references. The article writing process will still take time, but it saves some effort not starting from scratch. Check out our resource here.

I know what you’re thinking — can this get any cooler? And the answer is yes! Wiki Education has been developing and maintaining the Dashboard for the past few years. The Dashboard allows instructors and individuals to create courses that are scoped to a set of students/Wikipedians/edit-a-thon attendees, etc. – basically any set of individuals that want to participate in whatever the course is. Another feature is the ability to frame a course around a list of articles. Using the same query from our resource, anyone using a Dashboard can scope it to one of the lists we’ve developed. The idea here is to encourage Dashboard users to select articles about underrepresented groups or individuals and write them for English Wikipedia. Follow this link for an example of an article-scoped Dashboard. Heads up — clicking the PSID list will take some time to load because it is large.

And this, my friends, is one place where Wikipedia articles come from.

To review: we’re building a tool that encourages community members to write articles to increase the visibility of diverse groups and topics on Wikipedia. We’re doing this using Wikidata, queries, a list tool called Listeria, articles scoping on the Dashboard, and the hard work of anyone taking a Dashboard course or attending an event that uses the Dashboard. Although systemic bias and underrepresentation will remain a significant problem on Wikipedia and beyond, we hope this tool can push new and old users alike to edit in a way that helps to improve representation on the platform. As the community and these tools mature, we also hope others can refine and adapt it to their specific needs. An amazing thing about pulling from Wikidata is users can narrow and expand queries to generate new lists. For example, these lists are configured to improve English Wikipedia, but in a snap they can point to other language versions.

We’re still tinkering and ironing out the wrinkles, but we hope to have this up and running soon. Get ready to make some edits.

The instant availability of knowledge on your personal devices has revolutionized how we learn about the world around us. When you ask Google about a topic or pose a question to a virtual assistant like Alexa, the answer you get will likely come from Wikidata. That makes the open data repository an essential resource that we must make sure reflects the fullness of human knowledge. Limited coverage on Wikipedia and Wikidata of historically excluded populations and notable women has not reflected their historical importance. One of the potential causes of these gaps is that the majority of Wikipedia’s editing community are white and male. Wiki Education is committed to addressing these opportunities for growth and expanding both the editing population and coverage of historically marginalized communities on Wikidata and beyond.

Currently, groups of Wikipedia editors surface content gaps on Wikipedia manually, often through online common spaces called WikiProjects. We are inspired by the massive success of Women in Red, a WikiProject focused on expanding and adding articles about women on Wikipedia. Thanks to dedicated volunteer editors, the number of biographies about women has increased from 15% of all Wikipedia biographies to 19% since October 2014. Considering that there are almost 2 million biographies today on the English Wikipedia, 4% is quite a jump. While more progress needs to be made, the project has helped add much-needed visibility and credibility to women’s accomplishments that will inspire generations of leaders.

Using Wikidata in concert with Wikipedia provides a place to build a tool that can scale this important work further. Using Women in Red as a model, our online portal will allow the Wikipedia community to use information queried from Wikidata to tackle the gaps in knowledge in an organized way. Women in Red relies heavily on Wikidata queries to generate lists of women who do not yet have Wikipedia articles. With this approach, we will scope the queries to different demographics and create new lists of articles that do not exist on Wikipedia. We will leverage our portal to provide insights into the types of courses that we offer in our Scholars & Scientists Program.

We will also add this portal to the “Finding your article” training module on our Dashboard’s library of resources for student editors participating in our Wikipedia Student Program. This tool would guide students to edit Wikipedia articles that need the greatest amount of attention. We believe that the broad community who looks to Wiki Education for tools and resources will also benefit from this portal for their own initiatives and across languages.

Wiki Education’s new transformative portal will deepen the engagement of new and current program participants by empowering them to quickly assess the topics and communities most in need of improvement and representation on Wikipedia.

At the same time, we want to acknowledge that data about the personal identity of prominent figures is extremely sensitive and personal. We want everyone to know that in order for this kind of data to exist on Wikipedia, it must have a reliable source backing up that fact. It’s our hope that this portal will help encourage better sourcing, correcting errors, and a better ability to identify inaccurate or potentially harmful data from winding up (and staying) on Wikidata and Wikipedia.

Throughout this year, I’ll be developing a working prototype of the online portal and gathering feedback from the Wikimedia community. I’ll use Wikidata to test the functionality of the portal and add demographic properties that can be selected by Wikipedia editors to identify gaps in coverage of historically marginalized communities. We’re excited to leverage this portal to improve Wikipedia’s coverage of underrepresented groups and help volunteers provide millions of readers with more equitable information.

This group started with a classic challenge on Wikidata: how do we know what we are describing (given a little data, can we guess what a thing/entity is?) and what properties do we use to describe any given thing? Phrased differently – how do we know how complete or incomplete something is? This is hard to answer for many reasons.

1. There are millions of different kinds of things in Wikidata (people, countries, organizations)
2. There are multiple ways to describe these things (how do you describe an organization?)
3. Even if you know what something is, how do you know what’s missing or what to add to it? (is this a complete description of an organization?)

Sounds tough, but I’ve got good news. Even if we know very little about an item, a little data science magic can predict a lot about what your mystery item may be.

In their paper, “Review of Knowledge Graph Embedding Models for Link Prediction on Wikidata Subsets,” this group analyzed different subsets of items on Wikidata (countries, people, bridges, and films to name a few). They ran several algorithms through these sets to sort them and make guesses as to what the items may be. They found that some worked better than others and recommend them for future use in prediction tools. This could have an impact on evaluating data quality, consistency, and item completeness, which are some essential metrics on Wikidata. So how did they do this?

Let’s take a look at those subsets they selected. From this list you can start to guess how Wikidata describes these things. Countries and bridges have locations. Humans must have a place of birth. Films almost always have a director and actors. Bridges must start somewhere and end somewhere. This set of descriptions used to describe something is known as a schema or shape (don’t think geometry – think a specific set of things used to explicitly define or describe something). Their research also takes into account these shapes and considers how these items relate to other items. Sticking with humans as the example, a specific person has a two way relationship with their parents. A date of birth would be a one way relationship. And a teacher of a class of students would be a one-to-many relationship. For the information architecture superfans, these specific relationships are called cardinality. The group analyzed item cardinality and data models among these subsets within Wikidata.

So can something as small as analyzing these basic relationships reveal that much? It turns out that this is foundational for identification and recommendation features. Adding complexity reveals more and more about data models and makes identification easier and more accurate. In their analysis, they ran fifty-four different algorithms to analyze and identify items. A major takeaway is that these different algorithms can successfully process Wikidata at this level, but selecting subsets (a set of humans, a set of countries) will likely yield better results since there is more consistency in those subsets. Subsets process faster, requiring fewer resources. The paper details their rationale for rating these different programs and they recommend a few for link prediction on Wikidata. Best of all? They share all of their findings as a set of analysis tools on a Github page for anyone to use.

Let’s return to our initial question: how do we know what we’re describing? It turns out analyzing basic relationships between sets of things can reveal a great deal about those things. Since Wikidata is machine readable, knowing about these relationships can allow for the creation of recommendation tools (like Recoin) so Wikidata community members can make better edits on Wikidata. These kinds of tools could also be used to identify erroneous information and take guesses at what an unknown item or entity may be. All of this encourages better data consistency, quality, and completeness.

As great as Wikidata is, it’s not perfect. The community regularly deals with inconsistency, missing descriptions, and data that’s misplaced, out of date, or just wrong. This kind of work from UVA is exactly what is needed to make Wikidata even better. There’s a lot of work left to do and tools like what this group produced are an important step in engaging with some towering Wikidata challenges. We hope that the Wikidata community (and others outside of it) find these tools and approaches helpful in analyzing other knowledge bases and using the results to improve the data even more.

A special thanks again to Olu, Quinton, and Antoine, and the UVA data science department for supporting this work.

Want to learn Wikidata or brush up on your skills? We have online training courses starting in January, March, and May 2023. Visit wikiedu.org/learn to learn more.

• The State of Wikidata and Cultural Heritage: 10 Years In
  • Tuesday October 4, 2022 — Watch the recording on Youtube or read our summary blog.
    
  • We’ll learn how Wikidata is (or is not) integrated into Wikipedia, how it helps an enormous cultural institution like the Smithsonian achieve its goals, and how Kelly Doyle, Andrew Lih, and their colleagues at the Smithsonian work to keep a Wikidata Initiative going. Lane Rasberry also joins us from the University of Virginia as the Wikimedian-in-Residence at the School of Data Science!

• What You Need to Know to Kickstart a Wikidata Initiative
  • Thursday Oct 13, 2022 —Watch the recording on Youtube or read our summary blog.
    
  • We’ll hear from Wikidata’s biggest fans: librarians. Namely, Joe Cera from Berkeley Law Library, Kiley Jolicouer, a Metadata Strategies Librarian at Syracuse University Libraries, and Chris Long, the Director of the Resource Description Services Team at University of Colorado Boulder Libraries. They’ll each share how they got involved with Wikidata at their respective institutions, how Wikidata projects align with libraries’ missions, and how you can start a Wikidata Initiative at your institution, too!

• Scaling and Sustaining a Wikidata Initiative
  • Thursday Oct 20, 2022 — Watch the recording on Youtube or read our summary blog.
    
  • You’ve got a vision for a Wikidata Initiative that will amplify your work and make Wikidata more equitable and more complete. You may know how to get started, but how will you keep it going? Or foster community around this work? What does your institution need to do in order to support your Wikidata work? Join Bettina Smith from Dumbarton Oaks, Stephanie Caruso from the Art Institute of Chicago, Anne Chen of Dura-Europos and Bard College, and Ian Gill from SFMOMA. Let’s dive into what makes their projects successful. Their experiences may spark ideas for you as you develop your own Wikidata Initiative.

• The Future of Data: A Community that Grows Together Stays Together
  • Tuesday Oct 25, 2022 — Watch the recording on Youtube or read our summary blog.
    
  • For our final birthday celebration, we’re looking to the future. Speakers Julian Chambliss, Kate Topham, Justin Wigard, and Hilary Thorsen are out there building Wikidata community. We want to know where they envision this work going over the next few years. What kinds of insights do they want their communities to have from their Wikidata Initiatives five years from now, and how do they approach their projects to achieve this?

We hope these talks present a nice forum for connecting you not only with knowledge, but also with other attendees who can build community around an idea or project you may have. These free conversations will happen once a week over Zoom for one hour. We’ll also record and post the sessions online for you to view in the event of a scheduling conflict.

We hope you’ll be able to join us (virtually) and hear all of the insights these community members have to share about Wikidata and their projects. I can’t think of a better way to celebrate Wikidata than to show what an impact its made in all of these fields. See you soon!

Reach out if you have any questions: will@wikiedu.org

We regularly run Wiki Scholars courses throughout the year, but the way this course came to be was especially important to us. “We are so grateful to the estate of B.B. Clark for generously supporting this Wiki Scholars course,” said Andrés Vera, Wiki Education’s Equity Outreach Coordinator, who made this connection. “Mr. Clark was a victim of the AIDS pandemic and vowed that none of his anti-gay family would receive a portion of his estate. While so many people try to erase the stories of LGBTQ+ people, in the after life, Mr. Clark is helping us preserve LGBTQ history.”

You can see one example of improving the historical record in the article about AIDS activist Reggie Williams. It is now a substantial Wikipedia biography, thanks to Wiki Scholar Dan Royles. Before the course, Reggie’s biography did mention his activism work. But after the course, the biography tells a much deeper story of his life-mission making AIDS education and services more culturally relevant for gay and bisexual men of color. You can now read about Reggie’s impact at both a local and national level. He became an adviser to the San Francisco AIDS Foundation and the San Francisco Department of Public Health, submitted a proposal to the Centers for Disease Control’s national AIDS education program, and was instrumental in starting multiple organizations for on-the-ground AIDS education in San Francisco.

In addition to Reggie William’s biography, Ruth Bleier now has a more detailed one. Wiki Scholar Jenny Lenkowski worked on it, enhancing sections about the neurophysiologist’s activism work during the era of McCarthyism. Ruth was an early explorer of how gender bias affected her field and she advocated for change. In addition to following her own personal mission to better represent a diversity of scientists on Wikipedia, Jenny — as an Associate Professor of Biological Sciences at Goucher College who teaches with Wikipedia — has new perspective to take back to her Wikipedia assignments.

“This course was a great way for me, as an instructor who has assigned Wiki Education projects in my classes, to more intentionally contribute to Wikipedia in a meaningful way myself,” Jenny told us. “I try to emphasize to my students the benefits of us contributing to Wikipedia to increase the diversity of Wikipedians and to also consider profiling scientists from underrepresented groups for their project, so this was a great opportunity for me to explore and contribute to biographies of scientists in the LGBTQ+ community. It took me quite a while to finally decide what page I would be working on, something I see some students struggle with as well. I also benefited from weekly discussions of how projects were going, something I’ll be more intentional about doing next time I assign a similar project.”

One more example comes from Wiki Scholar Sara Moore, an Associate Professor of Sociology at Salem State University, who greatly expanded Rachel Levine’s biography page. Rachel Levine made history as the first openly transgender government official to hold an office requiring a Senate confirmation, serving as assistant secretary of health since 2021. Sara added a section to Levine’s biography about her commitment to solving US health disparities, especially as they relate to LGBTQ+ youth.

“You feel empowered when you learn how to contribute meaningfully to a body of knowledge that so many people draw on,” Sara told us. “It’s also important to bolster the stories and experiences of underrepresented groups of people and their histories.” Although there is a lot more work to do regarding the preservation of LGBTQ history, we’re pleased courses like this can have such an impact on Wikipedia.

If you’d like to peruse more great work that came out of our first iteration of this course, follow this link.

Thumbnail image shows Rachel Levine and White House Press Secretary Karine Jean-Pierre holding a Pride flag in 2022. Public domain, via Wikimedia Commons.

For this project we partnered with Lane Rasberry, Wikidatan-in-Residence at the University of Virginia (UVA), who had recently received funding from the Sloan Foundation to help bring more data about academic research software to Wikidata. Collaborating on this project will allow UVA to improve data on Scholia (a platform that displays academic profiles based on what’s in Wikidata) and it will help diversify the examples Wiki Education uses in outreach and training materials.

This project has been an excellent way of elevating some urgent and important research from individuals whose work has been underrepresented on Wikidata and beyond. This is a continuation of our commitment to equity across Wikimedia projects. We recognize that continued engagement is essential to affect community-wide changes. Our hope is these examples can lead to bigger changes and create space for more critical thought and engagement about representation on Wikidata.

We set out to identify academic research software contributors from North America who represent historically marginalized communities in this space: Native Americans, women, and people of the African diaspora. Wiki Education was ideally positioned to identify potential research software developers who fit these descriptions; through our Wikipedia Student Program and Scholars & Scientists Program, we are connected to thousands of academics across the United States and Canada. A few emails and video calls later, and we were able to develop a pool of individuals who met this specific criteria. The next step was to locate sources, publications, unique identifiers, and any other data we could use to update (or create) their items on Wikidata, enhance items related, and link their work to other entities on Wikidata.

One urgent research project happening right now came from Dr. Ben Frey, a linguistics professor who is part of the Eastern Band of Cherokees. He has been conducting research to preserve and teach the Cherokee language, which has around 2,500 speakers left at the time of publication. He has been working with large datasets of Cherokee and English sentences to improve machine learning with the Cherokee language. From there his hope is more instant translation can occur, as well as other uses. Before this work, he and the research software he’s developing didn’t have items on Wikidata. Not only does he have one now, but you can see a set of his publications here.

Another project we decided to focus on is Openscapes, led by Julia Stewart Lowndes. Openscapes endeavors to mentor researchers about open data practices (check out some of their cool work here). In working on these items, we performed merges, created new items, and linked to their Github repositories, which were previously unlinked. In developing these related items, Wikidata users will be able to discover this work through queries or by visiting Wikidata itself.

We also spent time working on several other researchers’ Wikidata items, items representing their research, and linking out to Github repositories, ORCID scholarly communication records, and additional identifier data. Although we can’t explain all of the edits here, the general idea is the same: having research better represented on Wikidata allows for better analysis of this data, more re-use of this data, and the opportunity to discover new insights about this data.  As with all of our examples, we are hopeful that drawing more attention to this kind of research will inspire others to think of work that should belong on Wikidata, but isn’t there yet.

We believe that using a diverse set of examples will draw more attention to the systemic bias that pervades Wikidata and the Wikidata community. Elevating the profile of these accomplished researchers (and their research) is just the beginning of what we need to do. We will continue to update these items, related items, and research papers to ensure the most information possible is on Wikidata. We hope our course participants and other members of the Wikidata community will also consider improving representation across all of Wikidata.

To learn more about Wikidata, follow this link and explore our courses.

Before we jump into all of that, it’s important to remember that there are more than 300 different language versions of Wikipedia, all governed by their own language community. Wikidata is also its own community. This means that the rules and guidelines that all of these projects follow can differ from one another. One way they do differ is what you are and are not allowed to do with Wikidata’s data. So the resources I’ll be sharing may be activated on some versions of Wikipedia, they may not, and they may change in the near future. I will also include some resources where you can see why or why Wikidata not allowed on Wikipedia (yet).

On to the most important part of the post: you can call (in the sense of use) various values and relationships from Wikidata onto any other Wiki-page (Wikipedia and any other Wikimedia projects). This is exciting because one value, like a city’s population, can be updated in Wikidata, and with that one edit, it will cascade across all language versions of Wikipedia. This has the potential to make data consistency better across Wikipedias, and it also makes updating all Wikipedias as easy as one edit in Wikidata.

A specific example of this is the Wikidata Bridge project. The aim of the Bridge project is to use power infoboxes with data from Wikidata. Some language versions of Wikipedia, like Catalan, already have this feature turned on. In English Wikipedia, the use of this tool is not widespread yet due to concerns about data quality on Wikidata. Either way, the implications of this kind of resource will be far reaching.

There are other projects that have been leveraging Wikidata for years. The beloved WikiProject Women in Red relies on Wikidata to generate lists of women who do not yet have Wikipedia articles on English Wikipedia. Women in Red uses hundreds of Wikidata queries to generate and organize these lists of women from all the other language versions of Wikipedia. The query results are presented as tables on the Redlist index page (note: WD stands for Wikidata list) using a tool called Listeria. Listeria is a Wikidata tool that takes Wikidata query results and displays them as a table on a Wiki page. This is a powerful tool because the lists are dynamic — updated frequently if not in real time — and you can pull in customized slices of data thanks to the query service. This is one way Women in Red is able to take advantage of Wikidata’s vast dataset to advance an urgent cause on Wikipedia.

One more way Wikipedia is leveraging Wikidata is through citations. As you know, citations are central to Wikipedia articles. What you may not know is that you can import citations to Wikipedia using resource identifiers like DOIs, PMIDs, ISBNs (instead of a title, the source is represented as a unique number — this helps avoid ambiguity and confusion). Now you can also do this with Wikidata Q-ids to do the same thing. If an article exists on Wikidata, you can insert any Q-id into the “automatic” citation menu when you are editing with Wikipedia’s Visual Editor and it will generate a citation in a Wikipedia article. This is convenient, but it also comes with the added benefit of the Wikidata items being queryable. As more Wikipedia articles include citations represented in Wikidata, we will soon be able to query any number of Wikidata variables — gender gap, ethnicity, location — in the context of Wikipedia references.

We’re just scratching the surface of what Wikipedia will be able to do with Wikidata. Returning to what I described at the beginning of this post, Wikipedia will be able to call any snippet (or enormous data set) from Wikidata soon. This will have a huge impact on the community and change the nature of a lot of the workflows on Wikipedia and Wikidata. Ideally it will improve quality, representation, and how we can evaluate data on all projects. Catch a glimpse of these new features appearing by keeping an eye on what appears here. This particular page tracks any template on English Wikipedia that uses data from Wikidata. You can expect this list to grow and grow over the next few years.

It’s exciting to think of the potential of all of these new tools. To learn more about Wikidata and Wikipedia, follow this link to find more information about our Wikipedia and Wikidata courses.

This post expands on a presentation its author Will Kent, together with Rosie Stephenson-Goodknight, gave to the LD4 Wikidata Affinity Group in June 2022.