In the past year, I have worked together with GLAM staff and Wikimedia community members to ‘test’ this new technology, and explore its potential, in a series of pilot projects. What does Structured Data on Commons make possible? Which new questions and challenges appear?
In the past year, I have worked together with GLAM staff and Wikimedia community members to ‘test’ this new technology, and explore its potential, in a series of pilot projects. What does Structured Data on Commons make possible, and which new questions and challenges appear?
As the three-year grant period for building Structured Data on Commons (SDC) comes to a close with the end of 2019, I’d like to share some lists of the past two year’s worth of planning, discussion, building, testing, and releases the team has done with the Commons community.
For more than 10 years now, cultural institutions around the world have partnered with Wikimedians to make their collections more visible and to encourage re-use via Wikimedia platforms. Collaborations of this kind, GLAM-Wiki projects (with Galleries, Libraries, Archives and Museums), often use Wikipedia and Wikimedia Commons as platforms. Images of cultural collections are uploaded to Wikimedia’s media repository Wikimedia Commons and are re-used as illustrations in Wikipedia articles.
For several years, a growing number of GLAM-Wiki partnerships also work with Wikidata, the free, multilingual knowledge base of the Wikimedia ecosystem. Cultural institutions and Wikimedians upload data about cultural collections to Wikidata: it provides an accessible way to publish collections data as Linked Open Data, and makes the collection data multilingual, re-usable and discoverable across the web. Since 2019, files on Wikimedia Commons can now also be described with multilingual structured data from Wikidata. This will make the (structured) data component of GLAM-Wiki collaborations even more prominent in the future.
I work as a part of the Community Programs (GLAM) team at the Wikimedia Foundation. As part of my work, I support Wikisource, a digital library of public domain and freely licensed texts, which is an important platform for GLAM projects and knowledge exchange in many Wikimedia communities. I have been writing case studies about Wikisource, documenting pain points around it, and prioritizing them with the communities.
ISA is a new tool that makes it very easy for anyone, including absolute beginners, to add structured data descriptions in the form of captions and so-called ‘Depicts’ statements to images on Wikimedia Commons. ISA is called a ‘micro-contributions’ tool: when you use ISA, you make many very small edits to Wikimedia Commons in a playful way. We intentionally designed ISA to be multilingual and mobile-first; it has been such a hit that it received a WikidataCon 2019 Award in the Multimedia category last October. And why this name? ‘Isa’ is the chiShona language word for ‘put’ or ‘place’, but it was also chosen because it is an acronym for Information Structured Acceleration or Information Structured Additions.
With depicts statements available to make the most basic claims about files on Commons, it was time to make more fully-formed statements. The Structured Data on Commons development team developed and released the first level of support for types of statements other than depicts.
Taken as a whole, depicts and other statements, contributors to Wikimedia Commons can now begin to fully contribute structured data. The development team continues to work on support for different data beyond words, such as geocoordinates, time stamps, and other such types. Additional support for community tools such as Lua functionality is making progress as well. After this multi-year effort, the partners involved in the project can start the work of building a more accessible Commons at last.
Now that the underlying software for Structured Data on Commons has been put in place, along with Captions helping to demonstrate the software worked, the development team was ready to release the first form of structured statements for Commons: depicts.
Depicts is a statement for representing the concepts or topics present or expressed in a media file. The depicts statement can be considered the most basic example for modeling information about a file.
After making sure basic depicts support was working, the development team added support for qualifiers. By using qualifiers for depicts, users are able to represent the file even further by refining, contextualizing, or expanding the simple statement. For example, the previous statement of depicts (P180) a house cat (Q146) can be refined to depicts (P180) a house cat (Q146) [color: gray (Q42519)] and will return only files with statements that match a gray cat. As with basic depicts, this functionality is multilingual and will find whatever languages are available.
Now that Commons has the most basic modeling for data in a file in place, the development team turned to supporting other types of statements beyond depicts. These other types of statements will be covered in the next part.