More often than not, artsgoers of all ages are discovering new artists, shows and festivals through some sort of digital device. Whether they’re searching “events near me” on Google or asking Alexa to find a playlist that fits their mood, they increasingly look no further beyond what their device (and the algorithm behind it) recommends.
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The Linked Digital Future initiative (LDFI), as we have known it until now, is giving way to an exciting new project, thanks to funding from the Canada Council of the Arts and the Government of Canada’s Canada Cultural Investment Fund and Community Services Recovery Fund. A project that will continue to enhance the discoverability of the performing arts, while growing and strengthening the community surrounding the Artsdata knowledge graph. We call it the Artsdata Linked Open Data Ecosystem.
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Calling all performing arts organizations! You are cordially invited to participate in the newest edition of CAPACOA’s Digital Discoverability Program, as part of the Linked Digital Future Initiative (LDFI) and in support of the Artsdata project.
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The Linked Digital Future initiative was launched in 2018 to promote the discoverability of the live performance sector. After just over four years, has this discoverability challenge remained just as critical? Or has increasingly advanced Artificial Intelligence (AI) solved the problem for us?
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Currently, much of the information about Canadian performing arts sector entities (including performers, directors, designers, choreographers, organizations, venues and events) is not properly formatted to be found, read and processed by search engines and other discovery technologies. As a result, it is too often ignored or underutilized.
The Linked Digital Future Initiative (LDFI) was created to make performing arts-related information findable and to help build better connections in our sector – between arts workers and audiences – in the digital age. One of the ways this has been achieved is by converting already publicly available information into reusable and accessible data in open databases, such as Wikidata and the Artsdata knowledge graph.
Although LDFI is leading this work, it will take many leaders in Canada’s performing arts sector contributing information to turn this vision into reality.
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Two years ago, the Indigenous Performing Arts Alliance (IPAA) and the Canadian Association for the Performing Arts (CAPACOA) set out to undertake an unusual project. Noting the scarcity of information about Indigenous artists in open databases, the two associations saw a gap that had to be filled.
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In a previous post pointing out the essential steps to a productive digital presence for the performing arts, we highlighted as a best practice the sharing of images in the Wikimedia Commons media library, a sister project of Wikipedia and Wikidata, under a free to use Creative Commons licence.
This practice deserves our attention because even if it may seem complex or disorienting, it is above all an extraordinary way to benefit from the positive bias of search engines.
We’ll be looking at Wikimedia Commons from three different angles.
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“How could the works of Indigenous arts and culture practitioners be more readily findable to people looking for them over the Web?” There are many potential answers to this question. Yet, each one is dependent on answering another, more fundamental, question: “Who are Indigenous arts and culture practitioners?” or, to be more precise, “how can these practitioners be accurately and respectfully identified as being Indigenous?”
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As we wrap up a busy first phase of the Linked Digital Future initiative, we’d like to share a few successes and lessons learned with the performing arts community.
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Over the last year, CAPACOA and Culture Creates collaborated with the Bern University of Applied Sciences to develop a conceptual model for performing arts data. At the same time, Culture Creates was building the artsdata.ca knowledge graph, and RIDEAU was rolling out the Scène Pro information system. The former assembles data about performances; the latter gathers data about organizations, venues and performing arts productions. What if the two datasets were interoperable and could exchange data? We tried it out.
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