English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Within the cultural heritage sector, there has been a growing and concertedeffort to consider a critical sociotechnical lens when applying machinelearning techniques to digital collections. Though the cultural heritagecommunity has collectively developed an emerging body of work detailingresponsible operations for machine learning in libraries and other culturalheritage institutions at the organizational level, there remains a paucity ofguidelines created specifically for practitioners embarking on machine learningprojects. The manifold stakes and sensitivities involved in applying machinelearning to cultural heritage underscore the importance of developing suchguidelines. This paper contributes to this need by formulating a detailedchecklist with guiding questions and practices that can be employed whiledeveloping a machine learning project that utilizes cultural heritage data. Icall the resulting checklist the "Collections as ML Data" checklist, which,when completed, can be published with the deliverables of the project. Bysurveying existing projects, including my own project, Newspaper Navigator, Ijustify the "Collections as ML Data" checklist and demonstrate how theformulated guiding questions can be employed and operationalized.

The "Collections as ML Data" Checklist for Machine Learning & Cultural Heritage