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Please access this project on the CWRC legacy site.
The Orlando Project is an ongoing feminist experiment in the use of digital methods and tools to advance literary history. Since the project began in the mid-1990s, it has brought together literary scholars, technical experts, and more than 130 student members trained in literary research, text encoding, data visualization and reconciliation, and public humanities communication.
Our textbase, Orlando: Women’s Writing in the British Isles from the Beginnings to the Present, is an unprecedented collection of original scholarship encoded with our bespoke semantic markup, whose design encourages users to explore and remix it in creative ways. Published by Cambridge University Press, we update it twice a year with new and revised material.
We have widened our focus to include the writing of long-form narratives informed by textbase data as well as the development of linked data, which we see as having great potential for more aggregation, for different kinds of exploration, for bringing data from different projects into conversation with each other. We undertake this phase of activity as a means of building on Orlando’s long-term feminist objectives and values.
The Orlando Project explores and harnesses the power of digital tools and methods to advance feminist literary scholarship.
The project is collaborative and multidisciplinary: the venture at its core brings together literary scholars, digital humanists based in varied disciplines, and computing scientists. Orlando Project research is cross-cultural, and student team members – of whom the project has trained upwards of 120, and rising – learn about editorial and archival research, document analysis, and markup by working with mentors and peers.
More about Orlando, onscreen
About the Orlando Project
Orlando: Women’s Writing in the British Isles from the Beginnings to the Present
Orlando: A Podcast on Women’s Writing
New and Related Research
Works-in-progress, from data production and visualization to long-form narrative