19 OCTOBER, 2024

TORONTO, CANADA

Data now drives advanced research and scholarship across diverse fields. Many humanities and social science disciplines are increasingly dependent on data. Scholarly practices in the latter disciplines generate new data as evidence or as demonstration of new creative practices or methods. With intensive focus on mitigating fraud in research and push for wider dissemination of research, publication venues have instituted new policies for sharing research or scholarly data. Given the criticality of data for nurturing and sustaining humanities and social science disciplines it is paramount that open data environments are available with minimum barriers for scholars to access and use. Although many data related topics and associated techniques and solutions are beginning to receive attention, the efforts on data aggregation and dissemination so far have been scattered and sometimes inconsistent. Surprisingly, there has not been a single and comprehensive international forum dedicated to developing open data resources for humanities and social science scholarship. The planned Dublin Core Data Institute (DCDI) will bring together a stellar group of data and information scientists as well as humanities and social science scholars, to mingle and exchange ideas in a single venue. The broad goal of DCDI is to establish the foundational requirements for a federated data service covering key challenges and potential avenues for addressing them.

DCDI Conference Theme

Speakers, Program, and Participants

Opening DCDI Keynote Speaker: Martin Puchner, the Byron and Anita Wien Professor at Harvard University, is a prize-winning author, educator, public speaker, and institution builder in the arts and humanities. His writings range from philosophy and theater to culture and technology and have been translated into many languages. Through his best-selling Norton Anthology of World Literature and his HarvardX MOOC Masterpieces of World Literature, he has brought four thousand years of literature to audiences across the globe.

His book, The Written World, which tells the story of literature from the invention of writing to the Internet, has been widely reviewed in The New York Times, The Times (London), the Financial Times, The Times Literary Supplement, The Atlantic, The Economist, among others, covered on radio and television, and has been translated into some twenty languages. It appeared on the Wall Street Journal bestseller list and received the Massachusetts Book Award.

More on Martin's background: https://www.martinpuchner.com/about.html

Closing DCDI Keynote Speaker: Dennis Yi Tenen is an associate professor of English at Columbia University, where he also co-directs the Center for Comparative Media. His research happens at the intersection of people, text, and technology. A long-time affiliate of Columbia’s Data Science Institute, formerly a Microsoft engineer in the Windows group and fellow at the Berkman Center for Internet and Society, his code runs on millions of personal computers worldwide.

Dennis received his doctorate in Comparative Literature at Harvard University under the advisement of Elaine Scarry and William Todd. The founder of Columbia’s Literary Modeling and Visualization Lab, he co-edits the On Method book series at Columbia University Press. His published work can be found in monographs including

Plain Text: The Poetics of Computation (Stanford University Press,2017) and Literary Theory for Robots (W.W. Norton, 2024).

More on Dennis's background: https://english.columbia.edu/content/dennis-yi-tenen

topics

Before Research

• Data findability

• Data access

• Data manipulation and use

Post Research

• Data catalogs, archives and dissemination sites

• Data set annotation/preparation for publication

• Data linkages across multiple data sets

• Data documentation to promote use

• Data presentation tools to enhance interpretation and sharing

• Data set authentication and security to increase trust and reliability

During Research

• Secure data access and use

• Data cleaning and preparation for research

• Data processing pipelines

• Data use among groups

• Data visualizations

• Data analytics

UNIVERSITY OF TORONTO

FACULTY OF INFORMATION

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