Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 14th June 2025, 07:30:50pm WEST

 
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Session Overview
Session
LP-32
Time:
Friday, 18/July/2025:
2:00pm - 3:30pm

Session Chair: Julia Matveeva, University of Turku
Location: B207 (TB)

64 places

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Presentations

Open archaeology in Catalonia: challenges, barriers, and potential solutions

Sabina Batlle Baró

Universitat de Barcelona, Spain

This presentation explores the challenges and opportunities of implementing open data in Catalan archaeology. It examines the current infrastructure, researchers' practices, and barriers to data openness. The study provides recommendations to promote a new research culture, with the goal to lead a smooth transition to open archaeological research.



Postclassical Time Maps: Theory and Interpretation

Sean A. Yeager

Independent Scholar

I build on my previous research on "time maps" by expanding their theory and demonstrating their interpretive utility. Time maps are the graphs which are produced when a narrative’s fabula is plotted against its syuzhet. I introduce three advanced theoretical concepts, then use time maps to close-read several narratives.



Subset Selection in Bibliographic Research: Exploring the Boundaries of Automated and Manual Curation

Julia Matveeva1, Veli-Matti Pynttäri2, Osma Suominen3, Kati Launis2, Leo Lahti1

1University of Turku, Finland; 2University of Eastern Finland; 3The National Library of Finland

This study examines subset selection in bibliographic research, focusing on Finnish literary history (1809–1917). Comparing manual and automated curation, we highlight their respective strengths and limitations. We propose a hybrid approach combining automation for scalability and manual curation for precision. Our findings enhance transparency, accuracy, and reproducibility in literary datasets.



 
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