Date(s) - 18/02/2021
3:00 pm - 5:00 pm
Categories No Categories
Chairs: Giovanni Colavizza and Melvin Wevers (University of Amsterdam)
You are kindly requested to register here: https://forms.gle/Dpd9W3i8MSEZZ8Z78
Networking Archives: Quantitative History and the Contingent Archive
By: Yann Ryan, Sebastian Ahnert, and Ruth Ahnert
Recent years have seen a growth in the use of network analysis on large datasets of correspondence, but studies of the epistemological basis for findings have not seen a commensurate increase. The latter are important because although large, these datasets can only ever represent a fraction of the total available correspondence, and most historically contingent letter archives have significant amounts of missing or uncertain data or records. This paper outlines three approaches to the study of missing network data: first, we suggest some strategies for dealing with missing data, beginning with understanding in detail the type and extent of missing data, second, we outline a method for understanding the effect that missing data has specifically on historical letter archives, which compares rank correlations of metrics between the full network and progressively smaller random sub-samples. The experiments show that the most basic metric of network structure, degree, is remarkably robust to random letter removal even when large samples of letters have been removed. Last, the paper argues that the combinatory effect of joined-up letter networks can be used to further the understanding of the structure of seventeenth-century letter networks and intellectual exchange.
Advancing Archival Education and Practice through Computational Archival Science (CAS)
By: Richard Marciano
This presentation discusses the emergence of Computational Archival Science (CAS) as a transdisciplinary field concerned with the application of computational methods and resources to large-scale records and archives processing, analysis, storage, long-term preservation, and access, with the aim of improving efficiency, productivity, and precision in support of appraisal, arrangement and description, preservation, and access decisions. We focus on the need to train current and future generations of information professionals to think computationally and rapidly adapt new technologies to meet their increasingly large and complex workloads.
About the speakers
Ruth Ahnert is Professor of Literary History and Digital Humanities at Queen Mary University of London. Her work focuses on Tudor culture, book history, and digital humanities. She is author of The Rise of Prison Literature in the Sixteenth Century (Cambridge University Press, 2013), editor of Re-forming the Psalms in Tudor England, as a special issue of Renaissance Studies (2015), and co-author of two further books: The Network Turn: Changing Perspectives in the Humanities (Cambridge University Press, forthcoming December 2020) and Tudor Networks of Power (forthcoming with Oxford University Press). Recent collaborative work has taken place through AHRC-funded projects ‘Living with Machines’ and ’Networking the Archives: Assembling and analysing a meta-archive of correspondence, 1509-1714’. With Elaine Treharne she is series editor of the Stanford University Press’s Text Technologies series.
Sebastian Ahnert is a University Lecturer at the Department of Chemical Engineering & Biotechnology, University of Cambridge, and a Senior Research Fellow at The Alan Turing Institute in London. He gained his PhD in theoretical physics from the University of Cambridge and then undertook postdoctoral research at the Institut Curie in Paris, before returning to Cambridge for a Leverhulme Fellowship, followed by a Royal Society University Research Fellowship and a Gatsby Career Development Fellowship. His research interests lie in the intersection of theoretical physics, biology, mathematics, and computer science, with a particular interest in the interdisciplinary application of network analysis. He has published over 60 articles across a wide range of academic journals in the sciences and humanities. The Network Turn, which he co-authored with Ruth Ahnert, Catherine Nicole Coleman, and Scott B. Weingart, will be published by Cambridge University Press in December 2020.
Yann Ryan is a research fellow on the ‘Networking Archives’ project, based at Queen Mary, University of London. In 2020 he completed a PhD thesis ‘Networks, Maps and Readers: Foreign News Reporting in London Newsbooks, 1645–1649’ , which looked at the flow of news from continental Europe to London, and examined how this can be traced and measured using computational techniques as well as more traditional scholarly methods. Prior to the Networking Archives project he worked at the British Library as a Curator of newspaper data—a post which sought to promote the use of the Library’s digital newspaper holdings to a wider audience. His current research interests include historical network analysis, the history of news and intelligencing in Europe, digital and spatial humanities, as well as early modern postal and communications history. He has published work on these topics in Media History and Publishing History.
Dr. Richard Marciano is the recipient of the 2017 Emmett Leahy Award for “outstanding accomplishments that have had a major impact on the records and information management profession.” Throughout his career, Richard has worked in highly interdisciplinary and collaborative environments at the intersection of technology, information, and records management. He has focused on blending disciplines (computer science, archives, and information management) to produce new ways of understanding the past. He was the founder and director of the Digital Curation Innovation Center (DCIC) from 2015 to 2020. Richard holds degrees in Avionics and Electrical Engineering, a Master’s and Ph.D. in Computer Science from the University of Iowa, and conducted a postdoc in Computational Geography. He has been collaborating with archivists and librarians for the last 20 years.
Zoomlink for the meeting: https://uva-live.zoom.us/j/96483376758?pwd=SlFZaytLcURiTGQxZDd4a1ZULzkwUT09