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| I share the call for proposals below for the 11th Computational Archival Science Workshop (part of the 2026 IEEE Big Data Conference), on behalf of program committee members Richard Marciano and Jane Greenberg. The workshop has brought together practitioners working in big data (and now AI) in libraries, archives, historical centers, etc., along with researchers, and across iSchools, as well as digital humanities and computer science programs. Longtime members of the CNI community may recall our deep involvement in and support of the LIS Education and Data Science Integrated Network Group (LEADING), designed to prepare a diverse, nation-wide cohort of LIS doctoral students and early to mid-career librarians for data science endeavors; several participants from that program are involved with this event, including mentors and previous fellows.
The event website is at https://ai-collaboratory.net/ieee-big-data-2026-cas-11/.
Diane Goldenberg-Hart CNI Assistant Director
— — —
Tue.
Dec. 15, 2026 (TBD) Part
of: 2026 IEEE Big Data Conference (IEEE BigData 2026) Dec.
14-17, 2026 -- Phoenix, AZ IMPORTANT
DEADLINES (tentative dates – to be updated): · Saturday,
Nov. 9, 2026 (final): Due date for full workshop papers submission · Saturday,
Nov 16, 2026: Notification of paper acceptance to authors · Saturday,
Nov 23, 2026 (hard deadline): Camera-ready of
accepted papers · Tuesday,
Dec 15, 2026: Day-long CAS workshop (in person) in Phoenix AZ, USA · If you
are planning on attending the workshop, please
contact mark.hedges at kcl.ac.uk for registration details!
COMPUTATIONAL ARCHIVAL
SCIENCE: digital records in the age of big data INTRODUCTION
TO WORKSHOP [also see our CAS Portal]: The large-scale
digitization of analogue archives, the emerging diverse forms of born-digital
archive, and the new ways in which researchers across disciplines (as well as
the public)wish to engage with archival material, are resulting in disruptions
to transitional archival theories and practices. Increasing quantities of ‘big
archival data’ present challenges for the practitioners and researchers who
work with archival material, but also offer enhanced possibilities for
scholarship, through the application both of computational methods and tools to
the archival problem space and of archival methods and tools to computational
problems such as trusted computing, as well as, more fundamentally, through the
integration of computational thinking with archival thinking.
Our working
definition of Archival Computational Science (CAS) is: A
transdisciplinary field grounded in archival, information, and computational
science that is concerned with the application of computational methods and
resources, design patterns, sociotechnical constructs, and human-technology
interaction, to large-scale (big data) records/archives processing, analysis,
storage, long-term preservation, and access problems, with the aim of improving
and optimizing efficiency, authenticity, truthfulness, provenance,
productivity, computation, information structure and design, precision, and
human technology interaction in support of acquisition, appraisal, arrangement
and description, preservation, communication, transmission, analysis, and
access decision. [refined by Nathaniel Payne (2018)]
OBJECTIVES This workshop will explore
the conjunction (and its consequences) of emerging methods and technologies
around big data with archival practice (including record keeping) and new forms
of analysis and historical, social, scientific, and cultural research engagement
with archives.We aim to identify and evaluate current trends, requirements, and
potential in these areas, to examine the new questions that they can provoke,
and to help determine possible research agendas for the evolution of
computational archival science in the coming years. At the same time, we will
address the questions and concerns scholarship is raising about the
interpretation of ‘big data’ and the uses to which it is put, in particular
appraising the challenges of producing quality–meaning, knowledge and
value–from quantity, tracing data and analytic provenance across complex ‘big
data’ platforms and knowledge production ecosystems, and addressing data
privacy issues. This will
be the 11th workshop at IEEE Big Data addressing
Computational Archival Science (CAS), following on from workshops in 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, and 2025. It also
builds on three earlier workshops on ‘Big Humanities Data’ organized
by the same chairs at the 2013-2015 conferences, and more directly on a 2016 symposium held
in April 2016 at the University of Maryland. All papers accepted for the
workshop will be included in the Conference Proceedings published by
the IEEE Computer Society Press. RESEARCH
TOPICS COVERED:
Topics covered by the workshop include, but are not
restricted to, the following: - Application
of analytics to archival material, including AI, ML,
text-mining, data-mining, sentiment analysis, network analysis.
- Analytics
in support of archival processing, including e-discovery,
identification of personal information, appraisal, arrangement and description.
- Scalable
services for archives, including identification, preservation, metadata
generation, integrity checking, normalization, reconciliation, linked data,
entity extraction, anonymization and reduction.
- New forms
of archives, including Web, social media, audiovisual archives, and
blockchain.
- Cyber-infrastructures
for archive-based research and for development and hosting
of collections
- Big data
and archival theory and practice
- Digital
curation and preservation
- Crowd-sourcing and
archives
- Big data
and the construction of memory and identity
- Specific
big data technologies (e.g. NoSQL databases) and their applications
- Corpora
and reference collections of big archival data
- Linked
data and archives
- Big data
and provenance
- Constructing
big data research objects from archives
- Legal and
ethical issues in big data archives
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