Big Data Studies Lab
Founded in Seoul in 2019 and based at the University of Hong Kong since 2022, the Big Data Studies Lab (BDSL) brings together digital humanities scholars, data scientists, engineers, entrepreneurs, and policymakers to investigate how contemporary information systems are changing the sources, methods, and material conditions of humanities research. BDSL takes the familiar 3Vs of big data (volume, velocity, and variety) as a point of departure for a humanities-oriented inquiry into the Zettabyte era.
BDSL follows vast streams of digital information beyond the scale of individual observation, from their generation and distribution to their transformation, preservation, and loss. Computational experiments, field research, interviews, software archaeology, and close readings of technical and legal records provide the empirical basis for interpretation and, where warranted, broader theorization.
What will survive of the digital present?
The abundance of the digital present will become the scarcity of the historical future. Much of today’s digital record persists in privately operated systems where replication, migration, modification, and deletion are routine, and long-term preservation is rarely a priority. Only a minute fraction is likely to remain accessible to future researchers. Archives of the Future studies the conditions under which digital records persist, disappear, or become inaccessible, much of this history unfolding before archival preservation can even begin.
What does big data require in order to exist?
Big data depends on data centres, electrical grids, cooling and water systems, fibre networks, semiconductors, critical minerals, and global supply chains. Material Infrastructures studies these dependencies as conditions of digital information itself, asking how efficiency and expanding demand interact with the growing concentration of computational capacity, and how ownership, digital sovereignty, and geopolitics shape the infrastructure that sustains computation.
How does big data move across space and time?
Big data are distributed, replicated, cached, migrated, and recombined across systems separated by distance. Information in Motion studies velocity beyond the rate of generation and processing, asking how infrastructure coordinates the circulation of data to approximate “real time,” and how physical limits, geography, and jurisdiction shape where information resides and when it becomes available.
How do machines come to know us?
Digital systems construct changing representations of their users from behavioural traces, inference, prediction, and increasingly direct personal disclosure. Data Doubles studies how these representations have developed from the comparatively dispersed surveillance of the Web 1.0 era to increasingly automated and centralized systems that observe their users, anticipate what they might do, and shape what they encounter next.
How can humanists investigate evidence beyond individual inspection?
Big data places much potentially relevant evidence beyond individual inspection. Machine-Assisted Reading explores how computation can recover some of the serendipity of discovery across vast and heterogeneous collections, using representations guided by the research question while preserving a route from computational findings back to their sources.
Examines bodies of evidence that become available for research by routes never intended to create an archive, from rescued websites and data dumps to leaks, hacks, and legal proceedings. The project uses these unintended afterlives to investigate digital records and information systems otherwise beyond scholarly observation.
Traces the development of personalization from Web 2.0 recommendation and engagement systems to generative AI. The Personalized Web examines how systems have learned to observe, infer, and predict their users, and what changes as behavioural traces are increasingly joined by personal disclosure in sustained interactions with machines.
Investigates how globally distributed information systems approximate “real time” despite the physical limits of distance, combining computational experiments and documentary research to trace how network architecture, synchronization, human perception, and jurisdiction shape the experience of a shared present.
Follows the material demands of big data and AI from energy, water, and data centres to semiconductors, critical minerals, and global supply chains. What Computing Takes investigates how large-scale computing reshapes the places and communities that sustain it, from resource use and noise pollution to industrial development, supply security, and geopolitics.
Investigate the physical sites and local conditions of large-scale computing, combining direct observation, interviews, and other evidence to understand how digital infrastructure is built, operated, and experienced.
Recover the workings of systems that are only partially public by reading technical papers, standards, patents, white papers, and legal proceedings for what they reveal, omit, or disclose indirectly.
Reconstruct changes in software from successive releases, reading decompiled code, interfaces, and version histories against the surrounding documentary record.
Design controlled experiments to measure system behaviour, test technical claims, and establish what specific operations do under defined conditions.
Develop computational representations of incomplete or heterogeneous evidence in a manner that preserves provenance, uncertainty, and distinctions required by the research question.
Assemble machine-learning systems for research questions and bodies of evidence, combining and adapting models, representations, and analytical procedures as needed to retrieve, compare, classify, cluster, and identify candidates for humanistic interpretation.