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Purpose – For decades, archivists have been appraising, preserving, and providing access to digital records by using archival theories and methods developed for paper records. However, production and consumption of digital records are informed by social and industrial trends and by computer and data methods that show little or no connection to archival methods. The purpose of this chapter is to reexamine the theories and methods that dominate records practices. The authors believe that this situation calls for a formal articulation of a new transdiscipline, which they call computational archival science (CAS).

Design/Methodology/Approach – After making a case for CAS, the authors present motivating case studies: (1) evolutionary prototyping and computational linguistics; (2) graph analytics, digital humanities, and archival representation; (3) computational finding aids; (4) digital curation; (5) public engagement with (archival) content; (6) authenticity; (7) confluences between archival theory and computational methods: cyberinfrastructure and the records continuum; and (8) spatial and temporal analytics.

Findings – Each case study includes suggestions for incorporating CAS into Master of Library Science (MLS) education in order to better address the needs of today’s MLS graduates looking to employ “traditional” archival principles in conjunction with computational methods. A CAS agenda will require transdisciplinary iSchools and extensive hands-on experience working with cyberinfrastructure to implement archival functions.

Originality/Value – We expect that archival practice will benefit from the development of new tools and techniques that support records and archives professionals in managing and preserving records at scale and that, conversely, computational science will benefit from the consideration and application of archival principles.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this paper is to identify the leading topics and developmental trends of archival information resource research in China by visualizing the intellectual structure and evolution patterns of archival information resource research.

This study took China National Knowledge Infrastructure (CNKI) as the data source and extracted keywords from relevant articles in archival information resource research as the sample. First, the frequency and co-occurrence of keywords were calculated by using SCI2. Second, this study analyzed the co-word network indicators by using Pajek. Then, topic community detection was conducted by using a VOS viewer, as well as the visualization of intellectual structures. Next, this study developed a graphical mapping of the evolution of research topics over time by using Cortext.

The research topics of archival information resources in China were unbalanced but distinct. Researchers focus on the construction and utilization of archival information resource, which consist of five evident research directions. The phenomena of fusion and differentiation coexist in research topic evolution. There were both continuities of traditional research and innovations in emerging research. The archival information resource research tended to be systematized and extended, reflecting the vertical and horizontal extension of the research content.

Based on a large number of previous studies, this study adopted quantitative methods to reveal the intellectual structure and evolution patterns of archival information resource research in China, providing guidance for researchers and institutions to grasp research status and developmental trends.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

The Spanish Flu 1918–1920 saw a high degree of excess mortality among young and healthy adults. The purpose of this paper is a further exploration of the hypothesis that high mortality risk during The Spanish Flu in Copenhagen was associated with early life exposure to The Russian Flu 1889–1892.

Based on 37,000 individual-level death records in a new unique database from The Copenhagen City Archives combined with approximate cohort-specific population totals interpolated from official censuses of population, the author compiles monthly time series on all-cause mortality rates 1916–1922 in Copenhagen by gender and one-year birth cohorts. The author then analyses birth cohort effects on mortality risk during The Spanish Flu using regression analysis.

The author finds support for hypotheses relating early life exposure to The Russian Flu to mortality risk during The Spanish Flu. Some indications of possible gender heterogeneity during the first wave of The Spanish Flu – not found in previous studies – should be a topic for future research based on data from other countries.

Due to lack of individual-level death records with exact dates of birth and death, previous studies on The Spanish Flu in Denmark and many other countries have relied on data with lower birth cohort resolutions than the one-year birth cohorts used in this study. The analysis in this paper illustrates how archival Big Data can be used to gain new insights in studies on historical pandemics.

This study aims to understand how Big Data foster a greater acceptance of performance management systems (PMS) discourses in health care.

This paper focusses on the case of head and neck cancer treatment and prevention and benefits from the analysis of archival sources and 19 interviews with physicians in the field. It uses the framework of the Middle Range theory (MRT) to understand whether, in the case of head and neck cancer, Big Data may favour the enactment of PMS discourses in health care, in turn benefiting from any improvement in PMS.

This study setting unveils the changing pathway known as reorientation through boundary management. Medical professionals internalized and even mobilized PMS discourses, showing the premises for evolutionary changes in the future, when the current limitations will be dealt with.

This paper offers new theoretical, practical and policymaking insights into how new technologies can foster positive PMS discourses among actors who usually resist them. This value also extends to different fields and contexts.

While social media data are used increasingly in studies of social movements, social media evolves far more rapidly than academic research and publication. This chapter argues that researchers should adopt historical and archival approaches to social media data. Treating social media data as an “instant archive” – one that is self-curated, is co-constituted, and changes rapidly – we caution researchers to pay attention to the features of this archive and their implications for working with the data therein. Applying insights from recent discussions of archival methods for social science research to the specific features of social media data, we explore how platform features, repressive effects, and user innovations affect the content of the instant archive. We then offer strategies for researchers' methodological approaches, including how best to select units of analysis and platforms, how to collect and interpret archival materials, and how to identify silences in the data.

Starting from the status quo, the paper outlines perspectives and challenges for the connection and interlinking of digitised and digital archival data. The following topics are addressed: Where are fields of action and what are the means of archives? Which functional and technical requirements are to be considered, and what is the role of portal infrastructures linking together various different institutions?

Considering needs of users and general framework conditions, the paper examines new approaches emerging in Germany. It outlines recent projects and considerations aiming to improve services and visibility of archives within the national data infrastructure in Germany.

Cross-connections are no new phenomenon, but change their appearance significantly in a digital context. In this respect, both smaller and bigger archives profit from participation in larger digital networks. Furthermore, archives need to keep in mind to reflect the quality of their digital (meta)data regularly and to offer or join systems that functionally and technically support cross-connection and interlinking of data.

The paper endeavours to show the importance of digital cross-connections and the role of portal infrastructures for visibility, online-distribution and use of digital archival metadata and data.

The aim of this study is to provide a conceptual framework to explain how museums sustain intellectual capital and promote value co-creation moving from designing virtual environments to introducing and managing Big Data.

This study is based on archival and qualitative data considering the literature related to the introduction of virtual environments and Big Data within museums.

Museums contribute to sustaining intellectual capital and in promoting value co-creation developing a Big Data-driven strategy and innovation.

By introducing and managing Big Data, museums contribute to creating a community by improving knowledge within cultural ecosystems while strengthening the users as active participants and the museum’s professionals as user-centred mediators.

As audience-driven and knowledge-oriented organisations moving from designing virtual environments to following a Big data-driven strategy, museums should select organisational and strategic choices for driving change.