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This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools.

The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing.

Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable.

Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows.

The purpose of this paper is to examine user studies as well as user education within the context of public services offered by archival institutions. It highlighted some of the key aspects that constitute both concepts drawing from history in order to provide a better understanding in the context of current professional discussions.

The review analysed peer-reviewed articles ranging from the late 1970s to the present time to illuminate debates in the archival professional underpinning the current understanding of user studies and user education.

The paper outlined the different paths used in user studies to ensure data collection is exhaustive and provides a nuanced assessment of user needs. It also outlined the two related paradigms of structuring user education programmes, highlighted the points at which they differ and the rich discussions resulting from comparative analysis.

The paper demonstrated that there is a rich corpus of professional literature on both user studies and user education, expounding on different aspects that would ensure both are designed and implemented effectively.

The purpose of this paper is to explore the task design and assignment of full-text generation on mass Chinese historical archives (CHAs) by crowdsourcing, with special attention paid to how to best divide full-text generation tasks into smaller ones assigned to crowdsourced volunteers and to improve the digitization of mass CHAs and the data-oriented processing of the digital humanities.

This paper starts from the complexities of character recognition of mass CHAs, takes Sheng Xuanhuai archives crowdsourcing project of Shanghai Library as a case study, and makes use of the theories of archival science, including diplomatics of Chinese archival documents, and the historical approach of Chinese archival traditions as the theoretical basis and analysis methods. The results are generated through the comprehensive research.

This paper points out that volunteer tasks of full-text generation include transcription, punctuation, proofreading, metadata description, segmentation, and attribute annotation in digital humanities and provides a metadata element set for volunteers to use in creating or revising metadata descriptions and also provides an attribute tag set. The two sets can be used across the humanities to construct overall observations about texts and the archives of which they are a part. Along these lines, this paper presents significant insights for application in outlining the principles, methods, activities, and procedures of crowdsourced full-text generation for mass CHAs.

This study is the first to explore and identify the effective design and allocation of tasks for crowdsourced volunteers completing full-text generation on CHAs in digital humanities.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

The purpose of this paper is to discuss the implications of current theories that advocate for minimal levels of description in digital collections. Specifically, this paper looks at the archival theory of “More Product, Less Process” and its encouragement of collection-level description. The purpose of the study was to analyze how levels of description impact resource retrieval.

This study analyzed 35 images from a New York Public Library (NYPL) digital collection present on the NYPL website and on Flickr. The methodology was designed to reflect users’ information seeking behavior for image collections. There were two research questions guiding this study: what are the descriptive terms used to describe items in digital collections? and what is the success rate of retrieving resources using assigned descriptive terms?

The results of this study revealed that the images from the NYPL collection were more difficult to find on the institution’s website as compared with Flickr. These findings suggest that lesser levels of description in digital collections hinder resource retrieval.

These findings suggest that lesser description levels hurt the findability of resources. In the wake of theories such as “More Product, Less Process”, information professionals must find ways to assign metadata to individual materials in digital image collections.

Recent research concerning description levels of digital collections is several years old and focuses mostly on the usefulness of collection-level metadata as a supplement to or substitute for item-level metadata. Few, if any, studies exist that explore the implications of description levels on resource retrievability and findability. This study is also unique in that it discusses these implications in the context of less-is-more theories of archival processing.

Operating at the crossroads of higher education, librarianship, technology application, and service delivery, the Research Libraries Group (RLG) is a powerful partner to institutional efforts. For 20 years, it has identified critical research needs, provided responsive solutions, and expanded access to new and untapped information resources. With its highly skilled staff, sophisticated technical resources, and successful track record in managing and supporting interactions among its members, RLG is well positioned to help research institutions address the information challenges of the 1990s.

The purpose of this paper is to synthesize different research projects and approaches to managing the “hidden collections” problem.

The paper describes current research and practices.

Hidden collections are a problem for both libraries and archives. “Hidden collections” are materials that cannot be found in the online catalog or may be found there only under a collective title or un‐ or under‐processed primary sources. These materials transcend all formats of library and archival materials (print, microform, video, etc.) and can be found throughout the library in a variety of collections from government documents to area studies to music to the reference department. It is in the area of archival and special collections, however, that this problem has received the most attention and where the greatest challenges and opportunities for exposing these collections exist.

Many archives have backlogs and “hidden collections”. This article discusses some recent efforts to identify and expose these collections to enable better archival control and researcher access that may be applied in other settings.

This article identifies and highlights different approaches and potential solutions to the problem of hidden collections.

The purpose of this paper is to investigate the impact of climate change on South Africa’s Mpumalanga Provincial Archives (MPA) and related records management activities.

A qualitative research approach/method was used to collect data from the source, including purposive sampling and expert knowledge solicited through semi-structured interviews. A review was undertaken to source climate data and related archival sector literature.

Recent extreme weather events in Mpumalanga seem to bear out climate change projections. The current absence of relevant strategies and plans implies that the MPA and provincial departments are not yet prepared for emergencies. The lack of a plan, an implementation strategy and related training may increase the impact of disasters on the provincial archives’ environment. Without disaster plans and robust preparedness, the question of how the MPA is going to preserve archives for future generations is raised. In addition, due to a lack of guidance for registry staff, appraisal is not allowing for identification and processing of archival materials, so combined with a lack of disaster management strategies and plans contributes to the loss of archives.

This research is limited to the MPA; however, the findings may be extended to other South Africa provincial archives and parts of the world experiencing similar issues. There is a need to conduct similar research on a larger scale so as to explore the impact of climate change in other provincial archives to provide a wider context of the problem within South Africa.

When archivists accept holdings into their repositories, they take the responsibility for their archival custody. But, these efforts may come to nothing if the archives are lost. Disaster management is the key to the protection of the archives.

This paper contributes to the literature on the impact of climate change on archives. The results achieved should be seen as a first step towards identification of the issue.

The purpose of this paper is to address the challenges that special collections repositories face when creating digital surrogates driven by researcher demand, to link these digitization issues with archival practice, and to provide recommendations for improvement.

Presents an overview of the development of the University of Maryland Libraries' digital imaging workflows and a critique of current practices.

A viable digital repository can be built from surrogates created in response to researcher demand, but there are limitations to this approach, with opportunity for improvement.

As a case study, this paper is limited to one institution's perspective.

Provides insight into constructing and managing digitization programs at special collections repositories.

This paper offers a case study approach for an institutional digital repository influenced heavily by researcher demand, in contrast to a digital repository constructed with a more structured plan.

The paper presents the implementation and classification of archival records according to business processes, a new field with many organizational and archival challenges. The purpose of the paper is to map the current situation, challenges, problems and topics for further investigation.

The paper is based on a wide benchmark study on information management in governmental agencies, municipalities and regions/country councils in Sweden.

There are many general similarities with process projects regarding management initiatives and roles. There is also a need for alignment between the business organizations, the business processes, and the archival descriptions. Unresolved issues occur in modeling, e.g. from whose perspective should the processes be described and on which level is the mapping useful. The business organizations require detail in order to analyze flaws improvements, while archivists ask for a static “frozen” description at higher levels. This may contradict the purpose of the archive – to provide useful information for the unknown future user.

Process based archival descriptions is a new field that broadens business process management. Projects are starting in several countries with challenges in the interaction between the business organizations and the archivists.