Artificial Intelligence (AI) and machine learning offer significant potential for archives: they can help identify sensitive content at scale, support the creation and enhancement of descriptive metadata, and enable new forms of access and interpretation. Yet AI also risks compounding long-standing challenges around archival description, bias, and power, especially where collections are fragmentary, under-described, or documented using outdated or harmful terminology.This article asks: how can archives prepare their collections for AI in ways that enhance discoverability and access, while remaining grounded in archival principles and ethical commitments? Co-authored by a computer scientist and a digital humanist with experience in cultural heritage, the article offers a cross-disciplinary literature review and practice-oriented guidelines for "AI data preparedness" in archives. Drawing on work in data documentation, data readiness, collections-as-data, and archival theory, we argue that concepts such as provenance, appraisal, authenticity, reliability, and trustworthiness should shape AI applications in records and archives.We distinguish between task-specific AI systems and generative AI, and propose workflows in which AI assists rather than replaces archivists, including description, terminology mapping, and sensitivity review. AI readiness, we argue, is an ethical and interventionist approach to data curation.
Effective archiving of research materials and records is essential for advancing modern science. While previous research highlights deficiencies in data management and calls for improved policies, it often overlooks the role of Freedom of Information (FOI) legislation, which mandates proper records management in many countries. This study examines the implications of FOI legislation for research data management, focusing on why research materials are rarely archived in Sweden despite legal obligations for public universities. Through 15 semi-structured interviews with university archivists, the study utilizes infrastructure theory to analyse challenges in implementing formal archiving. Findings reveal that, despite legal mandates, research materials often remain unarchived due to inadequate infrastructure, insufficient resources, contradictory legal frameworks, and cultural resistance. Outdated IT systems, lack of standardization, and unclear roles further hinder efficient archiving. Archivists advocate for harmonized regulations, standardized metadata practices, and technical solutions tailored to disciplinary needs. The study underscores that enacting laws and policies is insufficient; successful implementation requires a shared understanding of archival value, practical feasibility of archiving, and institutional commitment. These insights inform policymakers, universities, and infrastructure providers in developing strategies to integrate archiving into research workflows, ensuring long-term data preservation and accessibility.
Tomography is a technique used to image the three-dimensional structure of an object by cutting it into parallel two-dimensional slices. This technique has many applications to scientific research, but preserving tomography datasets for scientific repeatability poses considerable logistical and financial challenges. Conventional curation of fundamental tomography data and associated metadata demands long working-person hours because there is no standardized, easy-to-use software. Tomography datasets often exceed 1,000 files, and these large dataset sizes exceed the capacity of open online data repositories, and archiving these data locally demands abundant disk storage. Here, we present a new cost-effective software and hardware developed to facilitate the large-scale curation of tomography data. Our user-friendly software processes RAW tomography data, generates flipbook-style animations, and outputs metadata tables, while a cluster of desktop computers ensures efficient execution. Data are stored on magnetic tapes for robust long-term archiving. This comprehensive, open-source method is designed for easy adoption, especially in museums. It is compatible with the distribution of lightweight 3D models with open data repositories, mirroring the relationship between publishing research papers by publishers and archiving the described specimens by museums. It supports scientific integrity, particularly in the establishment of new species in natural history sciences.
Cultural heritage institutions, including archives, face significant and increasing climate-change-related threats. This paper presents a pilot study from the Providing Risk of the Environment's Changing Climate Threats for Galleries, Libraries, Archives and Museums (PROTECCT-GLAM) project. Focused on the United States Gulf Coast region, the study applies an advanced Geographic Information System analysis to assess county-level risk for archival institutions based on two climate variables: Flash Flood Warnings and Tropical Cyclone Wind occurrence. Based on historical data, the study developed a novel A-F grading scale to categorize risk. The pilot study successfully validated the methodological framework for integrating multi-hazard climate data and translating complex climate information into an intuitive, actionable, risk assessment tool for archival professionals. GLAMs within Louisiana and Mississippi are the most at-risk from these variables when compared to other states in the study region.
This study collected a total of 2,318 policy documents on adapting to climate change in key areas from various central government departments of China. The policies were divided into four stages. Subsequently, we used LDA to conduct policy topic mining, identifying 23 policy themes, drawing a Sankey diagram for the diffusion of policy themes. The research findings indicate that the overall trajectory of policy evolution shows a development pattern from dispersion to integration, from local to global, from initial exploration to focused breakthroughs. The change in policy propositions lies in the fact that the focus of policy themes varies at different stages: the comprehensiveness of the policy content has been enhanced, with laws and regulations continuing to deepen and become more complete; the unchanging aspect of the policy proposition is that policy formulation gives priority to regions across the country significantly affected by climate change; the policy content consistently attaches great importance to disaster prevention and mitigation-related work; finally, the relevant policies and actions have always been in line with the goals, principles, and institutional arrangements of international climate change response policies.
This study examines how community organizations in Bangladesh, a country among the most climate-vulnerable globally, are adapting their recordkeeping practices in response to escalating impacts of climate change. While community organizations play a critical role in supporting grassroots climate action, limited research has explored how climate change affects their internal operations, particularly recordkeeping. Most existing literature focuses on formal institutions in the Global North, overlooking the unique challenges faced by low-resource organizations in the Global South. Using qualitative thematic analysis, based on data from focus groups with 16 community organizations and interviews with 23 stakeholders across four disaster-prone regions in Bangladesh, the study investigates climate-related disruptions and emerging adaptive recordkeeping practices. Findings reveal that events like floods and cyclones lead to record losses, infrastructure damage, and administrative discontinuity. In response, organizations are adopting informal but practical strategies such as decentralized storage, partial digitization, and community-based support networks. The study proposes an Adaptive Recordkeeping Framework tailored to the needs of grassroots community organizations in low-resource, high-risk environments. While rooted in the Bangladeshi context, the findings have broader relevance for other climate-exposed regions around the globe where community organizations face similar recordkeeping challenges amidst increasing climate risks.
This paper investigates how archival protection and recovery can be strengthened in the face of natural disasters, based on the case of Rio Grande do Sul, Brazil, in 2024. The main objective is to propose a model of coordination and collaboration to improve the safety and recovery of archives in natural disasters. The analysis of the dataset from the Public Archives of Rio Grande do Sul (APERS) and the Integrated Disaster Information System (S2iD) revealed inconsistencies in disaster monitoring policies, particularly regarding data collection requirements. While municipal governments are mandated to report to the S2iD platform, APERS does not have similar data collection obligations, as the S2iD primarily supports resource allocation. The coordination model proposed includes standardized procedures, system integration, mandatory reporting of archival damage, training, use of technology, and continuous monitoring. We argue that efficient archival protection and recovery from natural disasters requires a coordinated approach with standardized procedures, integrated systems, mandatory reporting, and advanced technologies. Future strategies should prioritize integrating standardized models, improved data collection, emerging technologies, such as AI, and enhanced training programmes for archival professionals.
This study explores the application of Artificial Intelligence (AI)-driven technologies to improve the accessibility and usability of historical audio archives through the integration of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) techniques. A dataset of 14 historical and oral history recordings, sourced from the Library of Congress and the Smithsonian Institution, was used to evaluate three ASR models: Whisper larger-v3, OWSM v3.1, and MMS-1b-all. Comparative analysis based on Word Error Rate (WER) revealed that Whisper larger-v3 consistently achieved the lowest error rates, while the open-source OWSM v3.1 demonstrated competitive performance, positioning it as a viable transparent alternative. The multilingual MMS-1b-all exhibited higher WERs, highlighting the trade-off between language coverage and domain-specific optimization. Downstream Named Entity Recognition (NER) and automated text summarization were conducted on the transcribed outputs, with human evaluations confirming high levels of informativeness, fluency, and faithfulness. This integrated approach underscores the potential of AI technologies to automate metadata generation, enhance the accessibility of archival content, and support more efficient archival workflows. Furthermore, the study addresses ethical considerations and proposes strategies for future integration of AI-generated metadata into existing cataloguing systems, offering valuable insights for archivists and information professionals engaged in digital preservation and heritage management.
Ensuring the secure processing of patient health records - maintained across various healthcare organizations throughout a patient's lifetime - is a growing challenge, especially given the increasing volume of data and the rise in cyber-attacks targeting healthcare systems. These records contain critical information such as diagnoses and medications, which must be protected as they are shared across interconnected clinical systems. This paper aims to design and implement a secure, decentralized system for managing Electronic Health Records (EHRs) using blockchain technology and smart contracts. The objective is to improve the privacy, integrity, and accessibility of medical data while enabling patients to control access to their records. The proposed system was developed using a private blockchain integrated with a cloud database and was evaluated through functional testing, system architecture modelling, and algorithmic simulations. Key components include smart contracts for access management, cryptographic hashing for data integrity, and role-based access control for ensuring appropriate data permissions. The results demonstrate that the system achieves the desired levels of security, transparency, and traceability, while maintaining scalability and usability in real-world healthcare settings. This study confirms the practical feasibility of blockchain-based EHR systems and their potential to transform healthcare data management.
