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This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

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 main objective of this study is to present a compact overview analysis of intellectual property laws, specifically copyright-related provisions applicable to generative artificial intelligence (GenAI) in the Indian context.

The paper adopts a qualitative research methodology that is grounded in secondary sources of information. The data were gathered from the Scopus database for a systematic literature review.

GenAI technology has given rise to numerous questionable issues within the domain of intellectual property that need resolution in the form of policy solutions. Based on the findings of this paper, it can be deduced that Indian copyright laws are not adequate for addressing the rights pertaining to AI and its creations and outputs. Different countries like the United States, European Union and China have approached the regulation and protection of AI-generated content within the realm of copyright law in different ways. The future of law, as it has been established thus far, seems to be on a path of substantial evolution.

The study has implications for policymakers globally as there is a need to create feasible policy solutions that can efficiently safeguard against risks stemming from large language models (LLMs) and other GenAI models, while also promoting innovation, technical advancement and adoption.

The paper discusses the copyright-related issues in GenAI technology in the context of an emerging economy, India.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This study aims to examine the current status and different characteristic features of research data repositories established by BRICS nations in order to understand the research data infrastructure within BRICS countries.

The data were collected from the re3data repository (http://www.re3data.org/), focusing specifically on BRICS nations. The data were analyzed to grasp the current status of research data repositories in BRICS countries. The dataset was retrieved on March 2, 2024. A total of 195 Research Data Repositories (RDRs) originating from BRICS countries were identified and selected for comprehensive analysis.

As of March 2, 2024, re3data.org indexes a total of 3,192 Research Data Repositories (RDRs) worldwide, with BRICS nations contributing 195. China leads among BRICS nations, followed by India, Russia, and Brazil. Scientific and Statistical Formats are the most shared content categories, followed closely by Standard Office Documents. There is notable diversity in the subjects covered by RDRs across BRICS nations. English is the primary interface language, followed by Chinese and Portuguese. “House, tailor-made” software is widely used for creating RDRs, followed by Dataverse and DSpace. Fourteen metadata standards are found, with Dublin Core metadata being the most prevalent, followed by the DataCite Metadata Scheme. Most repositories are disciplinary, followed by institutional ones. Most repositories specify data upload types, with “restricted” being the most common, followed by closed types. Open access is predominant in data access, followed by restricted access and embargo periods, while a small number restrict access entirely.

The present study will help gauge the strengths and weaknesses of the RDRs of BRICS nations and also learn how open these RDRs are for data access and upload provisions. The study contributes to a broader comprehension of the accessibility and usability of research data within the BRICS community, ultimately fostering greater transparency, collaboration, and knowledge dissemination within the scientific community, thus fostering greater innovation and advancement in research endeavors. The study provides a nuanced understanding of the research data infrastructure within BRICS nations, highlighting key trends, strengths, and areas for potential improvement.

This systematic review aimed to identify and categorize applications using Fast Healthcare Interoperability Resources (FHIR) to support activities outside of direct healthcare provision.

A systematic electronic search was performed, and 53 studies were included after the selection process.

The results show that FHIR is being used to support (1) clinical research (i.e. clinical research based on interventional trials, data interoperability to support clinical research and advanced communication services to support clinical research), (2) public health and (3) medical education. Despite the FHIR potential to support activities outside of direct healthcare provision, some barriers were identified, namely difficulties translating the proposed applications to clinical environments or FHIR technical issues that require further developments.

This study provided a broad review of how FHIR is being applied in clinical activities outside of direct clinical care and identified three major domains, that is, clinical research, public health and medical education, being the first and most representative in terms of number of publications.

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.

The authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.

The authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.

Implications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.

Since AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.

This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data.

The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices.

Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts.

Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes.

This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

Data protection is a significant area of law in a country like India, which is digitalising at a fast rate. Recently, India passed comprehensive data protection legislation after discussing several draft data protection frameworks. This paper aims to analyse the provisions of India’s first comprehensive data protection legislation, the Digital Personal Data Protection Act (DPDPA), 2023.

The paper aims to analyse how the DPDPA’s provisions should be interpreted. The methodology involves studying the act’s provisions, identifying shortcomings and suggesting ways of addressing the shortcomings through legal interpretation. The paper interprets DPDPA provisions through a comparative analysis with the proposed American Privacy Rights Act 2024 and EU General Data Protection Regulation. The methodology can be broadly classified as doctrinal and comparative legal research.

The paper makes several recommendations for interpreting the provisions of the DPDPA, which are discussed throughout the paper and summarised in the way forward section.

The analysis of this paper is limited to present-day data protection concerns. In the future, research can assess how the DPDPA can be interpreted to solve the challenges presented by societal and technological progress.

The originality and contribution of the paper are analysis and interpretation of the provisions of the DPDPA that will provide data principals with strong control over personal data and ensure stringent data protection obligations on data fiduciaries.