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Science policy and practice for open access (OA) books is a rapidly evolving area in the scholarly domain. However, there is much that remains unknown, including how many OA books there are and to what degree they are included in preservation coverage. The purpose of this study is to contribute towards filling this knowledge gap in order to advance both research and practice in the domain of OA books.

This study utilized open bibliometric data sources to aggregate a harmonized dataset of metadata records for OA books (data sources: the Directory of Open Access Books, OpenAIRE, OpenAlex, Scielo Books, The Lens, and WorldCat). This dataset was then cross-matched based on unique identifiers and book titles to openly available content listings of trusted preservation services (data sources: Cariniana Network, CLOCKSS, Global LOCKSS Network, and Portico). The web domains of the OA books were determined by querying the web addresses or digital object identifiers provided in the metadata of the bibliometric database entries.

In total, 396,995 unique records were identified from the OA book bibliometric sources, of which 19% were found to be included in at least one of the preservation services. The results suggest reason for concern for the long tail of OA books distributed at thousands of different web domains as these include volatile cloud storage or sometimes no longer contained the files at all.

Data quality issues, varying definitions of OA across services and inconsistent implementation of unique identifiers were discovered as key challenges. The study includes recommendations for publishers, libraries, data providers and preservation services for improving monitoring and practices for OA book preservation.

This study provides methodological and empirical findings for advancing the practices of OA book publishing, preservation and research.

The purpose of this research is to introduce the Layar augmented reality (AR) application among library users and to understand the user’s satisfaction towards the information services provided by the Layar application with the help of the structural equation model (SEM).

According to Thomas (2016), action research is mainly undertaken to develop new skills or new approaches and to solve issues and problems with direct application to any applied setting. The present study helps to develop new skills and approaches to repackaging information using AR applications. Researchers have identified the question of what could be done to increase the awareness of Layar AR applications among students. Because the Layar augmented application is one of the new tools for an academic library to repackage information for mass accessibility. Therefore, in the present action research approach, researchers encompass two activities action and research. Researchers have used participatory action research methods by collecting data from 17 MBA institute libraries affiliated with Savitribai Phule Pune University. Researchers have systematically used the Layar application in the library by obtaining permission from each higher authority. Researchers have designed a Layar satisfaction model using the SEM with AMOS and SPSS.

The researcher found that the relationship between experience, performance and service quality is positively significant. The user’s experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study tested Layar AR application in MBA libraries affiliated with Savitribai Phule Pune University in the Pune and Pimpri Chinchwad areas.

The Layar app helps the academic library to convert selected print collections into an AR feel for library users. This is an additional method of providing information services to users through mobile devices. A total of 157 students downloaded the Layar application from their handsets and provided feedback through a questionnaire. Researchers have found that the relationships between users and Layar experience, performance and service quality are positively significant. The user experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study examined the performance, service quality and user experience of Layar applications. Structural equation and Modelling theories were used to examine the relationship between user satisfaction and information services using the Layar application.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

The purpose of studying the impact of artificial intelligence text generators (AITGs) on libraries is to examine the effect of AITGs on the library landscape, including the services offered, the resources provided and the roles of library staff.

The current study examined how AITGs impact libraries. The researcher was able to comprehend the problem by critically analyzing and reviewing the pertinent published works, such as books, journals and articles.

This study concludes AITGs can assist libraries in streamlining operations, enhancing services and making collections more accessible. It is vital to highlight that AITGs are not intended to dissuade its users from visiting physical libraries or to replace them with virtual ones. Instead, they are a tool that can improve and supplement the services and resources provided by virtual libraries.

The study’s observations add to the corpus of information on AITGs in libraries and help users comprehend their technological foundations. Further empirical research is recommended on the effects of AITGs and their impact on libraries.

This study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.

A semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.

The relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.

This is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping.

An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey.

The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping.

This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction.

This article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users.

A web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users.

Both this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface.

The subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.

The purpose of this study is to survey the landscape of online collections of digital games.

First, the study identifies existing sites hosting collections and criteria that make a collection valuable for research, then it reports on sites that fit the criteria and analyzes trends.

Most sites provide simple binary downloads, but some choose encapsulation. Common metadata terms consistently include genre, year of release and publisher. Most sites claim the right to provide their collections as “abandonware,” but remove games if they are asked to.

This study was conducted using a very limited subcategory of digital games, which could be expanded in other studies. Future research may require a multilingual team to account for collections based in non–English-speaking countries. Direct communication with sites’ management may be valuable in the future as well, but was not conducted in this study.

The study identifies practices that have developed organically in this field without any guiding standards. Understanding these may aid in Humanities research into digital games, as well as potential collection development in the future.

Digital games are increasingly important as cultural artifacts, and there is a growing effort to preserve them for the future, but there are no standards for collecting and providing them. Understanding how this is currently done can help in providing access into the future for both casual and analytical use.

While game preservation is a growing and active field of research, no study has been published in recent years on this particular subject. It will be valuable for the development of future collections and for research using current ones.