Are Open Access Ebooks Accessible?

The Directory of Open Access Books (DOAB) is a discovery service for open access e-books. It provides a searchable index to peer-reviewed e-books published under an open access business model, with links to the full text of the publications at the Open Access Publishing in European Networks (OAPEN) Library, publisher’s website or repositories. The present study aims to assess the current trends of the open access e-books in the field of science and technology available through DOAB. The data was collected online regarding the science and technology e-books in March 2014 for analysis. The results reveal that 307 e-books are available on science and technology through DOAB falling in three categories–monographs (209, 68.08 %), book series (93, 30.29 %) and conference proceedings (5,1.63 %). These e-books deal with eight major subject areas of Science and Technology having 36 sub-fields. The maximum number of e-books is available on General Science (95), Technology & Engineering (54), Earth & Environmental Sciences (50) & Health & Medical Sciences (47). In sub-fields most of the e-books cover Computer Sciences (24), Information Theory (24), General Medicine (20), Mathematics (17), Biology (13) & Geography (11). The authorship trends show that the maximum contribution is by single author (51.14 %) followed by two authors (74, 24.10 %) and three authors (36, 11.73 %). The linguistic assessment shows that 57.98 % (178) of these e-books have been published in English followed in German (88, 28.66%) and Italian (18, 5.86 %) respectively. The publishing trends reveal that 59.93 % (184) e-books have contributed by university presses whereas 40.07 % (123) by other publishing houses around the world. The maximum number of e-books (245, 79.80 %) is available through the Creative Commons (CC) license, whereas 20.20 % (62) e-books are available through comparable institutional licenses.

The Article explores the interplay between copyright law and the use of large language models (LLMs) in legal research. Both, the training of LLMs and their use for research and evaluation of linguistic works involve reproductions, bringing subsequent data analysis indirectly under the purview of copyright law. This dynamic raises concerns about the potential inhibitory effect of copyright on innovation in legal research involving LLMs, especially given the concentration of rights ownership in Germany and potentially in other countries among a few legal database providers. Access to data compatible with the use of LLMs in the field of legal research is thus contingent not only on legal database subscriptions but also on the (limited) functionality offered by these databases, such as search tools and interfaces. The Article identifies three main strategies to mitigate these dependencies and foster innovation within the bounds of copyright law. First, the use of public domain judicial decisions and open access publications including works licensed under Creative Commons can facilitate scraping and text and data mining (TDM) strategies for training LLMs or as input for LLM-assisted research. Second, strategies could enable LLM training without copyrightrelevant reproductions by relying on derivative components of works that fall outside the scope of copyright protection. Such approaches highlight the LLMs reliance on linguistic syntax and general cultural knowledge rather than specific copyrighted works, raising questions about whether these elements require independent legal protection. Third, German copyright provisions implementing Art. 3 DSM-Directive and Art. 5, 6 InfoSoc-Directive, particularly §§ 44a and 60d of the Copyright Act (UrhG), combined with § 95b UrhG on the circumvention of technological protection measures, provide legal pathways for detaching from limitations in functionality of the legal databases and to allow crawling and scraping as prerequisite for TDM in connection with LLMs. These provisions allow researchers on the basis of the text and data mining exception to use LLMs for innovative purposes, such as automated analysis of legal texts or extracting legal arguments, provided the research pertains to specific projects and avoids data hoarding. While the legal framework protects the interests of rights holders, it imposes significant restrictions on the use of copyrighted works in LLM-related research. Lawful access remains a prerequisite, with further TDM-based use potentially subject to licensing fees unless the materials are open access. Given the market dominance of a few database providers, adjustments in pricing models are likely. The advantages of § 60d UrhG thus have to be seen in reducing transaction costs and enabling effective crawling of legal databases under § 95b UrhG. These advantages can contribute to unlocking innovative LLM applications in legal scholarship. Future legislative responses to the evolving tensions between copyright and LLMs will likely shape this emerging field and warrant continued academic scrutiny.

The study investigated the awareness and use of open access (OA) channels for scholarly publishing by researchers at selected universities in Kenya. A sample of 341 faculty members, from three public universities, was selected for the study. A questionnaire was used as the main data collection instrument. The findings revealed that there is a considerably high level of awareness among the researchers; librarians were the main channel with regard to creating awareness; the OA channels that are more frequently used by researchers are OA journals, OA institutional repositories and OA e-books; the researchers mostly published/disseminated theses and dissertations, conference proceedings and workshop/seminar presentations through OA channels; and searching and accessing research articles, developing research proposals and formulating research problems were among the main reasons why researchers used open access channels. There are several reasons why researchers sometimes do not use OA channels for research. The study makes several recommendations for the adoption and effective use of OA channels in the selected universities and beyond.

The most rapidly changing pervasive and publicized aspects of library and information studies are e-resources. In a relatively short period of the time, e-resources have expanded drastically from the few dozen computerized bibliography databases to the overwhelming amount of information available today. Electronic resources have grown to include online library catalogues, lists of CD-ROMs, online journals, databases, newspapers, reference materials, open access journals, e-books, major publishers, and online bookshops. There is a pressing need for guidance regarding the use of such resources. The findings of this study conclude that the age-wise respondents have many problems accessing e-resources including computer viruses, having difficulty using digital resources due to lack of IT knowledge, and their limited access to computers.

The emergence of generative AI (GenAI) is a major driving force behind the modern data science ecosystem, a field that exploits data as the central asset for actionable insights. Analogously, GenAI is a form of artificial intelligence which learns from massive datasets to generate new data, showcasing human-like creativity in text, images to code, speech, and video. Two critical pillars of the GenAI technology are large language models (LLMs) and vector data. In particular, LLMs are a category of genAI models that emphasize on generating new text contents. On the other hand, there is also an upsurge of dense, high-dimensional, billion-scale vector data from deep learning models that embed complex data, e.g., text, multimedia, graphs, and tables into vector representations aiming to preserve semantic similarity. Since LLMs operate on vector data at various stages consisting of pre-training, fine-tuning, inference, and retrieval-augmented generation (RAG), coupling large language models with vector data management is essential for enhancing data science services with cross-modal data querying and generation. It creates new opportunities and challenges in areas such as accuracy, consistency, efficiency, scalability, privacy, fairness, explainability, data regulations, software-hardware collaboration, and cloud-native systems. The workshop aims to advance the understanding of how LLMs and vector data management can cooperatively contribute to data science solutions.

Adequate health literacy has been shown to be important for the general health of a population. To address this, it is recommended that patient-targeted medical information is written at a sixth-grade reading level. To make well-informed decisions about their health, patients may want to interact directly with peer-reviewed open access scientific articles. However, studies have shown that such text is often written with highly complex language above the levels that can be comprehended by the general population. Previously, we have published on the use of large language models (LLMs) in easing the readability of patient-targeted health information on the internet. In this study, we continue to explore the advantages of LLMs in patient education. This study aimed to explore the use of LLMs, specifically ChatGPT (OpenAI), to enhance the readability of peer-reviewed scientific articles in the field of ophthalmology. A total of 12 open access, peer-reviewed papers published by the senior authors of this study (ET and RA) were selected. Readability was assessed using the Flesch-Kincaid Grade Level and Simple Measure of Gobbledygook tests. ChatGPT 4.0 was asked "I will give you the text of a peer-reviewed scientific paper. Considering that the recommended readability of the text is 6th grade, can you simplify the following text so that a layperson reading this text can fully comprehend it? - Insert Manuscript Text -". Appropriateness was evaluated by the 2 uveitis-trained ophthalmologists. Statistical analysis was performed in Microsoft Excel. ChatGPT significantly lowered the readability and length of the selected papers from 15th to 7th grade (P<.001) while generating responses that were deemed appropriate by expert ophthalmologists. LLMs show promise in improving health literacy by enhancing the accessibility of peer-reviewed scientific articles and allowing the general population to interact directly with medical literature.

Medical report generation demands accurate abnormality detection and precise description generation from CT images. While large language models have shown promising results in natural language processing tasks, their application in medical imaging analysis faces challenges due to the complexity of fine-grained feature detection and the requirement for domain-specific knowledge. This paper presents a novel framework integrating large language models with specialized medical image processing techniques for fine-grained abnormality detection and natural language description generation. Our approach incorporates a multi-modal knowledge enhancement module and a hierarchical attention mechanism to bridge the gap between visual understanding and textual description. The framework employs an adapter-based architecture for efficient domain adaptation and introduces a medical knowledge-enhanced loss function to improve description accuracy. Experimental results on three public datasets demonstrate the effectiveness of our approach, achieving 94.6% detection accuracy and a BLEU-4 score of 0.421 for description generation, surpassing current state-of-the-art methods. The system shows particular strength in handling subtle abnormalities, with a 91.2% average precision in fine-grained detection tasks. Comprehensive ablation studies validate the contribution of each component, while qualitative analysis demonstrates the clinical relevance of generated descriptions. The proposed framework represents a significant advancement in automated medical image analysis, offering potential benefits for clinical workflow optimization and diagnostic support.

Random sampling for pose determination and refinement, G. Roth and M. Levine robust high breakdown estimation and consensus, P. Meer model-based synthesis of vision routines, T. Messer electromagnetic models for perceptual grouping, T. Pun multiple-font alpha-numeric recognition using multi-layer neural networks with a rejection function, K. Yamada 3-D object model synthesis in a monocular imaging system, A.M. Earnshaw and A.K.C. Wong a microcomputer based supervised system for automatic scoring of mitotic index in cytotoxicity studies, M. Garza-Jinich et al a multi-operator approach for the segmentation of 3-D images of dental imprints, J. Cote et al modeling sonar range sensors, D. Wilkes et al.

Notwithstanding recent advances in machine vision, video activity recognition from multiple cameras still remains a challenging task as many real-world interactions cannot be automatically recognised for many reasons, such as partial occlusion or coverage black-spots. In this paper we propose a new technique that infers the unseen relationship between two individuals captured by different cameras and use it to retrieve relevant video clips if there is a likely interaction between the two individuals. We introduce a human object interaction (HOI) model integrating the causal relationship between the humans and the objects. For this we first extract the key frames and generate the labels or annotations using the state-of-the-art image captioning models. Next, we extract SVO (subject, verb, object) triples and encode the descriptions into a vector form for HOI inference using the Stanford CoreNLP parser. In order to calculate the HOI co-existence and the possible causality score we use transfer entropy. From our experimentation, we found that integrating casual relations into the content indexing process and using transfer entropy to calculate the causality score leads to improvement in retrieval performance.

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Recent advances in Large Language Models (LLMs) present new opportunities for automating critical bottlenecks in scientific workflows such as literature reviews or protocol design. One such bottleneck is the purification of recombinant proteins, a vital aspect of biomedical research that frequently fails. To improve success rates, researchers must manually define optimal large-scale purification conditions and establish robust rescue protocols for proteins with low stability or solubility - a time-intensive process. To address this gap, we introduce a multi-agent LLM system that automates the creation and optimization of protein purification protocols to facilitate the production of high-concentration, high-purity protein samples. Our application streamlines the labor-intensive manual process of sequence similarity searches, literature reviews, and protocol comparison. Operating in a tool-like constrained workflow, the system identifies analogous proteins, leverages specialized LLM agents to extract successful purification methodologies from primary source literature, and cross-references them against failed protocols to generate optimization recommendations. Evaluation on a select number of targets demonstrated high accuracy in protocol extraction and the generation of scientifically sound, expert-validated optimization recommendations. While this system reduces complex analysis time from hours to minutes, we identify the lack of programmatic open access to literature, specifically primary citations in the Protein Data Bank, as a fundamental limitation to LLM agent-based scientific workflows. Ultimately, this system demonstrates the feasibility of using LLM agents to streamline wet-lab workflows while preserving methodological transparency and reproducibility.

The covid-19 pandemic has changed the pattern of learning and information tracing in the world of education. This condition becomes a challenge for academics amid various limitations and new habits. In addition to the problem of online learning, currently, students are also faced with difficulties in finding and accessing references to support online learning given the limitations of library services that have not provided loans and reading services online. This condition is faced by the partner of the service program, namely students of Fisip UHO. Problems faced by partners, first limited knowledge in access ebook services and open access e-journal. Second, the ability to use Mendeley as a means of citation scientific work. Solving the problem is done through ebook access training, e-journal and the use of Mendeley continued with the practice of finding open access reference sources and doing the citation independently in Mendeley The results showed significant changes in knowledge and skills for access to ebooks and e-journal services as well as Mendeley. There are only 60% of partners understand ebook access, 64% understand e-journal access, and there are 33.3% of partners who ady understand using Mendeley. This percentage value was obtained through the distribution of questionnaires before the training activities to 22 participants. After the training was carried out there was a change of one part to 100% of partners already understunderstooding ebook and e-journal services, but only 93.8% were able to use Mendeley.

Large Language Models (LLMs) have demonstrated revolutionary potential in autonomous systems. Studies have demonstrated that LLMs can be leveraged to support various robotic operations including visual navigation, planning, and drone controls. However, when facing tasks with complex reasoning, concerns and challenges are raised about the reliability of solutions produced by LLMs. This thesis proposed a closed-loop reasoning approach to enhance the reliability of LLM-driven drone control. The proposed method introduces a structured prompt framework designed with Guidelines, Skills, Constraints, and Examples integrated with a closed-loop feedback mechanism that iteratively refines the generated code based on its execution outcomes. The framework is featured by iteratively refining LLM-generated solutions, thereby improving reasoning quality and control reliability. We performed thorough experiments of our approach for drone control with a wide range of task complexities. The experiment result demonstrates that our framework significantly improves task success rates and completeness compared to baseline approaches, highlighting its potential for reliable LLM-driven autonomous drone systems.

Open-access scientific research is an essential source of health-related information and self-education. Artificial intelligence-based large language models (LMMs) may be used to identify erroneous health information. To investigate to what extent LMMs can be used to identify pseudo-information. Four common LMM applications (ChatGPT-4o, Claude 3.5 Sonnet, Gemini and Copilot) were used to investigate their capability to indicate erroneous information provided in an open-access article. Initially, ChatGPT-4o and Claude were able to mark the provided article as an unreliable information source, identifying most of the inaccuracy problems. The assessments provided by Gemini and Copilot were inaccurate, as several critical aspects were not identified or were misinterpreted. During the validation phase, the initially accurate assessment of ChatGPT-4o was not reproducible, and only Claude was able to detect several critical issues in this phase. The verdicts of Copilot and Gemini remained largely unaltered. Large heterogeneity exists between LMMs in identifying inaccurate pseudo-information. Replication in LMM output may constitute a significant hurdle in their application. The accuracy of LMMs needs to be further improved until they can be reliably used by patients for health-related online information and as assistant tools for health information and library services workers without restriction.

Co-creation is recognised as a promising methodology for addressing complex public health issues by leveraging the collective intelligence of multiple stakeholders to develop tailored solutions. However, there is a lack of clarity and significant fragmentation in the concept. This study aims to define the fundamental attributes and guiding principles of co-creation to establish it as a rigorous, evidence-based, and trustworthy methodology. A participatory concept mapping process based on the Differentiae Principle was employed. The study consisted of four stages: 1) systematic extraction of attributes and their definition from traditional scientific and participatory research methodological literature, 2) screening, 3) semantic analysis and clustering into principles using Natural Language Processing and Large Language Models and 4) selection of a final set of attributes and development of principles. The study involved 50 participants with diverse backgrounds within and outside the Health CASCADE Network over 18 months. The study identified 105 candidate attributes. A set of 38 was selected from which 10 key principles were derived (open access, recognised contribution, ethics and legal, transparency, rigorous evaluation, clear problem, plural evidence, critical reflection, diversity, and structured and flexible processes) across three domains: Governance, Scientific and Methodological Rigour, and Collective Intelligence Processes. The attributes and guiding principles provide a comprehensive foundations for evidence-based co-creation, enhancing its trustworthiness, transparency, reliability, and impact in public health and other fields. Future research and practice should use these principles and attributes to refine and implement co-creation effectively.