Introduction. Ontologies as knowledgebases have been heavily applied in computational biological studies by implementing into ML models for purposes such as disease-gene associations identification. Method. We conduct a case study using gene ontology (GO) annotation data and three ML models to replicate the prediction of autism spectrum disorder (ASD)-causing genes. Analysis. Data were collected from GO and Simmons Foundation Autism Research Initiative (SFARI). The semantic similarities between GO annotation terms on gene products were calculated. Results. The best-performing model can reach an AUC of .85, which means using GO annotation data for ASD disease-gene prediction can receive a significantly accurate result. However, we stress the importance of constructing knowledgebases in adapting to LLMs and the role of LIS professionals in curating community knowledge for interoperability and reuse. Conclusions. Biomedical ontologies play a crucial role in the discovery of biomedical knowledge. Knowledge organization and computer science domains require more communication and synchronization in the face of emerging AI and ML technologies.
Introduction. As artificial intelligence (AI) continues to grow rapidly, governments are implementing legislative frameworks to address its risks and opportunities. This paper provides a comparative analysis of AI Acts in the European Union (EU) and Canada, focusing on two legislative efforts: the EU artificial intelligence act (EU AI Act) and Canada’s artificial intelligence and data act (AIDA). Method. A summative approach of qualitative content analysis was used to examine the scope, risk classification, and regulatory strategies employed in the EU AI Act and Canada’s AIDA. This study highlights similarities and differences in their approaches to managing AI’s societal impacts. Results. Both Acts provide positive directions and encourage responsible AI by addressing AI-related risks and opportunities. The analysis further explores the challenges, such as the definition of AI, enforcement mechanisms, and the inclusion of ethical considerations. Conclusion. By drawing on these cases, the paper illustrates how regulatory steering can ensure responsible AI development and deployment in different geopolitical contexts. This paper offers insights into the evolving nature of AI governance and contributes to the broader discourse on balancing innovation with societal safeguards.
Introduction. Research data sharing and reuse have become increasingly important in modern science, and data papers represent a new academic publication genre aimed at enhancing the visibility, sharing, and reuse of research data. However, whether citations to data papers reflect actual data reuse remains largely unexplored. This paper presents preliminary findings from a project designed to address this gap. Method. we conducted a content analysis to manually annotate 437 citation sentences from 309 research articles referencing 50 data papers published in Data in Brief, a chief academic journal that only publishes data papers. The data papers were sampled from five knowledge domains based on a paper-level classification system. Results. Our results show that most citations to all selected data papers (89%) are unrelated to the research data being described in the paper, instead focusing on the research findings or methodologies. This suggests that data papers are being cited similarly to traditional research articles, despite their unique purpose and content. Conclusion. These findings raise questions about the effectiveness of data papers as representations of research data within the scholarly communication system, as well as their utility in quantitative studies on data reuse.
Introduction. Smart infrastructure development often prioritizes human needs such as safety and efficiency, overlooking the potential to facilitate coexistence with wildlife. This paper addresses this gap by demonstrating innovative use of internet of things (IoT) technology and data visualization for ecological monitoring. Method. We repurposed commercially available wildlife trail cameras to collect data on bats roosting in bridges, despite commercial devices not being built for this application. Furthermore, we developed a map- and cloud-based dashboard to integrate this ecological data with existing bridge metadata, transforming raw sensor data into actionable insights for transportation authorities. Analysis. We explore the ability of the dashboard to meet decision-making needs and offer design implications to further advance wildlife-inclusive smart infrastructure efforts. Results. Our findings demonstrate the efficacy of using IoT devices for wildlife monitoring and the power of data visualization in informing conservation efforts within smart infrastructure development. Conclusions. This study showcases the potential of IoT-enabled cameras and a user-friendly dashboard, BatMap, for effective bat monitoring and informed decision-making in infrastructure projects.
Introduction. Digital technologies have transformed the way we communicate, yet research on digital communication has often focused on either social or technical factors in isolation and has generally lacked a historical perspective. Method. This study integrates a historical perspective with a socio-technical approach to examine the trends and factors driving the evolution of graphicons (kaomojis, emojis, and stickers) on the Chinese social media platform Bilibili. Analysis. The analysis involves two parts: first, identifying graphicon evolution patterns from a 14-year corpus and irregularities in relation to a predicted rise-and-fall model, and then using digital archaeology to reconstruct the history and usage of graphicons on Bilibili, classifying them into social or technical categories. Results. Findings show that only kaomoji usage follows a clear rise-and-fall trend, while trends for emoji and sticker use are less clear. The evolution of graphicons is influenced by both social factors, like user discourse behaviour and graphic arts, and technical factors, such as design and input interfaces. Conclusion. The historically informed socio-technical approach offers a feasible framework for future studies of digital phenomena. Its quantitative aspect allows the identification of longitudinal patterns and irregularities in the data, while its qualitative aspect enables greater depth through considerations of the events underlying the data.
