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Abstract The stable isotope ratio of dissolved inorganic carbon (δ¹³C‐DIC) is a valuable tracer for investigating carbon cycling in aquatic environments. However, its potential remains underutilized due to limited data availability. Fewer than 15% of cruise samples are analyzed forδ¹³C‐DIC, as isotope analysis using isotope ratio mass spectrometry is labor‐intensive and restricted to onshore laboratories. We present over 3500δ¹³C‐DIC measurements from the 2023 Global Ocean Ship‐based Hydrographic Investigations Program A16N cruise in the North Atlantic. Notably, three‐quarters of these measurements were conducted onboard using a CO₂extraction device coupled with cavity ring‐down spectroscopy, a more efficient and cost‐effective method. This extensive dataset providesδ¹³C‐DIC values with spatial resolution comparable to other ocean carbonate chemistry and biogeochemical parameters. This dataset supports improved quantification of anthropogenic CO₂uptake and storage, and may facilitate the development of algorithms to estimateδ¹³C‐DIC in under sampled regions. 

Abstract The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe ongoing changes to our production pipeline to limit the sequences available in UniProtKB to high-quality, non-redundant reference proteomes. We continue to manually curate the scientific literature to add the latest functional data and use machine learning techniques. We also encourage community curation to ensure key publications are not missed. We provide an update on the automatic annotation methods used by UniProtKB to predict information for unreviewed entries describing unstudied proteins. Finally, updates to the UniProt website are described, including a new tab linking protein to genomic information. In recognition of its value to the scientific community, the UniProt database has been awarded Global Core Biodata Resource status. 

As generative AI technologies proliferate across higher education, many U.S. universities are still developing institutional policies to address their ethical, pedagogical, and accessibility implications. This posIT column critically examines AI policies and resources at 50 four year universities—one from each U.S. state—to assess alignment with the Association of Research Libraries’ (ARL) Guiding Principles for Artificial Intelligence. Through content analysis of LibGuides, AI taskforce membership, campus events, and public-facing policies, the study reveals widespread adoption of AI resources but a significant lack of clarity, consistency, and librarian involvement in policy development. While most institutions meet baseline criteria related to privacy, plagiarism, and algorithmic transparency, fewer address AI’s potential harms to marginalized communities or its impact on accessibility for students with disabilities. Notably, fewer than half of the AI taskforces surveyed included library staff, despite librarians’ expertise in digital literacy and ethical information use. This column urges academic librarians to actively seek leadership roles in institutional AI governance to help shape inclusive, responsible, and human-centered AI policy frameworks. 

This column explores the practical considerations and institutional strategies for adopting Generative Artificial Intelligence (GenAI) tools in academic libraries. As higher education institutions increasingly integrate AI into teaching, research, and student support, libraries play a pivotal role in guiding ethical, inclusive, and pedagogically sound implementation. Drawing on case studies from Clemson University, Wake Forest University, and Goldey-Beacom College, the column examines key areas of GenAI integration: contract negotiations, licensing models, trial and pilot program design, data privacy, accessibility, authentication, analytics, training, and ethical use. The article emphasizes the importance of aligning AI adoption with institutional missions, user agency, and evolving frameworks of AI literacy. Recommendations are provided for libraries of all sizes to navigate the dynamic GenAI landscape responsibly and equitably, ensuring that academic integrity and student-centered values remain at the core of AI integration. 

Large Language Models (LLMs) have gained attention in research and industry, aiming to streamline processes and enhance text analysis performance. Thematic Analysis (TA), a prevalent qualitative method for analyzing interview content, often requires at least two human experts to review and analyze data. This study demonstrates the feasibility of LLM-Assisted Thematic Analysis (LATA) using GPT-4 and Gemini. Specifically, we conducted semi-structured interviews with 14 researchers to gather insights on their experiences generating and analyzing Online Social Network (OSN) communications datasets. Following Braun and Clarke's six-phase TA framework with an inductive approach, we initially analyzed our interview transcripts with human experts. Subsequently, we iteratively designed prompts to guide LLMs through a similar process. We compare and discuss the manually analyzed outcomes with responses generated by LLMs and achieve a cosine similarity score up to 0.76, demonstrating a promising prospect for LATA. Additionally, the study delves into researchers' experiences navigating the complexities of collecting and analyzing OSN data, offering recommendations for future research and application designers. 

As artificial intelligence (AI) tools become integral to education, fostering ethical engagement and academic integrity is paramount. This column explores the integration of AI tools, including Grammarly, Scholarcy, and ImageFX, into a first-year writing program to promote ethical AI literacy. Grounded in Lo’s CLEAR framework, structured exercises emphasized critical thinking, intellectual agency, and human-AI collaboration. The study highlights the potential for AI to enhance learning while addressing challenges such as equity, accessibility, and ethical considerations. Findings demonstrate how intentional integration of AI tools fosters creativity, self-reflection, and responsible decision-making, providing a scalable model for incorporating AI into curricula while preserving student agency and integrity.