bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.11.14.688517

Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024

Abstract

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a secure cloud-based environment where research and education communities can analyze genomic and biomedical data. The platform supports a wide range of data analysis as well as the ability to safely store and access data in compliance with NIH policies. Work on the AnVIL platform can be easily shared to promote reproducible science and collaboration. The purpose of this study is to better understand the current user base of the AnVIL platform. The AnVIL Community Poll aimed to collect baseline information, identify development opportunities, guide the prioritization of user support strategies, and succinctly but comprehensively describe the current AnVIL Community. The AnVIL Team disseminated the inaugural AnVIL Community Poll by sharing it broadly on social media and through AnVIL and related consortia mailing lists. We categorized respondents as either returning or potential users of the AnVIL platform (based on their provided usage description) and examined user experiences: specifically user backgrounds, technological comfort, research interests, computational needs, and preferences for training and support. Our sample of the AnVIL community found opportunities for platform adoption beyond the current user base and identified areas where training should be enhanced, training preferences, and user computational needs. Specifically, while most respondents were involved in human genomics research, there may be potential for growth in adoption of the platform by prioritizing materials to support clinical researchers. All respondents felt availability of specific tools or datasets was a key feature of the platform. The broader community may also benefit from further development or showcasing of resources to facilitate cost management, finding and incorporating analysis tools, and data import. Our sample greatly preferred virtual training opportunities and returning users of the platform foresaw needing large amounts of storage. This poll provided an insightful snapshot of the current state of the AnVIL and demonstrated areas where the AnVIL Team can take specific steps to address barriers related to platform adoption and further support the existing and varied AnVIL Community. This work can be built upon through user interviews, community discussion, and coordinating a recurring poll.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Isaac, K. J., Cox, K. E. L., Ho, K. Y., Humphries, E. M., Kucher, N., Leek, J. T., Mosher, S., Schatz, M. C., Tan, F. J., Hoffman, A. M.. 2025-11-17. Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024. https://doi.org/10.1101/2025.11.14.688517

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

A strong start for sustained success: inclusivity through a national group mentorship program for first-year graduate students

In the United States, STEM graduate programs and workforce do not represent the demographics of the population. Obstacles, including a lack of transparency, community, and accessible information in navigating academia, disproportionately affect students from underserved backgrounds. Peer mentoring networks can address these disparities. Here, we describe Cientifico Latino, Inc.s Graduate Student Engagement and Community (CL-GSEC) program, a nationwide, group-based peer mentorship program that has served first-year graduate students across the U.S., especially those from underserved backgrounds. Surveys indicate CL-GSEC positively impacts the first-year graduate experience. We highlight key program features, challenges, and insights, such as financial strains faced by first-year graduate students. We offer suggestions for how faculty and departments can better support students during this critical early stage of graduate training. We hope that reporting on CL-GSECs program structure, evaluations, and findings will guide educational leaders in expanding programming for junior graduate students.

scientific communication and education↗

Biodesign Buddy: Integrating Generative Artificial Intelligence in Academic Biodesign

Biodesign is an interdisciplinary research domain that incorporates principles from design and the life sciences to develop new systems, processes, and objects. Collegiate biodesign educators face unique pedagogical challenges, including an absence of relevant scholarship on curriculum design and instructional best practices for cultivating student scientific literacy. These difficulties may be overcome with newly available technologies, like generative AI systems, that enable personalized learning through domain-specific semantic spaces. This article examines the instructional value of one such domain-specific LLM, Biodesign Buddy, through a mixed-methods analysis of an eight-week study involving 64 students participating in an international biodesign competition. Results indicate strong support for integrating AI into biodesign coursework. Surveys captured attitudes toward AI, scientific literature, and learning experiences to assess AIs impact on learning outcomes. Findings suggest that integrating AI into biodesign pedagogy can meaningfully redress conceptual issues in biodesign while informing broader debates on AIs role in higher education. Impact StatementThis article introduces Biodesign Buddy, a domain-specific generative AI system for collegiate biodesign education, and reports on its exploratory deployment, offering design principles and preliminary findings to inform the development of AI-supported pedagogies for interdisciplinary biodesign instruction.

scientific communication and education↗