Xie, ZhiwuChen, Yinlin2024-07-252024-07-252024-02-01https://hdl.handle.net/10919/120696We present a research data management project where librarians from University of California, Riverside and Virginia Tech are deeply embedded in a research team at Yale School of Medicine to directly answer specific research questions by applying AI/Deep Learning techniques to very large biomedical images. Leveraging library resources and expertise, we have developed a prototype pipeline that identifies nuclear pores from whole cell images captured at 8 nanometer resolution by a cutting edge microscope, in the hope to reveal the cellular mechanism of one type of epilepsy and autism. This project exemplifies out data management approach that strives to engage in much earlier stages of research, e.g., even during ideation and data collection, instead of waiting till most research activities are completed to "consult" or "advice" on the very general questions on data storage or preservation. This project also highlights the importance of non generative AI approaches, which have already been widely used as research tools in a much more mature manner.application/pdfenIn CopyrightConference proceeding