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Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine Abstract Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge. Resource link: https://github.com/Texera/texera Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET
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In this study, graphene oxide was electrochemically deposited and reduced on a graphite sheet. The electrode surface morphology was studied by scanning electron microscopy. The performance of the modified electrode in detecting dopamine ...
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