While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management "how can organizations use knowledge more effectively?". In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.
MAIA: A Collaborative Medical AI Platform for Integrated Healthcare Innovation
Department of Biomedical Engineering and Health Systems, KTH, Royal Institute of Technology（生物医学工程与健康系统系，皇家理工学院）；Department of Clinical Sciences, Intervention and Engineering, Karolinska Institutet（临床科学、干预与工程系，卡罗林斯卡研究所）；Division of Medical Radiation Physics, Department of Physics, Stockholm University（医学放射物理 division，物理系，斯德哥尔摩大学）；Department of Oncology-Pathology, Karolinska Institutet（肿瘤学-病理学系，卡罗林斯卡研究所）；Department of Radiology, Karolinska University Hospital（放射学系，卡罗林斯卡大学医院）；Department of Molecular Medicine and Surgery, Karolinska Institutet（分子医学与外科系，卡罗林斯卡研究所）；Department of Neurobiology, Care Sciences and Society, Karolinska Institutet（神经生物学、护理科学与社会系，卡罗林斯卡研究所）
本文提出开源协作平台 MAIA，基于 Kubernetes 集成数据、建模、标注、部署与临床反馈工具，以推动医学影像 AI 从研究到临床的可复现转化。
Lean Atlas: An Integrated Proof Environment for Scalable Human-AI Collaborative Formalization
Nyx Foundation（Nyx基金会）；Kyoto University（京都大学）；Large Language Model Research and Development Center, National Institute of Informatics（信息处理国家研究所大型语言模型研究与开发中心）
