Path Catalog
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正在从 AllPath API 加载 Path Catalog…
Path Catalog
列表数据实时取自 GET /api/v1/paths，仅包含存在已发布版本的 Path。
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A professional learning path covering core ethical frameworks, data ethics, algorithm bias, privacy, responsible AI, and ethical decision-making for computational science practitioners. The path emphasizes practical application and critical reflection.
This learning path equips research leaders and managers with the skills to plan, execute, and oversee computational science projects. It covers research planning, resource management, team coordination, milestone tracking, and software project management, tailored to the unique challenges of scientific computing.
A professional learning path for data scientists in scientific domains to master high-performance analytics. It covers scalable data processing with Spark and Dask, distributed computing fundamentals, scientific data formats, and streaming analytics, progressing from core concepts to advanced applications.
A comprehensive learning path for computational scientists to master advanced visualization techniques for scientific discovery. Covers scalar, vector, and tensor visualization, volume rendering, interactive visualization, and hands-on tools like ParaView and VTK, grounded in necessary programming and computer graphics foundations.
This path equips researchers and practitioners with the knowledge to leverage cloud infrastructure for scientific computing. It covers core cloud concepts, major providers, scalable computing, cost management, and practical considerations for deploying scientific workloads.
A practical learning path for computational researchers to master containerization for reproducible science. Covers core container concepts, Docker for environment management, Singularity for HPC deployment, and best practices for reproducibility.
A focused learning path for data professionals in science to master the core concepts and practices of managing scientific data. It covers data storage, formats, metadata, standards, databases, sharing, and FAIR principles, with a foundation in data science basics.
This learning path introduces social science students to computational methods for studying social phenomena. It covers core concepts in computational social science, basic programming for data analysis, and key methods including social network analysis, agent-based modeling, and text analysis. The path emphasizes practical applications and ethical considerations, preparing learners to design and interpret computational social science research.
A learning path for environmental science students to acquire computational skills for modeling pollution, hydrology, ecosystems, and conservation, while analyzing environmental data. It integrates programming fundamentals with environmental science principles.
This advanced graduate-level learning path equips climate and environmental science students with the computational knowledge needed to understand and work with climate models. It covers the physics of the climate system, numerical methods for solving model equations, and the structure and application of global climate models, including radiative transfer, ocean and ice sheet modeling, and climate feedbacks.