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SciML Open Source Scientific Machine Learning is an open source organization on GitHub.
This page provides real-time analytics for the GitHub organization SciML Open Source Scientific Machine Learning, including star growth, issue activity, participant engagement, contributor origins, code submission trends, pull request efficiency, and code review metrics. Data is sourced from GitHub events via GH Archive and updated in near real-time.
Discover the popularity and reach of your repositories through stars, enabling you to gauge the community's interest and identify potential collaboration opportunities.
Examine participation dynamics within your organization, analyzing participant activity, engagement depth, roles, affiliations, and geographic distribution. Uncover valuable insights into participate involvement, preferences, and demographics, enabling targeted strategies for enhanced engagement and tailored experiences.
Track participant growth, engagement patterns, and community health over time.
Analyze the development productivity of your organization in handling Pull Requests, Code Reviews, and Code Submissions. Identify bottlenecks in the development process, measure the efficiency of code review and issue resolution, and optimize the workflow for increased productivity.
Analyze your organization's issue management practices to gain insights into user feedback, suggestions, and discussions, indirectly revealing valuable product insights and user sentiments. Evaluate issue closure rates, response times, and active discussions to enhance organizational efficiency and collaboration while aligning with user needs for continuous improvement.