Software Overview and Use Cases

Both CollectOSS (pronounced "collect us") and GrimoireLab are excellent tools to help you derive meaningful health insights for open source projects and ecosystems, but they both take very different approaches. As a result of these differences, one of them might be a better choice depending on what you need to do.

GrimoireLab’s distinctive features are that you can use it to:

• get an aggregated view of your software development activity across a wide variety of channels (e.g., repositories, mailing lists, chat tools, wikis) and create a central place for data mining across sources to make sense of this information for your particular context.
• apply a large number of existing, pre-made visualizations to spot trends and continuously monitor the health of your open source projects and ecosystems and customize those visualizations using OpenSearch queries to drill in on interesting data.
• see GrimoireLab in action in the CHAOSS Dashboard.

CollectOSS’s distinctive features are that you can use it to:

• focus on data from GitHub and GitLab platforms (with support for additional platforms like Forgejo coming soon)
• collect data at scale with support for rotating API keys and collection that can scale to tens of thousands of repositories
• write custom queries that explore complex or unanticipated questions using data science or data engineering tools that are built for relational databases and structured data.
• explore data about compliance, security, dependencies, and related software topics to better understand potential risks associated with an open source project.
• use visualizations to learn about community health and explore the CollectOSS data in action using the 8Knot dashboard.

While anyone can use either tool to derive meaningful insights:

• Data scientists, researchers, and other data analysts might be more comfortable writing custom queries using CollectOSS’s relational database for in-depth research.
• Community managers, engineering / product teams, or project leaders might appreciate the ease of spotting trends using GrimoireLab’s visualizations across various use cases.

This overview contains only a few of the many scenarios that might bring someone to use CHAOSS tools. These are both complex tools with many features and functionality that can’t adequately be summarized within a few points. In addition, every open source project is unique, and the needs for data about open source projects and ecosystems can vary wildly. While we hope that the above summary might help you select the right tool for your needs, we encourage you to explore additional benefits and features below in more detail before making a final selection.

More Benefits and Features

CollectOSS

• Data is collected incrementally and includes all messages and commits associated with issues, pull requests, and pull request reviews, including historical data.
• Our high velocity parallel data collection is being used in production with over 40,000 repositories, enabling assessment of diverse open source software ecosystems.
• The CollectOSS community is always improving automated tests, documentation, and practicing transparent quality assurance processes to make CollectOSS a reliable source of upstream health data with full observability.
• Collection and analysis goes beyond the counting of activities to include license coverage and license type information, COCOMO based software complexity and cost of replacement data by project and file, software dependency scanning, measurement of dependency LibYears, and time series persistent OpenSSF Scorecard information.
• Users can explore complex or unanticipated questions while performing in-depth research using CollectOSS’s relational database or API to write custom queries.
• CollectOSS includes data visualizations through an extensible frontend built using tools familiar to data scientists (e.g., Dash and Plotly) upon which 8Knot is developed.

For more details, visit the CollectOSS repository.

GrimoireLab

• Data is produced in a consistent way for clear reporting and trusted insights. Data is collected from 30+ data sources, including historical data. Incremental data collection allows for quicker updates. Data quality is ensured through consistent data decay prevention methods.
• Raw data is enriched to provide deeper insights and allow analysis that goes beyond the basic count of events. For example, the onion analysis identifies core, regular, and casual contributors over time. The attraction and retention metrics identify contributors that recently joined a project and those that have become inactive.
• Dashboarding solutions are provided on top of the data for exploring data and creating custom visualizations and dashboards that are shareable with links and live data
• Management interfaces and APIs are available for updating organizational affiliations and deduplicating contributors
• Access to data is available in three levels: (1) User interface for exploring and sharing data; (2) Management interface for creating visualizations and dashboards, and for managing affiliations; (3) Data interface through OpenSearch API to raw and enriched data for custom analysis in different tools like Jupyter Notebooks.
• Network analysis allows uncovering relationships and interconnections between projects, repositories, contributors, and organizations.
• Data privacy is built in to support GDPR compliant operation.

For more details, visit the GrimoireLab website.