Cytomining Ecosystem Software Landscape Analysis - Landscape Report

This is a focused report to help describe landscape elements related to project user base size, usage, maturity, and general landscape for the Cytomining Ecosystem.

Code related to this effort may be found at: https://github.com/WayScience/software-landscape-analysis.

User base

This section helps visualize the relative user base size and age of projects.

• Description: This plot explores the user bases of relevant projects through relative GitHub stars and the age of the project in years. GitHub stars are an indication of unique users whom are interested in and likely use the project somehow.
• Findings: We observe that projects of greater age are not always those with the most stars. Projects of the loi-focus category (e.g., pycytominer) are generally in an early state of their user base (both in age and stars), relative to other projects (e.g., Napari). We note the limitations of using stars to gauge community engagement given many projects do not focus efforts on developers.

• Description: This plot demonstrates the magnitude of total GitHub stars by project and category. Click the category boxes to zoom into focus on only those projects.
• Findings: Here we can see the relevant open source category far outsize other categories. Within the microscopy analysis tools category we observe that Napari has many more stars than others. Projects of the loi-focus category are emerging with a growing community base, but not are not as established as other tools with much greater age.

Maturity

This section seeks to demonstrate relative project maturity by leveraging age, relative activity, GitHub stars, and total lines of code.

• Description: This plot shows the lines of code (LoC) and age in years to help indicate relative project maturity. Lines of code in this case are the sum total of all detected programming languages in the latest version of the GitHub repository.
• Findings: We observe that many tools within the Cytomining Ecosystem landscape reside within a tight range of total lines of code (relative to others), and that projects of greater age do not necessarily have more code.

• Description: This plot demonstrates project GitHub stars and most recent commit datetime as a way to observe potential decay for projects which may have been popular at one point but are no longer maintained.
• Findings: We find that relevant open source, microscopy analysis tools, and tools of loi-focus are all recently updated. Adjacent tools demonstrate historical usage but are somewhat less frequently maintained. Many tools from GitHub queries show a lack of updates for greater than 2 years. We hypothesize that many GitHub projects re-invent functionality related to the Cytomining Ecosystem. These projects are sometimes abandoned after authors perform bespoke or one-time analyses.

Usage

This section portrays relative project usage through GitHub watchers, forks, contributor and issue counts.

• Description: This plot shows project GitHub watcher and GitHub fork count as a way to demonstrate relative usage.
• Findings: We see a wide variety of project watcher and fork count depending on the category. Generally, projects of loi-focus are emerging tools that are at an earlier stage and thus have fewer forks and watchers.

• Description: This plot shows the number of project contributors and issue counts as a way to describe usage through maintainability.
• Findings: We find that many projects have a high contributor and issue count. A few examples in the adjacent tools and microscopy analysis tools categories are trending toward tools in the relevant open source category. Projects of loi-focus are nearby many landscape neighbors in the middle of this plot.

General landscape

This section visualizes broader aspects of the software landscape such as primary language preference and GitHub organization metrics.

• Description: This plot shows the primary programming language project count colored by category. A primary programming language is derived from the maximum lines of code from GitHub-detected programming languages.
• Findings: We observe that R, Python, and Jupyter Notebooks are among the most popular primary programming languages found within the Cytomining Ecosystem software landscape.

• Description: This plot shows the count of projects per GitHub organization (a collection of repositories) for those organizations with more than 2 projects.
• Findings: Here we find that the Theis Lab, UC Davis Bioinformatics Training, and Teich Lab are at the top of this listing. Cytomining is also near the top of the list, which is a code base that supports essential functionality that does not exist in any other open source organization that is actively maintained.

• Description: This plot shows the sum of GitHub stars per GitHub organization for those with a sum total of stars greater than 100.
• Findings: Here we can observe that scverse, Theis Lab, and Satija Lab organizations hold the top count of GitHub stars. The Cytomining organization is found near the middle of this visualization, indicating similar popularity and early success in audience outreach amidst those within the related landscape.

Table with selected dataset columns

The table below may be used to search and view a selected number of columns from the dataset.