Cytomining Ecosystem Software Landscape Analysis - Target Project Report

This is a targeted report to help describe three Cytomining Ecosystem software projects in terms of scholarly paper mentions, package usage, software dependents, and project visitors.

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

Scholarly Papers

This section helps visualize data related to how many scholarly papers mention or cite the target software.

• Description: This plot shows how many publications mention or cite Pycytominer as a reference. We used Google Scholar and BioRxiv as search resources for this work. Google Scholar is a scholarly article aggregator and search service. BioRxiv is a pre-print server with biological focus.
• Findings: We observe that Pycytominer appears in 22 Google Scholar results and 14 BioRxiv preprints. This demonstrates how Pycytominer is already being used in both previous and ongoing research efforts.

Package Usage

This section helps depict usage of the target software by leveraging package data from PyPI and Conda.

• Description: This plot shows the total number of PyPI and Conda downloads by project. PyPI (Python Package Index) is a platform which is commonly used to download Python packages. Conda is an environment and package management service which is commonly used for data science projects.
• Findings: This plot demonstrates how pycytominer has many downloads through both PyPI and Conda (with slightly more Conda downloads). CytoTable and CytoSnake are shown with strong early download counts at a much lower level (perhaps in alignment with their project age, which is less than pycytominer).

• Description: This plot shows how many PyPI package downloads per month by project. CytoSnake is excluded from this plot as it is not available on PyPI.
• Findings: Pycytominer has a consistent and trending upward number of monthly PyPI package downloads. CytoTable shows activity in this area, possibly trending upwards.

• Description: This plot shows how many Conda package downloads per month by project. CytoTable is excluded from this plot as it is not available on Conda.
• Findings: We can observe from this plot that pycytominer is actively downloaded on monthly basis. There is a large spike in download count for pycytominer in 2022 with lower counts in 2023. CytoSnake shows strong signs of good adoption for a relatively young project (in comparison with pycytominer).

Software dependents

This section helps show community dependents of the targeted software.

• Description: This plot shows dependents counts of the projects by leveraging the Dependency Graph and code search features of GitHub. Overlaps by organization and project name are removed to help count unique dependents.
• Findings: We observe that Pycytominer has many existing dependents on GitHub. CytoTable has a smaller number of dependents which may be relative to the project age.

Project dependents Graph (click and scroll with mouse to interact)

• Description: This graph shows how pycytominer and CytoTable are used as dependencies of existing projects on GitHub (from Dependency Graph and code search by name). Pycytominer is included as a pink node with edges in the same color. CytoTable is shown similarly in orange.
• Findings: We observe pycytominer as being connected to many existing projects. CytoTable is also connected to many Pycytominer projects. This interconnection in part shows how these components are used together in related landscape projects.

Project Visitors

This section helps show how many visitors have have interacted with the project over time.

• Description: This plot shows GitHub stars as a way to understand visitor activity on a monthly basis for the target projects. GitHub stars are an indication of unique users whom are interested in and likely use the project somehow.
• Findings: We can observe pycytominer has having a relatively consistent number of added GitHub stars since mid-2019. We also show that CytoSnake and CytoTable somewhat mimic the earlier growth in this area which pycytominer demonstrated.

• Description: This plot shows GitHub stars as a way to understand visitor activity on a monthly basis for the target projects. This plot differs from the one above in that the star counts are shown as cumulative. GitHub stars are an indication of unique users whom are interested in and likely use the project somehow.
• Findings: We can observe pycytominer has having a relatively consistent number of added GitHub stars since mid-2019. We also show that CytoSnake and CytoTable somewhat mimic the earlier growth in this area which pycytominer demonstrated.

Table with selected dataset columns

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