analysis

src/analysis

The scripts in this directory were used to produce derived data and plots.

• aggregate_datasets.py aggregates all data mined from GitHub into four datasets described in the wiki. Crucially, the data is reshaped into a time-indexed format for three of those datasets.
• mention_type_timeline.py visualises the relationship between how a repository is cited and the difference between its creation date and the publication date.
• repo_intent.py creates a dataset with all repositories mined from ePrints for which we manually determined the citation type. The resulting dataset contains data from ePrints as well as a label indicating whether the software was cited as created software.
• overall.py creates one plot containing visualisations and data about all repositories. The dataset can be filtered for a subset of repositories with the --filter argument.
• repository_timeline.py creates one plot for one repository, focussing on timelined data. The code to produce these uses the raw data rather than the aggregated data produced by aggregate_datasets.py as this script was written before aggregate_datasets.py. Both scripts use the same data manipulation methods - directly plotting data produced by aggregate_datasets.py should result in similar graphs.
• github.ipynb was used for exploratory data analysis. The most interesting visualisations were later transferred into overall.py and repository_timeline.py.
• eprints.ipynb produces visualisations illustrating the relationship between publications and GitHub links found in them.

The schemas for any produced datasets are included in the wiki.