| Characterizing the Open Source Software Ecosystem |
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What is the distribution of OSS projects and collaborations by category, sector, country and how does it change over time? What are the knowledge contributions of these actors (e.g., sectors, institutions, countries)? What are the prevailing OSS categories, languages, topics, and methods? |
Descriptive, visual, and statistical analysis of the collected data (e.g., projects, contributors, commits) Use of OSS metrics Text-mining (parsing, string-matching) to extract and group sectors, country, topics, etc. |
| Network Analysis |
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What are the overall structural features of the OSS networks (in Table 1)? How do they differ across fields, sectors, institutions, and countries? Units of analysis (OSS actors): projects, categories, developers, institutions, sectors, countries? What are the different communities that can be identified using structural features of the networks? Do they correspond to similarities in languages, methods, location, culture? |
Computing network metrics: global and local measures, e.g., density, diameter, clustering; and statistical properties Visualization methods developed in knowledge domains Community detection algorithms, random walk, module? |
| Measuring Impact: OSS Developers and Projects |
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- Who are the key players (projects, developers, institutions, sectors, and countries) on the networks and how has this changed over time? How do the positions of OSS actors impact OSS contributions?
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- Network centrality measures (e.g., degree, betweenness, eigenvector, PageRank), measures of graph cohesion, reuse, tenure, downloads, and others
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| Diffusion of Open Source Software Innovation |
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- What are the major paths from/to countries, sectors, institutions and fields that OSS innovation disseminates? What are the upstream and downstream actors, i.e., sectors, institutions, and countries?
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Average path length, edge weights, temporal analysis Diffusion models, information cascades |