Search for: All records

Open-source software (OSS) has become an essential in knowledge production and innovation in both academic and business sectors around the globe. OSS is developed by a variety of entities and is considered a “unique scholarly activity” due to the complexity of scientific computational tasks and the necessity of cooperation and transparency for research methodology. While the developers of OSS are thought to be very widespread, there remains many questions to be answered about who these contributors are, who are the largest contributors (countries, sectors, organizations), and how they influence each other. Using data collected on Python and R packages from GitHub, we leverage fractional-counting methods to measure the exact contribution of each developer and use weighted counting based on the lines of code added by each developer to accurately sum the contribution of countries. We find that for both Python and R, developers from a small group of top countries account for a considerable share of code additions. Developers from the top 10 countries, which include the United States, Germany, United Kingdom, France, and China comprise of 76.1% of the total R repositories, and 66.6% of Python repositories. Next, we use the dependency relationship between packages and study the pairwise connections between countries to measure their respective impact, finding that the packages attributed to United States are most frequently reused by packages from Germany, Spain, Italy, Australia, and United Kingdom based on the total dependency fractions. In parallel, United States mostly uses packages from Germany, France, and Denmark. Influential contributors to OSS can contribute heavily to the priorities and practices of scientific research when their work is widely used or built upon by other researchers. In this context, studying the global distribution, collaboration, and impact of the contributors is important to understanding the landscape of innovation in scientific research. 

Open source software (OSS) has become an essential in knowledge production and innovation in both academic and business sectors around the globe. OSS is developed by a variety of entities and is considered a “unique scholarly activity” due to the complexity of scientific computational tasks and the necessity of cooperation and transparency for research methodology. While the developers of OSS are thought to be very widespread, there remains many questions to be answered about who these contributors are, who are the largest contributors (countries, sectors, organizations), and how they influence each other. Using data collected on Python and R packages from GitHub, we leverage fractional-counting methods to measure the exact contribution of each developer and use weighted counting based on the lines of code added by each developer to accurately sum the contribution of countries. We find that for both Python and R, developers from a small group of top countries account for a considerable share of code additions. Developers from the top 10 countries, which include the United States, Germany, United Kingdom, France, and China comprise of 76.1% of the total R repositories, and 66.6% of Python repositories. Next, we use the dependency relationship between packages and study the pairwise connections between countries to measure their respective impact, finding that the packages attributed to United States are most frequently reused by packages from Germany, Spain, Italy, Australia, and United Kingdom based on the total dependency fractions. In parallel, United States mostly uses packages from Germany, France, and Denmark. Influential contributors to OSS can contribute heavily to the priorities and practices of scientific research when their work is widely used or built upon by other researchers. In this context, studying the global distribution, collaboration, and impact of the contributors is important to understanding the landscape of innovation in scientific research. 

Open source software (OSS) has become an essential in knowledge production and innovation in both academic and business sectors around the globe. OSS is developed by a variety of entities and is considered a "unique scholarly activity" due to the complexity of scientific computational tasks and the necessity of cooperation and transparency for research methodology. While the developers of OSS are thought to be very widespread, there remains many questions to be answered about who these contributors are, who are the largest contributors (countries, sectors, organizations), and how they influence each other. Using data collected on Python and R packages from GitHub, we leverage fractional-counting methods to measure the exact contribution of each developer and use weighted counting based on the lines of code added to accurately sum the contribution of countries to OSS. We find that for both Python and R, developers from a small group of top countries account for a considerable share of code additions. Developers from the top 10 countries, which include the United States, Germany, United Kingdom, France, and China comprise of 76.1% of the total R repositories, and 66.6% of Python repositories. Next, we use the dependency relationship between packages and study the pairwise connections between countries to measure their respective impact, finding that the packages attributed to United States are most frequently reused by packages from Germany, Spain, Italy, Australia, and United Kingdom based on the total dependency fractions. In parallel, United States mostly uses packages from Germany, France, and Denmark. Influential contributors to OSS can contribute heavily to the priorities and practices of scientific research when their work is widely used or built upon by other researchers. In this context, studying the global distribution, collaboration, and impact of the contributors is important to understanding the landscape of innovation in scientific research. 

The analysis of the gender dynamics in scientific research and respective outputs is crucial for ensuring that science policy is inclusive and equitable. Similar to other research outputs such as publications and patents, open source software (OSS) projects are also developed by contributors from universities, government research institutions, and nonprofits, in addition to businesses. Despite its reach and continued rapid growth, reliable and comprehensive survey data on OSS does not exist, limiting insights into contributions by gender and policy- makers’ ability to assess trends in gender representation. Like in scientific research, the inclusion of diverse perspectives in software development enhances creativity and problem-solving. Using GitHub data, researchers have found positive correlations between gender diversity of an OSS development team and its productivity (Vasilescu et al., 2015; Ortu et al., 2017). Yet there is evidence of gender bias, with women facing higher standards to have their contributions accepted (Terrell et al., 2017; Imtiaz et al., 2019). This exploratory study aims to quantify gender differences in development and use (impact) of OSS using publicly available information collected from GitHub. We focus on software packages developed for programming language R, with the majority of contributors from academia. The paper asks (1) what are gender differences in the volume of contributions? (2) has gender representation shifted over time? (3) is there a correlation between the gender of contributors and the impact of a package? 

Open source software (OSS) is ubiquitous, serving as specialized applications nurtured by devoted user communities, and as digital infrastructure underlying platforms used by millions of people. OSS is developed, maintained, and extended through the contribution of independent developers as well as people from businesses, universities, government research institutions, and nonprofits. Despite its prevalence, the scope and impact of OSS are not currently well-measured. Recent policies of the U.S. Federal Government promote sharing of software code developed by or for the Federal Government. While the policy to promote reusing and sharing of software created with public funding is relatively new, public funding plays an important and not fully accounted role in the creation of OSS. This paper aims to measure the scope and value of OSS development in the U.S. Federal Government. We collect data from Code.gov, the government’s platform for sharing OSS projects, and study contributions of agencies. The dataset contains 17K repositories from 21 agencies, with the majority of contributions originating from the DOE, NASA and GSA. In addition, we collect data on development activity (e.g., lines of code, contributors) of the repositories on GitHub, the largest hosting facility worldwide. Adopting a cost estimation model from software engineering, we generate estimates of investment in OSS that are consistent with the U.S. national accounting methods used for measuring software investment. Finally, we generate and analyze collaboration network resulting from cross-agency contributions to repositories and explore the centrality of agencies in the network. 

Web-based interactions allow agents to coordinate and to take actions (change state) jointly, i.e., to participate in collective action such as a protest, facilitating spread of contagion to large groups within networked populations. In game theoretic contexts, coordination requires that agents share common knowledge about each other. Common knowledge emerges within a group when each member knows the states and the types (preferences) of the other members, and critically, each member knows that everyone else has this information. Hence, these models of common knowledge and coordination on communication networks are fundamentally different from influence-based unilateral contagion models, such as those devised by Granovetter and Centola. Common knowledge arises in many settings in practice, yet there are few operational models that can be used to compute contagion dynamics. Moreover, these models utilize different mechanisms for driving contagion. We evaluate the three mechanisms of a common knowledge model that can represent web-based communication among groups of people on Facebook. We evaluate these mechanisms on five social (media) networks with wide-ranging properties. We demonstrate that different mechanisms can produce widely varying behaviors in terms of the extent of contagion spreading and the speed of contagion transmission.