Sustainable Open Source Software (OSS) projects are characterized by the ability to attract new project members and maintain an energetic project community. Building sustainable OSS projects from a nascent state requires effective project governance and socio-technical structure to be interleaved, in a complex and dynamic process. Although individual disciplines have studied each separately, little is known about how governance and software development work together in practice toward sustainability. Prior work has shown that many OSS projects experience large, episodic changes over short periods of time, which can propel them or drag them down. However, sustainable projects typically manage to come out unscathed from such changes, while others do not. The natural questions arise: Can we identify the back-and-forth between governance and socio-technical structure that lead to sustainability following episodic events? And, how about those that do not lead to sustainability? From a data set of social, technical, and policy digital traces from 262 sustainability-labeled ASF incubator projects, here we employ a large-scale empirical study to characterize episodic changes in socio-technical aspects measured by Change Intervals (CI), governance rules and regulations in a form of Institutional Statements (IS), and the temporal relationships between them. We find that sustainable projects during episodic changes can adapt themselves to institutional statements more efficiently, and that institutional discussions can lead to episodic changes intervals in socio-technical aspects of the projects, and vice versa. In practice, these results can provide timely guidance beyond socio-technical considerations, adding rules and regulations in the mix, toward a unified analytical framework for OSS project sustainability.

Data Science has recently experienced a significant surge in producing undergraduate degree/certificate and course enrollments. This growth has resulted in straining program resources at many institutions and causing concern about how to most effectively respond to the rapidly growing demand and provide equal opportunities for all students to pursue their degree. The primary goal of this work is to provide an intelligent solution to automatically identify biases related to race and gender in the content of Data Science related courses such as Machine Learning (ML), Natural Language Processing (NLP), Data Mining, Data Analytics, and Knowledge Discovery just to name a few. While machine learning allows to build powerful predicting tools, it hasn’t been used sufficiently to detect biases rooted in gender and race in educational material. Hence, using effective machine learning techniques we aim to enhance the success rate of undergraduate students in Data Science education and programs while promoting engagement through identifying and mitigating potential gender and racial biases. To this end, we propose a novel bias detection approach which uses a combination of gender identification and sentiment classification for characterization of gender and race in educational contents. Our proposed framework can help educators and course developers to mitigate race and gender biases in their course materials and create an equitable learning experience for students of minority and under-represented groups.