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ABSTRACT: While major funding agencies are striving for diversity and fairness, the mechanisms behind funding success have yet to be fully elucidated. Existing studies reveal valuable evidences about the effect of the applicant's individual attributes, e.g., gender and age, on the funding success. However, the relationship between funding success and academic activities, e.g., collaborator's characteristics, remains underexplored. This work collects massive scholarly data from open academic graphs and public data about National Institutes of Health awards to investigate the effect of various academic graph‐based factors on the “K to R” success. Leveraging a heterogeneous graph model for predicting the “K to R” success, we regard the gain in the model performance of a factor as a proxy variable for the magnitude of its effect on the “K to R” success. Our preliminary results suggest that interest by peers in the applicant's research and the timing of the interest are strongly correlated with the outcome. Meanwhile, the applicant's social connections, e.g., their collaborators, can also contribute to the outcome. 

ABSTRACT Artificial Intelligence (AI) methods are valued for their ability to predict outcomes from dynamically complex data. Despite this virtue, AI is widely criticized as a “black box” i.e., lacking mechanistic explanations to accompany predictions. We introduce a novel interdisciplinary approach that balances the predictive power of data-driven methods with theory-driven explanatory power by presenting a shared use case from four disciplinary perspectives. The use case examines scientific career trajectories through temporally complex, heterogeneous bibliographic big data. Topics addressed include: data representation in complex problems, trade-offs between theoretical, hypothesis driven, and data-driven approaches, AI trustworthiness, model fairness, algorithm explainability and AI adoption/usability. Panelists and audience members will be prompted to discuss the value of approach presented versus other ways to address the challenges raised by the panel, and to consider their limitations and remaining challenges. 

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for “soft” social skills, like teamwork and communication, increase with greater demand for “hard” technical skills and tools.