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Government spending on artificial intelligence (AI) has surged across the world. Quantifying the return on research investments is notoriously difficult, especially in newly emerging economic sectors. Here, we propose a novel way to describe and analyze where AI ideas are being used and how they spread—by tracing the people and academic communities involved in AI research as they transition from government-funded research labs to private sector companies, carrying cutting-edge “AI know-how” with them. Linking existing university administrative data with state employment records allows several quantifiable inferences about the value of AI research to be drawn from these academia-to-industry migrations. Here we describe a pilot implementation of this system, which is being developed in the State of Ohio. It offers a template for governments and policy makers all over the world. Importantly, the metrics discussed below offer a way to measure the economic impact of scientific research in general, with implications for critical and emerging technologies that go far beyond AI. 

This study conducts a novel approach to redesign EarSketch, an expressive computer science (CS) learning environment that inte- grates music composition into computing education, with a specific focus on inclusivity for blind and visually impaired (BVI) learners. This approach centers on the participation of teachers, students, and the community as co-designers, leveraging their insights and ex- periences to enhance the program’s accessibility and effectiveness. By actively involving the stakeholders in the development process, the study aims to address the unique educational challenges and needs of learners who are visually impaired more effectively. The participatory design approach is expected to not only maintain the intrinsic appeal of EarSketch but also to expand its accessibility, ensuring that it becomes a more inclusive tool in computer sci- ence education. The ultimate goal is to establish a more adaptable and inclusive educational paradigm within STEAM, particularly in computing education and music, that is responsive to the diverse needs of all students, including those with visual impairments. The contributions of this paper are design recommendations based on our data that can be applied to the design of EarSketch and other expressive CS environments for BVI learners. 

EarSketch is an online learning environment that uses the Web Audio API to teach computer science and computational thinking through music technology, production, and remixing. It is designed to broaden participation in computer science and music education for beginning learners by providing a free, web-based application coupled with curricula for informal and formal learning contexts and a library of audio content spanning multiple popular genres. This paper describes an analysis of EarSketch’s accommodations for students who are blind or visually impaired (BVI) and initial improvements to the system to improve accessibility. This paper also presents the findings of a user study designed to determine the biggest accessibility issues for EarSketch users who are BVI, and a discussion of how accessibility improvements can help broaden participation in computing and music education and, more broadly, how all web audio application developers can integrate accessibility considerations into their work. 

This article presents a new framework for realizing the value of linked data understood as a strategic asset and increasingly necessary form of infrastructure for policy-making and research in many domains. We outline a framework, the ‘data mosaic’ approach, which combines socio-organizational and technical aspects. After demonstrating the value of linked data, we highlight key concepts and dangers for community-developed data infrastructures. We concretize the framework in the context of work on science and innovation generally. Next we consider how a new partnership to link federal survey data, university data, and a range of public and proprietary data represents a concrete step toward building and sustaining a valuable data mosaic. We discuss technical issues surrounding linked data but emphasize that linking data involves addressing the varied concerns of wide-ranging data holders, including privacy, confidentiality, and security, as well as ensuring that all parties receive value from participating. The core of successful data mosaic projects, we contend, is as much institutional and organizational as it is technical. As such, sustained efforts to fully engage and develop diverse, innovative communities are essential. 

Abstract How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the ‘high precision over high recall’ performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009’s performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-ity2009 are publicly available for validation and reuse. 

Abstract A ‘state factor’ model of ecosystems can serve as a conceptual framework for researching and managing urban ecosystems. This approach provides alternative goals and narratives to those derived from historically grounded dichotomies between nature and culture, which can reify constructions of human influence as inherently destructive. The integration of human behaviour and state factors is critical to the application of a state factor model to urban ecosystems. We emphasize the role of culture in co-producing urban ecosystems and the importance of feedbacks between urban ecosystems and state factors. We advocate for ecosystem models that encourage local agency and actions that enhance the capacity of cities to constructively adapt to environmental change. We contrast this approach to efforts intended to minimize human impacts on ecosystems. The usefulness of the state factor model for informing such efforts is assessed through a consideration of the norms and practices of urban forest restoration in New York City. Despite the limitations and challenges of applying a state factor model to urban ecosystems, it can inform comparative research within and between cities and offers an intuitive framework for understanding the ecological conditions created in cities by human behaviour.