Search

The COVID-19 pandemic has highlighted many complexities involved in using data and advanced technologies to help resolve public health emergencies. These complexities highlight the need to embrace a broader framework of data governance with three foundational questions: (a) who decides about data flows, (b) on what basis, and (c) with what accountability and oversight. These questions can accommodate the issues that have arisen in the literature regarding new types of data harms. However, these questions also foreground important issues of power, authority, and legitimacy. Data governance can provide an organizing normative framework to address emerging data themes including access to data, collective decision making, data intermediaries, data sovereignty, design and digital infrastructure, regulatory technologies, the rule of law, and social trust and license. The pandemic experience with contact tracing apps, in particular, showed the many unresolved governance challenges. [ABSTRACT FROM AUTHOR]

As the volume of data associated with scientific research has exploded over recent years, the use of digital infrastructures to support this research and the data underpinning it has increased significantly. Physical chemists have been making use of eScience infrastructures since their conception, but in the last five years their usage has increased even more. While these infrastructures have not greatly affected the chemistry itself, they have in some cases had a significant impact on how the research is undertaken. The combination of the human effort of collaboration to create open source software tools and semantic resources, the increased availability of hardware for the laboratories, and the range of data management tools available has made the life of a physical chemist significantly easier. This review considers the different aspects of eScience infrastructures and explores how they have improved the way in which we can conduct physical chemistry research. [ABSTRACT FROM AUTHOR]

Law and social science scholars have long elucidated ways of governing built around state governance of populations and subjects. Yet many are now grappling with the growing prevalence of practices of governance that depart, to varying degrees, from received models. The profusion of digital data, and the deployment of machine learning in its analysis, are redirecting states' and international organizations' attention away from the governance of populations as such and toward the amassing, analysis, and mobilization of hybrid data repositories and real-time data flows for governance. Much of this work does not depend on state data sources or on conventional statistical models. The subjectivities nurtured by these techniques of governance are frequently not those of choosing individuals. Digital objects and mediators are increasingly prevalent at all scales. This article surveys how scholars are beginning to understand the nascent political technologies associated with this shift toward governance by data. [ABSTRACT FROM AUTHOR]

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice. [ABSTRACT FROM AUTHOR]

Rich federal data resources provide essential data inputs for monitoring the health and health care of the US population and are essential for conducting health services policy research. The six household surveys we document in this article cover a broad array of health topics, including health insurance coverage (American Community Survey, Current Population Survey), health conditions and behaviors (National Health Interview Survey, Behavioral Risk Factor Surveillance System), health care utilization and spending (Medical Expenditure Panel Survey), and longitudinal data on public program participation (SIPP). New federal activities are linking federal surveys with administrative data to reduce duplication and response burden. In the private sector, vendors are aggregating data from medical records and claims to enhance our understanding of treatment, quality, and outcomes of medical care. Federal agencies must continue to innovate to meet the continuous challenges of scarce resources, pressures for more granular data, and new multimode data collection methodologies. [ABSTRACT FROM AUTHOR]