The Future of Biological Big Data

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Description

In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput

In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.

Date Created
2021-05
Agent

An Intention- and Outcome-Focused Perspective on the Doubling of the NIH Budget

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This thesis studies the 1998-2003 doubling of the National Institutes of Health budget to evaluate how assertions about the impact of research investments compare with actual health and research outcomes. Stakeholders in the doubling noted a variety of outcomes intended

This thesis studies the 1998-2003 doubling of the National Institutes of Health budget to evaluate how assertions about the impact of research investments compare with actual health and research outcomes. Stakeholders in the doubling noted a variety of outcomes intended to result from the effort. Using public value mapping (Bozeman and Sarewitz, 2005), I have compared stakeholders' stated intentions of what the doubling ought to achieve with the health and research outcomes actually produced. In applying public value mapping, I first conducted interviews and reviewed press releases, Congressional record, news, and other data from the doubling period. Six public values were commonly represented in this data: (1) improving health outcomes (2) reducing the cost of healthcare (3) producing application-relevant knowledge (4) building biosecurity and biodefense capabilities (5) developing the research enterprise (6) economic growth I then inferred causal logic chains by which increasing funding could lead to achievement of the public values and identified four investment intermediaries through which funding would pass in advancing public values. Finally, using proxies, I evaluated if the public values had advanced in a way directly attributable to funding increases. This analysis identified (5) as achieved. (1), (3), (4), and (6) were indeterminate in one of the two components necessary for evaluating public value achievement: either no clear advancement or no direct link between outcomes and doubling investments. (2) was a failure due to the increase in healthcare costs throughout and following the doubling period. These results indicate that complex societal outcomes used to justify incremental research investments are challenging to causally attribute to those same investments, and thus uncertain premises on which to base policy.
Date Created
2017-05
Agent