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David Y. Hancock

Publications and source records attributed to David Y. Hancock.

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Application of the Cyberinfrastructure Production Function Model to R1 Institutions

High-performance computing (HPC) is widely used in higher education for modeling, simulation, and AI applications. A critical piece of infrastructure with which to secure funding, attract and retain faculty, and teach students, supercomputers come with high capital and operating costs that must be considered against other competing priorities. This study applies the concepts of the production function model from economics with two thrusts: 1) to evaluate if previous research on building a model for quantifying the value of investment in research computing is generalizable to a wider set of universities, and 2) to define a model with which to capacity plan HPC investment, based on institutional production - inverting the production function. We show that the production function model does appear to generalize, showing positive institutional returns from the investment in computing resources and staff. We do, however, find that the relative relationships between model inputs and outputs vary across institutions, which can often be attributed to understandable institution-specific factors.

cs.CY

Quantitative analysis of the value of investment in research facilities, with examples from cyberinfrastructure

Purpose: How much to invest in research facilities has long been a question in higher education and research policy. We present established and recently developed techniques for assessing the quantitative value created or received as a result of investments in research facilities. This discussion is timely. Financial challenges in higher education may soon force difficult decisions regarding investment in research facilities at some institutions. Clear quantitative analysis will be necessary for such strategic decision-making. Further, institutions of higher education in the USA are currently being called on to justify their value to society. The analyses presented here are extendable to research enterprises as a whole. Results: We present methods developed primarily for analyses of cyberinfrastructure. Most analyses comparing investment in university-based cyberinfrastructure facilities with purchasing services from commercial sources demonstrate positive results for economic and scientific research. A recent assessment, based on a comprehensive accounting approach, has shown that for one large publicly funded cyberinfrastructure project the value delivered to the USA economy and society exceeded the cost to USA taxpayers. Conclusions: Quantitative analyses of the benefits of investment in research and research facilities create a fact-based foundation for discussing the value of research and higher education. These methods enable a quantitative assessment of the relationship between investment in specific research facilities or research projects and economic, societal, and educational outcomes. These methods are of value in quantifying the economic benefit of higher education and in managing investments within such institutions.

econ.GN

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

cs.DC