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PurposeThe purpose of this article is to investigate particular aspects of the STEM job market in the US. In particular, we ask: could the possession of high performance computing (HPC) skills enhance the chances of a person getting a job and/or increase starting salaries for people receiving an undergraduate or graduate degree and entering the technical workforce (rather than academia)? We also estimate the value to the US economy of practical experience offered to US students through training about HPC and the opportunity to use HPC systems funded by the National Science Foundation (NSF) and accessible nationally. MethodsInterviews and surveys of employers of graduates in STEM fields were used to gauge demand for STEM graduates with practical HPC experience and the salary increase that can be associated with the possession of such skills. We used data from the XSEDE project to determine how many undergraduate and graduate students it enabled to acquire practical proficiency with HPC. ResultsPeople with such skills who had completed an undergraduate or graduate degree received an initial median hiring salary of approximately 7%–15% more than those with the same degrees who did not possess such skills. XSEDE added approximately $10 million or more per year to the US economy through the practical educational opportunities it offered. DiscussionPractical hands-on experience provided by the US federal government, as well as many universities and colleges in the US, holds value for students as they enter the workforce. ConclusionPractical training in HPC during the course of undergraduate and graduate programs has the potential to produce positive individual labor market outcomes (i.e., salary boosts, signing bonuses) as well as to help address the shortage of STEM workers in the private sector of the US. 

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure. Critically important findings from this first survey include the following: many of the respondents are engaged in some form of analysis of return in research computing investments, but only a minority currently report the results of such analyses to their upper-level administration. Most respondents are experimenting with use of commercial cloud resources but no respondent indicated that they have found use of commercial cloud services to create financial benefits compared to their current methods. There is clear correlation between levels of investment in research cyberinfrastructure and the scale of both cpu core-hours delivered and the financial level of supported research grants. Also interesting is that almost every respondent indicated that they participate in some sort of national cooperative or nationally provided research computing infrastructure project and most were involved in academic computing-related organizations, indicating a high degree of engagement by institutions of higher education in building and maintaining national research computing ecosystems. Institutions continue to evaluate cloud-based HPC service models, despite having generally concluded that so far cloud HPC is too expensive to use compared to their current methods. 

Abstract Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.