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Scientific user facilities---particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more---operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

This document is a report from the DOE Workshop on the Management, Analysis, and Visualization of Experimental and Observational Data. The workshop, held 29 September through 1 October 2015, brought together scientists and researchers from across DOE to discuss issues related to managing and working with data from DOE experimental and observational science projects. The report articulates a number of research topics in mathematics, computer science, and data science that are urgently needed to help minimize or eliminate impediments currently facing a broad set of experimental and observational science projects in DOE.

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the US Department of Energy Office of Science, the single largest supporter of basic research in the physical sciences in the United States. In support of the Office of Science programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities and scientists that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 20 years. In August, 2002 the DOE Office of Science organized a workshop to characterize the networking requirements for Office of Science programs. Networking and middleware requirements were solicited from a representative group of science programs. The workshop was summarized in two documents the workshop final report and a set of appendixes. This document updates the networking requirements for ESnet as put forward by the science programs listed in the 2002 workshop report. In addition, three new programs have been added. The information was gathered through interviews with knowledgeable scientists in each particular program or field.

Like the "phoenix," technology education (TE) can, under the right circumstances, give life to new programs--curricula with different emphases and directions from technology education, yet sharing a common heritage: the belief that applied technology will continue to shape the world. How that shaping process takes place--and the problems that it can solve--defines the industrial design phoenix as an increasingly formidable, emerging area of study. After more than 50 years of training industrial arts and technology educators at Montclair State University, the university's technology teacher education program was closed in 1996. Several reasons were offered to justify the program's termination: the need to put a public face on aggressive action towards program consolidation within the state college system; relative non-articulation of technology education with other academic disciplines; shrinking budgets; and an industrial, smokestack public image found to be increasingly inconsistent with the changing mission of the University. After closing the program, the majority of the remaining Technology faculty was moved into the Department of Art and Design, which offered teaching opportunities related to some of the areas with which the faculty were familiar: general design, graphic design and photography, jewelry and lapidary. In the years following the closure, these teaching reassignments have proven to be a fertile ground from which the ashes, so to speak, of a new phoenix, namely industrial design, would arise. This article presents a case history of industrial design and describes how industrial design has developed from arts-and-crafts-based occupations to a highly sophisticated social and technical science. It also discusses Montclair State University's B.F.A. in Industrial Design which offers students an intensive artistic and technical approach to the world of the industrial designer. (Contains 1 figure.)

Issues of the state of the economy, fuel consumption, environmental protection, interdependence, and global competition are relevant to technology education and must be addressed to shape the economic and technological future of the United States. (SK)

Highlights: [•] A description of a new framework and tool for recording scientific workflows, especially those resulting from simulation and analysis. [•] An explanation of the underlying technologies used to implement this web based tool. [•] Several examples of using the tool. [ABSTRACT FROM AUTHOR]

Dramatic progress in the scope and power of plasma simulations over the past decade has extended our understanding of these complex phenomena. However, as codes embody imperfect models for physical reality, a necessary step toward developing a predictive capability is demonstrating agreement, without bias, between simulations and experimental results. While comparisons between computer calculations and experimental data are common, there is a compelling need to make these comparisons more systematic and more quantitative. Tests of models are divided into two phases, usually called verification and validation. Verification is an essentially mathematical demonstration that a chosen physical model, rendered as a set of equations, has been accurately solved by a computer code. Validation is a physical process which attempts to ascertain the extent to which the model used by a code correctly represents reality within some domain of applicability, to some specified level of accuracy. This paper will cover principles and practices for verification and validation including lessons learned from related fields. [ABSTRACT FROM AUTHOR]

The spurious drift in pitch angle of order several degrees measured by the motional Stark effect (MSE) diagnostic in the Alcator C-Mod tokamak over the course of an experimental run day has precluded direct utilization of independent absolute calibrations. Recently, the underlying cause of the drift has been identified as thermal stress-induced birefringence in a set of in-vessel lenses. The shot-to-shot drift can be avoided by using MSE to measure only the change in pitch angle between a reference phase and a phase of physical interest within a single plasma discharge. This intrashot calibration technique has been applied to the lower hybrid current drive (LHCD) experiments and the measured current profiles qualitatively demonstrate several predictions of LHCD theory such as an inverse dependence of current drive efficiency on the parallel refractive index and the presence of off-axis current drive. [ABSTRACT FROM AUTHOR]

Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles. © 2001 American Institute of Physics. [ABSTRACT FROM AUTHOR]