scholarly journals Transparency and Reproducibility Practice in Large-Scale Computational Science: A Preface to the Special Section

2021 ◽  
Vol 32 (11) ◽  
pp. 2607-2608
Beth Plale ◽  
Stephen Lien Harrell
Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 197 ◽  
Sebastian Götschel ◽  
Martin Weiser

Solvers for partial differential equations (PDEs) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that need to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to the relatively small arithmetic intensity, and increasingly due to the growing disparity between computing power and bandwidth. Consequently, data compression techniques have been investigated and tailored towards the specific requirements of PDE solvers over the recent decades. This paper surveys data compression challenges and discusses examples of corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to the main memory. We illustrate concepts for particular methods, with examples, and give references to alternatives.

2006 ◽  
Vol 15 (01) ◽  
pp. 11-15
J. L. Talmon

SummaryTo raise awareness for actions that are urgently needed to accompany the large scale implementations of ICT in Health Care that are currently taking place in many countries around the world.An analysis of a few studies that have recently been described in the literature guided by recent suggestions for research and development of evaluation of health ICT.Six specific recommendations for action are specified:Development of good implementation practice,Development of an experience base of implementation of ICT in health care,Setting up a surveillance system for unintended effects,Build an evidence base of best evaluation practice,Developing guidelines for proper reporting of evaluation studies,Education of clinicians and decision makers.

2012 ◽  
Vol 14 (1) ◽  
pp. 1-2 ◽  
Changsheng Xu ◽  
Alan Hanjalic ◽  
Shuicheng Yan ◽  
Qingshan Liu ◽  
Alan F. Smeaton

Yinshi Li ◽  
Lei Zhang

Abstract Given the increasing energy demand and carbon dioxide emission, countries all over the world are vigorously developing sustainable and clean energy. Fuel cells and metal-ion batteries that directly convert chemical energy into electric energy have been receiving ever-increasing attention for energy conversion and storage in several applications such as portable, mobile, and stationary applications. Nowadays, not understanding mass and charge transport in fuel cells and metal-ion batteries, which results in low performance and durability, are still challenges for their large-scale commercialization. For example, the insufficient interaction of catalyst/ionomer/reactant as a result of fuel cells lacking the ion-conducting, reactant-delivering, or proton-conducting pathways leads to the deactivated triple-phase boundary. Meanwhile, the metal-ions transport in the interface of solid active materials and electrolyte, and the charge transport including ions transport in the electrolyte, and electron transport in the solid phase, are not well known in advanced metal-ion batteries. An ideal electrode architecture that boosts the performance and durability of cells and batteries needs the electrode design to meet all the requirements of electrochemical kinetics and mass and charge transport characteristics.

2015 ◽  
Vol 2 (2) ◽  
pp. 5-13 ◽  
Alyssa Friend Wise ◽  
David Williamson Shaffer

It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These range from the question of to which of the many possible variables a researcher should attend to how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section and the commentaries that were invited on them speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics work draws on and contributes to theory.

2020 ◽  
Vol 10 (1) ◽  
pp. 55-57
Abby C King

Abstract Despite the numerous successful behavioral interventions that have been published in the behavioral medicine field over a number of decades, surprisingly few have been translated and adapted for real-world settings using participatory research methods. The purpose of this commentary is to highlight the advances in participatory behavioral medicine reflected in the articles contained in the Diabetes special section. The articles contained in the Diabetes special section were reviewed, with a focus on the advances made with this type of research and the challenges that came to light. Numerous strengths of the large-scale translational studies were identified. The studies also highlighted important areas meriting further attention, including exploration of additional dissemination pathways, and further piloting and refinement of program components for different population segments. The articles in this special section represent major advances in implementing successful, impactful programs for diabetes prevention and control in low- and middle-income countries.

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