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The Atmospheric Radiation Measurement (ARM) Climate Research Facility is a multi-platform scientific user facility that supports research for addressing the major uncertainties of climate models - clouds and aerosols. ARM provides the national and international research community unparalleled infrastructure for obtaining precise observations of key atmospheric phenomena needed for the advancement of atmospheric process understanding and climate models. Within DOE, ARM's major clients are the Atmospheric System Research (ASR), Regional and Global Climate Modeling and Earth System Modeling programs. The primary ARM objective is improved scientific understanding of the fundamental physics related to interactions between clouds, aerosols, and radiative feedback processes in the atmosphere; in addition, ARM has enormous potential to advance scientific knowledge in a wide range of interdisciplinary Earth sciences.

ABCE is a Python Agent-Based Complete Economy Platform, written by Davoud Taghawi-Nejad. With ABCE, you can write economic, agent-based simulations in python. ABCE handles, trading production an consumption automatically. The agents, written by the modeler do only have to make the decisions and instruct the platform to trade, produce and consume. ABCE makes sure the economy is closed, that means no goods appear, disappear or are otherwise unaccounted for. It is therefore particularly useful for macre models. ABCE's model output are compatible with R, Excel and sqlite.

Complex coupled multi-physics simulations are playing increasingly important roles in scientific and engineering applications such as fusion plasma and climate modeling. At the same time, extreme scales, high levels of concurrency and the advent of multicore and many core technologies are making the high-end parallel computing systems on which these simulations run, hard to program. While the Partitioned Global Address Space (PGAS) languages is attempting to address the problem, the PGAS model does not easily support the coupling of multiple application codes, which is necessary for the coupled multi-physics simulations. Furthermore, existing frameworks that support coupled simulations have been developed for fragmented programming models such as message passing, and are conceptually mismatched with the shared memory address space abstraction in the PGAS programming model. This paper explores how multi-physics coupled simulations can be supported within the PGAS programming framework. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which enables efficient and productive development of coupled simulations across multiple independent PGAS Unified Parallel C (UPC) executables. XpressSpace provides the global-view style programming interface that is consistent with the memory model in UPC, and provides an efficient runtime system that can dynamically capture the data decomposition of global-view arrays and enable fast exchange of parallel data structures between coupled codes. In addition, XpressSpace provides the flexibility to define the coupling process in specification file that is independent of the program source codes. We evaluate the performance and scalability of Xpress Space prototype implementation using different coupling patterns extracted from real world multi-physics simulation scenarios, on the Jaguar Cray XT5 system of Oak Ridge National Laboratory.

Models of Infectious Disease Agent Study (MIDAS) is a collaboration of research and informatics groups to develop computational models of the interactions between infectious agents and their hosts, disease spread, prediction systems and response strategies. The models will be useful to policymakers, public health workers and other researchers who want to better understand and respond to emerging infectious diseases. If a disease outbreak occurs, the MIDAS network may be called upon to develop specific models to aid public officials in their decision-making processes. More information about MIDAS is available at the links below.

Milkyway@Home uses the BOINC platform to harness volunteered computing resources, creating a highly accurate three dimensional model of the Milky Way galaxy using data gathered by the Sloan Digital Sky Survey. This project enables research in both astroinformatics and computer science. In computer science, the project is investigating different optimization methods which are resilient to the fault-prone, heterogeneous and asynchronous nature of Internet computing; such as evolutionary and genetic algorithms, as well as asynchronous newton methods. While in astroinformatics, Milkyway@Home is generating highly accurate three dimensional models of the Sagittarius stream, which provides knowledge about how the Milky Way galaxy was formed and how tidal tails are created when galaxies merge.

DREAM (Dialogue for Reverse Engineering Assessments and Methods) poses fundamental questions about systems biology, and invites participants to propose solutions. The main objective is to catalyze the interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building. DREAM challenges address how we can assess the quality of our descriptions of networks that underlie biological systems, and of our predictions of the outcomes of novel experiments. These are not simple questions. Researchers have used a variety of algorithms to deduce the structure of biological networks and/or to predict the outcome of perturbations to their systems. They have also evaluated the success of their methodologies using a diverse set of non-standardised metrics. What is still needed, and what DREAM aims to achieve, is a fair comparison of the strengths and weaknesses of these methods and a clear sense of the reliability of the models that researchers create.

VORPAL enables researchers to simulate complex physical phenomena in less time and at a much lower cost than empirically testing process changes for plasma and vapor deposition processes. VORPAL offers a unique combination of physical models to cover the entire range of plasma simulation problems. Ionization and neutral gas models enable VORPAL to bridge the gap between plasma and neutral flow physics. VORPAL software runs on a wide range of computing platforms, from desktop machines to massively parallel supercomputers with thousands of processors. The use of standard data formats allows data analysis at various levels of sophistication, including your own preferred data analysis tool. VORPAL is used by scientists and engineers to simulate the physical behavior of devices and processes for many industrial and research applications, including laser wakefield accelerators, plasma thrusters, high-power microwave guides, and plasma processing chambers.

The Kepler Project is dedicated to furthering and supporting the capabilities, use, and awareness of the free and open source, scientific workflow application, Kepler. Kepler is designed to help scien­tists, analysts, and computer programmers create, execute, and share models and analyses across a broad range of scientific and engineering disciplines. Kepler can operate on data stored in a variety of formats, locally and over the internet, and is an effective environment for integrating disparate software components, such as merging "R" scripts with compiled "C" code, or facilitating remote, distributed execution of models. Using Kepler's graphical user interface, users simply select and then connect pertinent analytical components and data sources to create a "scientific workflow"-an executable representation of the steps required to generate results. The Kepler software helps users share and reuse data, workflows, and compo­nents developed by the scientific community to address common needs.

"All models are wrong, but some are useful." So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now.

"if your finding is... fragile... researchers should know [that] right away from reading the article." http://t.co/kvohH8iJON @StatModeling

Foldit was founded by the University of Washington Center for Game Science in collaboration with the Baker lab. In the last decade, scientists repeatedly failed to find a solution to the structure of a protein-cutting enzyme from an AIDS-like virus. The scientists have decided to collect a group of gamers and challenged them to produce an accurate model of the enzyme: users are tasked with folding known proteins and are scored on how well they manage to accomplish this task while taking into consideration the physical properties of the molecule. In less then ten days, the gamers came up with the desired solution.

Help scientists recover worldwide weather observations made by Royal Navy ships around the time of World War I. These transcriptions will contribute to climate model projections and improve a database of weather extremes. Historians will use your work to track past ship movements and the stories of the people on board.

Figshare allows researchers to publish all of their research outputs in seconds in an easily citable, sharable and discoverable manner. All file formats can be published, including videos and datasets that are often demoted to the supplemental materials section in current publishing models. By opening up the peer review process, researchers can easily publish null results, avoiding the file drawer effect and helping to make scientific research more efficient. Figshare uses creative commons licensing to allow frictionless sharing of research data whilst allowing users to maintain their ownership.

Vis5D is a system for interactive visualization of large 5-D gridded data sets such as those produced by numerical weather models. One can make isosurfaces, contour line slices, colored slices, volume renderings, etc of data in a 3-D grid, then rotate and animate the images in real time. There's also a feature for wind trajectory tracing, a way to make text anotations for publications, support for interactive data analysis, etc.

Models of whole-brain connectivity are valuable for understanding neurological function, development and disease. This paper presents a machine learning based approach to classify subjects according to their approximated structural connectivity patterns and to identify features which represent the key differences between groups. Brain networks are extracted from diffusion magnetic resonance images obtained by a clinically viable acquisition protocol. Connections are tracked between 83 regions of interest automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. Tracts between these regions are propagated by probabilistic tracking, and mean anisotropy measurements along these connections provide the feature vectors for combined principal component analysis and maximum uncertainty linear discriminant analysis. The approach is tested on two populations with different age distributions: 20-30 and 60-90 years. We show that subjects can be classified successfully (with 87.46% accuracy) and that the features extracted from the discriminant analysis agree with current consensus on the neurological impact of ageing.

This paper describes the design of OpenFOAM, an object- oriented library for Computational Fluid Dynamics (CFD) and struc- tural analysis. Efficient and flexible implementation of complex physi- cal models in Continuum Mechanics is achieved by mimicking the form of partial differential equation in software. The library provides Fi- nite Volume and Finite Element discretisation in operator form and with polyhedral mesh support, with relevant auxiliary tools and sup- port for massively parallel computing. Functionality of OpenFOAM is illustrated on three levels: improvements in linear solver technology with CG-AMG solvers, LES data analysis using Proper Orthogonal Decom- position (POD) and a self-contained fluid-structure interaction solver.

Widespread use of a comprehensive CI framework has the potential to revolutionize every science and engineering discipline as well as education. Computing power, data volumes, software, and network capacities are all on exponential growth paths. Highly diverse, multidisciplinary collaborations and partnerships are growing dramatically, greatly enabled by new and emerging technologies, spanning multiple agencies and international domains to address complex grand challenge problems. Scientific discovery is being advanced by linking computational facilities and instruments to build highly-capable simulation models, sophisticated algorithms, software, and other tools and services. CIF21 will enable new approaches to research and education - supporting new modalities such as distributed collaborative networks, allowing researchers to more easily adapt to changes in the research and education process, and providing an integrated framework for people, instruments, and tools to address complex problems and conduct multidisciplinary research. CIF21 will consist of secure, geographically distributed, and connected CI: advanced computing facilities, scientific instruments, software environments, advanced networks, data storage capabilities, and the critically important human capital and expertise.

Keep informed about advances in structural biology and structural genomics. Discover how protein sequences, three-dimensional structures and models relate to biological function. Stay up to date with the latest protocols, materials and technologies.

The FES SciDAC portfolio focuses on the development and application of high physics fidelity simulation codes that can advance the fundamental science of magnetically confined plasmas by fully exploiting leadership class computing resources and contribute to the FES goal of developing the predictive capability needed for a sustainable fusion energy source. The specific areas of interest of the Partnerships are: Edge Physics Multiscale Integrated Modeling Materials Science