Toward a first-principles integrated simulation of tokamak edge plasmas

Performance of the ITER is anticipated to be highly sensitive to the edge plasma condition. The edge pedestal in ITER needs to be predicted from an integrated simulation of the necessary first-principles, multi-scale physics codes. The mission of the SciDAC Fusion Simulation Project (FSP) Prototype Center for Plasma Edge Simulation (CPES) is to deliver such a code integration framework by (1) building new kinetic codes XGC0 and XGC1, which can simulate the edge pedestal buildup; (2) using and improving the existing MHD codes ELITE, M3D-OMP, M3D-MPP and NIMROD, for study of large-scale edge instabilities called Edge Localized Modes (ELMs); and (3) integrating the codes into a framework using cutting-edge computer science technology. Collaborative effort among physics, computer science, and applied mathematics within CPES has created the first working version of the End-to-end Framework for Fusion Integrated Simulation (EFFIS), which can be used to study the pedestal-ELM cycles

Enhancing Workflow with a Semantic Description of Scientific Intent

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Plasma Edge Kinetic-MHD Modeling in Tokamaks Using Kepler Workflow for Code Coupling, Data Management and Visualization

A new predictive computer simulation tool targeting the development of the H-mode pedestal at the plasma edge in tokamaks and the triggering and dynamics of edge localized modes (ELMs) is presented in this report. This tool brings together, in a coordinated and effective manner, several first-principles physics simulation codes, stability analysis packages, and data processing and visualization tools. A Kepler workflow is used in order to carry out an edge plasma simulation that loosely couples the kinetic code, XGC0, with an ideal MHD linear stability analysis code, ELITE, and an extended MHD initial value code such as M3D or NIMROD. XGC0 includes the neoclassical ion-electron-neutral dynamics needed to simulate pedestal growth near the separatrix. The Kepler workflow processes the XGC0 simulation results into simple images that can be selected and displayed via the Dashboard, a monitoring tool implemented in AJAX allowing the scientist to track computational resources, examine running and archived jobs, and view key physics data, all within a standard Web browser. The XGC0 simulation is monitored for the conditions needed to trigger an ELM crash by periodically assessing the edge plasma pressure and current density profiles using the ELITE code. If an ELM crash is triggered, the Kepler workflow launches the M3D code on a moderate-size Opteron cluster to simulate the nonlinear ELM crash and to compute the relaxation of plasma profiles after the crash. This process is monitored through periodic outputs of plasma fluid quantities that are automatically visualized with AVS/Express and may be displayed on the Dashboard. Finally, the Kepler workflow archives all data outputs and processed images using HPSS, as well as provenance information about the software and hardware used to create the simulation. The complete process of preparing, executing and monitoring a coupled-code simulation of the edge pressure pedestal buildup and the ELM cycle using the Kepler scientific workflow system is described in this paper

Reusable, extensible, and modifiable R scripts and Kepler workflows for comprehensive single set ChIP-seq analysis

BACKGROUND: There has been an enormous expansion of use of chromatin immunoprecipitation followed by sequencing (ChIP-seq) technologies. Analysis of large-scale ChIP-seq datasets involves a complex series of steps and production of several specialized graphical outputs. A number of systems have emphasized custom development of ChIP-seq pipelines. These systems are primarily based on custom programming of a single, complex pipeline or supply libraries of modules and do not produce the full range of outputs commonly produced for ChIP-seq datasets. It is desirable to have more comprehensive pipelines, in particular ones addressing common metadata tasks, such as pathway analysis, and pipelines producing standard complex graphical outputs. It is advantageous if these are highly modular systems, available as both turnkey pipelines and individual modules, that are easily comprehensible, modifiable and extensible to allow rapid alteration in response to new analysis developments in this growing area. Furthermore, it is advantageous if these pipelines allow data provenance tracking. RESULTS: We present a set of 20 ChIP-seq analysis software modules implemented in the Kepler workflow system; most (18/20) were also implemented as standalone, fully functional R scripts. The set consists of four full turnkey pipelines and 16 component modules. The turnkey pipelines in Kepler allow data provenance tracking. Implementation emphasized use of common R packages and widely-used external tools (e.g., MACS for peak finding), along with custom programming. This software presents comprehensive solutions and easily repurposed code blocks for ChIP-seq analysis and pipeline creation. Tasks include mapping raw reads, peakfinding via MACS, summary statistics, peak location statistics, summary plots centered on the transcription start site (TSS), gene ontology, pathway analysis, and de novo motif finding, among others. CONCLUSIONS: These pipelines range from those performing a single task to those performing full analyses of ChIP-seq data. The pipelines are supplied as both Kepler workflows, which allow data provenance tracking, and, in the majority of cases, as standalone R scripts. These pipelines are designed for ease of modification and repurposing

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page