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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2013

Scientific Data Management Center for Enabling Technologies

Managing scientific data has been identified by the scientific community as one of the most important emerging needs because of the sheer volume and increasing complexity of data being collected. Effectively generating, managing, and analyzing this information requires a comprehensive, end-to-end approach to data management that encompasses all of the stages from the initial data acquisition to the final analysis of the data. Fortunately, the data management problems encountered by most scientific domains are common enough to be addressed through shared technology solutions. Based on community input, we have identified three significant requirements. First, more efficient access to storage systems is needed. In particular, parallel file system and I/O system improvements are needed to write and read large volumes of data without slowing a simulation, analysis, or visualization engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Scientific Data Management Beyond Traditional Computing Boundaries

Scientific data management is undergoing a fundamental transformation driven by the convergence of artificial intelligence (AI)/machine learning workflows, distributed computing and storage environments, and exponential data growth. Here, we analyze how these developments address current limitations while enabling new capabilities for cross-facility collaboration and AI-driven research.

Widener, Patrick [Oak Ridge National Laboratory (O↗
DOE OSTI2012

Scientific Data Management Integrated Software Infrastructure Center (SDM/ISIC): Scientific Process Automation (SPA) (Final Report)

The KEPLER project has made a significant impact and established a sustained leadership in the field of scientific workflows. The open source KEPLER project started in 2003 when members of the SciDAC-SDM Center/SPA Team, sponsored by this DOE-funded project (DE-FC02-01ER25486) and members of the SEEK project (NSF/ITR awards DBI-0225674 and DBI-0533368) decided to collaborate and jointly develop a scientific workflow system based on the open source PTOLEMY II system (Ludäscher was a co-PI on both projects). In the first project phase, between 2001 and 2003, the SDSC team (Altintas, Ludäscher) worked closely with a domain scientist (Matt Coleman, LLNL) and created early versions of a Promoter Identification Workflow (PIW). Towards the end of that period, the open source PTOLEMY II system was adopted by SciDAC-SDM and SEEK as the basis for a general scientific workflow system and problem-solving environment to design and execute scientific workflows, giving rise to Kepler. Once started through SciDAC-SDM and SEEK, the KEPLER leadership team was able to attract further support from funding agencies including from DOE (as part of SciDAC-2), NSF, NIH, and the Gordon and Betty Moore Foundation. Important advances to KEPLER were made by the SciDAC/SDM-SPA team during the report period (2001–2007), in particular by the SDSC team (and after Ludäscher's move) the UC Davis team, including (i) research and development of the workflow infrastructure, (ii) library (actor) development, and (iii) development of concrete scientific workflows, in collaboration with scientists. Years later, the KEPLER system was listed as one of the prominent “Big Data” research outcomes of DOE in the White House Big Data Factsheet, announced as part of the White House Big Data press release.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Exacution: Enhancing Scientific Data Management for Exascale

As we continue toward exascale, scientific data volume is continuing to scale and becoming more burdensome to manage. In this paper, we lay out opportunities to enhance state of the art data management techniques. We emphasize well-principled data compression, and using it to achieve progressive refinement. This can both accelerate I/O and afford the user increased flexibility when she interacts with the data. The formulation naturally maps onto enabling partitioning of the progressively improving-quality representations of a data quantity into different media-type destinations, to keep the highest priority information as close as possible to the computation, and take advantage of deepening memory/storage hierarchies in ways not previously possible. Careful monitoring is requisite to our vision, not only to verify that compression has not eliminated salient features in the data, but also to better understand the performance of massively parallel scientific applications. Increased mathematical rigor would be ideal,to help bring compression on a better-understood theoretical footing, closer to the relevant scientific theory, more aware of constraints imposed by the science, and more tightly error-controlled. Throughout, we highlight pathfinding research we have begun exploring related these topics, and comment toward future work that will be needed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Rucio: Scientific Data Management

Rucio is an open-source software framework that provides scientific collaborations with the functionality to organize, manage, and access their data at scale. The data can be distributed across heterogeneous data centers at widely distributed locations. Rucio was originally developed to meet the requirements of the high-energy physics experiment ATLAS, and now is continuously extended to support the LHC experiments and other diverse scientific communities. In this article, we detail the fundamental concepts of Rucio, describe the architecture along with implementation details, and report operational experience from production usage.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2013

Scientific Data Management (SDM) Center for Enabling Technologies. 2007-2012

Over the past five years, our activities have both established Kepler as a viable scientific workflow environment and demonstrated its value across multiple science applications. We have published numerous peer-reviewed papers on the technologies highlighted in this short paper and have given Kepler tutorials at SC06,SC07,SC08,and SciDAC 2007. Our outreach activities have allowed scientists to learn best practices and better utilize Kepler to address their individual workflow problems. Our contributions to advancing the state-of-the-art in scientific workflows have focused on the following areas. Progress in each of these areas is described in subsequent sections. Workflow development. The development of a deeper understanding of scientific workflows "in the wild" and of the requirements for support tools that allow easy construction of complex scientific workflows; Generic workflow components and templates. The development of generic actors (i.e.workflow components and processes) which can be broadly applied to scientific problems; Provenance collection and analysis. The design of a flexible provenance collection and analysis infrastructure within the workflow environment; and, Workflow reliability and fault tolerance. The improvement of the reliability and fault-tolerance of workflow environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are known as user facilities. They are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources

In January 2019, the U.S. Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions for in situ data management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing system, cooperating toward a common objective. The workshop revealed two primary, interdependent motivations for processing and managing data in situ. The first motivation is that the in situ methodology enables scientific discovery from a broad range of data sources over a wide scale of computing platforms: leadership-class systems, clusters, clouds, workstations, and embedded devices at the edge. The successful development of ISDM capabilities will benefit real-time decision-making, design optimization, and data-driven scientific discovery. The second motivation is the need to decrease data volumes. ISDM can make critical contributions to managing large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency, often while simultaneously increasing scientific precision. A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery across the Office of Science mission space. Applications wanting to use the in situ capabilities include those where data analysis feeds back to the simulation, decisions are made autonomously, big data or machine learning is among the tasks to be coordinated, and computations need to be completed in real time. The workshop identified six priority research directions that highlight the components and capabilities needed for ISDM to be successful for the wide variety of applications discussed: making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources [Brochure]

In January 2019, the U.S. Department of Energy (DOE), Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) convened a workshop to identify priority research directions (PRDs) for In Situ Data Management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing (HPC) system, cooperating toward a common objective.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Experience with Rucio in the wider HEP community

Managing the data of scientific projects is an increasingly complicated challenge, which was historically met by developing experiment-specific solutions. However, the ever-growing data rates and requirements of even small experiments make this approach very difficult, if not prohibitive. In recent years, the scientific data management system Rucio has evolved into a successful open-source project that is now being used by many scientific communities and organisations. Rucio is incorporating the contributions and expertise of many scientific projects and is offering common features useful to a diverse re-search community. This article describes the recent experiences in operating Rucio, as well as contributions to the project, by ATLAS, Belle II, CMS, ES-CAPE, IGWN, LDMX, Folding@Home, and the UK's Science and Technology Facilities Council (STFC).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

MaDaTS: Managing Data on Tiered Storage for Scientific Workflows

Scientific workflows are increasingly used in High Performance Computing (HPC) environments to manage complex simulation and analyses, often consuming and generating large amounts of data. However, workflow tools have limited support for managing the input, output and intermediate data. The data elements of a workflow are often managed by the user through scripts or other ad-hoc mechanisms. Technology advances for future HPC systems is redefining the memory and storage subsystem by introducing additional tiers to improve the I/O performance of data-intensive applications. These architectural changes introduce additional complexities to managing data for scientific workflows. Thus, we need to manage the scientific workflow data across the tiered storage system on HPC machines. In this paper, we present the design and implementation of MaDaTS (Managing Data on Tiered Storage for Scientific Workflows), a software architecture that manages data for scientific workflows. We introduce Virtual Data Space (VDS), an abstraction of the data in a workflow that hides the complexities of the underlying storage system while allowing users to control data management strategies. We evaluate the data management strategies with real scientific and synthetic workflows, and demonstrate the capabilities of MaDaTS. Our experiments demonstrate the flexibility, performance and scalability gains of MaDaTS as compared to the traditional approach of managing data in scientific workflows.

Ghoshal, Devarshi↗
DOE OSTI2012

Integrating Data Management and Collaborative Sharing with Computational Science Processes

Structured Scientific Data Management - the management of storage, access, usage, lifecycle, content and meaning for scientific data - is not as commonly employed in computational science as it is in other fields of scientific endeavor. However, where it has been co-developed and integrated with the computational science research it has had a transformational influence on the scientific work. These infrastructures enabled not only new research previously impossible, but also helped to speed up the research process and improved the quality of the research output. Good data management systems are capable of facilitating effective scientific collaborations on a group, institutional, national or international level, through the easy sharing of resources and results. Today as computational science is becoming more data rich and collaborative, integrated scientific data management is becoming an essential tool for every computational science research and production environment. This chapter will describe the fundamental principles and components of a good data management system, provide real world examples of successful implementations and provides an outlook on future developments. We conclude with a short section on how to get started for those whose interest has been peaked by this chapter

42 ENGINEERING↗
DOE OSTI2019

DataFed: Towards Reproducible Research via Federated Data Management

The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS). An SDMS presents a logical and holistic view of data that greatly simplifies and empowers data organization, curation, searching, sharing, dissemination, etc. We present DataFed - a lightweight, distributed SDMS that spans a federation of storage systems within a loosely-coupled network of scientific facilities. Unlike existing SDMS offerings, DataFed uses high-performance and scalable user management and data transfer technologies that simplify deployment, maintenance, and expansion of DataFed. DataFed provides web-based and command-line interfaces to manage data and integrate with complex scientific workflows. DataFed represents a step towards reproducible scientific research by enabling reliable staging of the correct data at the desired environment.

Stansberry, Dale↗
DOE OSTI2013

The AmeriFlux Data Activity and Data System: An Evolving Collection of Data Management Techniques, Tools, Products and Services

The Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL), USA has provided scientific data management support for the U.S. Department of Energy and international climate change science since 1982. Over this period, climate change science has expanded from research focusing on basic understanding of geochemical cycles, particularly the carbon cycle, to integrated research addressing climate change impacts, vulnerability, adaptation, and mitigation. Interests in climate change data and information worldwide have grown remarkably and, as a result, so have demands and expectations for CDIAC’s data systems. To meet the growing demands, CDIAC’s strategy has been to design flexible data systems using proven technologies blended with new, evolving technologies and standards. CDIAC development teams are multidisciplinary and include computer science and information technology expertise, but also scientific expertise necessary to address data quality and documentation issues and to identify data products and system capabilities needed by climate change scientists. CDIAC has learned there is rarely a single commercial tool or product readily available to satisfy long-term scientific data system requirements (i.e., one size does not fit all and the breadth and diversity of environmental data are often too complex for easy use with commercial products) and typically deploys a variety of tools and data products in an effort to provide credible data freely to users worldwide. Like many scientific data management applications, CDIAC’s data systems are highly customized to satisfy specific scientific usage requirements (e.g., developing data products specific for model use) but are also designed to be flexible and interoperable to take advantage of new software engineering techniques, standards (e.g., metadata standards) and tools and to support future Earth system data efforts (e.g., ocean acidification). CDIAC has provided data management support for numerous long-term measurement projects crucial to climate change science. One current example is the AmeriFlux measurement network. AmeriFlux provides continuous measurements from forests, grasslands, wetlands, and croplands in North, Central, and South America and offers important insight about carbon cycling in terrestrial ecosystems. We share our approaches in satisfying the challenges of delivering AmeriFlux data worldwide to benefit others with similar challenges handling climate change data, further heighten awareness and use of an outstanding ecological data resource, and highlight expanded software engineering applications being used for climate change measurement data.

Boden, Thomas A.↗
DOE OSTI2015

Building a Cyberinfrastructure for Environmental System Science: Modeling Frameworks, Data Management, and Scientific Workflows, Workshop Report, Potomac, Maryland, April 30-May 1, 2015

The Environmental System Science (ESS) activity within the Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) seeks to advance a robust, predictive understanding of terrestrial environments, extending from bedrock to the top of the vegetative canopy and from molecular to global scales, through an iterative cycle of model-driven experimentation and observation dubbed MODEX. Considerable progress has been made toward achieving this overarching goal, but widely recognized is the fragmentation across projects and disciplines of the relevant modeling and simulation capabilities, observational and experimental data, analysis algorithms, and workflow tools. This fragmentation creates significant challenges for studying impacts and feedbacks in these complex multiscale systems. These challenges are further exacerbated by ongoing disruptive changes in high-performance computational architectures and the exponential growth in the types and volume of data that render obsolete the conventional approaches to software development and data management. Overcoming these challenges will require the development of a BER Climate and Environmental Sciences Division (CESD)–wide enabling cyberinfrastructure to support data management, cross-domain modeling, data analysis, and collaborative research. To explore the potential for working groups to initiate and guide a more integrated and community-based cyberinfrastructure, BER held the ESS Workshop on Model-Data Integration: Modeling Frameworks, Data Management, and Scientific Workflows on April 30-May 1, 2015, following the ESS Principal Investigator Meeting in Potomac, Maryland. Participants included model developers, software engineers, and data management specialists from eight national laboratories, which represented a wide range of projects and programs from CESD as well as cross-cutting projects from DOE’s Office of Advanced Scientific Computing Research (ASCR; both BER and ASCR are operated from within DOE’s Office of Science). A series of plenary talks provided background information and clarified the workshop’s three objectives: (1) develop requirements for this community-based cyberinfrastructure to ensure enhanced scientific productivity of the community as a whole; (2) identify challenges associated with developing this new cyberinfrastructure using a phased approach guided by project-driven use cases; and, given these requirements and challenges, (3) chart a path forward for ESS working groups to lead the phased development of the new cyberinfrastructure.

54 ENVIRONMENTAL SCIENCES↗