Search NASA⌕ Search

SEARCH · Search NASA

Results for “Knowledge Management”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A New Era of Nuclear Criticality Experiments: The First 10 Years of Comet Operations at NCERC

Comet is a general-purpose, heavy-duty, vertical-lift assembly designed for flexibility in conducting a variety of critical experiments. It is currently located at the National Criticality Experiments Research Center (NCERC) in Nevada. In the past, Comet resided at Technical Area-18 in Los Alamos, New Mexico, as part of the Los Alamos Critical Experiments Facility (LACEF). The Comet assembly was relocated to NCERC in 2008 and became fully operational in June of 2011. The first critical experiment performed on Comet at NCERC was a verification of one of the previous configurations of the Zeus experiment series. Over the next 10 years, many additional experiments followed including other Zeus configurations as well as completely new designs. This paper discusses the Comet vertical-lift assembly, the transition from LACEF to NCERC, and a selection of experiments that have been performed on Comet during its first 10 years of operation at NCERC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A New Era of Nuclear Criticality Experiments: The First 10 Years of Planet Operations at NCERC

Planet is a vertical-lift assembly machine currently located at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site. In the past, Planet resided at Technical Area-18 in Los Alamos, New Mexico, as part of the Los Alamos Critical Experiments Facility (LACEF). Following the de-inventorying of LACEF, the Planet assembly was relocated to NCERC in 2008 and became fully operational in June of 2011. The Class Foils experiment, which involves stacking highly enriched uranium foils to obtain a critical configuration, was the first critical experiment performed on Planet. As a major component of the Nuclear Criticality Safety Class taught for the U.S. Department of Energy (DOE) Nuclear Criticality Safety Program, the Class Foils experiment allows personnel from all over the DOE complex to handle nuclear material and to complete the approach to critical safely and successfully. This paper describes the Planet vertical assembly and recent engineering upgrade and a selection of the experiments that have been performed on Planet since its transition to NCERC 10 years ago.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Practices and Standards for Data and Processing at the APS

The handling of datasets at scientific user facilities is becoming progressively more challenging as advances in sources and detectors drive increasingly aggressive data acquisition rates. The ability to share and process these data depends on the ability of the originator of the data and the persons making use of that data to unequivocally agree on what these data mean. This understanding is driven by use of standardized data formats.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. For example, the availability of high-resolution geographic data and high-performance computing techniques together with deep learning fuel progress in fast and accurate object detection. Recent examples of GeoAI work include the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification (e.g. LiDAR point clouds), novel methods for spatial interpolation, and advances in traffic forecasting. Similarly, machine learning and natural language processing are facilitating the extraction of geographic information from unstructured (textual) data, such as news articles and Wikipedia as well as the matching of natural features in multiple gazetteers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of Digital Nautical Chart data for confirmation and expansion of GeoNames data

Here, this work examines how Digital Nautical Chart (DNC) data may contribute to the evolution and refinement of GeoNames data for near-shore features. GeoNames features are point data with one or more possible place names. DNC Earth Cover Text (ECRText) objects are map labels positioned nearby their real word counterpart. ECRText feature map position strikes a compromise between association with real features and cartographic readability. This work explores whether ECRText features can confirm (or expand names for) existing locations or contribute new locations through data conflation. Due to name variations and spatial position, conflating these data are nontrivial. Previous work engaged in a brief examination using the trigram string matching algorithm under coarse proximity constraints, indicating that ECRText could provide additional value to GeoNames. This work builds on that study, by engaging in a deeper examination of spatial proximity and exploring conflation agreement across an ensemble of string matching approaches. The result finds strong ensemble agreement about ECRText features which already exist in GeoNames but mixed results about which features contribute new information, as well as exploring why some of these matching techniques fail. With an eye toward automation, computational efficiency was found not to be a constraint in sustaining updates.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Determinants of residential mobility: an adaptive retrospective survey method

This study introduces a survey instrument to collect retrospective life-course events, focusing on residential relocation, and it utilizes the survey to evaluate determinants of residential mobility. Here, the survey consists of seven modules collecting information about household structure and household demographics, latest residential relocation, current and previous home, employment, education, vehicle ownership, and travel behavior. The time window of the life-course calendar in the survey is customized according to the latest residential relocation as an anchor point to assist with memory recollection and balance the required input from participants. The survey is used to collect data from a sample of 514 respondents in Sydney, Australia, and another sample of 404 respondents in Chicago, Illinois, in the US. The Cox proportional hazard model is used to analyze residential mobility. The results show primary school commencement is a salient determinant of residential relocation, but its impact is significantly higher in Chicago compared to Sydney.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

El valor de los metadatos para las estaciones de recuperación de recursos del agua

Spanish: Los metadatos hacen referencia a información descriptiva (como ubicación del sensor, unidad de medida, rango de medida, fecha de calibración, fecha de limpieza, si ocurrió algún evento como episodio de lluvia/fallo operativo/vertido tóxico . . .) que es esencial para convertir los grandes volúmenes de datos que se recogen actualmente en las instalaciones de tratamiento de agua y que están sin procesar en información y recursos útiles. Con el avance de la digitalización en el sector del agua, es fundamental evitar los cementerios de datos y, por otro lado, utilizar los datos almacenados para resolver problemas actuales y futuros. Este artículo se centra en el papel crucial que tienen los metadatos para responder a desafíos futuros y posiblemente impredecibles. El objetivo de este documento es presentar el ‘reto de los metadatos’ y destacar la necesidad de tener en cuenta los metadatos cuando se recoge información como parte de las buenas prácticas de digitalización. English: Metadata refers to descriptive information (such as sensor location, measurement unit, measurement range, calibration date, cleaning date, if any event occurred such as rain event/operating failure/toxic spill, . . .) essential to convert large volumes of raw data that are currently collected at water treatment facilities into useful information and resources. With the advance of digitalization in the water sector, it is fundamental to avoid data graveyards and, on the other hand, using collected data to address current and future problems. This paper focuses on the crucial role that metadata has in responding to future and possibly unpredictable challenges. The aim of this document is to present the ‘metadata challenge’ and to highlight the need to consider metadata when collecting information as part of good digitalization practices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

TomoBank: a tomographic data repository for computational x-ray science

There is a widening gap between the fast advancement of computational methods for tomographic reconstruction and their successful implementation in production software at various synchrotron facilities. This is due in part to the lack of readily available instrument datasets and phantoms representative of real materials for validation and comparison of new numerical methods. Recent advancements in detector technology made sub-second and multi-energy tomographic data collection possible [1], but also increased the demand to develop new reconstruction methods able to handle in-situ [2] and dynamic systems [3] that can be quickly incorporated in beamline production software [4]. The X-ray Tomography Data Bank, tomoBank, provides a repository of experimental and simulated datasets with the aim to foster collaboration among computational scientists, beamline scientists, and experimentalists and to accelerate the development and implementation of tomographic reconstruction methods for synchrotron facility production software by providing easy access to challenging dataset and their descriptors.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Multi-objective optimization of interatomic potentials with application to MgO

The parameterization of a functional form for an interatomic potential is treated as a problem in multi-objective optimization. An autonomous, machine-learning approach based on the identification of the Pareto hypersurface of errors in predicted properties allows the development of an ensemble of parameterizations with high materials fidelity and robustness. The efficacy of this approach is illustrated for the simple example of a Buckingham potential for MgO. This approach also provides a strong foundation for uncertainty quantification of potential parameterizations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Belle II Conditions Database

The Belle II experiment at KEK is preparing for taking first collision data in early 2018. For the success of the experiment it is essential to have information about varying conditions available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II Conditions Database was designed to make maintenance as easy as possible. To this end, a HTTP REST service was developed with industry-standard tools such as Swagger for the API interface development, Payara for the Java EE application server, and the Hazelcast in-memory data grid for support of scalable caching as well as transparent distribution of the service across multiple sites. On the client side, the online and offline software has to be able to obtain conditions data from the Belle II Conditions Database in a robust and reliable way under very different situations. As such the client side interface to the Belle II Conditions Database has been designed with a variety of access mechanisms which allow the software to be used with and without an internet connection. Different methods to access the payload information are implemented to allow for a high level of customization per site and to simplify testing of new payloads locally. Changes to the conditions data are usually handled transparently but users can actively check whether an object has changed or register callback functions to be called whenever a conditions data object is updated. In addition a command line user interface has been developed to simplify inspection and modification of the database contents.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The ATLAS Event Index: The Architecture of the Core Engine

The global view of the ATLAS Event Index system has been presented in the 17th ACAT Conference. This article concentrates on the architecture of the system core component. This component handles the final stage of the event metadata import. It organizes its storage and provides a fast and feature-rich access to all information. A user is able to interrogate metadata in various ways, including by executing user-provided code on the data to make selections and to interpret the results. A wide spectrum of clients is available, from a set of Linux-like commands to an interactive graphical Web Service. The stored event metadata contain the basic description of the related events, the references to the experiment event storage and the full trigger record and can be extended with other event characteristics. Derived collections of events can be created. Such collections can be annotated and tagged with further information.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Striped Data Server for Scalable Parallel Data Analysis

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. As a result, we have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Data preservation at the Fermilab Tevatron

The Fermilab Tevatron collider's data-taking run ended in September 2011, yielding a dataset with rich scientific potential. The CDF and DO experiments each have nearly 9 PB of collider and simulated data stored on tape. A large computing infrastructure consisting of tape storage, disk cache, and distributed grid computing for physics analysis with the Tevatron data is present at Fermilab. The Fermilab Run II data preservation project intends to keep this analysis capability sustained through the year 2020 or beyond. To achieve this, we are implementing a system that utilizes virtualization, automated validation, and migration to new standards in both software and data storage technology as well as leveraging resources available from currently-running experiments at Fermilab. Furthermore, these efforts will provide useful lessons in ensuring long-term data access for numerous experiments throughout high-energy physics, and provide a roadmap for high-quality scientific output for years to come.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Open access to high-level data and analysis tools in the CMS experiment at the LHC

The CMS experiment, in recognition of its commitment to data preservation and open access as well as to education and outreach, has made its first public release of high-level data under the CC0 waiver: up to half of the proton-proton collision data (by volume) at 7 TeV from 2010 in CMS Analysis Object Data format. CMS has prepared, in collaboration with CERN and the other LHC experiments, an open-data web portal based on Invenio. The portal provides access to CMS public data as well as to analysis tools and documentation for the public. The tools include an event display and histogram application that run in the browser. In addition a virtual machine containing a CMS software environment along with XRootD access to the data is available. Within the virtual machine the public can analyse CMS data, example code is provided. As a result, we describe the accompanying tools and documentation and discuss the first experiences of data use.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗