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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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All sources NASA NTRS DOE OSTI

NASA NTRS2004

Grid Enabled Geospatial Catalogue Web Service

Geospatial Catalogue Web Service is a vital service for sharing and interoperating volumes of distributed heterogeneous geospatial resources, such as data, services, applications, and their replicas over the web. Based on the Grid technology and the Open Geospatial Consortium (0GC) s Catalogue Service - Web Information Model, this paper proposes a new information model for Geospatial Catalogue Web Service, named as GCWS which can securely provides Grid-based publishing, managing and querying geospatial data and services, and the transparent access to the replica data and related services under the Grid environment. This information model integrates the information model of the Grid Replica Location Service (RLS)/Monitoring & Discovery Service (MDS) with the information model of OGC Catalogue Service (CSW), and refers to the geospatial data metadata standards from IS0 19115, FGDC and NASA EOS Core System and service metadata standards from IS0 191 19 to extend itself for expressing geospatial resources. Using GCWS, any valid geospatial user, who belongs to an authorized Virtual Organization (VO), can securely publish and manage geospatial resources, especially query on-demand data in the virtual community and get back it through the data-related services which provide functions such as subsetting, reformatting, reprojection etc. This work facilitates the geospatial resources sharing and interoperating under the Grid environment, and implements geospatial resources Grid enabled and Grid technologies geospatial enabled. It 2!so makes researcher to focus on science, 2nd not cn issues with computing ability, data locztic~, processir,g and management. GCWS also is a key component for workflow-based virtual geospatial data producing.

Chen, Ai-Jun↗

DOE OSTI2022

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1989

A rapid prototyping/artificial intelligence approach to space station-era information management and access

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced.

Carnahan, Richard S., Jr.↗

NASA NTRS2017

Harvesting NASA's Common Metadata Repository (CMR)

As part of NASA's Earth Observing System Data and Information System (EOSDIS), the Common Metadata Repository (CMR) stores metadata for over 30,000 datasets from both NASA and international providers along with over 300M granules. This metadata enables sub-second discovery and facilitates data access. While the CMR offers a robust temporal, spatial and keyword search functionality to the general public and international community, it is sometimes more desirable for international partners to harvest the CMR metadata and merge the CMR metadata into a partner's existing metadata repository. This poster will focus on best practices to follow when harvesting CMR metadata to ensure that any changes made to the CMR can also be updated in a partner's own repository. Additionally, since each partner has distinct metadata formats they are able to consume, the best practices will also include guidance on retrieving the metadata in the desired metadata format using CMR's Unified Metadata Model translation software.

Earth Resources↗

DOE OSTI2022

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Metadata for a systematic description of signal data

This chapter aims to provide a comprehensive overview of metadata types that may be useful during system design, optimization, and automation. Metadata are grouped into three main categories: (a) metadata describing signal generation, (b) metadata describing signal quality, and (c) contextual information in the form of annotations. Each of these categories is introduced and explained in three separate sections. Importantly, this chapter mainly answers what is considered metadata. To a lesser degree, recommendations are made regarding the selection of metadata for long-term storage. Chapter 4 will explain where and how to store metadata. Chapters 5 and 6 explain how to collect certain metadata through dedicated sensor validation tests (Chapter 5) or algorithmic analysis (Chapter 6).

Alferes, Janelcy↗

NASA NTRS2009

Evolution of Web Services in EOSDIS: Search and Order Metadata Registry (ECHO)

During 2005 through 2008, NASA defined and implemented a major evolutionary change in it Earth Observing system Data and Information System (EOSDIS) to modernize its capabilities. This implementation was based on a vision for 2015 developed during 2005. The EOSDIS 2015 Vision emphasizes increased end-to-end data system efficiency and operability; increased data usability; improved support for end users; and decreased operations costs. One key feature of the Evolution plan was achieving higher operational maturity (ingest, reconciliation, search and order, performance, error handling) for the NASA s Earth Observing System Clearinghouse (ECHO). The ECHO system is an operational metadata registry through which the scientific community can easily discover and exchange NASA's Earth science data and services. ECHO contains metadata for 2,726 data collections comprising over 87 million individual data granules and 34 million browse images, consisting of NASA s EOSDIS Data Centers and the United States Geological Survey's Landsat Project holdings. ECHO is a middleware component based on a Service Oriented Architecture (SOA). The system is comprised of a set of infrastructure services that enable the fundamental SOA functions: publish, discover, and access Earth science resources. It also provides additional services such as user management, data access control, and order management. The ECHO system has a data registry and a services registry. The data registry enables organizations to publish EOS and other Earth-science related data holdings to a common metadata model. These holdings are described through metadata in terms of datasets (types of data) and granules (specific data items of those types). ECHO also supports browse images, which provide a visual representation of the data. The published metadata can be mapped to and from existing standards (e.g., FGDC, ISO 19115). With ECHO, users can find the metadata stored in the data registry and then access the data either directly online or through a brokered order to the data archive organization. ECHO stores metadata from a variety of science disciplines and domains, including Climate Variability and Change, Carbon Cycle and Ecosystems, Earth Surface and Interior, Atmospheric Composition, Weather, and Water and Energy Cycle. ECHO also has a services registry for community-developed search services and data services. ECHO provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources (data, service and clients). In their native state, these data, service and client resources are not necessarily targeted for use beyond their original mission. However, with the proper interoperability mechanisms, users of these resources can expand their value, by accessing, combining and applying them in unforeseen ways.

Mitchell, Andrew↗

DOE OSTI2024

Optimizing Metadata Exchange: Leveraging DAOS for ADIOS Metadata I/O

In HPC I/O middleware like the Adaptable I/O System (ADIOS) often mediates data transfers between applications. The metadata I/O generated by such systems often presents significant scaling and performance limitations. This work seeks improvement opportunities for metadata I/O by leveraging the DAOS storage systems, a recent storage system solution deployed on high-end systems such as the Aurora supercomputer. We investigate the tradeoffs and the design space for integrating I/O engines for the ADIOS middleware based on the different storage mechanisms supported by DAOS. We present a new DAOS-Array-ChunkSize-aligned engine which provides up to 2.3× improved performance than when using the existing DAOS-POSIX interface, without requiring any application modifications.

Venkatesh, Ranjan Sarpangala↗

NASA NTRS2009

JPL Project Information Management: A Continuum Back to the Future

This slide presentation reviews the practices and architecture that support information management at JPL. This practice has allowed concurrent use and reuse of information by primary and secondary users. The use of this practice is illustrated in the evolution of the Mars Rovers from the Mars Pathfinder to the development of the Mars Science Laboratory. The recognition of the importance of information management during all phases of a project life cycle has resulted in the design of an information system that includes metadata, has reduced the risk of information loss through the use of an in-process appraisal, shaping of project's appreciation for capturing and managing the information on one project for re-use by future projects as a natural outgrowth of the process. This process has also assisted in connection of geographically disbursed partners into a team through sharing information, common tools and collaboration.

Information Management↗

NASA NTRS2018

New Metadata Capabilities within NASA's Common Metadata Repository (CMR)

This talk will convey the new capabilities and features of the UMM-Variables and the UMM-Services metadata model within the Common Metadata Repository (CMR) and how community engagement through the ESDIS Standards Office (ESO) review shaped the new versions of the models. The Unified Metadata Model (UMM) provides a common metadata model to unify legacy systems (i.e. GCMD (Global Change Master Directory), ECHO (Earth Observing System (EOS) Clearinghouse)) with new systems (i.e. CMR). The rationale and migration process of the Service Entry Resource Format (SERF) to the UMM-S will also be conveyed. The talk will conclude with discussing issues and lessons learned from the review process and how future reviews will be conducted to ensure a more targeted, meaningful review with a faster turn-around time for triage and implementation.

Earth Observing System↗

NASA NTRS1993

In search of meta-knowledge

Development of an Intelligent Information System (IIS) involves application of numerous artificial intelligence (AI) paradigms and advanced technologies. The National Aeronautics and Space Administration (NASA) is interested in an IIS that can automatically collect, classify, store and retrieve data, as well as develop, manipulate and restructure knowledge regarding the data and its application (Campbell et al., 1987, p.3). This interest stems in part from a NASA initiative in support of the interagency Global Change Research program. NASA's space data problems are so large and varied that scientific researchers will find it almost impossible to access the most suitable information from a software system if meta-information (metadata and meta-knowledge) is not embedded in that system. Even if more, faster, larger hardware is used, new innovative software systems will be required to organize, link, maintain, and properly archive the Earth Observing System (EOS) data that is to be stored and distributed by the EOS Data and Information System (EOSDIS) (Dozier, 1990). Although efforts are being made to specify the metadata that will be used in EOSDIS, meta-knowledge specification issues are not clear. With the expectation that EOSDIS might evolve into an IIS, this paper presents certain ideas on the concept of meta-knowledge and demonstrates how meta-knowledge might be represented in a pixel classification problem.

Lopez, Antonio M., Jr.↗

NASA NTRS2016

Evolution in Metadata Quality: Common Metadata Repository's Role in NASA Curation Efforts

Metadata Quality is one of the chief drivers of discovery and use of NASA EOSDIS (Earth Observing System Data and Information System) data. Issues with metadata such as lack of completeness, inconsistency, and use of legacy terms directly hinder data use. As the central metadata repository for NASA Earth Science data, the Common Metadata Repository (CMR) has a responsibility to its users to ensure the quality of CMR search results. This poster covers how we use humanizers, a technique for dealing with the symptoms of metadata issues, as well as our plans for future metadata validation enhancements. The CMR currently indexes 35K collections and 300M granules.

metadata quality↗

NASA NTRS2006

Power User Interface

Power User Interface 5.0 (PUI) is a system of middleware, written for expert users in the Earth-science community, PUI enables expedited ordering of data granules on the basis of specific granule-identifying information that the users already know or can assemble. PUI also enables expert users to perform quick searches for orderablegranule information for use in preparing orders. PUI 5.0 is available in two versions (note: PUI 6.0 has command-line mode only): a Web-based application program and a UNIX command-line- mode client program. Both versions include modules that perform data-granule-ordering functions in conjunction with external systems. The Web-based version works with Earth Observing System Clearing House (ECHO) metadata catalog and order-entry services and with an open-source order-service broker server component, called the Mercury Shopping Cart, that is provided separately by Oak Ridge National Laboratory through the Department of Energy. The command-line version works with the ECHO metadata and order-entry process service. Both versions of PUI ultimately use ECHO to process an order to be sent to a data provider. Ordered data are provided through means outside the PUI software system.

Pfister, Robin↗

NASA NTRS2006

A Reference Architecture for Space Information Management

We describe a reference architecture for space information management systems that elegantly overcomes the rigid design of common information systems in many domains. The reference architecture consists of a set of flexible, reusable, independent models and software components that function in unison, but remain separately managed entities. The main guiding principle of the reference architecture is to separate the various models of information (e.g., data, metadata, etc.) from implemented system code, allowing each to evolve independently. System modularity, systems interoperability, and dynamic evolution of information system components are the primary benefits of the design of the architecture. The architecture requires the use of information models that are substantially more advanced than those used by the vast majority of information systems. These models are more expressive and can be more easily modularized, distributed and maintained than simpler models e.g., configuration files and data dictionaries. Our current work focuses on formalizing the architecture within a CCSDS Green Book and evaluating the architecture within the context of the C3I initiative.

information systems↗

DOE OSTI2026

Groundwater and river water elevations and temperature from 2017 to 2022 across Meander Z in the East River Watershed, Colorado

This dataset includes groundwater and river water elevations and temperature data collected in the East River watershed located in the Upper Colorado River Basin. The data were collected in order to investigate the coupling between hydrology and biogeochemical processes in the floodplain. Data was collected at ten groundwater locations in Meander Z (MZ), located just upstream of the confluence with Brush Creek and two river locations directly adjacent to Meander Z from 2017-2019. From 2019-2022, data was collected at five groundwater locations in Meander Z. Note that location names, not location identifiers (IDs), are used in the related publication Dewey et al. (2022). Both location IDs and names are included in data files. Files in this dataset include the main data files for each location zipped into a single folder (waterlevel_data.zip), an installation methods file describing sensor installation (InstallationMethods.csv), a file containing field metadata including GPS (Global Positioning System) coordinates and ground surface elevations (transducers_locations.csv). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. This dataset conforms to the ESS-DIVE hydrological reporting format. 2026-04-27 Update: The river water elevation data files (ER-MZR1.csv and ER-MZR2.csv) were corrected. The data for these two locations were inadvertently swapped in the original published data. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

WorkJournalMaker (WJMaker) v0.5

The software generates and maintains daily work journal entries in text format, via a web browser. The journal entries are saved in a structured directory file tree on the system running the software. The software also incorporates a database so that it can track the location of files in the file system and various other metadata. The software allows the users to access their journal entries either through the browser or as discrete text files, facilitating sharing and open science. Additionally, to assist with the yearly PMP process, this tool connects to LLM APIs to provide summarization of the journal entries on a month-by-month or weekly basis. The advantage over similar technologies such as Apple Notes (extremely popular for notetaking) is that the instant software does not force the user to stay inside the Apple ecosystem, since it allows for export of the user's text files. This facilitates open science, so that researchers who use the tool can easily transfer their research notes to any other system. The WebJournalMaker repository is here: https://github.com/lbnl-science-it/WorkJournalMaker The WebJournalMaker repository is forked from the JournalSummarizer: https://github.com/tyfong-lbl/JournalSummarizer and builds on its code. I wrote the code for both of these software repos, using generative AI.

Fong, Timothy [Lawrence Berkeley National Laborato↗

DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Report on the Global Data Assembly Center (GDAC) to the 12th GHRSST Science Team Meeting

In 2010/2011 the Global Data Assembly Center (GDAC) at NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) continued its role as the primary clearinghouse and access node for operational Group for High Resolution Sea Surface Temperature (GHRSST) datastreams, as well as its collaborative role with the NOAA Long Term Stewardship and Reanalysis Facility (LTSRF) for archiving. Here we report on our data management activities and infrastructure improvements since the last science team meeting in June 2010.These include the implementation of all GHRSST datastreams in the new PO.DAAC Data Management and Archive System (DMAS) for more reliable and timely data access. GHRSST dataset metadata are now stored in a new database that has made the maintenance and quality improvement of metadata fields more straightforward. A content management system for a revised suite of PO.DAAC web pages allows dynamic access to a subset of these metadata fields for enhanced dataset description as well as discovery through a faceted search mechanism from the perspective of the user. From the discovery and metadata standpoint the GDAC has also implemented the NASA version of the OpenSearch protocol for searching for GHRSST granules and developed a web service to generate ISO 19115-2 compliant metadata records. Furthermore, the GDAC has continued to implement a new suite of tools and services for GHRSST datastreams including a Level 2 subsetter known as Dataminer, a revised POET Level 3/4 subsetter and visualization tool, a Google Earth interface to selected daily global Level 2 and Level 4 data, and experimented with a THREDDS catalog of GHRSST data collections. Finally we will summarize the expanding user and data statistics, and other metrics that we have collected over the last year demonstrating the broad user community and applications that the GHRSST project continues to serve via the GDAC distribution mechanisms. This report also serves by extension to summarize the activities of the GHRSST Data Assembly and Systems Technical Advisory Group (DAS-TAG).

sea surface temperature (SST)↗