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At least 289 records · Page 16

FJET Database Project: Extract, Transform, and Load

The Data Mining & Knowledge Management team at Kennedy Space Center is providing data management services to the Frangible Joint Empirical Test (FJET) project at Langley Research Center (LARC). FJET is a project under the NASA Engineering and Safety Center (NESC). The purpose of FJET is to conduct an assessment of mild detonating fuse (MDF) frangible joints (FJs) for human spacecraft separation tasks in support of the NASA Commercial Crew Program. The Data Mining & Knowledge Management team has been tasked with creating and managing a database for the efficient storage and retrieval of FJET test data. This paper details the Extract, Transform, and Load (ETL) process as it is related to gathering FJET test data into a Microsoft SQL relational database, and making that data available to the data users. Lessons learned, procedures implemented, and programming code samples are discussed to help detail the learning experienced as the Data Mining & Knowledge Management team adapted to changing requirements and new technology while maintaining flexibility of design in various aspects of the data management project.

excel vba↗

Instantiation of the Damara Tern Platform for Advanced Materials and Manufacturing Technologies (AMMT) Program Collaborative Data Management

This work package focused on deploying an instance of the Damara Tern platform to support AMMT collaborative research activities. The objectives were to provide selected AMMT collaborators with access to a shared environment for capturing operations, trackables, and associated metadata, and to implement data entry functionalities that reflect site-specific procedures. Key activities included creating configurable, schema-driven entry forms and validating the data collection process. The report details the deployment process, the platform infrastructure, and the implemented data entry workflows, providing a reference for end users and establishing a foundation for future production-scale deployments.

36 MATERIALS SCIENCE↗

NOAA National Centers for Environmental Information Fisheries Acoustics Archive Network Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders. Between May 2021 and August 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the National Oceanic and Atmospheric Administration (NOAA)'s N-Wave (the Enterprise network that supports the NOAA mission) and National Centers for Environmental Information (NCEI)'s Fisheries Acoustics Archive for the purpose of a recording a Deep Dive into research drivers. The goal of these meetings was to help characterize the requirements for the research use case, and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support.

59 BASIC BIOLOGICAL SCIENCES↗

University of Central Florida Campus-Wide Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders. Between December 2020 and August 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff at University of Central Florida (UCF) for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

TRUST, Trustworthiness and EOSDIS

In recent years there has been considerable attention by the international scientific research and applications community to ensure high quality of data and information management. The terms FAIR (Findable, Accessible, Interoperable, Reusable) data, TRUST (Transparency, Responsibility, User Community, Sustainability, and Technology) principles, and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles have come into vogue during the last decade. NASA has been managing data and information for over 60 years. NASA’s Earth Observing System Data and Information System (EOSDIS) has been in operation for over 25 years, managing most of NASA’s Earth science data. Trustworthiness is a goal that NASA has always strived to achieve or exceed, because it: enables the success of any NASA science mission; inspires general science research and applications; justifies the cost of operations; contributes to the value of NASA’s Open Data Policy; and influences the long term, historical view for the data collection. Given the recent growth of interest in TRUST principles, it is useful to assess and show how NASA’s attention to trustworthiness maps into those principles. This presentation addresses shows how the various steps that have been taken by the Earth Science Data and Information System (ESDIS) Project in the implementation and evolution of EOSDIS map into the TRUST principles.

Remote Sensing↗

AmeriFlux FLUXNET-1F US-ORv Olentangy River Wetland Research Park

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ORv Olentangy River Wetland Research Park. This is the FLUXNET version of the carbon flux data for the site US-ORv Olentangy River Wetland Research Park produced by applying the standard ONEFlux (1F) software. Site Description - The ORWRP site is a 21-ha large-scale, long-term wetland campus facility that is owned by Ohio State University. It is designed to provide teaching, research, and service related to wetland and river science and ecological engineering. The site has been developed in several phases: Phase 1 (1992 - 1994) - Construction of two 2.5-acre deepwater marshes and a river water delivery system began, with pumps installed on the floodplain to bring water from the Olentangy River. In May 1994, one wetland was planted with marsh vegetation, while the other remained as an unplanted control; Phase 2 (1994 - 1999) - Development of a research and teaching infrastructure took place with the construction of boardwalks, a pavilion, and a compound. The creation of the 7-acre naturally flooded oxbow was also included; Phase 3 (2000 - 2003) - As a research building was created, three additional wetlands were created in the vicinity of the building, including a stormwater wetland that receives runoff from the roof of the building; Phase 4 - The current phase involves research access to the Olentangy River.

Bohrer, Gil↗

AmeriFlux FLUXNET-1F US-RC3 WSU Lind Dryland Research Station

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RC3 WSU Lind Dryland Research Station. This is the FLUXNET version of the carbon flux data for the site US-RC3 WSU Lind Dryland Research Station produced by applying the standard ONEFlux (1F) software. Site Description - RC3 operated from 2011-2016 at the Washington State University Lind Dryland Experimental Station, as part of a cluster of 5 towers (RC1 to RC5) operated for the Regional Approaches to Climate Change (REACCH) USDA-supported research project. Lind is in the low precipitation region of the Columbia Plateau dryland cropping region. The area is characterized as a stable crop-fallow agroecological zone, with crops typically grown every second year to allow soil water to accumulate during intervening fallow years. Winter wheat was grown August 2012-August 2013, and August 2014-July 2015. The growing seasons of 2012 and 2014 were fallow years. Soils at Lind are predominantly silt loam texture Mollisols in the Shano and Ritzville series. Site topography is flat.

Chi, Jinshu [The Hong Kong University of Science a↗

A Data Management System for International Space Station Simulation Tools

Groups associated with the design, operational, and training aspects of the International Space Station make extensive use of modeling and simulation tools. Users of these tools often need to access and manipulate large quantities of data associated with the station, ranging from design documents to wiring diagrams. Retrieving and manipulating this data directly within the simulation and modeling environment can provide substantial benefit to users. An approach for providing these kinds of data management services, including a database schema and class structure, is presented. Implementation details are also provided as a data management system is integrated into the Intelligent Virtual Station, a modeling and simulation tool developed by the NASA Ames Smart Systems Research Laboratory. One use of the Intelligent Virtual Station is generating station-related training procedures in a virtual environment, The data management component allows users to quickly and easily retrieve information related to objects on the station, enhancing their ability to generate accurate procedures. Users can associate new information with objects and have that information stored in a database.

Betts, Bradley J.↗

Aircraft simulation data management - A prototype system

Piloted flight simulations are used throughout the aircraft development process to evaluate design concepts, handling qualities and operational procedures. Simulation project managers are often inundated with data but without a convenient and efficient way to make the correlations and analyses necessary to evaluate system performance. A computer-based Simulation Management System (SIMS) is under development. SIMS will permit simulation project engineers to quickly acquire, access, display, edit, analyze, and document the information necessary to more efficiently manage the research program. SIMS features interactive, associative access to simulation data. This paper describes SIMDEM, a prototype system designed to demonstrate these concepts and procedures in order to obtain feedback from simulator users to guide system design.

Crane, D. F.↗

AmeriFlux FLUXNET-1F US-DFK Dairy Forage Research Center - Kernza

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-DFK Dairy Forage Research Center - Kernza. This is the FLUXNET version of the carbon flux data for the site US-DFK Dairy Forage Research Center - Kernza produced by applying the standard ONEFlux (1F) software. Site Description - Crop field (10 acres) in annual and perennial rotation. Alfalfa (2015-2018), intermediate wheatgrass grown for Kernza grain and grass forage (planted September 2019-2023, terminated spring 2024), corn silage (2024), planted in winter wheat (late summer/fall 2024). Winter wheat (2025) followed by alfalfa (to be planted August 2025).

Duff, Alison [US Dairy Forage Research Center]↗

AmeriFlux FLUXNET-1F US-ADR Amargosa Desert Research Site (ADRS)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ADR Amargosa Desert Research Site (ADRS). This is the FLUXNET version of the carbon flux data for the site US-ADR Amargosa Desert Research Site (ADRS) produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located at the Amargosa Desert Research Site (ADRS). The U.S. Geological Survey (USGS) began studies of unsaturated zone hydrology at ADRS in 1976. Over the years, USGS investigations at ADRS have provided long-term "benchmark" information about the hydraulic characteristics and soil-water movement for both natural-site conditions and simulated waste-site conditions in an arid environment. The ADRS is located in a creosote-bush community adjacent to disposal trenches for low-level radioactive waste.

Moreo, Michael [U.S. Geological Survey]↗

AmeriFlux FLUXNET-1F US-EA4 EAA Field Research Park Woodland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-EA4 EAA Field Research Park Woodland. This is the FLUXNET version of the carbon flux data for the site US-EA4 EAA Field Research Park Woodland produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located at the Edwards Aquifer Authority (EAA) Field Research Park (FRP) just northeast of San Anotnio. It is located at the top of a hill in a small clearing amongst oak-juniper woodlands

McKinney, Tyson [The University of Texas at Austin↗

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗

Usability of EFBs for Viewing NOTAMs and AIS/MET Data Link Messages

Electronic Flight Bags (EFB) are increasingly integral to flight deck information management. A piloted simulation study was conducted at NASA Langley Research Center, one aspect of which was to evaluate the usability and acceptability of EFBs for viewing and managing Notices to Airmen (NOTAMs) and data linked aeronautical information services (AIS) and meteorological information (MET). The study simulated approaches and landings at Memphis International Airport (KMEM) using various flight scenarios and weather conditions. Ten two-pilot commercial airline crews participated, utilizing the Cockpit Motion Facility's Research Flight Deck (CMF/RFD) simulator. Each crew completed approximately two dozen flights over a two day period. Two EFBs were installed, one for each pilot. Study data were collected in the form of questionnaire/interview responses, audio/video recordings, oculometer recordings, and aircraft/system state data. Preliminary usability results are reported primarily based on pilot interviews and responses to questions focused on ease of learning, ease of use, usefulness, satisfaction, and acceptability. Analysis of the data from the other objective measures (e.g., oculometer) is ongoing and will be reported in a future publication. This paper covers how the EFB functionality was set up for the study; the NOTAM, AIS/MET data link, and weather messages that were presented; questionnaire results; selected pilot observations; and conclusions.

Evans, Emory T.↗

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗

Closing the Gap between FAIR Data Repositories and Hierarchical Data Formats

Many in the scientific community, particularly in publicly funded research, are pushing to adhere to more accessible data standards to maximize the findability, accessibility, interoperability, and reusability (FAIR) of scientific data, especially with the growing prevalence of machine learning augmented research. Online FAIR data repositories, such as the Open Science Framework (OSF), help facilitate the adoption of these standards by providing frameworks for storage, access, search, APIs, and other features that create organized hubs of scientific data. However, the wider acceptance of such repositories is hindered by the lack of support of hierarchical data formats, such as Technical Data Management Streaming (TDMS) and Hierarchical Data Format 5 (HDF5), that many researchers rely on to organize their datasets. Various tools and strategies should be used to allow hierarchical data formats, FAIR data repositories, and scientific organizations to work more seamlessly together. A pilot project at Los Alamos National Laboratory (LANL) addresses the disconnect between them by integrating the OSF FAIR data repository with hierarchical data renderers, extending support for additional file types in their framework. The multifaceted interactive renderer displays a tree of metadata alongside a table and plot of the data channels in the file. This allows users to quickly and efficiently load large and complex data files directly in the OSF webapp. Users who are browsing files can quickly and intuitively see the files in the way they or their colleagues structured the hierarchical form and immediately grasp their contents. This solution helps bridge the gap between hierarchical data storage techniques and FAIR data repositories, making both of them more viable options for scientific institutions like LANL which have been put off by the lack of integration between them.

97 MATHEMATICS AND COMPUTING↗

AmeriFlux FLUXNET-1F US-xUN NEON University of Notre Dame Environmental Research Center (UNDE)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xUN NEON University of Notre Dame Environmental Research Center (UNDE). This is the FLUXNET version of the carbon flux data for the site US-xUN NEON University of Notre Dame Environmental Research Center (UNDE) produced by applying the standard ONEFlux (1F) software. Site Description - NEON's Domain 5 core site is located at the University of Notre Dame Environmental Research Center (UNDERC) East. Straddling the border between Northern Wisconsin and Michigan’s Upper Peninsula, the UNDERC property comprises approximately 7500 acres and is maintained as an environmental education and research facility. UNDERC also has 30 lakes comprising 1350 acres, including Crampton Lake, a NEON aquatics site. Region-wide logging for pine in the late 1800s and early to mid-1900's led to clear cutting of most forested areas on the property. The main parcel was donated to the University in the 1930s. Timber harvest continued into the 1950s and later, leaving a mixture of successional forest regrowth. Since the 1970s, the site has been minimally managed to maintain access for recreational, educational and research goals.

Network), NEON (National Ecological Observatory↗

AmeriFlux FLUXNET-1F US-xSE NEON Smithsonian Environmental Research Center (SERC)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xSE NEON Smithsonian Environmental Research Center (SERC). This is the FLUXNET version of the carbon flux data for the site US-xSE NEON Smithsonian Environmental Research Center (SERC) produced by applying the standard ONEFlux (1F) software. Site Description - This site is operated at the Smithsonian Environmental Research Center, which is comprised of forests, wetlands, marshes and shoreline on the Chesapeake Bay

Network), NEON (National Ecological Observatory↗