PRIMED: Preparation & Requisition of Integrated Microscopy + Energy Data
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Explore the source record for details and available documents.
User requirements, guidelines, and standards for interconnecting an Applications Data Service (ADS) program for data sharing are discussed. Methods for effective sharing of information (catalogues, directories, and dictionaries) among member installations are addressed. An ADS Directory/Catalog architectural model is also given.
Current and future experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper, we report results of the on-beam validation of the Jefferson Lab SRO framework. In this work, we exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.
The standard SIR-B data products will consist of both optical and digital output. The format of the unprocessed echo data, optically processed image data, and digital image data is described.
Attribution of datasets that were used to generate research results described in peer-reviewed publications to the original source of these datasets (which are often archived at NASA Earth Science data centers) has been very challenging. Even though the data citation standard of citing datasets as research artifacts and citing them with Digital Object Identifiers (DOIs) was introduced over a decade ago, most authors do not properly reference the data used in their studies and merely mention them in the text. The lack of proper citations of datasets makes the peer-reviewed publication less transparent, imperils reproducibility, and impedes open science. We offer an open-source publication management methodology and a tool that can help to enhance usage-based data discovery, prominence, and provenance of the data; reproducibility of the research results; and potentially increase the return on investment on NASA-funded research.
Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.
The General Fluorescence Plant Meter (GFP-Meter) is a portable spectrofluorometer that utilizes a fiber-optic cable and a leaf clip to gather spectrofluorescence data. In contrast to traditional analytical systems, this instrument allows for the rapid detection and fluorescence measurement of proteins under field conditions with no damage to plant tissue. Here we discuss the methodology of gathering and standardizing spectrofluorescence data from tobacco and canola plants expressing GFP. Furthermore, we demonstrate the accuracy and effectiveness of the GFP-Meter. We first compared GFP fluorescence measurements taken by the GFP-Meter to those taken by a standard laboratory-based spectrofluorometer, the FluoroMax-2. Spectrofluorescence measurements were taken from the same location on intact leaves. When these measurements were tested by simple linear regression analysis, we found that there was a positive functional relationship between instruments. Finally, to exhibit that the GFP-Meter recorded accurate measurements over a span of time, we completed a time-course analysis of GFP fluorescence measurements. We found that only initial measurements were accurate; however, subsequent measurements could be used for qualitative purposes.
To investigate near UV aerosol absorbers, such as iron in mineral dust and brown carbon from biomass burning, AERONET spectral imaginary refractive index and size distributions retrievals were performed with the input of additional wavelength data. This includes adding both measured spectral AOD and sky radiances at 380 nm and 500 nm to the four channels (440, 675, 870 and 1020 nm) that are used in the standard retrieval data product. The vector radiative transfer code in the AERONET Version 3 retrievals enabled more accurate computations in the ultraviolet region (380 nm). Calibration of the 380 nm channel radiances was done by the vicarious method since the integrating sphere radiance source that is used for all other wavelengths does not produce sufficient energy at 380 nm. Spectral single scattering albedo, imaginary refractive indices and aerosol size distributions from these six wavelength retrievals are compared to the standard four wavelength retrieval values.
Terrestrial Aquatic Interfaces (TAIs), and tidal wetlands in particular, store large amounts of carbon yet are not well represented in Earth System Models (ESMs). Predictions of carbon cycling and greenhouse gas (GHG) emissions in tidal wetlands are highly uncertain. Eddy covariance (EC) towers provide ecosystem-scale GHG flux data at a temporal resolution (every 30min) that is helpful for parameterizing and improving mechanistic realism in ESMs. We propose to use a network of eddy covariance towers and standardized ancillary data streams, along with mesocosm experiments and statistical analyses, across diverse tidal wetlands of North America to develop and improve biogeochemical modeling at the TAI. Our overarching objective is to improve understanding and process-based modeling of gross primary productivity (GPP) and CH4 emission responses, both non-linear and asynchronous, to stressors including plant inundation, disturbance, salinity and nitrogen loading.
The activity of carbon in the two-phase regions - W + WC and W + W2C was obtained from the carbon content of iron rods equilibrated with mixtures of metal plus carbide powders. From this activity data the standard free energies of formation of WC and W2C were calculated. The temperature of the invariant reaction W2C = W + WC was fixed at 1570 + or - 5K. Using available solubility data for C in solid W, the partial molar free energy of C in the dilute solid solution was also calculated. The heat of solution of C in W, and the excess entropy for the interstitial solid solution, were computed, assuming that the carbon atoms reside in the octahedral interstices of bcc W.
Addressed in this paper is the dependence of telecommunication channel, forward error correcting coding and source data compression coding on integrated circuit technology. Emphasis is placed on real time high speed Reed Solomon (RS) decoding using full custom VLSI technology. Performance curves of NASA's standard channel coder and a proposed standard lossless data compression coder are presented.
An overview of the NASA EOS (Earth Observing System) Validation Program will be given with special emphasis on synergy of airborne field missions and EOS satellite missions. Presentation will describe objectives of program and recent field campaigns for validation of standard scientific data products from EOS Terra. Airborne simulators developed for Terra validation will also be described. Plans for future field activity and airborne simulators, especially for EOS Aqua validation and also encompassing EOS Aura validation, will also be briefly presented.
Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Post-acquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: 1) mapping the IM axis to the LC axis for IM-MS data, 2) converting the drift time vs. m/z space to CCS/z vs m/z space, and 3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. Finally, these new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.
Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.
Faraday is a high-temperature electrolysis data visualization tool, which reveals the performance of various button cells under test conditions. These tests and the resulting analytics on their data constitute a state of the industry as the US Department of Energy pushes for the production of hydrogen. Faraday leverages the Idaho National Laboratory's DeepLynx data warehouse to standardize and query button cell data. Faraday programmatically accesses this data in DeepLynx by traversing the schema, represented by a custom ontology. The user interface queries DeepLynx for timeseries data associated with specific button cells in the warehouse, and renders them using JavaScript charts. Additional charting and data analysis techniques are made possible by an auxiliary Python server.
The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.
SPACESIM is a computer program for detailed simulation of operations at a spaceport. SPACESIM is being developed to greatly improve existing spaceports and to aid in designing, building, and operating future spaceports, given that there is a worldwide trend in spaceport operations from very expensive, research- oriented launches to more frequent commercial launches. From an operational perspective, future spaceports are expected to resemble current airports and seaports, for which it is necessary to resolve issues of safety, security, efficient movement of machinery and people, cost effectiveness, timeliness, and maximizing effectiveness in utilization of resources. Simulations can be performed, for example, to (1) simultaneously analyze launches of reusable and expendable rockets and identify bottlenecks arising from competition for limited resources or (2) perform what-if scenario analyses to identify optimal scenarios prior to making large capital investments. SPACESIM includes an object-oriented discrete-event-simulation engine. (Discrete- event simulation has been used to assess processes at modern seaports.) The simulation engine is built upon the Java programming language for maximum portability. Extensible Markup Language (XML) is used for storage of data to enable industry-standard interchange of data with other software. A graphical user interface facilitates creation of scenarios and analysis of data.
NASA launched the Nimbus series of meteorological satellites in the 1960s and 70s. These satellites carried instruments for making observations of the Earth in the visible, infrared, ultraviolet, and microwave wavelengths. The original data archive consisted of a combination of digital data written to 7-track computer tapes and on various film media. Many of these data sets are now being migrated from the old media to the GES DISC modern online archive. The process involves recovering the digital data files from tape as well as scanning images of the data from film strips. Some of the challenges of archiving the Nimbus data include the lack of any metadata from these old data sets. Metadata standards and self-describing data files did not exist at that time, and files were written on now obsolete hardware systems and outdated file formats. This requires creating metadata by reading the contents of the old data files. Some digital data files were corrupted over time, or were possibly improperly copied at the time of creation. Thus there are data gaps in the collections. The film strips were stored in boxes and are now being scanned as JPEG-2000 images. The only information describing these images is what was written on them when they were originally created, and sometimes this information is incomplete or missing. We have the ability to cross-reference the scanned images against the digital data files to determine which of these best represents the data set from the various missions, or to see how complete the data sets are. In this presentation we compared data files and scanned images from the Nimbus-2 High-Resolution Infrared Radiometer (HRIR) for September 1966 to determine whether the data and images are properly archived with correct metadata.