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NASA NTRS2016

Outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications

It is widely accepted that time-sensitive remote sensing data serve the needs of decision makers in the applications communities and yet to date, a comprehensive portfolio of NASA low latency datasets has not been available. This paper will describe the NASA low latency, or Near-Real Time (NRT), portfolio, how it was developed and plans to make it available online through a portal that leverages the existing EOSDIS capabilities such as the Earthdata Search Client (https:search.earthdata.nasa.gov), the Common Metadata Repository (CMR) and the Global Imagery Browse Service (GIBS). This paper will report on the outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications (27-29 September 2016 at NASA Langley Research Center, Hampton VA). The paper will also summarize findings and recommendations from the meeting outlining perceived shortfalls and opportunities for low latency research and application science.

remote sensing↗

NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗

NASA NTRS2007

Accident/Mishap Investigation System

InvestigationOrganizer (IO) is a Web-based collaborative information system that integrates the generic functionality of a database, a document repository, a semantic hypermedia browser, and a rule-based inference system with specialized modeling and visualization functionality to support accident/mishap investigation teams. This accessible, online structure is designed to support investigators by allowing them to make explicit, shared, and meaningful links among evidence, causal models, findings, and recommendations.

Keller, Richard↗

DOE OSTI2024

Using Parameter Sweep in WaterTAP to Analyze New Water Treatment Technologies

We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Ramdb: The NASA Raman Spectral Database (version 1.00).

Given that, in most instances, minimal sample preparation is required and due to its contactless instrument design, Raman spectroscopy is one of the most versatile vibrational spectroscopic techniques for the chemical analysis of environmental and biological specimens. The diversity of applications of Raman spectroscopy ranges anywhere from art [1] to planetary science missions [2]. The advancement in the use of Raman spectroscopy in Solar System missions, notably in post-mission sample return analysis, requires a spectral library holding the broad range of specimens that could be found in Solar System sources. For this purpose, we have initiated the development of a Raman spectral database (Ramdb) at NASA Ames Research Center. Currently, the database includes experimental and theoretical Raman spectra of PAHs [3, 4], as well as laboratory Raman spectra of amino acids, carbon allotropes, minerals, and analogs relevance to Earth Sciences [5], Exobiology [6], Planetary [7], and Astrochemistry [8] to name just a few examples. Ramdb can be found on the web at www.astrochemistry.org/ramdb, where raw and processed Raman spectra can be downloaded in CSV format. The laboratory Raman spectra are measured using a laser Raman spectrometer (JASCO NRS-5500-532QRI). The Raman instrument is equipped with three excitation lasers, with wavelengths of 405, 532, and 785 nm. A clean silicon substrate is used as the internal standard for wavenumber calibration. Powdered samples were prepared (microscopic >10 um, grounded microscopic < 10 um) on glass slides. Some raw data exhibited a background signal arising as a combination of laser-induced fluorescence from the sample. To correct this background, we developed a Python pipeline that uses open-source Python libraries. Ramdb provides both raw and processed (using Python pipeline) data, which includes tabulated Raman shift transitions and other measurement details. The theoretical Raman band positions of PAHs (pyrene monomers and tetramer clusters) were computed using density functional theory (DFT) with the help of the Gaussian 16 suite of programs [9]. In the near future, Ramdb will serve as a repository of Raman spectral data from Laboratory Astrophysics and Planetary Science experiments involving the irradiation of organic compounds under simulated space and planetary conditions. In addition, online and offline tools will be developed for utilising the database for comparison to the user’s sample.

N Punnakayathil↗

DOE OSTI2022

Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9

We report the Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD’s metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Exploring composition mixing in kilonova ejecta with ray-by-ray simulations

Binary neutron star merger (BNSM) ejecta are considered a primary repository of r-process nucleosynthesis and a source of the observed heavy-element abundances. We implement composition mixing into ray-by-ray radiation-hydrodynamic simulations of BNSM ejecta, coupled with an online nuclear network (NN). We model mixing via a gradient-based mixing approximation that evolves simultaneously with the hydrodynamics. Here, we find that mixing occurs in regions where the electron fraction changes rapidly. While mixing smooths composition gradients in transition regions, it has a negligible impact on the heavy-element yields. This is because the primary r-process site (the equatorial ejecta) is initially homogeneous in free neutrons, leaving no strong gradients for mixing to act upon. In each angular ray, the abundances of the most produced elements are robust under mixing, while the less abundant ones are more affected. The total global abundances change only slightly from mixing, since each angular ray contributes its most abundant elements. Furthermore, the predicted kilonova light curves show only minor reddening, with differences below the detectability of state-of-the-art telescopes. In general, we do not observe significant effects from mixing in the time span of the r-process. Consequently, mixing only leads to minor variations in abundances and light curves in ray-by-ray simulations.

Explosive burning↗

DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2003

Mars Data Visualization and E/PO with Marsoweb

Marsoweb is a collaborative web environment that has been developed for the Mars research community to better visualize and analyze Mars orbiter data. Its goal is to enable online data discovery by providing an intuitive, interactive interface to data from the Mars Global Surveyor and other orbiters. Recently Marsoweb has served a prominent role as a resource center for the site selection process for the Mars Explorer Rover 2003 missions. In addition to hosting a repository of landing site memoranda and workshop talks, it includes a Java-based interface to a variety of datamaps and images. This interface enables the display and numerical querying of data, and allows data profiles to be rendered from user-drawn cross-sections. High-resolution Mars Orbiter Camera (MOC) images (currently, over 100,000) can be graphically perused; browser-based image processing tools can be used on MOC images of potential landing sites. An automated VRML atlas allows users to construct 'flyovers' of their own regions-of-interest in 3D. These capabilities enable Marsoweb to be used for general global data studies, in addition to those specific to landing site selection. As of September 2002, over 70,000 distinct users from NASA, USGS, academia, and the general public have accessed Marsoweb.

Gulick, V. C.↗

NASA NTRS2017

Astromaterials Curation Online Resources for Principal Investigators

The Astromaterials Acquisition and Curation office at NASA Johnson Space Center curates all of NASA's extraterrestrial samples, the most extensive set of astromaterials samples available to the research community worldwide. The office allocates ~1500 individual samples to researchers and students each year and has served the planetary research community for 45+ years. The Astromaterials Curation office provides access to its sample data repository and digital resources to support the research needs of sample investigators and to aid in the selection and request of samples for scientific study. These resources can be found on the Astromaterials Acquisition and Curation website at https://curator.jsc.nasa.gov. To better serve our users, we have engaged in several activities to enhance the data available for astromaterials samples, to improve the accessibility and performance of the website, and to address user feedback. We havealso put plans in place for continuing improvements to our existing data products.

Todd, Nancy S.↗

NASA NTRS2018

Planetary Spectrum Generator: An Accurate Online Radiative Transfer Suite for Atmospheres, Comets, Small Bodies and Exoplanets

We have developed an online radiative-transfer suite (https://psg.gsfc.nasa.gov) applicable to a broad range of planetary objects (e.g., planets, moons, comets, asteroids, TNOs, KBOs, exoplanets). The Planetary Spectrum Generator (PSG) can synthesize planetary spectra (atmospheres and surfaces) for a broad range of wavelengths (UV/Vis/near-IR/IR/far-IR/THz/sub-mm/Radio) from any observatory (e.g., JWST, ALMA, Keck, SOFIA), any orbiter (e.g., ExoMars, Juno), or any lander (e.g., MSL). This is achieved by combining several state-of-the-art radiative transfer models, spectroscopic databases and planetary databases (i.e., climatological and orbital). PSG has a 3D (three-dimensional) orbital calculator for most bodies in the solar system, and all confirmed exoplanets, while the radiative-transfer models can ingest billions of spectral signatures for hundreds of species from several spectroscopic repositories. It integrates the latest radiative-transfer and scattering methods in order to compute high resolution spectra via line-by-line calculations, and utilizes the efficient correlated-k method at moderate resolutions, while for computing cometary spectra, PSG handles non-LTE and LTE excitation processes. PSG includes a realistic noise calculator that integrates several telescope/instrument configurations (e.g., interferometry, coronagraphs) and detector technologies (e.g., CCD, heterodyne detectors, bolometers). Such an integration of advanced spectroscopic methods into an online tool can greatly serve the planetary community, ultimately enabling the retrieval of planetary parameters from remote sensing data, efficient mission planning strategies, interpretation of current and future planetary data, calibration of spectroscopic data, and development of new instrument/spacecraft concepts.

Villanueva, G. L.↗

DOE OSTI2020

RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study

Abstract Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2016

Using Cloud-Based Storage Technologies for Earth Science Data

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and software systems developed for NASA data repositories were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Object storage services are provided through all the leading public (Amazon Web Service, Microsoft Azure, Google Cloud, etc.) and private (Open Stack) clouds, and may provide a more cost-effective means of storing large data collections online. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows superior performance for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

Data↗

NASA NTRS2019

Integrated support of NASA satellite and in situ oceanographic data via the PO.DAAC

The NASA Physical Oceanography DAAC (PO.DAAC) serves as one of the premier repositories for oceanographic satellite data. More recently, however, it is also increasingly archiving and distributing complementary in situ datasets from NASA-sponsored field campaigns. Here we present an overview of these projects, the complex multivariate data they produce, and some of the data interoperability challenges faced when dealing with such a heterogeneous suite of observations. We summarize the range of online tools and services currently available via the PODAAC, including data discovery services, web-services for subsetting/extraction, compliance checking and visualization. We also preview some new capabilities under development that may feature in future. These efforts are indicative of an evolution of DAAC services in pursuit of our broader vision: a more integrated approach to multi-sensor oceanographic data access and delivery spanning NASA satellite missions and field campaigns in support of science and applications for societal benefit.

Vannan, Suresh↗

DOE OSTI2024

PVDeg: Development of a Streamlined Tool for PV Degradation Modeling

The photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 to 50 years. research efforts aim to achieve this while keeping performance degradation at a minimum for decades. This puts considerable pressure on improving the accuracy of long-term durability and reliability assessments. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. The tool will simplify many of the routine computational operations that are common to many degradation studies. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. This repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors.

degradation↗

DOE OSTI2023

EDX Spatial: Leveraging cloud and hybrid data management resources for spatial data

In the last few years, the National Energy Technology Laboratory has started leveraging cloud-hosted services for hosting spatial data collections published on the Energy Data eXchange (EDX). Using cloud-hosted storage and compute options offers multiple benefits for visualization and tool development through utilization of spatial data resources. The transition from on-premises services to cloud-hosted services has enabled a few key features: increased accessibility of large derivative datasets, dynamic integration of external authoritative data resources directly from outside entities through representational state transfer application programming interfaces (REST API), and the ability to produce complex mapping applications, dashboards, and online maps. As a result of the shift, NETL has launched EDX Spatial, a platform that leverages on-premises resources combined with cloud compute capabilities to enable enhanced online mapping interfaces and optimize data access. In addition, a unified workflow for handling the public release of spatial data products through EDX has been developed, including standardization of data hosting practices, metadata, symbology, and application elements. This poster reviews the opportunity of leveraging cloud-hosted and hybrid data management solutions for spatial data, and discusses the benefits and lessons learned while leveraging these services through the data repository EDX.

Morkner, Paige↗