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NASA NTRS1990

Pipeline issues

The declining pool of graduates, the lack of rigorous preparation in science and mathematics, and the declining interest in science and engineering careers at the precollege level promises a shortage of technically educated personnel at the college level for industry, government, and the universities in the next several decades. The educational process, which starts out with a large number of students at the elementary level, but with an ever smaller number preparing for science and engineering at each more advanced educational level, is in a state of crisis. These pipeline issues, so called because the educational process is likened to a series of ever smaller constrictions in a pipe, were examined in a workshop at the Space Grant Conference and a summary of the presentations and the results of the discussion, and the conclusions of the workshop participants are reported.

Eisley, Joe T.↗

NASA NTRSDate not supplied

Earth Science Data Processing With Nextflow

Earth science data processing tasks present many challenges. These tasks often process large input datasets and require scores of CPU-hours to generate results. All but the simplest tasks will be decomposed into a series of computational or data manipulation steps, also known as a scientific workflow. In order to reduce the burden of orchestrating and running the dependent processing steps, a workflow execution engine is required. This poster describes the lessons learned by the CLARREO Pathfinder (CPF) team while developing multiple scientific workflows and utilizing the open-source Nextflow engine to execute them in a cloud computing environment. The Nextflow engine is designed with the following stated goals: first, the engine does not dictate how individual steps in the task are implemented (i.e. it is language and interface agnostic); second, the engine supports easy configuration and modularity at the workflow level so that others can easily execute our workflows to reproduce results; lastly, the engine eases development by transparently scaling execution from local to remote environments. Nextflow was developed for the bioinformatics domain but is a good fit for other scientific workflows where the overall task is well-described by a dataflow diagram. The CPF team has developed Nextflow pipelines (i.e. scientific workflows) to simulate CLARREO radiance, generate large look-up tables for inter-calibration algorithms, and generate L4 intercalibration data products. These pipelines consume from single-digits to hundreds of thousands of CPU-hours. In the development and evolution of these pipelines we have discovered many design patterns, pitfalls, and solutions to common problems. Our goal is to demonstrate important aspects of how to design, implement, run, and ultimately share Nextflow pipelines in the domain of Earth science.

Aron D Bartle↗

NASA NTRS2000

Chandra Highlights

This talk will discuss the present status of Chandra's operations and pipeline processing and will present some science highlights.

Fabbiano, Giuseppina↗

NASA NTRS2004

Packaging data products using data grid middleware for Deep Space Mission Systems

We are investigating a distributed computing infrastructure for releasing science products out of the mission pipeline. We are creating a DSMS Product Service and DSMS Data Packaging Service based on the OODT middleware, a Data Grid middleware technology that has proven successful in providing access to heterogeneous, disparate data, and federating that data using a common data model.

Deep Space Mission Systems↗

NASA NTRS2019

The Cumulus Ecosystem: Open Source and Beyond to Foster Collaboration

NASA’s Earth Observing System Data and Information System (EOSDIS) open source Cumulus software is designed as a common set of code and services that can be used to create a pipeline to deliver and manage earth science data in the cloud. Cumulus strives to create an ecosystem on the foundation of open source that unites those with shared problems and goals by encouraging users to contribute solutions back to the platform. Large parts of ingesting and managing data are common and much of what is created can be used by others. Our goal is to maximize collaboration and code reuse while allowing users to design a custom solution that meets their needs without having to take on extraneous functionality. In this talk we will describe how the Cumulus ecosystem works beyond just open source software. We will review the technology, the successes and challenges, and the evolution and future of Cumulus as an ecosystem.

Cumulus↗

NASA NTRS2020

Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents ∆-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.

Lakhmiri, Dounia↗

NASA NTRS2003

Aerospace Workforce Development: The Nebraska Proposal; and Native View Connections: A Multi-Consortium Workforce Development Proposal

The NASA Nebraska Space Grant Consortium (NSGC) continues to recognize the necessity of increasing the quantity and quality of highly skilled graduates and faculty involved with NASA. Through NASA Workforce Development funds awarded in 2002, NSGC spearheaded customer- focused workforce training and higher education, industry and community partnerships that are significantly impacting the state s workforce in the science, technology, engineering, and mathematics (STEM) competencies. NSGC proposes to build upon these accomplishments to meet the steadily increasing demand for STEM skills and to safeguard minority representation in these disciplines. A wide range of workforce development activities target NASA s need to establish stronger connections among higher education, industry, and community organizations. Participation in the National Student Satellite Program (NSSP), Community Internship Program, and Nebraska Science and Technology Recruitment Fair will extend the pipeline of employees benefiting NASA as well as Nebraska. The diversity component of this proposal catapults from the exceptional reputation NSGC has built by delivering geospatial science experiences to Nebraska s Native Americans. For 6 years, NSGC has fostered and sustained partnerships with the 2 tribal colleges and 4 reservation school districts in Nebraska to foster aeronautics education and outreach. This program, the Nebraska Native American Outreach Program (NNAOP), has grown to incorporate more than educational institutions and is now a partnership among tribal community leaders, academia, tribal schools, and industry. The content focus has broadened from aeronautics in the school systems to aerospace technology and earth science applications in tribal community decision-making and workforce training on the reservations. To date, participants include faculty and staff at 4 Nebraska tribal schools, 2 tribal colleges, approximately 1,000 Native American youth, and over 1,200 community members. This Native American Initiative of the NSGC addresses Nebraska workforce development and serves as a model to others. Following a structured evaluation process, NSGC proposes to sustain delivery of the training funded by NASA in 2002 to tribal entities through partnerships linking academic programs and industry leaders.

Bowen, Brent D.↗

NASA NTRS2010

Implementation and Comparison of Acoustic Travel-Time Measurement Procedures for the Solar Dynamics Observatory-Helioseismic and Magnetic Imager Time-Distance Helioseismology Pipeline

The Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO) satellite is designed to produce high-resolution Doppler-velocity maps of oscillations at the solar surface with high temporal cadence. To take advantage of these high-quality oscillation data, a time - distance helioseismology pipeline (Zhao et al., Solar Phys. submitted, 2010) has been implemented at the Joint Science Operations Center (JSOC) at Stanford University. The aim of this pipeline is to generate maps of acoustic travel times from oscillations on the solar surface, and to infer subsurface 3D flow velocities and sound-speed perturbations. The wave travel times are measured from cross-covariances of the observed solar oscillation signals. For implementation into the pipeline we have investigated three different travel-time definitions developed in time - distance helioseismology: a Gabor-wavelet fitting (Kosovichev and Duvall, SCORE'96: Solar Convection and Oscillations and Their Relationship, ASSL, Dordrecht, 241, 1997), a minimization relative to a reference cross-covariance function (Gizon and Birch, Astrophys. J. 571, 966, 2002), and a linearized version of the minimization method (Gizon and Birch, Astrophys. J. 614, 472, 2004). Using Doppler-velocity data from the Michelson Doppler Imager (MDI) instrument onboard SOHO, we tested and compared these definitions for the mean and difference traveltime perturbations measured from reciprocal signals. Although all three procedures return similar travel times in a quiet-Sun region, the method of Gizon and Birch (Astrophys. J. 614, 472, 2004) gives travel times that are significantly different from the others in a magnetic (active) region. Thus, for the pipeline implementation we chose the procedures of Kosovichev and Duvall (SCORE'96: Solar Convection and Oscillations and Their Relationship, ASSL, Dordrecht, 241, 1997) and Gizon and Birch (Astrophys. J. 571, 966, 2002). We investigated the relationships among these three travel-time definitions, their sensitivities to fitting parameters, and estimated the random errors that they produce.

HMI↗

NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

NASA NTRS2016

Astro-H Data Analysis, Processing and Archive

Astro-H (Hitomi) is an X-ray Gamma-ray mission led by Japan with international participation, launched on February 17, 2016. The payload consists of four different instruments (SXS, SXI, HXI and SGD) that operate simultaneously to cover the energy range from 0.3 keV up to 600 keV. This paper presents the analysis software and the data processing pipeline created to calibrate and analyze the Hitomi science data along with the plan for the archive and user support.These activities have been a collaborative effort shared between scientists and software engineers working in several institutes in Japan and USA.

Archive↗

NASA NTRSDate not supplied

Reanalysis of Rat Data from Spacelab Life Sciences 2 (SLS-2) to Reveal Research Gaps in Spaceflight Data

Using and analyzing the legacy data obtained in space life sciences missions has the potential to provide researchers a complete picture of the molecular changes associated with space without further experimentation. This project’s objective is to extract, filter, organize, and analyze all Rattus norvegicus data and metadata obtained from Columbia’s Spacelab Life Sciences 2 (SLS-2, STS-58) mission to explore the ways that we can compile information from model organisms, in our case rats, to create a reliable model to understand biological mechanisms in response to these space flight changes. By reusing rare space legacy data coupled with data analysis techniques, we can combine individual preexisting datasets with current ones to gain new, comprehensive insights about the effects of spaceflight on our bodies. Our methods can also lead to the creation of a standardized pipeline that could be applied to other space life science datasets for analysis. In this review, every biological experiment conducted on rats in the SLS-2 Mission was studied with our pipeline to create a new biological library and model that could be used by scientists from around the world to make novel discoveries and develop new hypotheses from this priceless information without the limitation of the costs of spaceflight experimentation.

rats↗

NASA NTRSDate not supplied

Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach

Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.

Jonathan Oribello↗

NASA NTRS2013

The Hopkins Ultraviolet Telescope: The Final Archive

The Hopkins Ultraviolet Telescope (HUT) was a 0.9 m telescope and moderate-resolution (Delta)lambda equals 3 A) far-ultraviolet (820-1850 Å) spectrograph that flew twice on the space shuttle, in 1990 December (Astro-1, STS-35) and 1995 March (Astro-2, STS-67). The resulting spectra were originally archived in a nonstandard format that lacked important descriptive metadata. To increase their utility, we have modified the original datareduction software to produce a new and more user-friendly data product, a time-tagged photon list similar in format to the Intermediate Data Files (IDFs) produced by the Far Ultraviolet Spectroscopic Explorer calibration pipeline. We have transferred all relevant pointing and instrument-status information from locally-archived science and engineering databases into new FITS header keywords for each data set. Using this new pipeline, we have reprocessed the entire HUT archive from both missions, producing a new set of calibrated spectral products in a modern FITS format that is fully compliant with Virtual Observatory requirements. For each exposure, we have generated quicklook plots of the fully-calibrated spectrum and associated pointing history information. Finally, we have retrieved from our archives HUT TV guider images, which provide information on aperture positioning relative to guide stars, and converted them into FITS-format image files. All of these new data products are available in the new HUT section of the Mikulski Archive for Space Telescopes (MAST), along with historical and reference documents from both missions. In this article, we document the improved data-processing steps applied to the data and show examples of the new data products.

Hopkins↗

NASA NTRS2018

Astro-H/Hitomi Data Analysis, Processing, and Archive

Astro-H is the x-ray/gamma-ray mission led by Japan with international participation, launched on February 17, 2016. Soon after launch, Astro-H was renamed Hitomi. The payload consists of four different instruments (SXS, SXI, HXI, and SGD) that operate simultaneously to cover the energy range from 0.3 keV up to 600 keV. On March 27, 2016, JAXA lost contact with the satellite and, on April 28, they announced the cessation of the efforts to restore mission operations. Hitomi collected about one months worth of data with its instruments. This paper presents the analysis software and the data processing pipeline created to calibrate and analyze the Hitomi science data, along with the plan for the archive. These activities have been a collaborative effort shared between scientists and software engineers working in several institutes in Japan and United States.

Angelini, Lorella↗

NASA NTRSDate not supplied

GeoNEX: A geostationary earth observatory at NASA Earth eXchange: Earth monitoring from operational geostationary satellite systems

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

Ramakrishna R Nemani↗

NASA NTRSDate not supplied

GeoNEX: A Geostationary Earth Observatory

GeoNEX↗

NASA NTRS2012

The Kepler End-to-End Data Pipeline: From Photons to Far Away Worlds

The Kepler mission is described in overview and the Kepler technique for discovering exoplanets is discussed. The design and implementation of the Kepler spacecraft, tracing the data path from photons entering the telescope aperture through raw observation data transmitted to the ground operations team is described. The technical challenges of operating a large aperture photometer with an unprecedented 95 million pixel detector are addressed as well as the onboard technique for processing and reducing the large volume of data produced by the Kepler photometer. The technique and challenge of day-to-day mission operations that result in a very high percentage of time on target is discussed. This includes the day to day process for monitoring and managing the health of the spacecraft, the annual process for maintaining sun on the solar arrays while still keeping the telescope pointed at the fixed science target, the process for safely but rapidly returning to science operations after a spacecraft initiated safing event and the long term anomaly resolution process.The ground data processing pipeline, from the point that science data is received on the ground to the presentation of preliminary planetary candidates and supporting data to the science team for further evaluation is discussed. Ground management, control, exchange and storage of Kepler's large and growing data set is discussed as well as the process and techniques for removing noise sources and applying calibrations to intermediate data products.

data archiving↗

NASA NTRS2011

Dynamic Black-Level Correction and Artifact Flagging for Kepler Pixel Time Series

Methods applied to the calibration stage of Kepler pipeline data processing [1] (CAL) do not currently use all of the information available to identify and correct several instrument-induced artifacts. These include time-varying crosstalk from the fine guidance sensor (FGS) clock signals, and manifestations of drifting moire pattern as locally correlated nonstationary noise, and rolling bands in the images which find their way into the time series [2], [3]. As the Kepler Mission continues to improve the fidelity of its science data products, we are evaluating the benefits of adding pipeline steps to more completely model and dynamically correct the FGS crosstalk, then use the residuals from these model fits to detect and flag spatial regions and time intervals of strong time-varying black-level which may complicate later processing or lead to misinterpretation of instrument behavior as stellar activity.

Kolodziejczak, J. J.↗