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This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 20 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.
Five years ago NASA selected the Planetary Plasma Interactions (PPI) Node at UCLA to help the scientific community locate, access and preserve particles and fields data from planetary missions. We propose to continue to serve for 5 more years. During the first five years we have served the scientific community by providing them with high quality data products. We worked with missions and individual scientists to secure the highest quality data possible and to thoroughly document it. We validated the data, placed it on long lasting media and made sure it was properly archived for future use. So far we have prepared and archived over 10(exp 11) bytes of data from 26 instruments on 4 spacecraft. We have produced 106 CD-ROMs with peer reviewed data. In so doing, we have developed an efficient system to prepare and archive the data and thereby have been able to steadily increase the rate at which the data are produced. Although we produced a substantial archive during the initial five years, we have an even larger amount of work in progress. This includes preparing CD-ROM data sets with all of the Voyager, Pioneer and Ulysses data at Jupiter and Saturn. We will have the Jupiter data ready for the Galileo encounter in December, 1995. We are also completing the Pioneer Venus data restoration. The Galileo Venus archive and radio science data from Magellan will be prepared early in the next period. We are assisting the Small Bodies Node of PDS in the preparation of comet data and will be archiving the asteroid data from Galileo. We will be moving in several new directions as well. We will archive the PPI Node's first Earth based data with data from the International Jupiter Watch and Hubble data taken in support of Ulysses particles and field observations. We will work with the Cassini mission in archive planning efforts. For the inner planets we will begin an archive of Mars data starting with Phobos data and will support the US and Russian Mars missions in the late 1990's. We will restore the Mercury data from Mariner 10 and prepare the lunar data from Clementine in time for the lunar data analysis program in 1995. We will work with the Discovery mission teams to plan their archive and have already started with one, NEAR. Finally we will begin archiving our first heliospheric data from Voyager, Galileo, and Mars observers. We will continue to serve the science community by providing access to the data products. During the past 19 months we have filled nearly 6000 requests for on-line and CD-ROM data. The data delivered directly by the PPI Node has been - 5 x 10(exp 11) bytes. In addition to providing the data, we have provided users with software tools to manage and read the data which are computer, operating system and format independent. We have developed scalable systems so that the same software we use to manage and access the data for the entire PPI Node can be used by individual investigators to manage the data on a single CD-ROM, thereby greatly reducing the software development effort for both the PPI Node and users. We deliver this software with the disks. Recent technical advances have made it possible for us to serve a broader community than before. In the next five year period we plan to extend our outreach to the general public and in particular to increase our support for education. Since planetary plasma data are varied and require expertise in many areas the PPI Node will continue to be distributed. In addition to the primary node at UCLA, the PPI Node has three subnodes with an Outer Planets Subnode at the University of Iowa, an Inner Planets Subnode at UCLA, and a Radio Science Subnode at Stanford University. During the first two years of the renewal period there will be a Radio Astronomy Data Node at GSFC. These organizations will provide scientific expertise on the data, participate in node data selection activities and help with data restoration and mission activities.
NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelveNASA Earth Observing System (EOS) data centers that process, archive, document, and distributedata from Earth science missions and related projects. The GES DISC hosts a wide range ofremotely-sensed and model data and provides reliable and robust data access and services to usersworldwide. This presentation, focusing on hydrological land surface data, provides a summary tablefor the hydrological data holdings, along with discussions of recent updates to data and data services.
The data system onboard the United States Operating Segment (USOS) of the ISS is currently used to capture, route, record and downlink high-rate science data from experiments inside the US Lab. Once NASDA's Japanese Experiment Module (JEM) and ESA's Attached Pressurized Module (APM) are launched - in the 2004 to 2005 timeframe - data from these facilities will also be routed to the ground using the USOS data system. A critical component of the USOS data system is the High Rate Frame Multiplexer (HRFM). The HRFM combines multiple data/video inputs and combines them into one data stream. This Ku-band data stream is then routed through the Tracking and Data Relay Satellite (TDRS) system to the ground. The Data Management Coordinator (DMC) - located at the Marshall Space Flight Center's Payload Operations Center (POC) - is responsible for commanding and controlling the HRFM. The HRFM can multiplex a maximum of eight digital data sources and four digital video sources. Thus far, this limitation has not been constraining to operations. However, once the JEM and APM are integrated, the HRFM limitations will become a major constraint to science operations onboard. The purpose of this paper is to characterize the limitations of the HRFM and to explain how these limitations can be successfully managed. With this information, Payload Developers and International Partners will be able to more effectively utilize the data systems onboard the ISS. Ultimately, more science data can be captured and downlinked to Flight Controllers and Scientists on the ground.
Accessing and using NASA Earth science data has commonly presented a challenge to many educators and students, due to issues such as heterogeneous data formats, complex data structures, large volumes of data storage, special programming requirements, and diverse analytical software options that often require a significant investment in time and resources, especially for novices. By facilitating data access and evaluation, as well as promoting open access to create a more level playing field for non-funded scientists, NASA Earth observation data can be more readily used for scientific discovery and societal benefits. To advance this goal, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni). To date, Giovanni has assisted researchers around the world publish over 1300 peer-reviewed papers in a wide range of Earth science disciplines. In this presentation, we will demonstrate how easy it is to use Giovanni for the rapid creation of many different analyses of both weather and climate events.
This slide presentation reviews the work of NASA's Earth Observing System Data and Information System (EOSDIS), a petabyte-scale archive of environmental data that supports global climate change research. The Earth Science Data Systems provide end-to-end capabilities to deliver data and information products to users in support of understanding the Earth system. The presentation contains photographs from space of recent events, (i.e., the effects of the tsunami in Japan, and the wildfires in Australia.) It also includes details of the Data Centers that provide the data to EOSDIS and Science Investigator-led Processing Systems. Information about the Land, Atmosphere Near-real-time Capability for EOS (LANCE) and some of the uses that the system has made possible are reviewed. Also included is information about how to access the data, and evolutionary plans for the future of the system.
The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.
RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.
RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides archive and distribution services for several data products in the Planetary Boundary Layer (PBL) category. As a new variable added to the Atmospheric Infrared Sounder (AIRS) Version 6 support product, the PBL height from AIRS is derived based on the gradients of the retrieved relative humidity profile, and provides the atmospheric pressure at the top of the PBL over the ocean. The GES DISC also hosts the Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) product generated by the Goddard Earth Observing System Model Version 5 (GEOS-5) data assimilation system. The PBL height from MERRA-2 is based on the total eddy diffusion coefficient of heat. The monthly PBL height has been made available in the Giovanni system (Giovanni is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data). Recently, the GES DISC began serving the global PBL height climatology product derived from the COSMIC/FORMOSAT-3 and TerraSAR-X Global Positioning System (GPS) radio occultation (RO) measurements from June 2006 to December 2015. In a previous study, we presented the monthly PBL height data from AIRS and MERRA-2 and demonstrated the GES DISC services which support data intercomparison, such as access, plotting, sub-setting, regridding, and generation of a multi-year monthly mean. We also provided intercomparison results, and found that different PBL height definitions contributed to significant differences of PBL height values between AIRS and MERRA-2. In this work, we present the 10-year seasonal climatologies from the AIRS, MERRA-2 and GPS-RO. We also used the cross section and vertical profile services in Giovanni to display and analyze the vertical atmosphere structure over regions where the PBL height derived from the AIRS and MERRA-2 are quite different. The examination of the AIRS and MERRA-2 three-dimensional data found that the relative humidity profiles had larger differences than the temperature profiles. The MERRA-2 gives more details than the AIRS for the vertical distribution of the humidity.
There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.
NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, digital elevation, atmosphere dynamics and composition, and inter-disciplinary research, and many others. One of the key components of EOSDIS is a set of twelve discipline-based Distributed Active Archive Centers (DAACs) distributed across the United States. Managed by NASA's Earth Science Data and Information System (ESDIS) Project at Goddard Space Flight Center, these DAACs serve over 3 million users globally. The ESDIS Project provides the infrastructure support for EOSDIS, which includes other components such as the Science Investigator-led Processing systems (SIPS), common metadata and metrics management systems, specialized network systems, standards management, and centralized support for use of commercial cloud capabilities. Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, EOSDIS has evolved continually over the past three decades. However, many challenges remain. Challenges addressed in this paper include: growing volume and variety, achieving consistency across a diverse set of data producers, managing information about a large number of datasets, migration to a cloud computing environment, optimizing data discovery and access, incorporating user feedback from a diverse community, keeping metadata updated as data collections grow and age, and ensuring that all the content needed for understanding datasets by future users is identified and preserved.
The data system of the spaceborne eight-beam NASA scatterometer for measuring ocean backscatter is a nonreal-time ground-based science data processing system which inputs backscatter telemetry, processes the data into wind vectors, and archives and distributes the wind vector data and other products. Special attention is given to changes to the baseline design intended to meet new requirements for the data granularity, the data needs of the science team receiving the data, and the telemetry data source.
For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.
The layout of portable document format (PDF) files is constant to any screen, and the metadata therein are latent, compared to mark-up languages such as HTML and XML. No semantic tags are usually provided, and a PDF file is not designed to be edited or its data interpreted by software. However, data held in PDF files need to be extracted in order to comply with opensource data requirements that are now government-regulated. In the chemical domain, related chemical and property data also need to be found, and their correlations need to be exploited to enable data science in areas such as data-driven materials discovery. Such relationships may be realized using text-mining software such as the “chemistry-aware” natural-language-processing tool, ChemDataExtractor; however, this tool has limited data-extraction capabilities from PDF files. This study presents the PDFDataExtractor tool, which can act as a plug-in to ChemDataExtractor. It outperforms other PDF-extraction tools for the chemical literature by coupling its functionalities to the chemical-named entityrecognition capabilities of ChemDataExtractor. The intrinsic PDF-reading abilities of ChemDataExtractor are much improved. The system features a template-based architecture. This enables semantic information to be extracted from the PDF files of scientific articles in order to reconstruct the logical structure of articles. While other existing PDF-extracting tools focus on quantity mining, this template-based system is more focused on quality mining on different layouts. PDFDataExtractor outputs information in JSON and plain text, including the metadata of a PDF file, such as paper title, authors, affiliation, email, abstract, keywords, journal, year, document object identifier (DOI), reference, and issue number. With a self-created evaluation article set, PDFDataExtractor achieved promising precision for all key assessed metadata areas of the document text.
A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.
Data available from the National Space Science Data Center (NSSDC) are listed. The spacecraft, principal investigator, the experiment, and time span of the data are given. A listing is also included of ground-based data, models, computer routines and composite spacecraft data that are available from NSSDC.
The concept for the Platform/Power System combination (supported by Shuttle visits) is presented from the NASA-sponsored Phase A study. The combination will cost-effectively free-fly small groups of unmanned low-earth payloads which have common accommodation needs. A manned support module and power system can provide elemental and growth capability for 3-6 month missions. The configuration and payload accommodations of the combination systems are reviewed and schematized in detail. Flight operations for the payload science management will include the conduct of science operations, the monitoring of instrument performance, verification of the science data and the maintenance of the science mission plan.