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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.

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NASA NTRS2013

Designing the X-Ray Microcalorimeter Spectrometer for Optimal Science Return

Recent advances in X-ray microcalorimeters enable a wide range of possible focal plane designs for the X-ray Microcalorimeter Spectrometer (XMS) instrument on the future Advanced X-ray Spectroscopic Imaging Observatory (AXSIO) or X-ray Astrophysics Probe (XAP). Small pixel designs (75 microns) oversample a 5-10" PSF by a factor of 3-6 for a 10 m focal length, enabling observations at both high count rates and high energy resolution. Pixel designs utilizing multiple absorbers attached to single transition-edge sensors can extend the focal plane to cover a significantly larger field of view, albeit at a cost in maximum count rate and energy resolution. Optimizing the science return for a given cost and/or complexity is therefore a non-trivial calculation that includes consideration of issues such as the mission science drivers, likely targets, mirror size, and observing efficiency. We present a range of possible designs taking these factors into account and their impacts on the science return of future large effective-area X-ray spectroscopic missions.

X-rays↗

NASA NTRS2015

ESIP Earth Sciences Data Analytics (ESDA) Cluster - Work in Progress

The purpose of this poster is to promote a common understanding of the usefulness of, and activities that pertain to, Data Analytics and more broadly, the Data Scientist; Facilitate collaborations to better understand the cross usage of heterogeneous datasets and to provide accommodating data analytics expertise, now and as the needs evolve into the future; Identify gaps that, once filled, will further collaborative activities. Objectives Provide a forum for Academic discussions that provides ESIP members a better understanding of the various aspects of Earth Science Data Analytics Bring in guest speakers to describe external efforts, and further teach us about the broader use of Data Analytics. Perform activities that:- Compile use cases generated from specific community needs to cross analyze heterogeneous data- Compile sources of analytics tools, in particular, to satisfy the needs of the above data users- Examine gaps between needs and sources- Examine gaps between needs and community expertise- Document specific data analytics expertise needed to perform Earth science data analytics Seek graduate data analytics Data Science student internship opportunities.

science data analysis↗

NASA NTRS2015

The NASA Earth Science Program and Small Satellites

Earth's changing environment impacts every aspect of life on our planet and climate change has profound implications on society. Studying Earth as a single complex system is essential to understanding the causes and consequences of climate change and other global environmental concerns. NASA's Earth Science Division (ESD) shapes an interdisciplinary view of Earth, exploring interactions among the atmosphere, oceans, ice sheets, land surface interior, and life itself. This enables scientists to measure global and climate changes and to inform decisions by Government, other organizations, and people in the United States and around the world. The data collected and results generated are accessible to other agencies and organizations to improve the products and services they provide, including air quality indices, disaster prediction and response, agricultural yield projections, and aviation safety. ESD's Flight Program provides the spacebased observing systems and supporting infrastructure for mission operations and scientific data processing and distribution that support NASA's Earth science research and modeling activities. The Flight Program currently has 21 operating Earth observing space missions, including the recently launched Global Precipitation Measurement (GPM) mission, the Orbiting Carbon Observatory-2 (OCO-2), the Soil Moisture Active Passive (SMAP) mission, and the International Space Station (ISS) RapidSCAT and Cloud-Aerosol Transport System (CATS) instruments. The ESD has 22 more missions and instruments planned for launch over the next decade. These include first and second tier missions from the 2007 Earth Science Decadal Survey, Climate Continuity missions to assure availability of key climate data sets, and small-sized competitively selected orbital missions and instrument missions of opportunity belonging to the Earth Venture (EV) Program. Small satellites (~500 kg or less) are critical contributors to these current and future satellite missions. Some examples are the aforementioned Orbiting Carbon Observatory-2 (OCO-2), the Gravity Recovery and Climate Experiment Follow On (GRACE FO), and the Cyclone Global Navigation Satellite System (CYGNSS) microsatellite constellation. Small satellites also support ESD in space validation and risk reduction of enabling technologies (components and systems). The status of the ESD Flight Program and the role of small satellites will be discussed.

Earth Science Prog & Small Sat↗

NASA NTRS2014

Titan Science Return Quantification

Each proposal for a NASA mission concept includes a Science Traceability Matrix (STM), intended to show that what is being proposed would contribute to satisfying one or more of the agency's top-level science goals. But the information traditionally provided cannot be used directly to quantitatively compare anticipated science return. We added numerical elements to NASA's STM and developed a software tool to process the data. We then applied this methodology to evaluate a group of competing concepts for a proposed mission to Saturn's moon, Titan.

Saturn↗

NASA NTRS2016

Ensuring and Improving Information Quality for Earth Science Data and Products: Role of the ESIP Information Quality Cluster

Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality - scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly.

Earth Science↗

NASA NTRS2016

Ensuring and Improving Information Quality for Earth Science Data and Products Role of the ESIP Information Quality Cluster

Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly.

Information Quality↗

NASA NTRS2016

NASA Earth Observing System Data and Information System (EOSDIS): A U.S. Network of Data Centers Serving Earth Science Data: A Network Member of ICSU WDS

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, and serving a diverse user community around the world with Earth science data from satellites, aircraft, field campaigns and research investigations. The ESDIS Project, responsible for EOSDIS is a Network Member of the International Council for Sciences (ICSU) World Data System (WDS). Nine of the 12 Distributed Active Archive Centers (DAACs), which are part of EOSDIS, are Regular Members of the ICSUWDS. This poster presents the EOSDIS mission objectives, key characteristics of the DAACs that make them world class Earth science data centers, successes, challenges and best practices of EOSDIS focusing on the years 2014-2016, and illustrates some highlights of accomplishments of EOSDIS. The highlights include: high customer satisfaction, growing archive and distribution volumes, exponential growth in number of products distributed to users around the world, unified metadata model and common metadata repository, flexibility provided to uses by supporting data transformations to suit their applications, near-real-time capabilities to support various operational and research applications, and full resolution image browse capabilities to help users select data of interest. The poster also illustrates how the ESDIS Project is actively involved in several US and international data system organizations.

Earth Science↗

NASA NTRS2016

Stewardship of NASA's Earth Science Data and Ensuring Long-Term Active Archives

Program, NASA has followed an open data policy, with non-discriminatory access to data with no period of exclusive access. NASA has well-established processes for assigning and or accepting datasets into one of 12 Distributed Active Archive Centers (DAACs) that are parts of EOSDIS. EOSDIS has been evolving through several information technology cycles, adapting to hardware and software changes in the commercial sector. NASA is responsible for maintaining Earth science data as long as users are interested in using them for research and applications, which is well beyond the life of the data gathering missions. For science data to remain useful over long periods of time, steps must be taken to preserve: (1) Data bits with no corruption, (2) Discoverability and access, (3) Readability, (4) Understandability, (5) Usability' and (6). Reproducibility of results. NASAs Earth Science data and Information System (ESDIS) Project, along with the 12 EOSDIS Distributed Active Archive Centers (DAACs), has made significant progress in each of these areas over the last decade, and continues to evolve its active archive capabilities. Particular attention is being paid in recent years to ensure that the datasets are published in an easily accessible and citable manner through a unified metadata model, a common metadata repository (CMR), a coherent view through the earthdata.gov website, and assignment of Digital Object Identifiers (DOI) with well-designed landing product information pages.

Data Management↗

NASA NTRS2017

A Relevancy Algorithm for Curating Earth Science Data Around Phenomenon

Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earthscience metadata records. Second, the methodology has been implemented as a standalone web service that is utilized to augment search and usability of data in a variety of tools.

earth science phenomena↗

NASA NTRS2017

Technology for NASA's Planetary Science Vision 2050.

NASAs Planetary Science Division (PSD) initiated and sponsored a very successful community Workshop held from Feb. 27 to Mar. 1, 2017 at NASA Headquarters. The purpose of the Workshop was to develop a vision of planetary science research and exploration for the next three decades until 2050. This abstract summarizes some of the salient technology needs discussed during the three-day workshop and at a technology panel on the final day. It is not meant to be a final report on technology to achieve the science vision for 2050.

Vision↗

NASA NTRS2017

Embracing Open Source for NASA's Earth Science Data Systems

The overarching purpose of NASAs Earth Science program is to develop a scientific understanding of Earth as a system. Scientific knowledge is most robust and actionable when resulting from transparent, traceable, and reproducible methods. Reproducibility includes open access to the data as well as the software used to arrive at results. Additionally, software that is custom-developed for NASA should be open to the greatest degree possible, to enable re-use across Federal agencies, reduce overall costs to the government, remove barriers to innovation, and promote consistency through the use of uniform standards. Finally, Open Source Software (OSS) practices facilitate collaboration between agencies and the private sector. To best meet these ends, NASAs Earth Science Division promotes the full and open sharing of not only all data, metadata, products, information, documentation, models, images, and research results but also the source code used to generate, manipulate and analyze them. This talk focuses on the challenges to open sourcing NASA developed software within ESD and the growing pains associated with establishing policies running the gamut of tracking issues, properly documenting build processes, engaging the open source community, maintaining internal compliance, and accepting contributions from external sources. This talk also covers the adoption of existing open source technologies and standards to enhance our custom solutions and our contributions back to the community. Finally, we will be introducing the most recent OSS contributions from NASA Earth Science program and promoting these projects for wider community review and adoption.

Earth Science↗

NASA NTRS2017

GES DISC Datalist Improves Earth Science Data Discoverability

At American Geophysical Union(AGU) 2016 Fall Meeting, Goddard Earth Sciences Data Information Services Center (GES DISC) unveiled a novel way to access data: Datalist. Currently, datalist is a collection of predefined data variables from one or more archived datasets, curated by our subject matter expert (SME). Our science support team has curated a predefined Hurricane Datalist and received very positive feedback from the user community. Datalist uses the same architecture our new website uses and have the same look and feel as other datasets on our web site. and also provides a one-stop shopping for data, metadata, citation, documentation, visualization and other available services. Since the last AGU Meeting, we have further developed a few new datalists corresponding to the Big Earth Data Initiative (BEDI) Societal Benefit Areas and A-Train data. We now have four datalists: Hurricane, Wind Energy, Greenhouse Gas and A-Train. We have also started working with our User Working Group members to create their favorite datalists and working with other DAAC to explore the possibility to include their products in our datalists that may also lead to a future of potential federated (cross-DAAC) datalists. Since our datalist prototype effort was a success, we are planning to make datalist operational. It's extremely important to have a common metadata model to support datalist, this will also be the foundation of federated datalist. We mapped our datalist metadata model to the unpublished UMM(Universal Metadata Model)-Var (Variable) (June version) and found that the UMM-var together with UMM-C (Collection) and possible UMM-S (Service) will meet our basic requirements. For example: Dataset shortname, and version are already specified in UMM-C, variable name, long name, units, dimensions are all specified in UMM-Var. UMM-Var also facilitates Science Keywords to allow tagging at variable level and Characteristics for optional variable characteristics. Measurements is useful for grouping of the variables and Set is promising to define datalist. And finally, the UMM-Service model to specify the available services for the variable will be very beneficial. In summary, UMM-Var, UMM-C and UMM-S are the basis of federated datalist and the development and deployment of datalist will contribute to the evolution of the UMM.

datalist↗

NASA NTRS2017

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni↗

NASA NTRS2017

Use of Schema on Read in Earth Science Data Archives

Traditionally, NASA Earth Science data archives have file-based storage using proprietary data file formats, such as HDF and HDF-EOS, which are optimized to support fast and efficient storage of spaceborne and model data as they are generated. The use of file-based storage essentially imposes an indexing strategy based on data dimensions. In most cases, NASA Earth Science data uses time as the primary index, leading to poor performance in accessing data in spatial dimensions. For example, producing a time series for a single spatial grid cell involves accessing a large number of data files. With exponential growth in data volume due to the ever-increasing spatial and temporal resolution of the data, using file-based archives poses significant performance and cost barriers to data discovery and access. Storing and disseminating data in proprietary data formats imposes an additional access barrier for users outside the mainstream research community. At the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), we have evaluated applying the schema-on-read principle to data access and distribution. We used Apache Parquet to store geospatial data, and have exposed data through Amazon Web Services (AWS) Athena, AWS Simple Storage Service (S3), and Apache Spark. Using the schema-on-read approach allows customization of indexing spatially or temporally to suit the data access pattern. The storage of data in open formats such as Apache Parquet has widespread support in popular programming languages. A wide range of solutions for handling big data lowers the access barrier for all users. This presentation will discuss formats used for data storage, frameworks with This presentation will discuss formats used for data storage, frameworks with support for schema-on-read used for data access, and common use cases covering data usage patterns seen in a geospatial data archive.

cloud applications↗

NASA NTRS2018

The Best Educational Tool for Interdisciplinary Earth Science Giovanni

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.

interdisciplinary↗

NASA NTRSDate not supplied

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗

NASA NTRS2020

Data Science and Urban Air Mobility: Challenges and Opportunities

Urban Air Mobility↗

NASA NTRSDate not supplied

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗