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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↗

NASA NTRS2020

Data Science Challenges for Urban Air Mobility

Data Science↗

NASA NTRSDate not supplied

Mars Science Helicopter: Conceptual Design of the Next Generation of Mars Rotorcraft

Robotic planetary aerial vehicles increase the range of terrain that can be examined, compared to traditional landers and rovers, and have more near-surface capability than orbiters. Aerial mobility is a promising possibility for planetary exploration as it reduces the challenges that difficult obstacles pose to ground vehicles. The first use of a rotorcraft for a planetary mission will be in 2021, when the Ingenuity Mars helicopter technology demonstrator will be deployed via the Perseverance rover. NASA’s Jet Propulsion Laboratory and NASA Ames Research Center are exploring possibilities for a Mars Science Helicopter, a second-generation Mars rotorcraft with the capability of conducting science investigations independently of a lander or rover (although this type of vehicle could also be used to assist rovers or landers in future missions). Two, large rotorcraft configurations are described: a hexacopter and a co-axial helicopter with a payload in the range of two to three kilograms and an overall vehicle mass of approximately twenty kilograms. Additionally, advancements in technology over the course of the study are applied to a rotorcraft of the same size and form as Ingenuity. Initial estimates of weight and performance were based on the capabilities of Ingenuity. Rotorcraft designs for Mars are constrained by the dimensions of the aeroshell and lander for the trip to the planet, constraining maximum rotor dimensions and, hence, overall performance potential. The effects of airfoils designed specifically for the low Reynolds number and high Mach number inherent to operation on Mars were studied. Rotor structural designs were developed that met blade frequency and weight targets, subject to material stress limits. The final designs are representative of the vehicle configurations required for a large range of future missions and will require relatively minor adaptations once science tasks are chosen. These designs will be compared to Ingenuity to demonstrate technology advancements developed during the study.

Mars Science Helicopter↗

NASA NTRSDate not supplied

Science Backroom Support for Sustained Lunar Surface

Sustained surface operations on the lunar surface will be supported by the Foundational Surface Habitat (FSH) at the Artemis Base Camp. Planning for FSH is still preliminary but includes notional science outfitting that will enable lunar science and support the science conducted during EVA traverses.

Geolab↗

NASA NTRS2020

The XRISM Science Data Center: Optimizing the Scientific Return from a Unique X-ray Observatory

The X-Ray Imaging and Spectroscopy Mission, XRISM, is currently scheduled to launch in 2022 with the objective of building on the brief, but significant, successes of the ASTRO-H (Hitomi) mission in solving outstanding astrophysical questions using high resolution X-ray spectroscopy. The XRISM Science Operations Team (SOT) consists of the JAXA-led Science Operations Center (SOC) and NASA-led Science Data Center (SDC), which work together to optimize the scientific output from the Resolve high-resolution spectrometer and the Xtend wide-field imager through planning and scheduling of observations, processing and distribution of data, development and distribution of software tools and the calibration database (CaldB), support of ground and in-flight calibration, and support of XRISM users in their scientific investigations of the energetic universe. Here, we summarize the roles and responsibilities of the SDC and its current status and future plans. The Resolve instrument poses particular challenges due to its unprecedented combination of high spectral resolution and throughput, broad spectral coverage, and relatively small field-of-view and large pixel-size. We highlight those challenges and how they are being met.

XRISM↗