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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 NTRS1994

A land-surface Testbed for EOSDIS

The main objective of the Testbed project was to deliver satellite images via the Internet to scientific and educational users free of charge. The main method of operations was to store satellite images on a low cost tape library system, visually browse the raw satellite data, access the raw data filed, navigate the imagery through 'C' programming and X-Windows interface software, and deliver the finished image to the end user over the Internet by means of file transfer protocol methods. The conclusion is that the distribution of satellite imagery by means of the Internet is feasible, and the archiving of large data sets can be accomplished with low cost storage systems allowing multiple users.

Emery, William↗
NASA NTRS2004

Aura Atmospheric Data Products and Their Availability from NASA Goddard Earth Sciences DAAC

NASA's EOS-Aura spacecraft was launched successfully on July 15, 2004. The four instruments onboard the spacecraft are the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), the Tropospheric Emission Spectrometer (TES), and the High Resolution Dynamics Limb Sounder (HBDLS). The Aura instruments are designed to gather earth sciences measurements across the ultraviolet, visible, infra-red, thermal and microwave regions of the electromagnetic spectrum. Aura will provide over 70 distinct standard atmospheric data products for use in ozone layer and surface UV-B monitoring, air quality forecast, and atmospheric chemistry and climate change studies (http://eosaura.gsfc.nasa.gov/). These products include earth-atmosphere radiances and solar spectral irradiances; total column, tropospheric, and profiles of ozone and other trace gases, surface W-B flux; clouds and aerosol characteristics; and temperature, geopotential height, and water vapor profiles. The MLS, OMI, and HIRDLS data products will be archived at the NASA Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC), while data from TES will be archived at NASA Langley Research Center DAAC. Some of the standard products which have gone through quick preliminary checks are already archived at the GES DAAC (http://daac.nsfc.nasa.gov/) and are available to the Aura science team and data validation team members for data validation; and to the application and visualization software developers, for testing their application modules. Once data are corrected for obvious calibration problems and partially validated using in-situ observations, they would be made available to the broader user community. This presentation will provide details of the whole suite of Aura atmospheric data products, and the time line of the availability of the rest of the preliminary products and of the partially validated provisional products. Software and took available for data access, visualization, and data mining will also be discussed.

Ahmad, S.↗
NASA NTRS2005

An Ecological Forecasting Agent

The project goals are: Make data analysis faster and cheaper. Increase use of NASA data by removing barriers to data access. Cope with data heterogeneity. Support code reuse and rapid application development. Support multiple applications, users. Including fire and health domains. Improve QOS. Always provide an answer. Tell user how good it is, where it come from.

Golden, Keith↗
NASA NTRS2013

NASA Tech Briefs, March 2013

Topics covered include: Remote Data Access with IDL Data Compression Algorithm Architecture for Large Depth-of-Field Particle Image Velocimeters Vectorized Rebinning Algorithm for Fast Data Down-Sampling Display Provides Pilots with Real-Time Sonic-Boom Information Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery Monitoring and Acquisition Real-time System (MARS) Analog Signal Correlating Using an Analog-Based Signal Conditioning Front End Micro-Textured Black Silicon Wick for Silicon Heat Pipe Array Robust Multivariable Optimization and Performance Simulation for ASIC Design; Castable Amorphous Metal Mirrors and Mirror Assemblies; Sandwich Core Heat-Pipe Radiator for Power and Propulsion Systems; Apparatus for Pumping a Fluid; Cobra Fiber-Optic Positioner Upgrade; Improved Wide Operating Temperature Range of Li-Ion Cells; Non-Toxic, Non-Flammable, -80 C Phase Change Materials; Soft-Bake Purification of SWCNTs Produced by Pulsed Laser Vaporization; Improved Cell Culture Method for Growing Contracting Skeletal Muscle Models; Hand-Based Biometric Analysis; The Next Generation of Cold Immersion Dry Suit Design Evolution for Hypothermia Prevention; Integrated Lunar Information Architecture for Decision Support Version 3.0 (ILIADS 3.0); Relay Forward-Link File Management Services (MaROS Phase 2); Two Mechanisms to Avoid Control Conflicts Resulting from Uncoordinated Intent; XTCE GOVSAT Tool Suite 1.0; Determining Temperature Differential to Prevent Hardware Cross-Contamination in a Vacuum Chamber; SequenceL: Automated Parallel Algorithms Derived from CSP-NT Computational Laws; Remote Data Exploration with the Interactive Data Language (IDL); Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals; Partitioned-Interval Quantum Optical Communications Receiver; and Practical UAV Optical Sensor Bench with Minimal Adjustability.

Source record↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗
NASA NTRS2002

The Right Amount of Glue: Technologies and Standards Relevant to a Future Solar-Terrestrial Data Environment

In order to meet the challenge of developing a new system science, we will need to employ technology that enables researchers to access data from fields with which they are at least initially unfamiliar as well as from sources they use more regularly. At the same time, the quantity of data to be obtained by missions such as the Solar Dynamics Observatory demands ease and simplicity of data access. These competing demands must in turn fit within severely constrained funding for data analysis in such projects. Based on experience in only a single discipline but with a diversity of data types and sources, we will give examples of technology that have made a significant difference in the way people do science. Similarly, we will show how adoption of a well-documented data format has made it easier for one community to search, reduce, and analyze data. We will also describe a community-supported data reduction and analysis software tree with useful features. We will attempt to generalize the lessons learned in these instances to features the broader, solar-terrestrial community might find compelling, while avoiding overdesign of a common data environment.

Gurman, J. B.↗
NASA NTRS2005

Atmospheric Composition Data and Information Services Center (ACDISC)

NASA's GSFC Earth Sciences (GES) Data and Information and Data Services Center (DISC) manages the archive, distribution and data access for atmospheric composition data from AURA'S OMI, MLS, and hopefully one day, HIRDLS instruments, as well as heritage datasets from TOMS, UARS, MODIS, and AIRS. This data is currently archived in the GES Distributed Active Archive Center (DAAC). The GES DISC has begun the development of a community driven data management system that's sole purpose is to manage and provide value added services to NASA's Atmospheric Composition (AC) Data. This system, called the Atmospheric Composition Data and Information Services Center (ACDISC) will provide access all AC datasets from the above mentioned instruments, as well as AC datasets residing at remote archive sites (e.g, LaRC DAAC) The goals of the ACDISC are to: 1) Provide a data center for Atmospheric Scientists, guided by Atmospheric Scientists; 2) Be absolutely responsive to the data and data service needs of the Atmospheric Composition (AC) community; 3) Provide services (i.e., expertise) that will facilitate the effortless access to and usage of AC data; 4) Collaborate with AC scientists to facilitate the use of data from multiple sensors for long term atmospheric research. The ACDISC is an AC specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques that facilitate science data usage. The purpose of this presentation is to provide the evolution path that the GES DISC in order to better serve AC data, and also to receive continued community feedback and further foster collaboration with AC data users and providers.

Kempler, S.↗
NASA NTRS2020

The Multi-Mission Maximum Likelihood Framework threeML: Multi-wavelength Astronomy in Practice

The Multi-Mission Maximum Likelihood framework (threeML)is a flexi-ble python-based framework for multiwavelength data analysis in astronomy. ThreeMLallows joint likelihood fits of data recorded by many different instruments, from radio to gamma rays. This is achieved by encapsulating data access into instrument-specific plugins, leaving the rest of the analysis agnostic of the data format. In this paper, I out-line threeML’s design and major components, with a focus on the modeling language(astromodels) and the data-access plugins

Henrike Fleischhack↗
NASA NTRS2023

Technical Note: NASAaccess – A Tool for Access, Reformatting, and Visualization of Remotely Sensed Earth Observation and Climate Data

The National Aeronautics and Space Administration (NASA) has launched a new initiative, the Open-Source Science Initiative (OSSI), to enable and support science towards openness. The OSSI supports open-source software development and dissemination. In this work, we present NASAaccess, which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the NASAaccess platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The NASAaccess platform is available as software packages (i.e., the R and conda packages) as well as an interactive-format web-based environmental modeling application for earth observation data developed with Tethys Platform. NASAaccess has been envisioned as lowering the technical barriers and simplifying the process of accessing scalable distributed computing resources and leveraging additional software for data and computationally intensive modeling frameworks. Specifically, NASAaccess has been developed to meet the need for seamless earth observation remote-sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, NASAaccess is also contributing to keeping interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote-sensing products. NASAaccess' current capabilities cover various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products.

Ibrahim Nourein Mohammed↗
NASA NTRS2007

Single-Point Access to Data Distributed on Many Processors

A description of the functions and data structures is defined that would be necessary to implement the Chapel concept of distributions, domains, allocation, access, and interfaces to the compiler for transformations from Chapel source to their run-time implementation for these concepts. A complete set of object-oriented operators is defined that enables one to access elements of a distributed array through regular arithmetic index sets, giving the programmer the illusion that all the elements are collocated on a single processor. This means that arbitrary regions of the arrays can be fragmented and distributed across multiple processors with a single point of access. This is important because it can significantly improve programmer productivity by allowing the programmers to concentrate on the high-level details of the algorithm without worrying about the efficiency and communication details of the underlying representation.

James, Mark↗
NASA NTRS2011

Long-Term Preservation of NASA Heliophysics Data and Access: Where We Were and Where We're Going

The importance of ensuring preservation and useful access to the unique science potential of past, present and future NASA solar and space physics (i.e. heliophysics) data has been recognized since the inception of NASA but remains challenging. In this talk, I will briefly review the history of this topic and and then discuss the present NASA model for heliophysics science data management, including key current resources for finding and using data projects like the Space Physics Data Facility. I will highlight expected future directions, building on working elements of the present program and exploiting new technology, to further improve the data environment, address existing issues and anticipate emerging challenges.

McGuire, Robert E.↗
NASA NTRS2010

The Heliophysics Data Environment, Virtual Observatories, NSSDC, and SPASE

Heliophysics (the study of the Sun and its effects on the Solar System, especially the Earth) has an interesting data environment in that the data are often to be found in relatively small data sets widely scattered in archives around the world. Within the last decade there have been more concentrated efforts to organize the data access methods and create a Heliophysics Data and Model Consortium (HDMC). To provide data search and access capability a number of Virtual Observatories (VO's) have been established both via funding from the U.S. National Aeronautics and Space Administration (NASA) and through other funding agencies in the U.S. and worldwide. At least 15 systems can be labeled as Heliophysics Virtual Observatories, 9 of them funded by NASA. Other parts of this data environment include Resident Archives, and the final, or "deep" archive at the National Space Science Data Center (NSSDC). The problem is that different data search and access approaches are used by all of these elements of the HDMC and a search for data relevant to a particular research question can involve consulting with multiple VO's - needing to learn a different approach for finding and acquiring data for each. The Space Physics Archive Search and Extract (SPASE) project is intended to provide a common data model for Heliophysics data and therefore a common set of metadata for searches of the VO's and other data environment elements. The SPASE Data Model has been developed through the common efforts of the HDMC representatives over a number of years. We currently have released Version 2.1. of the Data Model. The advantages and disadvantages of the Data Model will be discussed along with the plans for the future. Recent changes requested by new members of the SPASE community indicate some of the directions for further development.

Thieman, James↗
NASA NTRS2005

Virtual Observatories for Space Physics Observations and Simulations: New Routes to Efficient Access and Visualization

New tools for data access and visualization promise to make the analysis of space plasma data both more efficient and more powerful, especially for answering questions about the global structure and dynamics of the Sun-Earth system. We will show how new existing tools (particularly the Virtual Space Physics Observatory-VSPO-and the Visual System for Browsing, Analysis and Retrieval of Data-ViSBARD; look for the acronyms in Google) already provide rapid access to such information as spacecraft orbits, browse plots, and detailed data, as well as visualizations that can quickly unite our view of multispacecraft observations. We will show movies illustrating multispacecraft observations of the solar wind and magnetosphere during a magnetic storm, and of simulations of 3 0-spacecraft observations derived from MHD simulations of the magnetosphere sampled along likely trajectories of the spacecraft for the MagCon mission. An important issue remaining to be solved is how best to integrate simulation data and services into the Virtual Observatory environment, and this talk will hopefully stimulate further discussion along these lines.

Roberts, Aaron↗
NASA NTRS2006

Implementing Access to Data Distributed on Many Processors

A reference architecture is defined for an object-oriented implementation of domains, arrays, and distributions written in the programming language Chapel. This technology primarily addresses domains that contain arrays that have regular index sets with the low-level implementation details being beyond the scope of this discussion. What is defined is a complete set of object-oriented operators that allows one to perform data distributions for domain arrays involving regular arithmetic index sets. What is unique is that these operators allow for the arbitrary regions of the arrays to be fragmented and distributed across multiple processors with a single point of access giving the programmer the illusion that all the elements are collocated on a single processor. Today's massively parallel High Productivity Computing Systems (HPCS) are characterized by a modular structure, with a large number of processing and memory units connected by a high-speed network. Locality of access as well as load balancing are primary concerns in these systems that are typically used for high-performance scientific computation. Data distributions address these issues by providing a range of methods for spreading large data sets across the components of a system. Over the past two decades, many languages, systems, tools, and libraries have been developed for the support of distributions. Since the performance of data parallel applications is directly influenced by the distribution strategy, users often resort to low-level programming models that allow fine-tuning of the distribution aspects affecting performance, but, at the same time, are tedious and error-prone. This technology presents a reusable design of a data-distribution framework for data parallel high-performance applications. Distributions are a means to express locality in systems composed of large numbers of processor and memory components connected by a network. Since distributions have a great effect on the performance of applications, it is important that the distribution strategy is flexible, so its behavior can change depending on the needs of the application. At the same time, high productivity concerns require that the user be shielded from error-prone, tedious details such as communication and synchronization.

James, Mark↗
NASA NTRS2023

Spatially-Coordinated Airborne Data and Complementary Products for Aerosol, Gas, Cloud, and Meteorological Studies: the Nasa Activate Dataset

The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ∼ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).

Aerosol Cloud meTeorology Interactions oVer the we↗
NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗
NASA NTRS2012

WMS Server 2.0

This software is a simple, yet flexible server of raster map products, compliant with the Open Geospatial Consortium (OGC) Web Map Service (WMS) 1.1.1 protocol. The server is a full implementation of the OGC WMS 1.1.1 as a fastCGI client and using Geospatial Data Abstraction Library (GDAL) for data access. The server can operate in a proxy mode, where all or part of the WMS requests are done on a back server. The server has explicit support for a colocated tiled WMS, including rapid response of black (no-data) requests. It generates JPEG and PNG images, including 16-bit PNG. The GDAL back-end support allows great flexibility on the data access. The server is a port to a Linux/GDAL platform from the original IRIX/IL platform. It is simpler to configure and use, and depending on the storage format used, it has better performance than other available implementations. The WMS server 2.0 is a high-performance WMS implementation due to the fastCGI architecture. The use of GDAL data back end allows for great flexibility. The configuration is relatively simple, based on a single XML file. It provides scaling and cropping, as well as blending of multiple layers based on layer transparency.

Plesea, Lucian↗