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Ensemble ReST is a software system that eases the development, deployment, and maintenance of server-side application programs to perform functions that would otherwise be performed by client software. Ensemble ReST takes advantage of the proven disciplines of ReST (Representational State Transfer. ReST leverages the standardized HTTP protocol to enable developers to offer services to a diverse variety of clients: from shell scripts to sophisticated Java application suites
The Coordinated Data Analysis Web (CDAWeb) data browsing system provides plotting, listing and open access v ia FTP, HTTP, and web services (REST, SOAP, OPeNDAP) for data from mo st NASA Heliophysics missions and is heavily used by the community. C ombining data from many instruments and missions enables broad resear ch analysis and correlation and coordination with other experiments a nd missions. Crucial to its effectiveness is the use of a standard se lf-describing data format, in this case, the Common Data Format (CDF) , also developed at the Space Physics Data facility , and the use of metadata standa rds (easily edited with SKTeditor ). CDAweb is based on a set of IDL routines, CDAWlib . . The CDF project also maintains soft ware and services for translating between many standard formats (CDF. netCDF, HDF, FITS, XML) <! .
The Automation Hooks Architecture Trade Study for Flexible Test Orchestration sought a standardized data-driven alternative to conventional automated test programming interfaces. The study recommended composing the interface using multicast DNS (mDNS/SD) service discovery, Representational State Transfer (Restful) Web Services, and Automatic Test Markup Language (ATML). We describe additional efforts to rapidly mature the Automation Hooks Architecture candidate interface definition by validating it in a broad spectrum of applications. These activities have allowed us to further refine our concepts and provide observations directed toward objectives of economy, scalability, versatility, performance, severability, maintainability, scriptability and others.
The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.
The NASA GSFC Space Physics Data Facility (5PDF) provides heliophysics science-enabling information services for enhancing scientific research and enabling integration of these services into the Heliophysics Data Environment paradigm, via standards-based approach (SOAP) and Representational State Transfer (REST) web services in addition to web browser, FTP, and OPeNDAP interfaces. We describe these interfaces and the philosophies behind these web services, and show how to call them from various languages, such as IDL and Perl. We are working towards a "one simple line to call" philosophy extolled in the recent VxO discussions. Combining data from many instruments and missions enables broad research analysis and correlation and coordination with other experiments and missions.
Cloud Computing has become the ubiquitous approach to our Big Data challenge. However, one will quickly discover that moving (a.k.a. forklifting) existing on-premise data analytics solutions to the Cloud doesn’t always translate to costing saving and performance boost. The Cloud’s elasticity, its availability, and its wide selection of computing options and selections of costing models making Cloud an attractive environment to tackle our Big Data challenge. The fact is Cloud, on its own, is not the silver bullet to our daunting challenge need for analyze and derive scientific inferences through vast collections of multi-sensor measurements. We would like to have all scientific data in one easy to access environment, but getting the world of scientific data in one analytic system is immensely difficult to achieve. This paper describes the data analytics web architecture NASA is developing by infusing instances of Integrated Data Analytics systems next to the data. The goal is to minimize unnecessary data movement through collection of data access and analytics webservices for researchers to interact with and analyze measurements without have to download data to their local computer. These services are RESTful and provisioned by the data centers with the help from subject matter and science experts. These services encapsulate the physical computing infrastructure, which could local computing cluster, on-premise or public Cloud environment.
Behavioral health services, similar to those offered to the U.S. astronauts who complete six-month missions on board the International Space Station, were provided to 13 long-duration head-down bed rest participants. Issues in psychological screening, selection, and support are discussed as they relate to other isolated and confined environments. Psychological services offered to participants are described, and challenges in subject selection and retention are discussed. Psychological support and training provided to both subjects and study personnel have successfully improved the well-being of study participants. Behavioral health services are indispensable to long-duration head-down tilt bed rest studies.
NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC) provides access to a wide range of global climate data from various satellite missions and models. However, the visualization and analysis of these data can be challenging due to their large volume, complex structure, and diverse formats. This study presents the implementation of interoperable map services (API-Maps) with performance tuning using API-Tiles and dynamic API-Styles. API-Maps is a standard for defining and exposing map services through RESTful (representational state transfer) APIs (application programming interfaces). API-Tiles is a technique for generating and delivering map tiles on demand from any data source. API-Styles is a method for dynamically applying styles to map tiles based on user preferences or data attributes. The use of API-Tiles and dynamic API-Styles enhances the performance and scalability of the map services, allowing for smooth and interactive visualization of large datasets. Two types of Earth Science data sources from the NASA GES DISC are used in the experiment: regularly gridded data, such as Global Precipitation Measurement (GPM) precipitation data, and low processing level data, such as low-level data of atmospheric composite measurements from the TROPOspheric Monitoring Instrument (TROPOMI) mission. Re-gridding of swath data (low level data - e.g. Level 2) of atmospheric composites (e.g. TROPOMI products, such as nitrogen dioxide, ozone and aerosol optical depth) is applied to enable the Web-based, interoperable, tiled, and styled mapping (rendering) services of such data. The results demonstrate the effectiveness of the proposed approach in providing fast and efficient access to Earth science data through interoperable map services.
We describe the conclusions of a technology and communities survey supported by concurrent and follow-on proof-of-concept prototyping to evaluate feasibility of defining a durable, versatile, reliable, visible software interface to support strategic modularization of test software development. The objective is that test sets and support software with diverse origins, ages, and abilities can be reliably integrated into test configurations that assemble and tear down and reassemble with scalable complexity in order to conduct both parametric tests and monitored trial runs. The resulting approach is based on integration of three recognized technologies that are currently gaining acceptance within the test industry and when combined provide a simple, open and scalable test orchestration architecture that addresses the objectives of the Automation Hooks task. The technologies are automated discovery using multicast DNS Zero Configuration Networking (zeroconf), commanding and data retrieval using resource-oriented Restful Web Services, and XML data transfer formats based on Automatic Test Markup Language (ATML). This open-source standards-based approach provides direct integration with existing commercial off-the-shelf (COTS) analysis software tools.
Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end‐user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.
Bone density studies of bed rest subjects by radiographic method
This slide presentation accompanies a tutorial on the ReSTful (Representational State Transfer) web application. Using Open Services Gateway Initiative (OSGi), ReST uses HTTP protocol to enable developers to offer services to a diverse variety of clients: from shell scripts to sophisticated Java application suites. It also uses Eclipse for the rapid development, the Eclipse debugger, the test application, and the ease of export to production servers.
There are two distinct functions of the Behavioral Health and Performance (BHP) specialists at NASA Johnson Space Center (JSC), with a shared goal of promoting optimal performance during all mission phases, beginning with selection to the NASA Astronaut Corps through training for and completing a spaceflight mission. The BHP Operations team is active in areas such as selection, training, routine behavioral healthcare services, and work-rest schedule. The BHP Laboratory is engaged in research in space and analog platforms to address the risks and other challenges associated with future deep space missions. Their research focuses on topics that align with the NASA Human Research Program’s Human Factors Behavioral Performance Element goals. This panel will provide details of recent work by BHP Operations and the BHP Laboratory at NASA JSC, and will demonstrate their shared goals, and contributions toward, optimizing performance and maintaining the behavioral health and well-being of astronauts during all mission phases.In 2020-2021, NASA engaged another astronaut applicant screening process to identify its 23rd group of astronaut candidates. The BHP Operations team completed a significant amount of work to evaluate applicants and provide valuable input to the Astronaut Selection Board and the Aerospace Medical Board. One presentation will provide an overview of the work completed by BHP; and one presentation will discuss a critical competency important for successful job performance of the astronauts – Team Orientation. The former will summarize aspects of BHP’s Select-in and Select-out process and provide lessons learned for future selection cycles; the latter will briefly discuss the competencies of BHP’s applicant screening work, with focus on the importance of Team Orientation and the challenges of its measure. A third presentation will provide an overview of BHP’s current fatigue management work with astronauts and mission-critical operators at JSC (e.g., flight directors, pilots), and provide a look at the direction these fatigue management services may take in future exploration missions. Two other presentations will focus on the research completed by the BHP Lab to measure in a standardized way the critical aspects of behavioral health and well-being. One of the presentations will provide an overview of the Exploration Measures being collected during all space and analog missions, and the importance these objective measures have for future research and operations. The final presentation will summarize analyses completed with these measures, as they have been harmonized across both ISS and Analog missions.
The NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) team provides low latency LEO and GEO satellite derived cloud and radiation products to end users for use in near real-time (NRT) applications. Hourly mosaics are fused from LEO (SNPP, JPSS-1, AQUA, MODIS) and GEO Satellite imagers (GOES-West, GOES-East, Metesat-11, Metesat-8, and Himawari-8) to create global cloud composites (GCC). SatCORPS GCC data are integrated into Esri ArcGIS system and transformed into Analysis Rady Data (ARD) to provide more efficient data access to support disaster management, weather diagnoses/forecasting, and Earth Sciences remote sensing applications. GCC data are exposed as RESTful APIs, ArcGIS Image Services, and Open Geospatial Consortium (OGC) Web Mapping Services/Web Coverage Services to provide a variety of end points for integration into user applications. We will preview the new interactive SatCORPS GCC web visualization tool and discuss initial integration of GCC product into NASA Airborne Mission Tool Suite (MTS) to support NASA and NOAA field campaigns.
Apparatus uses prestressed cables to support vibrational table. Cables are durable, do not require frequent servicing, and provide increased safety. Because much weight rests on these cables, vibration actuating pistons can provide longer service. In event of structural failure of other supporting components, they will support entire weight of vibrational table.
This software is provided by the Mars Relay Operations Service (MaROS) task to a variety of Mars projects for the purpose of coordinating communications sessions between landed spacecraft assets and orbiting spacecraft assets at Mars. The Information Management Service centralizes a set of functions previously distributed across multiple spacecraft operations teams, and as such, greatly improves visibility into the end-to-end strategic coordination process. Most of the process revolves around the scheduling of communications sessions between the spacecraft during periods of time when a landed asset on Mars is geometrically visible by an orbiting spacecraft. These relay sessions are used to transfer data both to and from the landed asset via the orbiting asset on behalf of Earth-based spacecraft operators. This software component is an application process running as a Java virtual machine. The component provides all service interfaces via a Representational State Transfer (REST) protocol over https to external clients. There are two general interaction modes with the service: upload and download of data. For data upload, the service must execute logic specific to the upload data type and trigger any applicable calculations including pass delivery latencies and overflight conflicts. For data download, the software must retrieve and correlate requested information and deliver to the requesting client. The provision of this service enables several key advancements over legacy processes and systems. For one, this service represents the first time that end-to-end relay information is correlated into a single shared repository. The software also provides the first multimission latency calculator; previous latency calculations had been performed on a mission-by-mission basis.
As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.