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At least 487 records · Page 27

Readout of DSN Monitor Data

DSN Monitor Data Reader is a computer program that, as its name suggests, reads file of monitor data from the Deep Space Network (DSN). The monitor data constitute information on the status and performance of tracking, telemetry, command, and pointing equipment at the DSN antennas. The DSN has recently introduced a new, more advanced monitor data format, denoted 0158-Mon, that is based on the standard formatted data unit (SFDU) and compressed header data objects (CHDO) of the Consultative Committee for Space Data Systems (CCSDS). The 0158-Mon data format is a very flexible generic format that provides for specific variable-length formats and for self-identifying parameters that obviate the proprietary NASA Communications (NASCOM) bit-packed formats of the past. The monitor data SFDUs are also encapsulated in Standard DSN Blocks and routed to DSN customers for processing at their local mission control centers. This program helps a DSN customer to read and parse the monitor data to assess the statuses of the DSN stations in support of spacecraft flight operations.

Levister, Katherine↗

Maximizing Spaceflight Biological Data with Omics Analytics: The NASA GeneLab Database

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

Sylvain Vincent Costes↗

GeneLab: The NASA System Biology Platform for Space Omics Repository, Analysis and Visualization

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

GeneLab↗

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↗

Numeric data distribution: The vital role of data exchange in today's world

The major aim of the NIST standard Reference Data Program (SRD) is to provide critically evaluated numeric data to the scientific and technical community in a convenient and accessible form. A second aim of the program is to provide feedback into the experimental and theoretical programs to help raise the general standards of measurement. By communicating the experience gained in evaluating the world output of data in the physical sciences, NIST/SRD helps to advance the level of experimental techniques and improve the reliability of physical measurements.

Chase, Malcolm W.↗

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Rings Node for the Planetary Data System

The Planetary Data System's Rings Node is devoted to the archiving and distributing of scientific data sets relevant to planetary ring systems. The two major classes of ring data are images and occultation profiles, although a variety of additional data types (e.g. spectra, particle absorption signatures, etc.) are also of interest. A large fraction of our data sets are from the Voyager missions to the outer planets, but Earth-based and Hubble Space Telescope data sets are also represented. Archiving work often includes re-formatting the data into standardized formats and reconstructing some of the data processing steps. The Rings Node also performs a variety of services to support research into these data sets. These services include developing on-line catalogs and information systems, filling orders for data, developing software tools, and coordinating special observing campaigns.

Showalter, Mark R.↗

Continued Development of a Global Heat Transfer Measurement System at AEDC Hypervelocity Wind Tunnel 9

Heat transfer rates are an extremely important consideration in the design of hypersonic vehicles such as atmospheric reentry vehicles. This paper describes the development of a data reduction methodology to evaluate global heat transfer rates using surface temperature-time histories measured with the temperature sensitive paint (TSP) system at AEDC Hypervelocity Wind Tunnel 9. As a part of this development effort, a scale model of the NASA Crew Exploration Vehicle (CEV) was painted with TSP and multiple sequences of high resolution images were acquired during a five run test program. Heat transfer calculation from TSP data in Tunnel 9 is challenging due to relatively long run times, high Reynolds number environment and the desire to utilize typical stainless steel wind tunnel models used for force and moment testing. An approach to reduce TSP data into convective heat flux was developed, taking into consideration the conditions listed above. Surface temperatures from high quality quantitative global temperature maps acquired with the TSP system were then used as an input into the algorithm. Preliminary comparison of the heat flux calculated using the TSP surface temperature data with the value calculated using the standard thermocouple data is reported.

Kurits, Inna↗

Data Quality Challenges for Analysis Ready Data (ARD)

Data quality plays a critical role in research and applications. The Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) defines four aspects of information quality: Science, Product, Stewardship, and Services. The ESIP IQC has become internationally recognized as an authoritative and responsive resource of information and guidance to data producers and distributors on how to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. In recent years, cloud computing environments have provided scale-up capabilities such as data archives and services, enabling interdisciplinary science and applications. More value-added products are expected from data service providers, including Analysis Ready Data (ARD). ARD refers to data that has been preprocessed into a form that allows immediate analysis by the end user, processed to a minimum set of requirements and provides interoperability over time and across multiple datasets. Once a dataset has been developed from its original form to produce ARD, what quality characteristics should the derived dataset or ARD possess? Also, is it safe to assume that the quality of the ARD is consistent with the quality of the source data, or are there special attributes to an ARD that would warrant a secondary, independent quality assessment? What provenance (also called “data lineage”) information needs to be included in ARD? It is important to answer these questions, especially given the ease of use of ARD, and the consequent temptation by users to trust ARD without understanding the limitations or possible variations in quality compared to the source data. In this presentation, we will discuss data quality challenges for ARD products and services and introduce IQC for participation.

data quality↗

A Multi-Center Space Data System Prototype Based on CCSDS Standards

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing Radio Frequency (RF) propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. However, the maturity level of this protocol stack is insufficient for mission inclusion at this time. This Space Data System prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of this protocol set. In order to reduce costs, future missions can take advantage of these standard protocols, which will result in increased interoperability between control centers. This prototype demonstrates these capabilities by implementing a realistic space data system in which telemetry is published to control center applications at the Jet Propulsion Lab (JPL), the Marshall Space Flight Center (MSFC), and the Johnson Space Center (JSC). Reverse publishing paths for commanding from each control center are also implemented. The target vehicle consists of realistic flight computer hardware running Core Flight Software (CFS) in the integrated Power, Avionics, and Power (iPAS) Pathfinder Lab at JSC. This prototype demonstrates a potential upgrade path for future Deep Space Network (DSN) modification, in which the automatic error recovery and communication gap compensation capabilities of DTN would be exploited. In addition, SM&C provides architectural flexibility by allowing new service providers and consumers to be added efficiently anywhere in the network using the common interface provided by SM&C's Message Abstraction Layer (MAL). In FY 2015, this space data system was enhanced by adding telerobotic operations capability provided by the Robot API Delegate (RAPID) family of protocols developed at NASA. RAPID is one of several candidates for consideration and inclusion in a new international standard being developed by the CCSDS Telerobotic Operations Working Group. Software gateways for the purpose of interfacing RAPID messages with the existing SM&C based infrastructure were developed. Telerobotic monitor, control, and bridge applications were written in the RAPID framework, which were then tailored to the NAO telerobotic test article hardware, a product of Aldebaran Robotics.

Rich, Thomas M.↗

Apollo experience report: Flight instrumentation calibration

Three types of instrumentation-calibration data were used in the Apollo Program to provide the correct engineering data for tests and mission support. The command and service module instrumentation-component procurement specifications required individual-component calibration, and calibration data for these individual components (conventional-calibration data) were always used for mission data support. A mean standard type of calibration data derived from a statistical sampling of conventional-calibration data was used for test and checkout during the latter part of the Apollo Program. The lunar module instrumentation procurement specification permitted the use of standard-calibration data. These data were applicable to similarly instrumented measurements. The definition, merit, and application of each type of data are discussed.

Demoss, J. F.↗

HAPI: An API Standard for Accessing Heliophysics Time Series Data

Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires intercomparison of data from multiple data providers difficult. The Heliophysics Application Programmer's Interface (HAPI) is a recently developed standard for accessing distributed time series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI-related open-source projects offer server-side implementation tools and client-side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

Robert S. Weigel↗

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.

Haste, Deepak↗

University Data Management Pilot Utilizing the Nuclear Research Data System

Background In 2022, the Office of Science and Technology Policy (OSTP) issued a memo that significantly reshaped the landscape of access to federally funded research. The memo mandated that all taxpayer-funded research be made available to the public without delay upon publication, without an embargo period, superseding the 2013 OSTP public access policy. This public access policy promotes transparency and the democratization of knowledge, ensuring that the fruits of scientific endeavors funded by federal agencies could be immediately accessed and built upon by scientists, educators, students, and the public at large. To implement the requirements of the OSTP guidance and DOE Public Access Plan, the Office of Nuclear Energy (NE) has implemented public access plan guidance and has identified several areas where better data management practices would further expand public access to important nuclear energy related scientific data, reports, and other technical products. Significant NE supported efforts are already underway for data management and public access to important nuclear energy related data.1 2 To address gaps in data management practices, and improve retention and accessibility of data, NE is actively exploring enhanced data management options utilizing its high-performance computing resources administered by its Nuclear Scientific User Facility Program. A newly piloted system, the Nuclear Research Data System (NRDS) acts as a portal for data collection and dissemination. Nuclear Energy University Program Research and Development Portfolio According to Web of Science, NEUP has produced 2,345 journal publication that have been cited more than 61,000 times3 and countless conference proceedings. These publications are publicly available through OSTI.gov and in the open literature. Additional scientific and technical products including project milestones that are not publications and NEUP project final reports are vetted through OSTI.gov and released once reviewed and approved by DOE. Since 2009, NEUP has awarded close to 1,000 different R&D projects in technical areas across the NE research programs. As of June 2023, 512 NEUP reports are publicly available on OSTI. The underlying data for projects is still held at universities, and data transfer, co-location, and dissemination has not occurred in a systematic way. NEUP data is currently accessible through myriad university-based data repositories, or through direct requests to PIs. The program identified this patchwork of repositories, or often lack of publicly available data, as a significant barrier to an organized, accessible, and comprehensive solution to sharing data with the larger nuclear energy community. Approach The goal of this pilot project is to establish a pathway to a consolidated long-term repository for NEUP project data. To accomplish this goal, the pilot strives to accomplish the following objectives: Establish data collection standards, including a standard set of required supplementary information to contextualize and support raw data files. Work with the HPC group collect and upload information and to modify the NRDS system, as needed, to support a standardized approach. Resolve potential barriers to successful roll out of an expanded data collection strategy, including modifying data management plan guidelines and establishing a document and data release process that accounts for potential intellectual property and/or export control concerns. Results Overall, the pilot was successful in collecting 8,982 raw and processes data files, 220 reports, 56 calibration files, and 5,931 other supplementary documents. Supplementary documents included experimental plans, methods, journal publications and conference proceedings, milestone reports, and final reports. Figure 2 shows the number of data sets and supplementary project information provided by each project. Projects has significantly different input, depending on experimental data produced and completeness of the datasets provided.

Data collection↗

Rubidium and cesium frequency standards status and performance on the GPS program

The on-oribt operational performance of the frequency standards on the Global Positioning System (GPS) 1 to 10 NAVSTAR satellites are discussed. The history of the Rb frequency standards showing the improvements incorporated at various stages of the program and the corresponding results are presented. Also presented is the operational history of the NAVSTAR cesium frequency standards. The frequency standards configuration data presented covers the chronology of events from the concept validation satellites, NAVSTAR 1 to 10, starting in 1978 to the present, including the configurations of clocks to be used on the GPS Production Program. Data are presented showing the results of long-term laboratory testing of a production Rb frequency standard with the necessary data taken to calculate Delta F, drift, time error, and Allan variance.

Bethke, H.↗