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NASA NTRS2006

Vehicle Transponder for Preemption of Traffic Lights

The purpose of this article is to describe, in more detail, the transponder installed in each vehicle that participates in the emergency traffic-light-preemption system described in the immediately preceding article. The transponder (see figure) is a fully autonomous data--collection, data-processing, information-display, and communication subsystem that performs robustly in preemption of traffic lights and monitoring of the statuses of street intersections. This transponder monitors the condition of the emergency vehicle in which it is installed and determines when the vehicle has been placed in an emergency-response condition with its siren and/or warning lights activated. Upon detection of such a condition, the transponder collects real-time velocity and acceleration data from the onboard diagnostic (OBD) computer of the vehicle. For this purpose, the transponder contains an OBD interface circuit, including a microprocessor that determines the manufacturer and model of the vehicle and then sends the appropriate commands to the OBD computer requesting the speed and acceleration data. At the same time, data from an onboard navigation system are collected to determine the location and the heading of the vehicle. Then acceleration, speed, position, and heading data are processed and combined with a vehicle-identification number and the resulting set of data is transmitted to monitoring and control units located at all intersections within communication range. When the unit at an intersection determines that this vehicle is approaching and has priority to preempt the intersection, it transmits a signal declaring the priority and the preemption to all participating vehicles (including this one) in the vicinity. If the unit at the intersection has determined that other participating vehicles are also approaching the intersection, then this unit also transmits, to the vehicle that has priority, a message that the other vehicles are approaching the same intersection. The texts of these messages, plus graphical symbols that show the directions and numbers of the approaching vehicles, are presented on the display panel of a computer that is part of the transponder.

Foster, Conrad↗

NASA NTRS2003

Data Access Tools And Services At The Goddard Distributed Active Archive Center (GDAAC)

As one of the largest providers of Earth Science data from the Earth Observing System, GDAAC provides the latest data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Solar Radiation and Climate Experiment (SORCE) data products via GDAAC's data pool (50TB of disk cache). In order to make this huge volume of data more accessible to the public and science communities, the GDAAC offers multiple data access tools and services: Open Source Project for Network Data Access Protocol (OPeNDAP), Grid Analysis and Display System (GrADS/DODS) (GDS), Live Access Server (LAS), OpenGlS Web Map Server (WMS) and Near Archive Data Mining (NADM). The objective is to assist users in retrieving electronically a smaller, usable portion of data for further analysis. The OPeNDAP server, formerly known as the Distributed Oceanographic Data System (DODS), allows the user to retrieve data without worrying about the data format. OPeNDAP is capable of server-side subsetting of HDF, HDF-EOS, netCDF, JGOFS, ASCII, DSP, FITS and binary data formats. The GrADS/DODS server is capable of serving the same data formats as OPeNDAP. GDS has an additional feature of server-side analysis. Users can analyze the data on the server there by decreasing the computational load on their client's system. The LAS is a flexible server that allows user to graphically visualize data on the fly, to request different file formats and to compare variables from distributed locations. Users of LAS have options to use other available graphics viewers such as IDL, Matlab or GrADS. WMS is based on the OPeNDAP for serving geospatial information. WMS supports OpenGlS protocol to provide data in GIs-friendly formats for analysis and visualization. NADM is another access to the GDAAC's data pool. NADM gives users the capability to use a browser to upload their C, FORTRAN or IDL algorithms, test the algorithms, and mine data in the data pool. With NADM, the GDAAC provides an environment physically close to the data source. NADM will benefit users with mining or offer data reduction algorithms by reducing large volumes of data before transmission over the network to the user.

Pham, Long↗

NASA NTRS2014

The International Space Station: A Unique Platform for Remote Sensing of Natural Disasters

Assembly of the International Space Station (ISS) was completed in 2012, and the station is now fully operational as a platform for remote sensing instruments tasked with collecting scientific data about the Earth system. Remote sensing systems are mounted inside the ISS, primarily in the U.S. Destiny Module's Window Observational Research Facility (WORF), or are located on the outside of the ISS on any of several attachment points. While NASA and other space agencies have had remote sensing systems orbiting Earth and collecting publicly available data since the early 1970s, these sensors are carried onboard free-flying, unmanned satellites. These satellites are traditionally placed into Sun-synchronous polar orbits that allow imaging of the entire surface of the Earth to be repeated with approximately the same Sun illumination (typically local solar noon) over specific areas, with set revisit times that allow uniform data to be taken over long time periods and enable straightforward analysis of change over time. In contrast, the ISS has an inclined, Sun-asynchronous orbit (the solar illumination for data collections over any location changes as the orbit precesses) that carries it over locations on the Earth between approximately 52degnorth and 52deg south latitudes (figure 1). The ISS is also unique among NASA orbital platforms in that it has a human crew. The presence of a crew provides options not available to robotic sensors and platforms, such as the ability to collect unscheduled data of an unfolding event using handheld digital cameras as part of the Crew Earth Observations (CEO) facility and on-the-fly assessment of environmental conditions, such as cloud cover, to determine whether conditions are favorable for data collection. The crew can also swap out internal sensor systems installed in the WORF as needed. The ISS orbit covers more than 90 percent of the inhabited surface of the Earth, allowing the ISS to pass over the same ground locations at different times of the day and night. This is important for two reasons: 1) certain surface processes (i.e., development of coastal fog banks) occur at times other than local solar noon, making it difficult to collect relevant data from traditional satellite platforms, and 2) it provides opportunities for the ISS to collect data for short-duration events, such as natural disasters, that polar-orbiting satellites may miss due to their orbital dynamics - in essence, the ISS can be "in the right place at the right time" to collect data. An immediate application of ISS remote sensing data collection is that the data can be used to provide information for humanitarian aid after a natural disaster. This activity contributes directly to the station's Benefits to Humanity mission. The International Charter, Space and Major Disasters (also known as the International Disaster Charter, or IDC) is an agreement between agencies of several countries to provide - on a best-effort basis - remotely sensed data related to natural disasters to requesting countries in support of disaster response. In the United States, the lead agency for interaction with the IDC is the United States Geological Survey (USGS); when an IDC request, or activation, is received, the USGS notifies the science teams for NASA instruments with targeting information for data collection. In the case of the ISS, Earth scientists in the JSC ARES Directorate, in association with the ISS Program Science Office, coordinate targeting and data collection with the USGS. If data is collected, it is passed back to the USGS for posting on its Hazards Data Distribution System and made available for download. The ISS was added to the USGS's list of NASA remote sensing assets that could respond to IDC activations in May 2012. Initially, the NASA ISS sensor systems available to respond to IDC activations included the ISS Agricultural Camera (ISSAC), an internal multispectral visible-near infrared wavelength system mounted in the WORF; CEO, a project that collects imagery through the ISS windows using off-the-shelf handheld digital visible-wavelength cameras; and the Hyperspectral Imager for the Coastal Oceans (HICO), a visible to near-infrared system mounted externally on the Japanese Experiment Module - Exposed Facility. Since May 2012, there have been 37 IDC activations; ISS sensor systems have collected data for 10 of these events.

Stefanov, William L.↗

DOE OSTI2020

HarDWR - Raw Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Switched source data from collecting records from each state independently to using the WestDAAT dataset v1.0 - Initial public release Description In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, as well as geospatial data defining the areas in which water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. In addition, while all states make their water rights publicly available, each provides their records in unique formats, meaning that file types, field availability, and terms vary from state to state. This leads to additional challenges to managing resources which cross state lines, or conducting consistent multi-state water analyses. For the first version of HarDWR, we collected the water rights databases from 11 Western States of the United States. In order to preform regional analyses with the collected data, the raw records had to be harmonized into one single format. The Water Data Exchange (WaDE) is a program dedicated to the sharing of water-related data for the Western U.S. in a singular consistent format. Created by the Western States Water Council (WSWC) to facilitate the collection and dissemination of water data among WSWC's member states and the public, WaDE provides an important service for those interested in water resource planning and management in their focus region. Of the services which WaDE provides, the one of the most interesting is the WestDAAT dataset, which is a collection of water rights data provided by the 18 WSWC member states that have been standardized into a single format, much like we had done on a more limited scale with HarDWR v1. For this version of HarDWR we decided to use WestDAAT, specifically a snapshot created in Feburary 2024, as our water rights source data. A full explanation of the benefits gained from this switch can be found in the description of the updated Harmonized Water Rights Records v2.0, but in short it has allowed us to focus more of our efforts on answering research questions and gaining a more realistic understanding of how water rights are allocated. For more information on how the data for WestDAAT was collected, please see the WaDE data summary. Terms of Use While WaDE works directly with the state agencies to collect and standardize the water rights records, the ultimate authority for the water rights data remains the individual states. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. As several of the states update their water rights databases daily, the information provided here is not the latest possible, and should not be used for legal purposes. WestDAAT itself has irregular updates. Additional questions about the data the source states provided should be directed to the respective state agencies (see methods.csv and organization.csv files described below). In addition, although data was presented here was not collected directly from the states, several states requested specifically worked disclaimers when sharing their data. These disclaimers are included here as an acknowledgement from where the water rights data is primarily sourced. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." File Descriptions The unmodified February, 2024 WestDAAT snapshot is composed of nine files. Below is a brief description of each file, as well as how they were utilized for HarDWR. WaDEDataDictionaryTerms.xlsx: As the file's name implies, this is a data dictionary for all of the below named files. This file describes the column names for each of the following files, with the exception of citation.txt which does not have any columns. The descriptions for each file are divided by tab,with the same name as their associated file, within this document. allocationamount.csv: The "main" file of the group, it contains the water right records for each state. Of particular note, each water right is broken down into one or more water allocations. Allocations may be withdrawn from one or more locations, or even multiple allocations associated with a particular location. This is a more subtle and realistic representation of how water is used than what was available in the first version of HarDWR. For the records from some states, this can mean that multiple allocations listed under a single right will appear as rows within this file. citation.txt: A combination of contact information for WaDE personnel, disclaimer about how the data should be used, and guidelines for citing WestDAAT. methods.csv: A file describing the source and method by which WaDE collected water rights data from each state. organization.csv: A file listing the water rights authoritative agencies for each state. sites.csv: This file provides the geographic, and other descriptors, of the physical location of allocations, called 'sites'. To reiterate, it is possible for one allocation to be associated with multiple sites, as well as one site to be associated with multiple allocations. The two descriptors which we were most interested in where the site's coordinates, as well as whether the site was classified as a Point of Diversion (POD) or a Place of Use (POU). As a general rule, PODs are geographic points, while POUs are areas typically represented as property boundaries or irregularly shaped polygons. sites_pouGeometry.csv: For those allocations with a POU site, this file contains the defining points for the associated polygons. variables.csv: A file describing the units in which an allocation's water amount is reported within WestDAAT. This information is essentially a repeat of the 'AllocationFlow_CFS' and 'AllocationVolume_AF' columns within allocationamount.csv, at least for our purposes. watersources: This file describes the source of water from which each site extracts from. For our purposes, this table was used to determine whether the water came from Surface Water, Groundwater, or Unspecified Water.

Lisk, Matthew↗

NASA NTRSDate not supplied

RadLab and the Environmental Data Application Dashboard: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on the International Space Station (ISS) and multiple spacecraft elsewhere in Earth orbit and in deep space continuously monitor and collect environmental data, transmitting this information back to Earth. These data include ionizing radiation and, on the ISS, CO2, relative humidity levels, and temperature, and are of great importance to space biology research. Ionizing radiation in particular has been established in ground-based experiments as being correlated with increased risk of carcinogenesis and cardiovascular and neurological effects. Looking ahead to future long duration crewed missions beyond low Earth orbit, the ability to study how factors including CO2 levels, light cycle, temperature modulate the response to ionizing radiation and microgravity is essential. To date, access to these data has been fragmented across space agencies, spacecraft, and databases. To address this issue, NASA’s Open Science Data Repository (osdr.nasa.gov) has developed two Web applications: the Environmental Data Application (EDA) and a radiation-specific RadLab. Each consists of an API (application programming interface) and an associated GUI (graphical user interface) that provide single points of access to the data. To date, OSDR has focused on the sensors from payloads and radiation detectors located on the ISS. The Web applications process telemetry information and associated data, such as spacecraft location and orientation, from multiple international databases. The applications’ request syntax enables users to interrogate these data by craft, sensor type, time range, radiation type (galactic cosmic rays, solar particle events, the contribution of the South Atlantic Anomaly), facilitating arbitrary comparisons of original source data at varying time resolutions. The applications provide programmatic access for use in computational pipelines and GUIs for data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗

NASA NTRS2004

Automated Attitude Sensor Calibration: Progress and Plans

This paper describes ongoing work a NASA/Goddard Space Flight Center to improve the quality of spacecraft attitude sensor calibration and reduce costs by automating parts of the calibration process. The new calibration software can autonomously preview data quality over a given time span, select a subset of the data for processing, perform the requested calibration, and output a report. This level of automation is currently being implemented for two specific applications: inertial reference unit (IRU) calibration and sensor alignment calibration. The IRU calibration utility makes use of a sequential version of the Davenport algorithm. This utility has been successfully tested with simulated and actual flight data. The alignment calibration is still in the early testing stage. Both utilities will be incorporated into the institutional attitude ground support system.

Sedlak, Joseph↗

NASA NTRS1979

Cockpit displays of traffic information: Airline pilots opinions about content, symbology, and format

A number of candidate computer-generated cockpit displays of traffic information (CDTI) displays and display options were simulated statically and were shown to 23 airline pilots who were asked to respond to more than 250 questions about them. The pilots indicated that the amount and complexity of navigation information displayed should increase with altitude and map scale. Terrain information should appear automatically if a pilot's own aircraft descends below the minimum safe altitude and should include only those obstruction within 2,000 ft or less. Few pilots that weather information should be displayed on a CDTI, but if it was, it should be at pilot request only. A chevron-shaped symbol, located so that the majority of the map area was ahead was preferred. The position, altitude, ground speed, ground track, weight class, and flightpath history of other aircraft should be presented graphically by coding the shape of the symbol for other aircraft or presented digitally in data tags displayed at pilot request. All pilots thought that color coding was necessary to recognize different categories of information quickly and accurately. The majority of pilots felt that a CDTI would provide useful information even though its presence might increase their workload somewhat particularly during its introductory stages.

Hart, S. G.↗

DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗

NASA NTRS1982

Archiving and retrieval of data from the International Ultraviolet Explorer /IUE/ mission

The data base of IUE spectra available for dissemination to the scientific community presently contains approximately 22,000 separate images. Due to storage space and computer processing time limitations, it was decided to store the data on high density (6250 bpi) specially formatted tapes and to retrieve and reconvert them to the originally supplied format upon request. The archival/retrieval system for the data and an automatic data entry system for creation and maintenance of an indexing data base are described. Suggestion are made for the use of standard formats for future image data storage, retrieval and dissemination.

Warren, W. H., Jr.↗

NASA NTRS1989

Tracking and data relay satellite fault isolation and correction using PACES: Power and attitude control expert system

The Power and Attitude Control Expert System (PACES) is an object oriented and rule based expert system which provides spacecraft engineers with assistance in isolating and correcting problems within the Power and Attitude Control Subsystems of the Tracking and Data Relay Satellites (TDRS). PACES is designed to act in a consultant role. It will not interface to telemetry data, thus preserving full operator control over spacecraft operations. The spacecraft engineer will input requested information. This information will include telemetry data, action being performed, problem characteristics, spectral characteristics, and judgments of spacecraft functioning. Questions are answered either by clicking on appropriate responses (for text), or entering numeric values. A context sensitive help facility allows access to additional information when the user has difficulty understanding a question or deciding on an answer. The major functionality of PACES is to act as a knowledge rich system which includes block diagrams, text, and graphics, linked using hypermedia techniques. This allows easy movement among pieces of the knowledge. Considerable documentation of the spacecraft Power and Attitude Control Subsystems is embedded within PACES. The development phase of TDRSS expert system technology is intended to provide NASA with the necessary expertise and capability to define requirements, evaluate proposals, and monitor the development progress of a highly competent expert system for NASA's Tracking and Data Relay Satellite Program.

Erikson, Carol-Lee↗

NASA NTRS1994

SeaWiFS technical report series. Volume 20: The SeaWiFS bio-optical archive and storage system (SeaBASS), part 1

This document provides an overview of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-Optical Archive and Storage System (SeaBASS), which will serve as a repository for numerous data sets of interest to the SeaWiFS Science Team and other approved investigators in the oceanographic community. The data collected will be those data sets suitable for the development and evaluation of bio-optical algorithms which include results from SeaWiFS Intercalibration Round-Robin Experiments (SIRREXs), prelaunch characterization of the SeaWiFS instrument by its manufacturer -- Hughes/Santa Barbara Research Center (SBRC), Marine Optical Characterization Experiment (MOCE) cruises, Marine Optical Buoy (MOBY) deployments and refurbishments, and field studies of other scientists outside of NASA. The primary goal of the data system is to provide a simple mechanism for querying the available archive and requesting specific items, while assuring that the data is made available only to authorized users. The design, construction, and maintenance of SeaBASS is the responsibility of the SeaWiFS Calibration and Validation Team (CVT). This report is concerned with documenting the execution of this task by the CVT and consists of a series of chapters detailing the various data sets involved. The topics presented are as follows: 1) overview of the SeaBASS file architecture, 2) the bio-optical data system, 3) the historical pigment database, 4) the SIRREX database, and 5) the SBRC database.

Hooker, Stanford B.↗

NASA NTRSDate not supplied

Spaceflight Biospecimen Sharing in Support of Science Discovery and Exploration

For decades, NASA and international partners have flown non-human biological experiments in space to understand the effects of spaceflight and address potential biological hazards. Sending organisms into space is a costly endeavor which makes space-flown biological specimens a valuable resource. To enable maximum scientific return, samples not required by the Principal Investigators are harvested and collected mostly by NASA’s Space Biology Biospecimen Sharing Program. These specimens are collected according to well-established SOPs that maintain quality and integrity. The specimens are then preserved, archived, and made available to the international scientific community through NASA’s Institutional Scientific Collection (ISC) at Ames Research Center (ARC). The ISC-ARC biospecimens and descriptive metadata are findable and accessible for request through the Life Sciences Data Archive (LSDA). The NASA ISC-ARC currently stores over 32,000 specimens from Shuttle, International Space Station, and ground-based investigations (spaceflight analog experiments involving either hindlimb unloading, centrifugation, or partial weight-bearing study designs). Tissues are predominantly from mice and rats, though samples are also available from bacteria and quail. The specimens include tissues from many physiological systems including musculoskeletal, neurosensory, reproductive, respiratory, circulatory, and digestive. Tissues are stored at -80°C, -20°C, +4°C, or ambient and preserved in various fixatives. Descriptive metadata is available for all samples. Historically, these tissues have been used for a wide range of analyses, including histology, genomics, and transcriptomics. Plans are underway to expand the ISC-ARC beyond the mostly-rodent contents, to include a space-relevant microbial culture collection including bacteria, fungi, and yeast. This expansion of the ISC-ARC will now involve identifying and standardizing best practices for microbial curations. To ensure safe long-term storage of microbial isolates, a microbiology laboratory will be dedicated for identification, cell culture, and lyophilization. Awarding of tissue to public science investigators has resulted in 33 publications since 2011, with 48 requests being submitted since 2016. Of note, NASA GeneLab has been awarded ISC-ARC biospecimens in the past few years. GeneLab processes the biospecimens to generate various levels of ‘omics’ data, which are published on GeneLab’s open access online platform for bioinformatics analysis and visualization. This has helped a systems biology community grow around the processed-biospecimens’ datasets, resulting in many new publications and insights. Websites: https://www.nasa.gov/ames/research/space-biosciences/isc-bsp ; https://lsda.jsc.nasa.gov/Biospecimen

Ryan T. Scott↗

NASA NTRS1997

Internet Access to ISEE-1 and 2 Magnetometer Data

It is reported that the entire ISEE-1 and -2 magnetometer data are placed on-line, using an 8 Gbyte disk drive. The data are stored at 4-s and 60-s resolution. Also, an interactive world wide web page, which allows to plot, on request, any interval for which magnetometer data are available, is developed.

Source record↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.

Kirill Grigorev↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

Kirill Grigorev↗

NASA NTRS2009

Maneuver Automation Software

The Maneuver Automation Software (MAS) automates the process of generating commands for maneuvers to keep the spacecraft of the Cassini-Huygens mission on a predetermined prime mission trajectory. Before MAS became available, a team of approximately 10 members had to work about two weeks to design, test, and implement each maneuver in a process that involved running many maneuver-related application programs and then serially handing off data products to other parts of the team. MAS enables a three-member team to design, test, and implement a maneuver in about one-half hour after Navigation has process-tracking data. MAS accepts more than 60 parameters and 22 files as input directly from users. MAS consists of Practical Extraction and Reporting Language (PERL) scripts that link, sequence, and execute the maneuver- related application programs: "Pushing a single button" on a graphical user interface causes MAS to run navigation programs that design a maneuver; programs that create sequences of commands to execute the maneuver on the spacecraft; and a program that generates predictions about maneuver performance and generates reports and other files that enable users to quickly review and verify the maneuver design. MAS can also generate presentation materials, initiate electronic command request forms, and archive all data products for future reference.

Uffelman, Hal↗

NASA NTRS2019

Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture

Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.

Zhang, Hailiang↗

NASA NTRS2003

HEASARC Software Archive

(1) Chandra Archive: SAO has maintained the interfaces through which HEASARC gains access to the Chandra Data Archive. At HEASARC's request, we have implemented an anonymous ftp copy of a major part of the public archive and we keep that archive up-to- date. SAO has participated in the ADEC interoperability working group, establishing guidelines or interoperability standards and prototyping such interfaces. We have provided an NVO-based prototype interface, intending to serve the HEASARC-led NVO demo project. HEASARC's Astrobrowse interface was maintained and updated. In addition, we have participated in design discussions surrounding HEASARC's Caldb project. We have attended the HEASARC Users Group meeting and presented CDA status and developments. (2) Chandra CALDB: SA0 has maintained and expanded the Chandra CALDB by including four new data file types, defining the corresponding CALDB keyword/identification structures. We have provided CALDB upgrades for the public (CIAO) and for Standard Data Processing. Approximately 40 new files have been added to the CALDB in these version releases. There have been in the past year ten of these CALDB upgrades, each with unique index configurations. In addition, with the inputs from software, archive, and calibration scientists, as well as CIAO/SDP software developers, we have defined a generalized expansion of the existing CALDB interface and indexing structure. The purpose of this is to make the CALDB more generally applicable and useful in new and future missions that will be supported archivally by HEASARC. The generalized interface will identify additional configurational keywords and permit more extensive calibration parameter and boundary condition specifications for unique file selection. HEASARC scientists and developers from SAO and GSFC have become involved in this work, which is expected to produce a new interface for general use within the current year. (3) DS9: One of the decisions that came from last year's HEADCC meeting was to make the ds9 image display program the primary vehicle for displaying line graphics (as well as images). The first step required to make this possible was to enhance the line graphics capabilities of ds9. SAO therefore spent considerable effort upgrading ds9 to use Tcl 8.4 so that the BLT line graphics package could be built and imported into ds9 from source code, rather than from a pre-built (and generally outdated) shared library. This task, which is nearly complete, allows us to extend BLT as needed for the HEAD community. Following HEADCC discussion concerning archiving and the display of archived data, we extended ds9 to support full access to many astronomical Web-based archives sites, including HEASARC, MAST, CHANDRA, SKYVIEW, ADS, NED, SIMBAD, IRAS, NVRO, SAO TDC, and FIRST. Using ds9's new internal Web access capabilities, these archives can be accessed via their Web page. FITS images, plots, spectra, and journal abstracts can be referenced, down-loaded, and displayed directly and easily in ds9. For more information, see: http://hea-www.harvard.edu/saord/ds9. Also after the HEADCC discussion concerning region filtering, we extended the Funtools sample implementation of region filtering as described in: http://hea-www.harvard.edu/saord/funtools/regions.html. In particular, we added several new composite regions for event and image filtering, including elliptical and box annuli. We also extended the panda (Pie AND Annulus) region support to include box pandas and elliptical pandas. These new composite regions are especially useful in programs that need to count photons in each separate region using only a single pass through the data. Support for these new regions was added to ds9. In the same vein, we developed new region support for filtering images using simple FITS image masks, i.e. 8-bit or 16-bit FITS images where the value of a pixel is the region id number for that pixel. Other important enhancements to DS9 this year, include supporor multiple world coordinate systems, three dimensional event file binning, image smoothing, region groups and tags, the ability to save images in a number of image formats (such as JPEG, TIFF, PNG, FITS), improvements in support for integrating external analysis tools, and support for the virtual observatory. In particular, a full-featured web browser has been implemented within D S 9 . This provides support for full access to HEASARC archive sites such as SKYVIEW and W3BROWSE, in addition to other astronomical archives sites such as MAST, CHANDRA, ADS, NED, SIMBAD, IRAS, NVRO, SA0 TDC, and FIRST. From within DS9, the archives can be searched, and FITS images, plots, spectra, and journal abstracts can be referenced, downloaded and displayed The web browser provides the basis for the built-in help facility. All DS9 documentation, including the reference manual, FAQ, Know Features, and contact information is now available to the user without the need for external display applications. New versions of DS9 maybe downloaded and installed using this facility. Two important features used in the analysis of high energy astronomical data have been implemented in the past year. The first is support for binning photon event data in three dimensions. By binning the third dimension in time or energy, users are easily able to detect variable x-ray sources and identify other physical properties of their data. Second, a number of fast smoothing algorithms have been implemented in DS9, which allow users to smooth their data in real time. Algorithms for boxcar, tophat, and gaussian smoothing are supported.

White, Nicholas↗