Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data Discovery”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Usage-Based Discovery of Earth Observations

Most providers of Earth Observation data enable search via dataset characteristics, (e.g., quantity being measured, instrument, location, and time). However, the Earth Science Information Partners Federation (ESIP) is attempting to improve dataset discovery by capturing information on how datasets are used. At a usage-based discovery hackfest at the July 2020 ESIP Summer Meeting, participants pooled their collective skills to implement a prototype based on the connections between datasets and data usage, which can reveal unanticipated patterns in dataset connectivity that are not apparent through traditional approaches. A survey of natural hazards websites, focusing on hydrology-related hazards such as floods and hydrology datasets such as rainfall, illustrated the difficulty of gleaning which dataset was actually used and how it was accessed. This difficulty highlights the need for greater transparency on dataset usage in website design. An application wireframe for a usage-based discovery database was conceptualized as a tool to help applications data specialists rapidly assemble a fit-for-purpose website as part of a disaster response, where customer feedback was integral to the manner in which the website would be developed and delivered. Capturing connections among datasets and instances of usage, either for research or applications, could help users identify datasets with a proven track record of utility or those that have been supplanted by more accurate and(or) precise representations. It may also help Earth Observation providers maximize returns on investments in data services. Social discovery, i.e, following discovery paths laid down by previous users of the data, could be a critical tool for improving the timeliness and applicability of dataset delivery at times and in circumstances where practitioners don’t have the capacity to evaluate the deluge of datasets that are returned by today’s search tools.

search↗
NASA NTRS2009

Kepler Data Validation and Follow-up Programs

The approach that the Kepler Mission uses to remove false positive events and to validate the discoveries consists of two parts; data validation (DPI) and follow up observations program (FOP). DV consists of several methods of examining the data from the spacecraft observations. First, to rule out statistical fluctuations in the data, accept only signals that show 3 or more transits and that have a total signal-to-noise ratio that exceeds 7 sigma. Second. to identify small stellar companions to the target star, we check for secondary eclipses and determine if the transit characteristics are appropriate for a planetary companion. Third, check for background binaries that are in the target aperture. Here we measure the movement of the image centroid before, during, and after the transit. If the target is producing the signal, a dimming wi11 move the image centroid in a known direction and magnitude. If the signal comes from a nearby star, the amplitude and direction of the motion wi11 be different, This test is expected to rule out the hundreds of binary signals expected from background stars. The precision of the measurement depends on the stellar fluxes and positions but can be better than 0.01 pixel; i.e., 0.04". Those candidates that pass these tests are examined using ground-based telescopes and radial velocity spectrometers. First medium precision RV is used to rule out any remaining stellar companions. Then high spatial resolution imaging is used to check for nearby stars that are in the aperture- (The Kepler apertures depend on magnitude but are of order 36 sq are sec in area.) If no stars are present that quid generate the observed signal, then the candidate goes to a large telescope such as Keck, HET, or Wi1lilam Herschel for high precision observations to get the planet mass or an upper limit to it, if there are some stars in the aperture, then the photometric observations are employed to look for the transit by cane of the confounding stars. If none are seen, then the candidate is sent to a large telescope such as Keck

William, Borucki J.↗
NASA NTRS2015

Characterization of Synthetic and Natural Manganese Oxides as Martian Analogues

Recent discoveries of highly concentrated manganese oxides in Gale Crater and on the rim of Endeavour Crater by the Mars Science Laboratory Curiosity and Mars Exploration Rover Opportunity, respectively, imply more highly oxidizing aqueous conditions than previously recognized. Manganese oxides are a significant environmental indicator about ancient aqueous conditions, provided the phases can be characterized reliably. Manganese oxides are typically fine-grained and poorly crystalline, making the mineral structures difficult to determine, and they generally have very low visible reflectance with few distinctive spectral features in the visible to near infrared, making them a challenge for interpretation from remote sensing data. Therefore, these recent discoveries motivate better characterization using methods available on Mars, particularly visible to near infrared (VNIR) spectroscopy, X-ray diffractometry (XRD), and compositional measurements. Both rovers have complementary instruments in this regard. Opportunity is equipped with its multispectral visible imager, Pancam, and an Alpha Particle X-ray Spectrometer (APXS), and Curiosity has the multispectral Mastcam, ChemCam (laser-induced breakdown spectroscopy and passive spectroscopy), and APXS for in situ characterization, and ChemMin (XRD) for collected samples.

Fox, V. K.↗
NASA NTRS2006

Serving Fisheries and Ocean Metadata to Communities Around the World

NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://qcrnd.nasa.qov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage. The directory also offers data holders a means to post and search their data through customized portals, i.e. online customized subset metadata directories. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1994. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as a coordinating node of the International Directory Network (IDN), has been active at the international, regional and national level for many years through its involvement with the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and partnerships (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP), sharing experience, knowledge related to metadata and/or data management and interoperability.

Meaux, Melanie↗
NASA NTRS2019

GeoNEX: A Cloud Gateway for Near Real-time Processing of Geostationary Satellite Products

The emergence of a new generation of geostationary satellite sensors provides land andatmosphere monitoring capabilities similar to MODIS and VIIRS with far greater temporal resolution (5-15 minutes). However, processing such large volume, highly dynamic datasets requires computing capabilities that (1) better support data access and knowledge discovery for scientists; (2) provide resources to enable real-time processing for emergency response (wildfire, smoke, dust, etc.); and (3) provide reliable and scalable services for the broader user community. This paper presents an implementation of GeoNEX (Geostationary NASA-NOAA Earth Exchange) services that integrate scientific algorithms with Amazon Web Services (AWS) to provide near realtime monitoring (~5 minute latency) capability in a hybrid cloud-computing environment. It offers a user-friendly, manageable and extendable interface and benefits from the scalability provided by Amazon Web Services. Four use cases are presented to illustrate how to (1) search and access geostationary data; (2) configure computing infrastructure to enable near real-time processing; (3) disseminate and utilize research results, visualizations, and animations to concurrent users; and (4) use a Jupyter Notebook-like interface for data exploration and rapid prototyping. As an example of (3), the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) was implemented on GOES-16 and -17 data to produce an active fire map every 5 minutes over the conterminous US. Details of the implementation strategies, architectures, and challenges of the use cases are discussed.

GeoNEX↗
NASA NTRS1984

Solar Total Irradiance Variability Measurements by the SMM/ACRIM I Experiment

Convincing evidence of solar total irradiance variability and its relationships with solar activity was provided by the Active Cavity Radiometer Irradiance Monitor I (ACRIM I) experiment on the NASA Solar Maximum Mission (SMM). SMM/ACRIM I, the first flight experiment dedicated to the task of solar irradiance monitoring, has produced a multiyear solar total irradiance data base with + or - 0.02% or better long term precision since its launch in February, 1980. While the climatological significance of the results will not be apparent until many more years of continuous data are acquired, the discovery of variability on solar active region time scales has provided new insight into the physics of solar activity in the early years of the mission.

Willson, R. C.↗
NASA NTRS1984

The Structure of the Uranian Rings and the Search for Rings Around Neptune

The nine narrow rings of Uranus, presently the only confirmed features of this system, have been observed with the diffraction-limited resolution (3.5 km) of ground-based occultations since their discovery in 1977. These data have been used to establish an orbit model, from which the five Keplerian orbit parameters for each ring, the pole of the mean ring plane, and the gravitational harmonic coefficients J sub 2 and J sub 4 have been determined. The rings are typically a few kilometers wide with eccentricities of about 0.001 and inclinations of a few hundredths of a degree, although a few have no measurable eccentricity or inclination. Interesting Voyager investigations would include probing the structure of the rings at higher spatial resolution, searching for new rings in the system (including inter-ring material), locating the postulated shepherd satellites, and searching for satellites inside the orbit of Miranda that might have dynamical influence on the rings. The high precision (approx 2 km) of the ring orbits might prove useful for spacecraft navigation. For Neptune, occultation searches have revealed no rings to a limit of a few hundreths optical depth, within a few hundred kilometers from the top of the planet's atmosphere (for equatorial rings).

Elliot, J. L.↗
NASA NTRS1991

Exploratory geological analysis of AVIRIS data

The size and complexity of AVIRIS data sets makes analysis difficult. We present a technique that allows the researcher to rapidly scan the full data set in image form in order to empirically find significant spatial patterns anywhere across the spectrum. Our approach takes advantage of the speed and power of computer graphics workstations to allow real-time, interactive data manipulation and display. Empirical data selection avoids the bias inherent in theoretical selection criteria, and allows discovery of unexpected relationships within the data set. Link Winds, a prototype data analysis system under development, provides the visualization tools used in this study. We are applying this approach to geological studies in the eastern Mojave Desert.

Beratan, K. K.↗
NASA NTRS1989

Voyager's Last Encounter

This video describes Voyager 2's encounter with Neptune. Computer animation and actual data convey Voyager's discoveries such as turbulent storms and dark spots in Neptune's atmosphere, six new moons, Neptune's three rings, and the presence of frozen methane on Triton, as researchers at NASA's Jet Propulsion Laboratory describe Voyager's achievements.

Source record↗
NASA NTRS1994

An application of miniscale experiments on Earth to refine microgravity analysis of adiabatic multiphase flow in space

Adiabatic two-phase flow is of interest to the design of multiphase fluid and thermal management systems for spacecraft. This paper presents original data and unifies existing data for capillary tubes as a step toward assessing existing multiphase flow analysis and engineering software. Comparisons of theory with these data once again confirm the broad accuracy of the theory. Due to the simplicity and low cost of the capillary tube experiments, which were performed on earth, we were able to closely examine for the first time a flow situation that had not previously been examined appreciably by aircraft tests. This is the situation of a slug flow at high quality, near transition to annular flow. Our comparison of software calculations with these data revealed overprediction of pipeline pressure drop by up to a factor of three. In turn, this finding motivated a reexamination of the existing theory, and then development of a new analytical and is in far better agreement with the data. This sequence of discovery illustrates the role of inexpensive miniscale modeling on earth to anticipate microgravity behavior in space and to complete and help define needs for aircraft tests.

Rothe, Paul H.↗
NASA NTRS1997

Correlated studies at activity maximum: The Sun and the solar wind

The breadth and power of the set of solar and heliospheric observatories presently in space is unprecedented. Their observations generally began at solar minimum or in the declining phase of the past maximum, but it is anticipated that most of the instruments will be able to observe the rise to the next maximum and that the events will happen then. The second orbit of Ulysses will be especially interesting and the Yokhoh orbital decay is not projected until 2002. New spacecraft, including TRACE, HESSI, and SOLAR-B, may also become available. The current remote sensing and in situ measurements are characterized by a much stronger understanding of how the solar and interplanetary phenomena match. The novel discoveries from the current data are reviewed, and speculations are expressed on how to take advantage of the future data, emphasizing the use of heliospheric observations to help probe the connectivity of the corona/solar wind interface region. It is suggested that there now exists a possibility of understanding the heliospheric structure empirically in new ways: by using particles as tracers of the field, and by correlating multi-point measurements of structures in the solar wind with solar images.

Hudson, H. S.↗
NASA NTRS1996

The Results of Dynamic Data Acquisition During Mars Pathfinder Prototype Airbag Drop Testing

The Mars Pathfinder mission, scheduled for launch in December 1996, will use an airbag system to safely deliver a lander to the Martian surface.The airbag landing system has undergone a comprehensive test program during its evolution from initial design phase to final qualification and acceptance testing. This paper outlines the test approach used in the airbag development program, describes the data acquisition system used to obtain and evaluate airbag performance data, and presents test results.

airbags Mars Mars Pathfinder Mars lander data acqu↗
NASA NTRS2007

Serving Fisheries and Ocean Metadata to Communities Around the World

NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://gcmd.nasa.gov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches, or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage, and data resolution for selected datasets. The directory also offers data holders a means to advertise and search their data through customized portals, which are subset views of the directory. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1988. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as the International Directory Network (IDN), has been providing its services and sharing its experience and knowledge of metadata at the international, national, regional, and local level for many years. Active partners include the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and organizations (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP).

Meaux, Melanie F.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2014

Namibia Dashboard Enhancements

The purpose of this presentation is for a Technical Interchange Meeting with the Namibia Hydrological Services (NHS) in Namibia. The meeting serves as a capacity building exercise. This presentation goes over existing software functionality developed in collaboration with NHS over the past five years called the Namibia Flood Dashboard. Furthermore, it outlines new functionality developed over the past year and future functionality that will be developed. The main purpose of the Dashboard is to assist in decision support for flood warning. The Namibia Flood Dashboard already exists online in a cloud environment and has been used in prototype mode for the past few years.Functionality in the Dashboard includes river gauge hydrographs, TRMM estimate rainfall, EO-1 flood maps, infrastructure maps and other related functions. Future functionality includes attempting to integrate interoperability standards and crowd-sourcing capability. To this end, we are adding OpenStreetMap compatibility and an Applications Program Interface (API) called a GeoSocial API to enable discovery and sharing of data products useful for decision support via social media.

Disaster Decision Support↗
NASA NTRS2019

An Evolutionary System of Mineralogy: Proposal for A Classification of Planetary Materials Based on Natural Kind Clustering

Minerals reveal the nature of the co-evolving geosphere and biosphere through billions of years of Earth history. Mineral classification systems have the potential to elucidate this rich evolutionary story; however, the present mineral taxonomy, based as it is on idealized major element chemistry and crystal structure, lacks a temporal aspect, and thus cannot reflect planetary evolution. A complementary evolutionary system of mineralogy based on the quantitative recognition of “natural kind clustering” for a wide range of condensed planetary materials with different paragenetic origins has the potential to amplify, though not supersede, the present classification system.

Philosophy of mineralogy↗