Find research datasets worth reusing

ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001

This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.

Eddy Dynamics, mIxing, Export, and Species composition (EDDIES)

Measurements from the EDDIES program near Bermuda in the Atlantic Ocean from 2004 and 2005.

IceBridge CMG 1A Dynamic Gravity Meter Time-Tagged L1B Vertical Accelerations V001

This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

restrictedus-pdMar 2025View details →

ASTEROID DYNAMICAL FAMILIES V4.0

This dataset has been superseded by version 4.1 of the data. Dynamical family classification of asteroids by Zappala, et al., based on the hierarchical clustering method.

IMAGE RPI Dynamic Spectrogram data in CDF at NASA CDAWeb

RPI passive wave measurement capturing voltage spectral density of the radio emissions in space as a function of frequency, typically between 3 and 1009 kHz. This operating frequency range was selected by the RPI team to provide optimal temporal resolution of the wave observations. Commonly used in the analysis of noise generators, spectral density is a frequency-dependent characteristic that describes how much power is generated by the emission source in a 1 Hz bandwidth. The original description of emissions was done in terms of thermal noise measurements, though the same approach also applies to non-thermal emissions such as AKR. CDF_DS_PT5M stores calibrated data from all three RPI antennas X, Y, and Z individually and a combined X+Y antenna channel. The data are presented as the Voltage Spectral Density (VSD), which is the root of power spectral density, measured in [V/root-Hz] units. Note that conversion of antenna voltage to electric field strength depends on the effective length of the receive antenna, and such conversion is not performed here. (See spase://SMWG/Instrument/IMAGE/RPI for a time history of the lengths of the three mutually orthogonal RPI dipole antennas.) RPI is capable of detecting input radio emissions above its noise floor of 5 nV/root-Hz, which is determined by the internal white noise of the RPI antenna pre-amplifiers. The VSD in RPI spectrogram data is presented in dB relative to 1 V/root-Hz (logarithmic scale), units of dB(V/root-Hz). The RPI instrument noise floor is 5 nV/root-Hz = -166 dB(V/root-Hz) at the receiver input. Software suggested by the science team for CDF file visualization: (1) Plotting tool at the CDAWeb portal, (2) For analysis beyond static image inspection, including color scale optimization, zooming, text export, alternative data representations in physical units, detailed frequency and time information, overlaid model fpe and fce graphs, and EPS quality figures, use BinBrowser software at UML, http://ulcar.uml.edu/rpi.html

Dynamical Model of Rocket Propellant Loading with Liquid Hydrogen

A dynamical model describing the multi-stage process of rocket propellant loading has been developed. It accounts for both the nominal and faulty regimes of cryogenic fuel loading when liquid hydrogen is moved from a storage tank to an external tank via a transfer line. By employing basic conservation laws, the reduced, lumped-parameter model takes into consideration the major multi-phase mass and energy exchange processes involved, such as highly non-equilibrium condensation-evaporation of hydrogen, pressurization of the tanks, and liquid hydrogen and hydrogen vapor flows in the presence of pressurizing helium gas. A self-consistent theory of dynamical condensation-evaporation has been developed that incorporates heat flow by both conduction and convection through the liquid-vapor interface inside the tanks. A simulation has been developed in Matlab for a generic refueling system that involves the solution of a system of ordinary integro-differential equations. The results of these simulations are in good agreement with the Space Shuttle refueling data.

ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003

This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.

Asiago Supernova Catalog (Dynamic Version)

This table contains the dynamic version of the Asiago Supernova Catalog. It supersedes the original 1999 version by Barbon et al. (1999A&AS..139..531B, Cat. II/227), and contains data about the supernovae observed since 1885 and their parent galaxies through a few days prior to the most recent update. In addition to the list of newly discovered SNe, the literature has been searched for new information on past SNe as well. The data for the parent galaxies have also been homogenized. This table was created by the HEASARC based on the <a href="https://cdsarc.cds.unistra.fr/ftp/cats/B/sn">CDS Catalog B/sn</a>. The CDS updates it regularly, and this HEASARC version is accordingly updated within a week of such updates. This is a service provided by NASA HEASARC .

Distributed Diagnosis in Uncertain Environments Using Dynamic Bayesian Networks

This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system bond graph (BG) into a set of structurally observable bond graph fac- tors (BG-Fs). Each BG-F is systematically translated into a corresponding DBN Factor (DBN-F), which is then used in its corresponding local diagnoser for quantitative fault detec- tion, isolation, and identification. By construction, the ran- dom variables in each DBN-F are conditionally independent of the random variables in all other DBN-Fs, given a subset of communicated measurements considered as system inputs. Each DBN-F and BG-F pair is used to derive a local diag- noser that generates globally correct diagnosis results by lo- cal analysis. Together, the local diagnosers diagnose all single faults of interest in the system. We demonstrate on an electri- cal system how our distributed diagnosis scheme is compu- tationally more efficient than its centralized counterpart, but without compromising the accuracy of the diagnosis results.

Dynamic gene expression response to altered gravity in human T cells (parabolic flight)

We investigated differentially regulated genes in human Jurkat T lymphocytic cells in 20s and 5min microgravity and in hypergravity and compared expression profiles to identify potential gravity-regulated genes and adaptation processes.

restrictedus-pdMar 2025View details →

ASTEROID DYNAMICAL FAMILIES V2.0

This data set contains the Zappala et al. (1995) [ZAPPALAETAL1995] asteroid dynamical families classifications, based on the hierarchical clustering method applied to the proper elements of Milani & Knezevic (1994) [MILANI&KNEZEVIC1994]. It includes 12,487 numbered and unnumbered asteroids.

MMS 2 Magnetic Ephemeris and Coordinates (MEC) and Support (Tsyganenko 1989 model, Dynamic conditions), Level 2 (L2), Burst Mode, 30 ms Data

Magnetospheric Multiscale 2 (MMS 2) spacecraft position, velocity, attitude, angular momentum vector, and magnetic ephemeris and coordinates (MEC), Level-2 science data at Burst (30 ms) time resolution. The Magnetic ephemeris data are calculated by using the Tsyganenko 1989 magnetic field model for disturbed magnetospheric conditions. Many variables are included in this data product including the magnetic field measured at the spacecraft. If possible, the northern and southern hemisphere footpoints of the spacecraft are found by tracing along the magnetic field line threading through the spacecraft per the given Tsyganenko and internal magnetic field models. The northern and southern hemisphere loss cone angles are also given. The magnetic field strength at the footpoints and the minimum magnetic field strength along the field line are also calculated by using the field models. Other variables list the spacecraft L-shell, the magnetic local time, the magnetic latitude and longitude, and whether the threading field line is open, closed, etc. Rotational quaternions are provided to allow coordinate transformation from GEI into 11 other coordinate systems including BSC, GEO, GSE, GSE2000, GSE, and SM. The list of ancillary variables includes the dipole tilt angle, the Dst and Kp actvity indices, and separate flags that denote satellite eclipse by the Earth and Moon.

Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The offline testing framework is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.

Catalog of High-Mass X-Ray Binaries in the Galaxy (Dynamic Version)

High-Mass X-ray Binaries (HMXBs) are a particular class of high-energy sources which require multi-wavelength observational efforts to be properly characterized. New identifications and refinement of previous measurements are regularly published in the literature by independent teams of researchers and could, once collected in a catalog, offer a tool to facilitate further studies on HMXBs. The authors aim to update on previous instances of catalogs of HMXBs in the Galaxy, and provide the community easy access to the most complete set of observables on Galactic HMXBs. On top of the fixed version that is available in VizieR, they also aim to host and maintain a dynamic version that can be updated upon request from users, and where any modification will be logged. Using previous catalogs of HMXBs supplemented by listings of hard X-ray sources detected in the past 20 years, the authors produce a base set of HMXBs and candidates by means of identifier and sky coordinate cross-matches. They query SIMBAD for unreferenced HMXBs. They search for as many hard X-ray, soft X-ray, optical and infrared counterparts to the HMXBs as we can in well-known catalogs and compile their coordinates. Each HMXB is subject to a meticulous search in the literature to find relevant measurements and their original reference. The authors provide a catalog of HMXBs in the Galaxy with their best known coordinates, companion star spectral type, systemic radial velocities, component masses, orbital period, eccentricity and spin period when available. This catalog also provides the coordinates and identifiers for each counterpart found from hard X-rays to near-infrared, including counterparts from the recent Gaia DR3 catalog. This catalog was created from data-mining the published literature. It takes into account information available through 2022. Values for binary parameters are joined with a reference in which the value was derived. Position data for which the authors have manually found a counterpart also have a specific reference; if not, then the data comes directly from the corresponding catalog. This database table was first created by the HEASARC in October 2023, based on the 2023-09 version of this catalog, found from the <a href="https://binary-revolution.github.io/HMXBwebcat/">Binary rEvolution website</a>. It is automatically updated in the HEASARC database within a few days of a new version being released on that website. This is a service provided by NASA HEASARC .

ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001

MMS 4 Magnetic Ephemeris and Coordinates (MEC) and Support (Tsyganenko 1989 model, Dynamic conditions), Level 2 (L2), Survey Mode, 30 s Data

Magnetospheric Multiscale 4 (MMS 4) spacecraft position, velocity, attitude, angular momentum vector, and magnetic ephemeris and coordinates (MEC), Level-2 science data at Quick-Look (30 s) time resolution. The Magnetic ephemeris data are calculated by using the Tsyganenko 1989 magnetic field model for disturbed magnetospheric conditions. Many variables are included in this data product including the magnetic field measured at the spacecraft. If possible, the northern and southern hemisphere footpoints of the spacecraft are found by tracing along the magnetic field line threading through the spacecraft per the given Tsyganenko and internal magnetic field models. The northern and southern hemisphere loss cone angles are also given. The magnetic field strength at the footpoints and the minimum magnetic field strength along the field line are also calculated by using the field models. Other variables list the spacecraft L-shell, the magnetic local time, the magnetic latitude and longitude, and whether the threading field line is open, closed, etc. Rotational quaternions are provided to allow coordinate transformation from GEI into 6 other coordinate systems including BSC, GEO, GSE, GSE2000, GSE, and SM. The list of ancillary variables includes the dipole tilt angle, the Dst and Kp actvity indices, and separate flags that denote satellite eclipse by the Earth and Moon.

MMS 1 Magnetic Ephemeris and Coordinates (MEC) and Support (Tsyganenko 1989 model, Dynamic conditions), Level 2 (L2), Burst Mode, 30 ms Data

Magnetospheric Multiscale 1 (MMS 1) spacecraft position, velocity, attitude, angular momentum vector, and magnetic ephemeris and coordinates (MEC), Level-2 science data at Burst (30 ms) time resolution. The Magnetic ephemeris data are calculated by using the Tsyganenko 1989 magnetic field model for disturbed magnetospheric conditions. Many variables are included in this data product including the magnetic field measured at the spacecraft. If possible, the northern and southern hemisphere footpoints of the spacecraft are found by tracing along the magnetic field line threading through the spacecraft per the given Tsyganenko and internal magnetic field models. The northern and southern hemisphere loss cone angles are also given. The magnetic field strength at the footpoints and the minimum magnetic field strength along the field line are also calculated by using the field models. Other variables list the spacecraft L-shell, the magnetic local time, the magnetic latitude and longitude, and whether the threading field line is open, closed, etc. Rotational quaternions are provided to allow coordinate transformation from GEI into 11 other coordinate systems including BSC, GEO, GSE, GSE2000, GSE, and SM. The list of ancillary variables includes the dipole tilt angle, the Dst and Kp actvity indices, and separate flags that denote satellite eclipse by the Earth and Moon.

DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING

DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO* Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity research community. The current research in structural health monitoring (SHM) is to provide continuous (or on demand) information about the state of a structure. The SHM system can be based on either active or passive sensor measurements. Though the current research on ultrasonic wave propagation based active sensing approach has the potential to estimate very small damage, it has severe drawbacks in terms of low sensing radius and external power requirements. To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently, few efforts have been made towards, time-series fatigue damage state estimation over the entire fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques demonstrates the clear trend in damage growth during the final failure regime (stage-III regime) or during when the damage is comparatively large enough. The present paper proposes a passive sensing technique that demonstrates a clear trend in damage growth almost over the entire stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks that can estimate the time-series fatigue damage state under random loading is proposed. For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the reference condition dynamic strain at a given instant of time. The predicted strains are compared with the actual sensor measurements to estimate the corresponding error signals. The error signals estimated at two different locations are correlated to estimate the corresponding fatigue damage state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with biaxial random loading.

restrictedus-pdMar 2025View details →

MSL DYNAMIC ALBEDO OF NEUTRONS LEVEL 3/4 RDR V1.0

Reduced housekeeping and scientific data collected from the Dynamic Albedo of Neutron Detector (DAN) aboard the Mars Science Laboratory.

MMS 4 Magnetic Ephemeris and Coordinates (MEC) and Support (Tsyganenko 1989 model, Dynamic conditions), Level 2 (L2), Burst Mode, 30 ms Data

Magnetospheric Multiscale 4 (MMS 4) spacecraft position, velocity, attitude, angular momentum vector, and magnetic ephemeris and coordinates (MEC), Level-2 science data at Burst (30 ms) time resolution. The Magnetic ephemeris data are calculated by using the Tsyganenko 1989 magnetic field model for disturbed magnetospheric conditions. Many variables are included in this data product including the magnetic field measured at the spacecraft. If possible, the northern and southern hemisphere footpoints of the spacecraft are found by tracing along the magnetic field line threading through the spacecraft per the given Tsyganenko and internal magnetic field models. The northern and southern hemisphere loss cone angles are also given. The magnetic field strength at the footpoints and the minimum magnetic field strength along the field line are also calculated by using the field models. Other variables list the spacecraft L-shell, the magnetic local time, the magnetic latitude and longitude, and whether the threading field line is open, closed, etc. Rotational quaternions are provided to allow coordinate transformation from GEI into 11 other coordinate systems including BSC, GEO, GSE, GSE2000, GSE, and SM. The list of ancillary variables includes the dipole tilt angle, the Dst and Kp actvity indices, and separate flags that denote satellite eclipse by the Earth and Moon.