Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8,460
datasets available to search
ShareScore release 0.9.0
Dataset results
8,460 results for “MONITORING”
LAT Monitored Source List Light Curves
The LAT team monitors flux values for a number of bright sources and transient sources that have shown flares during the mission. (See up-to-date weekly reports on flaring sources at the Fermi-LAT Flare Advocate Blog.) As sources cross the monitoring flux threshold of 1x10-6 cm-2s-1, they are added to the monitored source list. (The initial flux threshold was 2x10-6 cm-2s-1, but this value was lowered in June 2009.) In addition to the light curves below, the flux values in several bands are available via Browse. This list will continue to grow as the mission progresses.
Trajectory Clustering with Applications to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft. IEEE Transactions on Intelligent Transportation Systems, Dec. 2011, 12(4), pp. 1511 - 1524.
Health Monitoring and Prognostics for Computer Servers
**Abstract** Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. **Bios** Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.
Prognostics Design Solutions in Structural Health Monitoring Systems
The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.
Influence of gonadectomy on muscle health in micro- and partial-gravity environments in rats (Estrous Cycle Monitoring)
Gonadal hormones, such as testosterone and estradiol, modulate muscle size and strength in males and females. However, the influence of sex hormones on muscle strength in micro- and partial-gravity environments (e.g., the Moon or Mars) is not fully understood. The purpose of this study was to determine the influence of gonadectomy (castration/ovariectomy) on progression of muscle atrophy in both micro- and partial-gravity environments in male and female rats. Male and female Fischer rats (n equals 120) underwent castration/ovariectomy (CAST/OVX) or sham surgery (SHAM) at 11 weeks of age. After 2 weeks of recovery, rats were exposed to hindlimb unloading (0g), partial weight bearing at 40% of normal loading (0.4g, Martian gravity), or normal loading (1.0g) for 28 days. In males, CAST did not exacerbate body weight loss or other metrics of musculoskeletal health. In females, OVX animals tended to have greater body weight loss and greater gastrocnemius loss. Within 7 days of exposure to either microgravity or partial gravity, females had detectable changes to estrous cycle, with greater time spent in low-estradiol phases diestrus and metestrus (∼47% in 1g vs. 58% in 0g and 72% in 0.4g animals, P equals 0.005). We conclude that in males testosterone deficiency at the initiation of unloading has little effect on the trajectory of muscle loss. In females, initial low estradiol status may result in greater musculoskeletal losses. This study derives results from Estrous Cycle Monitoring (Cytology).
Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics
This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.
Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms
This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.
Fermi LAT Monitored Source List
The Fermi LAT table of monitored sources provides daily and weekly fluxes for sources of interest as described in <a href="http://fermi.gsfc.nasa.gov/ssc/data/policy/LAT_Monitored_Sources.html">http://fermi.gsfc.nasa.gov/ssc/data/policy/LAT_Monitored_Sources.html</a>. In addition, similar information will be released for any source which flares above 2x10<sup>-6</sup> photons cm<sup>-2</sup> s<sup>-1</sup> until the flux drops below 2x10<sup>-7</sup> photons cm<sup>-2</sup> s<sup>-1</sup>. Fermi is currently in survey mode and observes the entire sky every day. However, if a source does not exceed the detection threshold, no entry will appear in this catalog. The tabulated fluxes are derived at the LAT Instrument Science Operations center in a 'quick look' analysis to produce results quickly to facilitate follow-up multi-wavelength observations of flaring sources. The table of released fluxes will be updated as analysis and calibrations improve. <b>These early flux estimates do not include systematic uncertainties and do not</b> <b>have an absolute flux calibration</b>. Use of these data as absolute flux measurements for constraining models or for comparison to other data is strongly discouraged at this time. In addition to overall normalization uncertainties, source fluxes may have variations of up to 10% due to currently-uncorrected dependencies of the gamma-ray detection efficiency on variations of the particle background in orbit. Please note that these results are produced using preliminary instrument response functions and calibrations. The quality and stability of these results will improve when updated calibrations become available over the coming months. This database table is created by the HEASARC from FITS tables received from the Fermi Science Support Center (FSSC). The ASP FITS files are produced by the LAT Instrument Science Operations Center (LISOC) and transferred from the LISOC to the FSSC about once per week. This is a service provided by NASA HEASARC .
Rotor health monitoring combining spin tests and data-driven anomaly detection methods
Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory are evaluated and integrated into multiple data-driven anomaly detection techniques to identify faults and anomalies in the disk. In summary, this study presents a select evaluation of online health monitoring of a rotating disk using high caliber capacitive sensors and demonstrates the capability of the in-house spin system.
Propulsion Health Monitoring of a Turbine Engine Disk using Spin Test Data
On line detection techniques to monitor the health of rotating engine components are becoming increasingly attractive options to aircraft engine companies in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenging feature to easily implement, especially, in the presence of scattered loading conditions, crack size, component geometry and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini cracks before any catastrophic event occurs. These techniques go further to evaluate materials' discontinuities and other anomalies that have grown to the level of critical defects which can lead to failure. Generally, health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications [1-5]. These efforts are focused on developing high temperature, wireless, low cost and durable products.Therefore, in an effort to address the technical issues concerning health monitoring of a rotor disk, this paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating engine-like-disk-to predict the disk faults and assess its structural integrity. The experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center's Rotordynamics Laboratory will be evaluated using multiple data-driven anomaly detection techniques [6-9] to identify anomalies in the disk. This study is expected to present a select evaluation of online health monitoring of a rotating disk using these high caliber sensors and test the capability of the in-house spin system.
SOHO Charge, Element, and Isotope Analysis System (CELIAS) Proton Monitor (PM) 30-second Solar Wind Data in CDF Format
The data include 30-second proton speed, density, most probable thermal speed, and arrival direction, as well as predicted alpha-particle speed. Also included are the SOHO spacecraft predicted positon in GSE coordinates, Heliocentric Range, Heliographic Latitude and Longitude, and Earth Carrington Rotation Number. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. Description of the CELIAS-PM instrument and scientific scope can be found on the CELIAS instrument home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.
Application of Inductive Monitoring System to Plug Load Anomaly Detection
NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. LEED Platinum office building. Plug loads are expected to account for a significant portion of the overall energy consumption. This is because building design choices have resulted in greatly reduced energy demand from Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are major contributors to energy consumption in traditional buildings. In anticipation of the importance of plug loads in Sustainability Base, a pilot study was conducted to collect data from a variety of plug loads. A number of cases of anomalous or unhealthy behavior were observed including schedule-based rule failures, time-to-standby errors, changed loads, and inter-channel anomalies. These issues prevent effective plug load management; therefore, they are important to promptly identify and correct. The Inductive Monitoring System (IMS) data mining algorithm was chosen to identify errors. This paper details how an automated data analysis program was created, tested and implemented using IMS. This program will be applied to Sustainability Base to maintain effective plug load management system performance, identify malfunctioning equipment, and reduce building energy consumption.
STING-induced blood-brain barrier opening in combination with radiation therapy potentiates anti-glioma immune and therapeutic activity monitored with [18F]-FLT PET
GEO Series GSE305400. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic-Based Effects Monitoring for Endocrine Active Chemicals—Assessing Relative Contribution of Treated Wastewater to Downstream Pollution
GEO Series GSE49098. Pimephales promelas. 62 samples. Type: Expression profiling by array.
Simultaneous Detection of Gene Expression of insects, nematode and bacterial specific genes to monitor the infection.
GEO Series GSE197737. Bombyx mori; Heterorhabditis bacteriophora; Spodoptera frugiperda; Galleria mellonella; Drosophila melanogaster; Xenorhabdus; Photorhabdus. 10 samples. Type: Expression profiling by array.
Monitoring the ethanol stress response of a sigZ deletion strain of B. cereus ATCC 14579.
GEO Series GSE9858. Bacillus cereus ATCC 14579. 6 samples. Type: Expression profiling by array.
Heterologous hybridization to an oligonucleotide microarray monitors differential gene expression in Pinus radiata exposed to long-term Ethephon exposure
GEO Series GSE42300. Pinus radiata; Pinus taeda. 12 samples. Type: Expression profiling by array.
Monitoring ORF10's Impact on Host Transcriptome Using Doxycycline-Inducible A549 and 293T Cell Lines.
GEO Series GSE281252. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Transplant tissue specfic exosome platform for noninvasive monitoring of immunologic rejection [mRNA]
GEO Series GSE88844. Homo sapiens. 2 samples. Type: Expression profiling by array.
Monitoring the ethanol stress response of a sigM deletion strain of B. cereus ATCC 14579.
GEO Series GSE9856. Bacillus cereus ATCC 14579. 6 samples. Type: Expression profiling by array.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.