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625 results for “anomaly”
Correlation Between Serum Anti-Müllerian Hormone & Müllerian Ducts Anomalies in Infertility
ClinicalTrials.gov study NCT02436538. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prevalence of Tongue Anomalies in Egyptian Children
ClinicalTrials.gov study NCT05779618. IPD Sharing: YES. Countries: 1. Publications: 6.
Cell-free DNA Analysis of Chromosome Anomalies in Early Pregnancy Loss
ClinicalTrials.gov study NCT04935138. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Positive selection of deleterious alleles through interaction with a sex-ratio suppressor gene in African buffalo: a plausible new mechanism for a high frequency anomaly
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Data from: Life history plasticity of a tropical seabird in response to El Niño anomalies during early life
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Data for: Global frequency analyses of canine progressive rod-cone degeneration–progressive retinal atrophy and collie eye anomaly using commercial genetic testing data
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Data from: Parallelism isn't perfect: could disease and flooding drive a life history anomaly in Trinidadian guppies?
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Sea-surface temperature anomalies mediate changes in fish richness and abundance in Western North Atlantic and Gulf of Mexico estuaries
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Data from: Effects of climate and topography on the diversity anomaly of plants disjunctly distributed in eastern Asia and eastern North America
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Data from: Why concatenation fails near the anomaly zone
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Data from: Allopatric speciation in Asia contributed to the diversity anomaly between eastern Asia and eastern North America: evidence from anchored phylogenomics of Stewartia (Theaceae)
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Data from: Impacts of weather anomalies and climate on plant disease
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Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
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The relative influence of sea surface temperature anomalies on the benthic composition of an Indo-Pacific and Caribbean coral reef over the last decade
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Data from: Long-term (1979-present) total water storage anomalies over the global land derived by reconstructing GRACE data
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Seasonal seabird density by species and seasonal density anomaly for four seabird species off Southern California from from sampling aboard California Cooperative Oceanic Fisheries Investigations (CalCOFI) cruises, 1987 - 2006.
The observational data have been collected as part of long-term fisheries oceanography and seabird monitoring programs designed to inform management of California Current Ecosystem living marine resources. Information collected on seabird distribution and abundance at sea off southern California has been obtained since May 1987 as part of the California Cooperative Oceanic Fisheries Investigation (CalCOFI; http://www.calcofi.net). This program, initiated by R.R. Veit and J.A. McGowan, with funding from the National Science Foundation, has resulted in density (no. birds/km2) estimates of ~60 species of seabirds over the period 1987-present (Veit et al. 1996, 1997, Hyrenbach and Veit 2003, Yen et al. 2006).
Using Bursty Announcements for Detecting BGP Routing Anomalies
<p>This dataset contains all the require data to reproduce the Indonesia incident. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Raquel Hill, and L. Jean Camp. "Using bursty announcements for detecting BGP routing anomalies." vol. 188, 107835, 2021. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.comnet.2021.107835" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.comnet.2021.107835</span></a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Establishment of AIRS Climate-Level Radiometric Stability using Radiance Anomaly Retrievals of Minor Gases and SST
<p> Temperature, H<sub>2</sub>O, and O<sub>3</sub> profiles, as well as CO<sub>2</sub>, N<sub>2</sub>O, CH<sub>4</sub>, CFC12, and SST scalar anomalies are computed using a clear subset of AIRS observations over ocean for the first 16-years of NASA's EOS-AQUA AIRS operation. The AIRS Level 1c radiances are averaged over 16 days and 40 equal-area zonal bins and then converted to brightness temperature anomalies. Geophysical anomalies are retrieved from the brightness temperature anomalies using a relatively standard optimal estimation approach. The CO<sub>2</sub>, N<sub>2</sub>O, CH<sub>4</sub>, and CFC12 anomalies are derived by applying a vertically uniform multiplicative shift to each gas in order to obtain an estimate for the ngas mixing ratio. The minor gas anomalies are compared to the NOAA ESRL in-situ values and used to estimate the radiometric stability of the AIRS radiances. Similarly the retrieved SST anomalies are compared to the SST values used in the ERA-Interim reanalysis and to NOAA's OISST SST product. These inter-comparisons strongly suggest that many AIRS channels are stable to better than 0.02 to 0.03 K/Decade, well below climate trend levels, indicating that the AIRS blackbody is not drifting. However, detailed examination of the anomaly retrieval residuals (observed minus computed) show various small unphysical shifts that correspond to AIRS hardware events (shutdowns, etc.). Some examples are given highlighting how the AIRS radiances stability could be improved, especially for channels sensitive to N<sub>2</sub>O and CH<sub>4</sub>. The AIRS short wave channels exhibit larger drifts that make them unsuitable for climate trending, and they are avoided in this work. The AIRS Level 2 surface temperature retrievals only use short wave channels. We summarize how these short wave drifts impacts recently published comparisons of AIRS surface temperature trends to other surface climatologies.</p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Data for "Wave anomaly detection in wave buoy measurements"
<p>Data used for the production of figures in the manuscript "Wave anomaly detection in wave buoy measurements" submitted for review.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.