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952 results for “Noise”
Cochlear transcriptome at 3 hours post-noise trauma in mice
GEO Series GSE137817. Mus musculus. 2 samples. Type: Expression profiling by array.
High level and nature of transcriptional noise in yeast cells [MNase-Seq]
GEO Series GSE216309. Saccharomyces cerevisiae. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcriptomic changes in Arabidopsis thaliana seedlings in response to traffic noise exposure
GEO Series GSE240794. Arabidopsis thaliana. 6 samples. Type: Expression profiling by high throughput sequencing.
Monarchy of Concordia: A Globalized Society on Maintaining Peace and Harmony in the World by Controlling Human Noise Behavior
<p>Monarchy of Concordia is a globalized society with objective of maintaining peace and harmony in the world by controlling human noise behavior. The Monarchy of Concordia has motto of “<em>Controlling Human Noise Behavior</em>” with guiding principles of energy perspectives in a society. This paper is a short communication focusing on socio-economic and ergonomics aspects of monitoring and controlling human noise behavior by establishing sovereign political power of Monarchy of Concordia. Noise is defined as a sensation of unwanted intensity of a wave. It is perception of a pollutant and a type of environmental stressor. The unwanted intensity of a wave is a propagation of noise due to transmission of energy source waves (viz. physical agents) such as sun, light, sound, heat, electricity, fluid and fire. Human Noise Behavior is checked by identifying a source and a sink of noise i.e. a person making noise and a person affected by such noise. The whole data of correspondences, court/public notices and presentations by the author is deposited on a public repository.</p> <p><a href="https://www.iises.net/international-journal-of-social-sciences/publication-detail-25601">https://www.iises.net/international-journal-of-social-sciences/publication-detail-25601</a></p>
Dataset for "Heterogenous lithospheric modifications beneath central Mongolia revealed by ambient noise tomography"
<p>This project contains the datasets needed to produce the crustal and upper-mantle shear-wave velocity model in a manuscript submitted for publicaiton with Journal of Geophysicsl Research - Solid Earth. </p><p>The folders contains the raw waveforms of vertical component from L3 array. Please note that this data is provided for reproduction purposes only.</p>
Movies of Oikopleura dioica behaving (1st part of an anthropogenic noise exposure dataset)
<div> </div> <div> <p>These are movies of Oikopleura dioica which have been video recorded in the Ciona Tracker 2.0 video acquisition setup. Oikopleura dioica adults were exposed to anthropogenic noise (Level 10 on our amplifier settings). </p> </div>
Studying the Impact of Noises in Build Breakage Data [Online Appendix]
<p>This document represents an Online Appendix of our article, which has been accepted for publication in the IEEE Transactions on Software Engineering (TSE) in August 2019. The Online Appendix includes a replication package of the scripts and data we used in our work, in addition to more detailed results of the findings reported in the article.</p> <p> </p> <p><strong>Abstract.</strong></p> <p>Much research has investigated the common reasons for build breakages. However, prior research has paid little attention to builds that may break due to reasons that are unlikely to be related to development activities. For example, Continuous Integration (CI) builds may break due to timeout or connection errors while generating the build. Such kinds of build breakages potentially introduce noises to build breakage data. Not considering such noises may lead to misleading results when studying CI builds. In this paper, we propose three criteria to identify build breakages that can potentially introduce noises to build breakage data. We apply these criteria to a dataset of 350,246 builds from 153 GitHub projects that are linked with Travis CI. Our results reveal that 33% of the build breakages are due to environmental factors (e.g., errors in CI servers), 29% are due to (unfixed) errors in previous builds, and 9% are due to build jobs that were later deemed by developers as noisy (there is an overlap of 17% between these three types of breakages). We measure the impact of noises in build breakage data on modeling build breakages. We observe that models that use uncleaned build breakage data can lead to misleading associations between build breakages and development activities (e.g., the role of developer). However, such associations could not be observed after eliminating noisy build breakages. Moreover, we replicate a prior study that investigates the association between build breakages and development activities using data from 14 GitHub projects. We observe that some observations reported by the prior study (e.g., pull requests cause more breakages) do not hold after eliminating the noises from build breakage data.</p> <p> </p> <p><strong>Citing this research.</strong></p> <p>If you intend to use any materials (i.e., scripts, data, approach, or findings) of this work, please cite our article as follows:</p> <p>@article{ghaleb2019studying,<br> title={Studying the Impact of Noises in Build Breakage Data},<br> author={Ghaleb, Taher Ahmed and da Costa, Daniel Alencar and Zou, Ying and Hassan, Ahmed E.},<br> journal={IEEE Transactions on Software Engineering},<br> pages={1--14},<br> year={2019},<br> publisher={IEEE},<br> DOI={10.1109/TSE.2019.2941880}<br> }</p> <p>The official publication of this research can be found at <a href="https://dx.doi.org/10.1109/TSE.2019.2941880">https://dx.doi.org/10.1109/TSE.2019.2941880</a></p> <p> </p> <p><strong>Replication Package.</strong></p> <p>You may download the whole replication package (i.e., scripts, data, approach, or findings) using the zip file shown below. Please note that the size of the raw build logs used in this work is approximately 107 GB. You can download raw build logs using from Travis CI (using Travis API) or its AWS S3 backend (using Amazon S3 API).</p> <p> </p>
CSRM Level 2 dataset: inter-station empirical Green's functions (Z-Z component) in continental China from seismic ambient noise data
<p>This dataset contains the <strong>377,770</strong> inter-station empirical Green's functions and associated Rayleigh wave phase/group velocity dispersion curves that are calculated from continuous seismic ambient noise waveforms of <strong>2513</strong> seismic stations deployed in the continent China. This dataset results from a project of constructing the high-resolution China Seismological Reference Model (<strong>CSRM-1.0</strong>) in the top 100 km of the crust and uppermost mantle in continental China (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">model link</a>). </p> <p>本数据库包含了利用中国大陆区域 <strong>2513</strong> 个地震台站记录的连续波形数据计算的 <strong>377,770 </strong>条台站间经验格林函数及其瑞利波相/群速度频散曲线。该数据库源于构建中国大陆区域高精度地壳和上地幔顶部 100 公里三维地震学模型(<strong>CSRM-1.0</strong>)的工作 (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">模型链接</a>) 。</p> <p>If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Xiao Xiao (<strong>xiaox.seis@gmail.com</strong>). </p>
Summary statistics of enQTL from "Genetic determinants of gene expression noise and its role in complex trait variation"
<p><span>Here, we established an atlas of gene expression noise from 1.23 million human peripheral blood cells of 981 individuals, identifying its age- and gender-dependent pattern. We then identified 10,770 independent expression noise quantitative trait loci (enQTLs) for 6,743 unique enGenes (genetically driven gene expression noise) across 7 immune cell types.</span></p>
Narrowband Nolise Masking of High-Pass-Noise Derived ABR: Wave V amplitudes and latencies.
<p>Study investigated effect of narrowband noise maskers on click-evoked high-pass noise/derived ABR responses. Amplitudes and Latencies are raw (uV and ms), NOT %amp or Lat-shift.</p>
Raw data for "Auditory White Noise Exposure Modulates Human Cortical Excitability Patterns"
<p>This link contains the raw data of the article "Auditory White Noise Exposure Modulates Human Cortical Excitability Patterns", by Schuler et al. (2023).</p>
Serum response factor reduces gene expression noise and confers cell state stability (Bulk RNA-Seq)
GEO Series GSE210072. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
High level and nature of transcriptional noise in yeast cells [3'READS]
GEO Series GSE216322. Saccharomyces cerevisiae. 6 samples. Type: Other.
High level and nature of transcriptional noise in yeast cells
GEO Series GSE216450. Saccharomyces cerevisiae. 25 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Other.
Cochlear transcriptome after noise trauma and dexamethasone therapy in mice
GEO Series GSE85290. Mus musculus. 6 samples. Type: Expression profiling by array.
Characterization of gene expression noise modulated by microRNA
GEO Series GSE164080. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
microRNA expression data of ascencding central auditory pathway from noise-induced hearing loss rat model
GEO Series GSE271449. synthetic construct; Rattus norvegicus. 64 samples. Type: Non-coding RNA profiling by array.
Impact of acute noise trauma on the gene expression profile of the hippocampus
GEO Series GSE140087. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
RT quantitative PCR analysis of p53 Signaling Pathway related genes in mice choclea after noise exposure.
GEO Series GSE116460. Mus musculus. 16 samples. Type: Expression profiling by RT-PCR.
Dataset related to the article "Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm"
<p>This record contains raw data related to the article “Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm”</p> <p>Abstract</p> <p><strong>Background: </strong>Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM).</p> <p><strong>Methods: </strong>Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE.</p> <p><strong>Results: </strong>The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01).</p> <p><strong>Conclusion: </strong>When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.</p>
ScienceDex guides
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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.