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ShareScore release 0.9.0
Dataset results
77 results for “Local networks”
A Local Regulatory Network Around Three NAC Transcription Factors in Stress Responses and Senescence in Arabidopsis leaves (Botrytis cinerea infection).
GEO Series GSE45594. Arabidopsis thaliana. 16 samples. Type: Expression profiling by array.
A Local Regulatory Network Around Three NAC Transcription Factors in Stress Responses and Senescence in Arabidopsis leaves
GEO Series GSE46318. Arabidopsis thaliana. 132 samples. Type: Expression profiling by array.
sci-ATAC-seq identifies unique neuronal adhesion molecules and common transcription factor networks in high-grade localized prostate cancers
GEO Series GSE171559. Homo sapiens. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Dynamic Chromatin Localization of Sirt6 Shapes Stress- and Aging- Related Transcriptional Networks (Illumina)
GEO Series GSE28585. Mus musculus. 12 samples. Type: Expression profiling by array.
A Local Regulatory Network Around Three NAC Transcription Factors in Stress Responses and Senescence in Arabidopsis leaves (Dark Induced Senescence).
GEO Series GSE45595. Arabidopsis thaliana. 16 samples. Type: Expression profiling by array.
Data from: Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection
Identifying local adaptation is crucial in conservation biology in order to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so-called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FSTs, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms.
fMRI Study on Cerebral Localization and Network Mechanisms of rTMS in Chronic Ankle Instability Treatment
ClinicalTrials.gov study NCT06971705. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Microflow3D - Non-invasive Mapping of Coronary Arteries and the Cerebral Vascular Network Using 3D Ultrasound Localization Microscopy in Patients With Atherosclerosis Prior to Carotid Endarterectomy S
ClinicalTrials.gov study NCT07193225. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection
Open the record for dataset details and reuse information.
Data from: Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
Open the record for dataset details and reuse information.
Local Observations from the Seasonal Ice Zone Observing Network (SIZONet) and Alaska Arctic Observatory and Knowledge Hub (AAOKH), Version 2
The Seasonal Ice Zone Observing Network (SIZONet) and the Alaska Arctic Observatory and Knowledge Hub (AAOKH) share the Local Observations Interface, which allows access to observations of sea ice, weather, wildlife and community activities collected since 2006 by Iñupiaq and Yup'ik sea ice experts and community members in several communities along the northern and western coasts of Alaska. The SIZONet web interface, which transitioned to AAOKH in 2015, provides access to a database of local observations spatially referenced around Alaska coastal communities. The database brings together two distinct knowledge systems of western science and Indigenous Knowledge. As an archive and instruction tool, the interface offers collaborating opportunities for researchers and local observers. Since it is designed to change in response to the evolving nature of the observations, the database provides a framework for researchers to track and compare specific climatic, environmental and ecological features, and events across geographic locations and over time. The goal of this project is to document and share Indigenous Knowledge alongside western scientific data in the context of changing sea ice and environmental conditions. In documenting local environmental changes, including sea ice conditions, records may offer insight into how those changes affect community and cultural activities. Arctic coastal communities have long recognized that sea ice conditions are not what they once were: the ocean is freezing later in the fall and ice is melting earlier in the spring, shore-fast ice is less stable, there is far less thick multiyear ice, and environmental conditions overall are less predictable. To view the observations in the database, visitors must agree to the Use Agreement and enter as a Guest. Members of the participating Alaska communities can log in as a Registered User for a more robust use of the interface. More information about AAOKH, project administration, and context for the observations can be found at https://arctic-aok.org/.
Dynamic Chromatin Localization of Sirt6 Shapes Stress- and Aging- Related Transcriptional Networks (ChIP-chip)
GEO Series GSE28638. Mus musculus. 14 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Dynamic Chromatin Localization of Sirt6 Shapes Stress- and Aging- Related Transcriptional Networks
GEO Series GSE28641. Mus musculus. 26 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by genome tiling array.
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P) environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, target tracking, and density estimation to name a few, necessary for many emerging P2P applications in bioinformatics, webmining and sensor networks. Centralizing all or some of the data to build global models is impractical in such P2P environments because of the large number of data sources, the asynchronous nature of the P2P networks, and dynamic nature of the data/network. The proposed algorithm takes a two-step approach. In the monitoring phase, the algorithm checks if the model ‘quality’ is acceptable by using an efficient local algorithm. This is then used as a feedback loop to sample data from the network and rebuild the GMM when it is outdated. We present thorough experimental results to verify our theoretical claims.
A Local Scalable Distributed EM Algorithm for Large P2P Networks
his paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P) environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, target tracking, and density estimation to name a few, necessary for many emerging P2P applications in bioinformatics, webmining and sensor networks. Centralizing all or some of the data to build global models is impractical in such P2P environments because of the large number of data sources, the asynchronous nature of the P2P networks, and dynamic nature of the data/network. The proposed algorithm takes a two-step approach. In the monitoring phase, the algorithm checks if the model ‘quality’ is acceptable by using an efficient local algorithm. This is then used as a feedback loop to sample data from the network and rebuild the GMM when it is outdated. We present thorough experimental results to verify our theoretical claims.
A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks
In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data is located at a central location. However, it becomes extremely challenging to perform the same when the data is distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world datasets in order to performance of the proposed algorithm.
Earthquake catalog in QuakeML format from: "Local earthquake monitoring with a low-cost seismic network: a case study in Nepal"
<p>Earthquake catalog of the microseismicity in central Nepal recorded by a Low cost seismic network(Raspberry Shake) in 2021 in QuakeML format. The information included for each event contains location, phase pick, local magnitude information. </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.