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1,721 results for “network data”
Prospective Evaluation of Prognosis of Pci Patients Using Network Data in SHengjing Hospital
ClinicalTrials.gov study NCT02667548. IPD Sharing: NO. Countries: 0. Publications: 0.
Accurate Diagnosis of the Invasion Depth in ESCC by a Deep Neural Network Analysis of NBI Endoscopy Data
ClinicalTrials.gov study NCT06252974. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks
This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.
Distributed Data Mining in Peer-to-Peer Networks
Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.
Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data
GEO Series GSE164498. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data (miRNA)
GEO Series GSE60431. Homo sapiens. 47 samples. Type: Non-coding RNA profiling by array.
Single-cell data for FGFR3-driven gene regulatory network analysis reveals a pro-tumoral role for p63 in luminal bladder tumors
GEO Series GSE315628. Homo sapiens; Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Dynamic rewiring of transcription factor networks during smooth muscle cell phenotypic modulation (ChIP-Rx data sets)
GEO Series GSE111710. Rattus norvegicus. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data (mRNA)
GEO Series GSE60432. Homo sapiens. 47 samples. Type: Expression profiling by array.
Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data
GEO Series GSE205422. Homo sapiens. 34 samples. Type: Expression profiling by high throughput sequencing.
TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks
GEO Series GSE112563. Arabidopsis thaliana. 6 samples. Type: Expression profiling by high throughput sequencing.
The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data
GEO Series GSE60433. Homo sapiens. 94 samples. Type: Non-coding RNA profiling by array; Expression profiling by array.
Network Scenario Data PVact Exploration
<p>This data comprises the files for the scenarios investigating the influence of the network parameters in the PVact exploration paper.</p> <p>The .csv and .xls files comprise the data of the respective (sub-)scenarios, whereas the .sh scripts document how the data was downloaded. The calculations were done on the infrastructure of the Leipzig Computing Center as is documented in the respective paper. </p>
Data set for "Stability limitations of optical frequency transfer in telecommunication DWDM networks"
<p>Here we share the relevant data of the manuscript “Stability limitations of optical frequency transfer in telecommunication DWDM networks”.</p> <p>The files:</p> <ul> <li>“fig_2a_total.txt”</li> <li>“fig_2a_diff.txt”</li> </ul> <p>contains respectively the timeseries data of phase fluctuations of total delay and differential delay between two fibers in 50 km-long soil-deployed cable. Same results for 110 km-long aerial cable are in files:</p> <ul> <li>“fig_2b_total.txt”</li> <li>“fig_2b_diff.txt”</li> </ul> <p>The files:</p> <ul> <li>“fig_3_aerial_MDEV_points.txt”</li> <li>“fig_3_soil_MDEV_points.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the differential delay fluctuations in 110 km-long aerial and 70 km-long soil-deployed cable. For short averaging the stability is limited by the noise of time interval counter (MTC 108). Before MDEV calculations the differential delay fluctuations was divided by factor of two, as a half of these fluctuations is present at the output of a stabilized system.</p> <p>The files:</p> <ul> <li>“fig_4_soil_MDEV_points_CW-total.txt”</li> <li>“fig_4_soil_MDEV_points_CW-diff.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the total and differential delay fluctuations in 70km soil-deployed cable, measured with the unmodulated coherent optical carrier.</p> <p>The files:</p> <ul> <li>“fig_7a_EDFA_phase.txt” and “fig_7a_EDFA_temp.txt”</li> <li>“fig_7b_Raman_phase.txt” and “fig_7b_Raman_temp.txt”</li> <li>“fig_7c_ROADM_phase.txt” and “fig_7c_ROADM_temp.txt”</li> </ul> <p>contains the timeseries data of simultaneous measurement of phase and temperature changes in individual cards (modules) of the DWDM system: EDFA amplifier, card containing the Raman module, as well as ROADM (Reconfigurable Optical Add Drop Multiplexer) module.</p> <p>Time series data of the fluctuations of the total and differential propagation delay of the signal transfer in the unmodified DWDM system (over 1500 km long route) include files:</p> <ul> <li>“fig_9_total_phase_fluct_part1”, “fig_9_total_phase_fluct_part2”, “fig_9_total_phase_fluct_part1” </li> <li>“fig_9_diff_phase_fluct_part1”, “fig_9_diff_phase_fluct_part2”, “fig_9_diff_phase_fluct_part3”</li> </ul> <p>The files:</p> <ul> <li>“fig_10_mdev_acusticNOISE.txt”</li> <li>“fig_10_mdev_air-conditioning_ON.txt”</li> <li>“fig_10_mdev_air-conditioning_MODIFIED.txt”</li> </ul> <p>contains points of modify Allan deviation that shows respectively: the influence of acoustic noise, stability of the frequency transfer in DWDM network in the case of sub-optimal (asymmetric) cooling within a single ILA node, while the last file shows the stability of the transfer after optimizing the airflow within this node.</p> <ul> </ul>
The North American Monsoon GPS Hydrometeorological Network 2017: Flux Data
<p>Water, energy and carbon fluxes and acnillary meteorological measurements taken during The North American Monsoon GPS-Hydrometeorological Network 2017. The experiment was carried out during the summer of 2017 in the state of Sonora in northwestern Mexico.</p>
Data files for the publication "Dissecting the hydrogen bond network of water: charge transfer and nuclear quantum effects"
<p>This dataset includes text/dat files for all the data included in the manuscript "Dissecting the hydrogen bond network of water: charge transfer and nuclear quantum effects".</p>
Code and data used in "Designing marine reserve networks to mitigate larval dispersal volatility with the connectivity portfolio effect"
<p>This repository contains the code and data used in "Designing marine reserve networks to mitigate larval dispersal volatility with the connectivity portfolio effect".</p> <p>The files have been placed inside a .rar to maintain the file structure necessary to run Marxan correctly.</p> <p>The main file to run the analyses is "Comparing spatial conservation prioritisation run.R" which uses functions from "Comparing spatial conservation prioritisation functions.R".</p> <p>The main input files are located in data01.Rdata, data02.Rdata, and data03.Rdata, which contain 1. a data frame of planning unit centroids, sizes, and amount of reef habitat, 2. a subset of planning unit data as a matrix, 3-14. migration, probability, and flow larval dispersal matrices, 15-18. larval release dates, 19-34 indices of Oceanic nino index (ONI), El Nino Modoki index (MOD), Dipole Mode Index (DMI), and Pacific Decadal Oscillation (PDO) of coral trout, snapper, rabbitfish, and octopus.</p> <p> </p>
scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data
<p>This data set contains simulation data and experimental data.</p> <p>The simulation data set includes simulated data with five different sizes, each of which contains five different dropout rates, generated by Splatter. Each .rds file contains simulated dropout data, true data and bulk data. </p> <p>The experimental data set includes CellBench benchmarking scRNA-seq datasets, ENCODE cell line data, and HCA data. </p>
Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"
<p>Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers).</p> <p>The data is organized as follows:</p> <p>- ct_data_sets.tar.gz: contains preprocessed validation (aka calibration) and test sets with:</p> <ul> <li>ground truth images,</li> <li>FBP reconstructions,</li> <li>measurements, for the various settings explored (3 noise levels).</li> </ul> <p>- mri_data_sets.tar.gz: contains preprocessed validation (aka calibration) and test sets with:</p> <ul> <li>subsampling cartesian masks,</li> <li>sensitivity masks,</li> <li>ground truth image,</li> <li>measurements, for the various settings explored: single- and multi-coil MRI, various acceleration rates (2, 4, and 8), synthetic noise, and different image types (fat suppression or not).</li> </ul> <p>For completeness, the code used to generate the preprocessed data from the raw data can be found on the GitHub repository.</p>
The MEF2B Regulatory Network - H3K27ac and H3K4me3 ChIP-seq data
GEO Series GSE67455. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.