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155 results for “dependency network”
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
Präzi: From Package-based to Call-based Dependency Networks
<p>The data is originally derived from commit 6c550c8 of <a href="https://web.archive.org/web/20210129124622/https://github.com/rust-lang/crates.io-index">https://github.com/rust-lang/crates.io-index</a>. The dataset includes the following files:</p> <ul> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/releases.csv?versionId=0fbac7f6-820c-4411-80ee-26967eda0652">releases.csv</a>: extracted package releases.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/docsrs.csv?versionId=b61f6277-0f19-4c21-bb54-04dcb19f8d43">docsrs.csv</a>: build status and compile toolchain of package releases scrapped from <a href="https://web.archive.org/web/20210118074327/https://docs.rs/">Docs.rs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/rustcg-corpus.tar.xz?versionId=914e7e03-0509-443f-8841-a76408f329d3">rustcg-corpus.tar.xz</a>: call graphs and type hierarchies corpus of <a href="https://web.archive.org/web/20210125175834if_/https://crates.io/">crates.io</a> in JSON format. Constructed using <a href="https://web.archive.org/web/20210129130825/https://github.com/ktrianta/rust-callgraphs">rust-callgraphs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/CDN.tar.xz?versionId=9e0d7baa-32c8-4594-b8b9-fb486eee7d11">CDN.tar.xz</a>: static call-based dependency network (CDN) and package-based dependency network (PDN) in JSON format. </li> </ul>
Dependency Networks of Open Source Libraries Available Through CocoaPods, Carthage and Swift PM
<p>Third party libraries are used to integrate existing solutions for common problems and help speed up development. The use of third party libraries, however, can carry risks, for example through vulnerabilities in these libraries. Studying the dependency networks of package managers lets us better understand and mitigate these risks. So far, the dependency networks of the three most important package managers of the Apple ecosystem, CocoaPods, Carthage and Swift PM, have not been studied. We analysed the dependencies for all publicly available open source libraries up to December 2021 and compiled a dataset containing the dependency networks of all three package managers. The dependency networks can be used to analyse how vulnerabilities are propagated through transitive dependencies. In order to ease the tracing of vulnerable libraries we also queried the NVD database and included publicly reported vulnerabilities for these libraries in the dataset. </p>
Dependency networks of PyPI, npm, CRAN and Bioconductor repositories
<p>A set of datasets with a list of dependencies from the software repositories PyPI, npm, CRAN and Bioconductor.</p>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
Dataset for "Secure and Dependable Multi-Cloud Network Virtualization"
<p>Processed data from experiments using Sirius, the SUPERCLOUD network hypervisor</p> <p>Type of data: processed data</p> <p>Hardware/software used:</p> <p>We use one public (Amazon EC2 in Germany – Frankfurt) and one private (the private cloud is our datacenter in Portugal – Lisbon) clouds in the evaluation. In Amazon EC2 we use t2.medium as gateway VMs and t2.micro as normal VMs. The private cloud is based on a rack of Dell R420, with 2 Intel Xeon E5520 quad-core, 2.2 GHz, and 32 GB RAM. VirtualBox managed the VMs, which were configured</p> <p>with 1 CPU and 2GB RAM. The VMs run Ubuntu with Docker (1.13.1) and OvS (2.5.0). The containers were also based in Ubuntu. The hypervisor was hosted in the server of the private Datacenter.</p> <p>Data Format: Text files</p> <p>Source: Simulations and experiments</p> <p>Number of samples: 10 samples in each experiment</p> <p>Total size of samples:</p> <p>Time to create and destroy containers: 1KB</p> <p>Time to setup a virtual network: 1KB</p> <p> </p>
Self Consistent Recurrent Neural Network for Path Dependent Deformation
<p>Data and Machine Learning codes for the paper:</p> <ul> <li>Title<strong> : Self Consistent Recurrent Neural Network for Path Dependent Deformation</strong></li> </ul> <p><strong>Abstract</strong> : Current neural network (NN) structures can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is not known in advance which NN architectures will perform best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting material failure based on the earlier states in the form of deformation history. Following this investigation, the crucial issues arising from the mathematical computation process of the best-performing NN architectures and the physical properties of the deformation paths are examined in detail. Additionally, we propose a novel and adaptable RNN approach to address the fundamental challenges of truncation and consistency related to obtaining estimations that are compatible with the natural physical properties of deformation paths. This study will serve as a foundation for localization estimation and pave the way for future endeavors to propose further solutions to encountered challenges.</p>
An Analysis of Dependency Network Evolution in PyPI
<p>Retrieved from Libraries.io.</p>
Dataset supporting: Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks
<p>This dataset supports the Figures in the preprint: 'Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks' by Anil et. al., <a href="https://www.biorxiv.org/content/10.1101/2023.03.20.533396v1">2023</a>.</p> <p>The code used to genrate this data can be found on the github repository: <a href="https://github.com/swathianil">swathianil </a><a href="https://github.com/swathianil/homeostatic_structural_plasticity_rTMS"> /homeostatic_structural_plasticity_rTMS</a>.</p> <p>To interactively regenerate the figures, follow the Jupyter notebook, GraphPlotter, available in the repository.</p>
Data from: Ethanol abolishes vigilance-dependent astroglia network activation in mice by inhibiting norepinephrine release
<p>This collection of data sets was obtained during a study of the effect of acute ethanol intoxication on vigilance-dependent astroglia network activation in mice. The main findings have been that ethanol inhibits astroglia activation by inhibiting vigilance-dependent norepinephrine release. This leads to a failure of activation of alpha<sub>1A</sub>-adrenergic receptors on astroglia. Further, this study has revealed that ethanol inhibition of cerebellar Bergmann glia Ca<sup>2+</sup> activation does not account for ataxic motor behavior, but may rather contribute to cognitive deficits.</p>
A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation
<p>B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.</p>
Replication Package for "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems"
<p>This is the replication package for the article "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems" published in the Empirical Software Engineering journal.</p> <p>This package requires Python 3.5 and all the dependencies that are listed in "requirements.txt".<br> The notebooks (in "notebooks" folder) should be opened and executed with Jupyter.</p> <p>The notebooks require the graphs (in "graphs" folder) to be computed first. To do so, execute "helpers.py" with Python.<br> The graphs are built using the data provided by https://libraries.io under CC BY-SA<br> https://creativecommons.org/licenses/by-sa/4.0/<br> Those data can be found in the "data" folder.</p> <p> </p>
Replication Package for "On the impact of security vulnerabilities in the npm package dependency network
<p>This is the replication package for paper "On the impact of security vulnerabilities in the npm package dependency network" accepted for publication in MSR 2018.</p>
On the evolution of technical lag in the npm package dependency network
<p>This is the replication package for paper "On the evolution of technical lag in the npm package dependency network" accepted for publication in ICSME 2018.</p>
Strain‐dependent differences in coordination of yeast signalling networks
<p>The yeast mitogen activated protein kinase pathways serve as a model system for understanding how network interactions affect the way in which cells coordinate the response to multiple signals. We have quantitatively compared two yeast strain backgrounds YPH499 and Σ1278b (both of which have previously been used to study these pathways) and found several important differences in how they coordinate the interaction between the high osmolarity glycerol (HOG) and mating pathways. In the Σ1278b background, in response to simultaneous stimulus, mating pathway activation is dampened and delayed in a dose dependent manner. In the YPH499 background, only dampening is dose dependent. Further, leakage from the HOG pathway into the mating pathway (crosstalk) occurs during osmostress alone in the Σ1278b background only. The mitogen activated protein kinase Hog1p suppresses crosstalk late in an induction time course in both strains but does not affect the early crosstalk seen in the Σ1278b background. Finally, the kinase Rck2p plays a greater role suppressing late crosstalk in the Σ1278b background than in the YPH499 background. Our results demonstrate that comparisons between laboratory yeast strains provide an important resource for understanding how signaling network interactions are tuned by genetic variation without significant alteration to network structure.</p>
Data from: Ethanol abolishes vigilance-dependent astroglia network activation in mice by inhibiting norepinephrine release
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A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation
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Strain‐dependent differences in coordination of yeast signalling networks
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Data from: Temporal scale-dependence of plant-pollinator networks
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Alzheimer's disease risk gene BIN1 induces Tau-dependent network hyperexcitability — MEA Axion Biosciences Maestro Recordings, Figure 6
<p>Genome-wide association studies identified the <i>BIN1</i> locus as a leading modulator of genetic risk in Alzheimer's disease (AD). One limitation in understanding <i>BIN1</i>'s contribution to AD is its unknown function in the brain. AD-associated <i>BIN1</i> variants are generally noncoding and likely change expression. Here, we determined the effects of increasing expression of the major neuronal isoform of human BIN1 in cultured rat hippocampal neurons. Higher BIN1 induced network hyperexcitability on multielectrode arrays, increased frequency of synaptic transmission, and elevated calcium transients, indicating that increasing BIN1 drives greater neuronal activity. In exploring the mechanism of these effects on neuronal physiology, we found that BIN1 interacted with L-type voltage-gated calcium channels (LVGCCs) and that BIN1–LVGCC interactions were modulated by Tau in rat hippocampal neurons and mouse brain. Finally, Tau reduction prevented BIN1-induced network hyperexcitability. These data shed light on BIN1's neuronal function and suggest that it may contribute to Tau-dependent hyperexcitability in AD.</p>
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.