Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
144
datasets available to search
ShareScore release 0.7.1
Dataset results
144 results for “complex network”
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Accompanying dataset for: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks
<p>This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks] containing the full set of 10,000 lognormal networks studied, their network communities and the compotype species observed during simulations with the GARD model. Details are given in the aforementioned paper. Please see also: http://ico2s.org/</p> <p>This work was funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) under projects (EP/J004111/2) "Towards a Universal Biological-Cell Operating System (AUdACiOuS)" and (EP/N031962/1) "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies"</p> <p> </p>
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
Figure 5. Unrooted haplotype network. Each circle represents a in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey
Figure 5. Unrooted haplotype network. Each circle represents a haplotype, and the lines above each link indicate one mutation. Small black dots indicate intermediate, missing, or unsampled haplotypes.
A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities
<p>This file contains the data that was used in the paper titled "A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities" which will be published in Frontiers of Pharmacology.</p>
Benefits and limits of phasing alleles for network inference of allopolyploid complexes
<p>Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared to haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American <em>Dryopteris </em>fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared to using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical non-identifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.</p>
An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks
<p>Freshwater invasions are a global conservation issue. Emerging tools for biogeographical analyses can provide critical information for their effective management and monitoring. Here, we propose a method to assess the distribution of environmental resistance of stream ecosystems to biological invasions by coupling multi‐stage habitat potential models for non‐native species. Location: Andean Patagonia (Chile and Argentina).Taxa: North American beaver (<em>Castor canadensis</em>), Chinook salmon (<em>Oncorhynchus tshawytscha</em>), and coho salmon (<em>O. kisutch</em>). Methods: Environmental resistance to invasive species was mapped throughout a large region of Patagonia by stacking multi‐stage habitat relationships for each target species and assessing the complementation between critical habitats at multiple scales. We generated an environmental model of stream networks derived from high‐resolution topographic and climatic data representing 15,406 drainage basins (>1 km2) covering an area of 369,791 km2. We quantified the intrinsic potential of stream reaches (100 m and 1000 m) to sustain high‐quality habitats and assessed habitat complementation (i.e., abundance and proximity) at the sub‐basin scale as a proxy for environmental resistance. Results: Our model revealed high heterogeneity in the distribution of environmental resistance to invasions throughout the study region, providing case‐specific insights for the research and management of invaders. Conclusions: Environmental resistance modelling is a novel method to study the biogeography of riverine invasions. Our approach is compatible with additional sources of information about species and the environment and shows versatility to diverse invasion scenarios and data sources. This method can be useful in prioritising research and management of incipient and spreading invasions, especially for large and data‐poor regions.</p>
Complex Lava Tube Networks Developed Within the 1792-93 Lava Flow Field on Mount Etna (Italy): Insights for hazard assessment Supporting Informations: Maps and sections of the lava tubes
<div> <div> <div> <p>This supporting information for the above paper submitted to Frontiers in Earth Science - Volcanology, comprises Table 1, as well as the maps and sections of the 8 lava tubes analyzed in this paper, that are located within the 1792-93 lava flow field at Etna volcano. The methods used for the new surveys of the lava tubes are also explained.</p> </div> </div> </div>
Training data set for: Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries
<h2>Overview</h2> <p>This is a synthetic volcano deformation dataset accompanying the publication of <em><strong>Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries</strong></em>,<em><strong> </strong></em>on the journal <em>Volcanica</em>. Synthetic, quasi-static deformation is computed for magma chambers of various geometries, parameterized as spheroids or superpositions of spherical harmonics. Surface deformation is computed using the boundary element method (BEM) of Nikkhoo & Walter (2015). Please reference our paper for details of computational methods.</p> <p>The dataset contains 50,000 realizations of magma chamber geometries/orientations/centroid depths and associated deformation fields. Surface deformation fields are sampled at discrete locations, with a uniform random distribution within [Lh x Lh], and a distribution that concentrates near the chamber (at radial distances, r = 10^(-3 <em> random number) * </em>Lh/2). Note this dataset contains only a small fraction of the total dataset. In total, 824,393 realizations of magma chambers were used to train our emulators. For accessing the complete training data set, please contact the authors. </p> <p>Each .mat file contains the deformation field associated with a single chamber geometry. Use visData.m to visualize chamber geometry and associated surface displacement. Each file contains two MATLAB structures, "input" and "output". </p> <h2>Naming of each zip file</h2> <p>The numbers after the underscore, N:M, indicate that this file contains N of the M total chamber realizations for this particular setup. </p> <p><a href="../api/records/13800065/draft/files/sph_20AspRatios_1e4:151211.zip.zip/content" target="_blank" rel="noopener noreferrer">sph_20AspRatios_1e4:151211.zip</a>: deformation corresponding to spheroidal magma chambers parameterized by aspect ratios. </p> <p><a href="../api/records/13800065/draft/files/sh_complex_1e4:152283.zip/content" target="_blank" rel="noopener noreferrer">sh_complex_1e4:152283.zip</a>: deformation corresponding to chamber geometry produced by superposition of spherical harmonic modes. </p> <p><a href="../api/records/13800065/draft/files/sh_mode_approx_1e4:138380.zip/content" target="_blank" rel="noopener noreferrer">sh_mode_approx_1e4:138380.zip</a>: deformation corresponding to chamber geometries corresponding to individual spherical harmonic modes, combined with a spherical mode (the spherical mode prevents chamber surfaces from having zero radii locally)</p> <p><a href="../api/records/13800065/draft/files/sh_spheroid_approx1e4:202272.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_approx1e4:202272.zip</a>: deformation corresponding to chambers approximating spheroids, but parameterized by spherical harmonics.</p> <p><a href="../api/records/13800065/draft/files/sh_spheroid_perturb_1e4:180247.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_perturb_1e4:180247.zip</a>: same as above, but with additional random perturbations parameterized in spherical harmonics.</p> <h2>Variables in each file</h2> <p><strong>Input</strong> contains the following fields:</p> <p><strong>dp2mu</strong>: pressure change to shear modulus ratio.</p> <p><strong>dx</strong>, <strong>dy</strong>, <strong>dz</strong>: the coordinates of chamber centroid [meters]</p> <p><strong>mu: </strong>dimensionless crustal shear modulus (always set to 1)</p> <p><strong>nu</strong>: crustal Poisson's ratio (always set to 0.25)</p> <p><strong>Ns</strong>: number of points on the surface where displacements are computed</p> <p><strong>Lh</strong>, <strong>Lv</strong>: horizontal and vertical dimensions of the model domain [meters]. Lh is determined such that at the edge of the model domain, the displacement magnitude is below 10 percent of the maximum. Lv = Lh/2 + abs(dz)</p> <p>for the spheroids -----------------------------------------------------------------------------------------------------------</p> <p>the input files contain</p> <p><strong>asp</strong>: aspect ratio of chamber (length of the semi-major axis divided by that of the semi-minor axis)</p> <p><strong>ra</strong>, <strong>rb</strong>: semi-major, -minor, axis length [meters]</p> <p><strong>thetax</strong>, <strong>thetay</strong>, <strong>thetaz</strong>: counterclockwise rotation angles with regard to x, y, z axis [degrees]. thetax = [0, 90] degrees, thetay = 0 degrees, thetaz = 360 degrees.</p> <p>for the general geometries--------------------------------------------------------------------------------------------------</p> <p>the input files contain</p> <p><strong>ls</strong>, <strong>ms</strong>, <strong>fs</strong>: degree, order, coefficients of spherical harmonic modes. Spherical harmonics are sampled up to degree 5. fs is a complex vector of coefficients such that the resulting shape is real. </p> <p><strong>normF</strong>: normalization factor applied to the shape parameterized by ls, ms, fs, such that the shape as a maximum radius of unity.</p> <p><strong>rmax</strong>: scale factor to scale the spherical harmonics parameterized shape to real dimensions [meters].</p> <p>=============================================================================================</p> <p>Output contains the following fields,</p> <p><strong>X</strong>, <strong>Y</strong>, <strong>Z</strong>: coordinates of points where displacement vectors are computed [meters]</p> <p><strong>Ux</strong>, <strong>Uy</strong>, <strong>Uz</strong>: displacements in x, y, z directions [meters]</p> <p><strong>P</strong>, <strong>T</strong>: coordinates [meters] of vertices for the triangular mesh used in BEM calculation, and the connectivity matrix </p> <p><strong>C</strong>: coordinates [meters] of the center of each triangular element</p> <p><strong>that</strong>, <strong>dhat</strong>, <strong>nhat</strong>: unit vectors for orthogonal coordinate systems local to each triangular element. that ("t-hat") extends from vertex one to vertex two, nhat is outward normal, and dhat = cross (nhat, that).</p> <p>Reference:</p> <p>1. Nikkhoo, M., & Walter, T. R. (2015). Triangular dislocation: an analytical, artefact-free solution. <em>Geophysical Journal International</em>, <em>201</em>(2), 1119-1141.</p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Figure 4. Haplotype network for Rattus rattus Complex II in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 4. Haplotype network for Rattus rattus Complex II. The Nusa Tenggara samples are illustrated on the right of the network.
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks
Open the record for dataset details and reuse information.
Benefits and limits of phasing alleles for network inference of allopolyploid complexes
Open the record for dataset details and reuse information.
Data from: Abundant top predators increase species interaction network complexity in Northeastern Chinese forests
Open the record for dataset details and reuse information.
Spatial Tournament Data for Complex Networks Non Deterministic Size - MSc Dissertation
<p>A data set that contains the results of various spatial tournaments, for the iterated prisoner's dilemma. Three different types of complex networks have been used as topology for the spatial tournaments. Small world, random and complete networks.</p>
Network structural origin of instabilities in large complex systems
<p>Raw data used to generate figures in the following publication:</p> <p>Title: "Network structural origin of instabilities in large complex systems"<br> Authors: Chao Duan, Takashi Nishikawa, Deniz Eroglu, Adilson E. Motter<br> Journal: <a href="https://doi.org/10.1126/sciadv.abm8310">Science Advances 8, eabm8310 (2022)</a></p> <p>The CSV files are named by the corresponding figure numbers and the quantities (e.g., "Fig1A_data.csv" for data for Fig. 1A and "FigS1A_data_adj_mat.csv" for the adjacency matrix data for Fig. S1A).<br> </p>
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
<p>TEM images of Au nanoparticles of various sizes on ultra-thin carbon substrates and their corresponding labels for semantic segmentation. The images have a dataset label of "images" and the labels have a dataset label of "labels". </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.