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57 results for “network topology”
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Topologies collected from 3 Community Networks
<p>This data-set contains graph topologies of several networks that were analysed in two scientific works and used in several more. </p> <p>The data in the 'topologies' folder contains two sets of data: The '2014' folder contains about 5000 snapshots of three community networks, namely Freifunk Wien, Freifunk Graz and ninux Rome. <br> This data-set was collected between 2014 and 2015 and is at the base of the work "A week in the life of three large Wireless Community Networks" (link to the paper below), it describes three large-scale wireless mesh networks running in three cities. <br> The data-set is fully described in the paper, here I report the information needed to use it.<br> - For FFWien and ninux, each snapshot is taken once every 5 minutes, for Graz, one every 10.<br> - one snapshot corresponds to the real state of the network in a specific moment, correlating the database of active nodes with the topology exported by the routing protocol. Some elaboration has been made to merge into one logical nodes some nodes that were running multiple instances of the routing protocol in the same physical location (see the paper for details)<br> - the format is the well known graphml XML format, you can open the files with networkx, gephi and many more tools<br> - the link weight represents the ETX metric (high = bad, see the paper)</p> <p>The network-evolution folder contains the network graphs collected for the two networks of Wien and Graz only, but in a different period of time, and with a much larger time-span between the snapshots. This data-set was used for the paper "On the Technical and Social Structure of Community Networks", and again, represents the physical structure of the network, annotated with link quality from the routing protocol. Format is graphml, metric is ETX.</p> <p>Finally, the mailing_list folder contains the ninux-ml.xml that contains the interactions in the mailing list of the ninux network, as described in the same paper. </p> <p>The second part of the data-set was collected and elaborated during the netCommons (see http://netcommons.eu) research project, while the first was collected before, but contributed to the results of the project too.</p> <p>If you use the data, pleas cite the relevant papers below.</p> <p>If you need more information, feel free to contact me:</p> <p>Leonardo Maccari, Assistant Professor @DISI, University of Trento<br> Tel: +39 0461 285323, www.disi.unitn.it/~maccari, gpg ID: AABE2BD7<br> leonardo.maccari(at)unitn.it.</p> <p>Related Papers:</p> <p>"A week in the life of three large Wireless Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2014Week.pdf</p> <p>"On the Technical and Social Structure of Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2016Technical.pdf</p>
TReNCo: Topologically associating domain (TAD) aware regulatory network construction (extended data)
<p>The enclosed files contain all of the extended data from: TReNCo: Topologically associating domain (TAD) aware regulatory network construction</p>
Decomposition, topology, properties, and graphs of woody crown networks of 15 tree species of Cerrado vegetation
<p>Data of decomposition, topology, properties, and the corresponding graphs of 15 adult tree species of Cerrado vegetation, <em>sensu stricto</em> physiognomy. The woody crown networks (WCN) representations in a bidimensional space were obtained by drawing followed the methodology described by Prado et al. (2020, Prado, C.H.B.A., Trovão, D.M.B.M., Souza, J.P.<strong>,</strong> 2020. A network model for determining the woody crown's decomposition, topology, and properties. Journal of Theoretical Biology, v. 499, p. 110318. https://doi.org/<a href="https://www.x-mol.com/paperRedirect/1258515479077781504">10.1016/j.jtbi.2020.110318</a>.). The branching regions were the nodes, and the woody crown segments connecting the nodes or merely emerging from them were the connectors. Those trees grew under natural conditions in a most common (<em>sensu stricto</em>) physiognomy of Cerrado vegetation, in a reservoir of 86 ha, located at 850 m above sea level in São Carlos city, São Paulo state, Brazil, at 21°58'- 22°00'S and 47°51'-47°52'W. Following the Köppen climatic classification, this region is between Aw and Cwa, a tropical climate with dry winter and wet summer. The rainy season occurs between October-March, and the dry season between April and September. </p>
Topological Network of the Dutch Fairway Information System
<p>Topological fairway network derived from the <a href="https://www.vaarweginformatie.nl/">Dutch Fairway Information System</a>. The data is processed to be topological connected and usable for transport network analysis. </p> <p>Files</p> <p><code>network_digital_twin_v0.3.json</code> This is the json (for web) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p><code>network_digital_twin_v0.3.pickle</code> This is the pickled (for performance) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p><code>network_digital_twin_v0.3.zip</code> This is the shapefile (for gis) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p>Methodological information</p> <p>For details about the creation of the network see <a href="https://github.com/Deltares/digitaltwin-waterway/blob/master/notebooks/Build_FIS_network.ipynb">Build_FIS_network.ipynb</a>.</p> <p>Updates in version 0.3.0:</p> <p>- Added information on discharge dependent <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/velocities/read-velocities.ipynb">velocities</a> and <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/waterlevels/read_waterlevels.ipynb">waterlevels</a> in <code>river_waterlevel.geojson</code> and <code>river_velocity.geojson</code></p> <p>- Moved information on <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/fis-network/generate_bathymetry.ipynb">bathymetry</a> to separate file<code>edges_0.3_with_bathy.geojson</code></p> <p>- More structures added (fix in source dataset)</p> <p>- Edge and node id's are now always strings</p> <p>- Removed yaml file. It was not efficient enough and reading functionality is removed from networkx</p> <p> </p> <p>Updates in version 0.2.0:</p> <p>- added bathymetry info: mean, standard deviation , percentiles [0 (min), 5, 10, 50 (median), 90, 95, 100 (max)]</p> <p>- added new output formats: added shapely compatible geometry type</p> <p>Source data for the network is available at <a href="https://www.vaarweginformatie.nl/">https://www.vaarweginformatie.nl/</a>.</p> <p>Sharing and Access information</p> <p>CC BY-SA 4.0 license applies: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>.</p>
Research data supporting the publication "Hierarchy of Topological Transitions in a Network Liquid"
<p>This dataset contains the data used to produce each of the figures in the manuscript "Hierarchy of Topological Transitions in a Network Liquid" published in PNAS. The dataset includes configuration files for the dendrimer system generated via NPT Monte Carlo simulations and the topological analysis of the networks formed by the dendrimer particles.</p>
Datasets for Manuscript: Neural-Network-Assisted Detection of Superconducting Topological Semimetals
<p>This file contains datasets for an original machine-learning-approach that we developed for the identification of superconducting topological semimetals.</p>
Data from: Gene network topology drives the mutational landscape of gene expression
Open the record for dataset details and reuse information.
Interactions outside local patches contribute to the compound topology of plant-pollinator networks in fragmented dune slacks
Open the record for dataset details and reuse information.
Supporting information for: Accounting for the topology of road networks to better explain human-mediated dispersal in terrestrial landscapes
<p><span>Human trade and movements are central to biological invasions worldwide. Human activities not only transport species across biogeographical barriers but also accelerate their post-introduction spread in the landscape. Thus, by constraining human movements, the spatial structure of road networks might greatly affect the regional spread of invasive species. However, few invasion models have accounted for the topology of road networks so far, and its importance for explaining the regional distribution of invasive species remains mostly unexplored.</span><span> To address this issue, we developed a spatially explicit and mechanistic human-mediated dispersal model that accounts and tests for the influence of transport networks on the regional spread of invasive species. Using as a model the spread of the invasive ant <em>Lasius</em> <em>neglectus</em> in the middle Rhône valley (France), we show that accounting for the topology of road networks improves our ability to explain the current distribution of the invasive ant. In contrast, we found that using human population density as a proxy for the frequency of transport events decreases models' performance and might thus not be as appropriate as previously thought. Finally, by differentiating road networks into sub-networks, we show that national and regional roads are more important than smaller roads for explaining spread patterns. Overall, our results demonstrate that the topology of transport networks can strongly bias regional invasion patterns and highlight the importance of better incorporating it into future invasion models. The mechanistic modelling approach developed in this study should help invasion scientists explore how human-mediated dispersal and topography shape invasion dynamics in landscapes. Ultimately, our approach could be combined with demographic, natural dispersal and environmental suitability models to refine spread scenarios and improve invasive species monitoring and management at regional to national scales.</span></p>
Anomalous and Chern topological waves in hyperbolic networks
<p>Data and codes for the paper "Anomalous and Chern topological waves in hyperbolic networks", by Q. Chen et al.</p>
NANCY SNS-JU Project - Fronthaul network of fixed topology Usage Scenario - Dataset 1
<p>In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. The variety of collected features ranges from radio front-end metrics to physical server operating system and network function metrics. The dataset consists of raw network traffic and extracted flow-based data captured in separate files. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted on two different bands, N3 and N78, with different resolutions, 480p, 720p, 1080p; both in uplink (UL) and in downlink (DL); the type of protocol monitored is “HTTP protocol”; in case of N78 band, data related to the resource usage were also captured, for a total of more that 100 data files.</p> <p>The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile devices and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Deciphering complex networks via topology-encoded latent hyperbolic geometry
<p><span>Complex networks, which are the abstraction of many real-world systems, present a persistent challenge across disciplines for people to decipher their underlying information. Recently, hyperbolic geometry of latent spaces has gained traction in network analysis, due to its ability to preserve certain local intrinsic properties of the nodes. In this study, we explore the problem from a much broader perspective: understanding the impact of nodes' global topological structures on latent space placements. Our investigations reveal a direct correlation between the topological structure of nodes and their positioning within the latent space. Building on this deep and strong connection between node distance and network topology, we propose a novel embedding framework called Topology-encoded Latent Hyperbolic Geometry (TopoLa) for analyzing complex networks. With the encoded topological information in the latent space, TopoLa is capable of enhancing both conventional and low-rank networks, using the singular value gap to clarify the mathematical principles behind this enhancement. Meanwhile, we show that the equipped TopoLa distance can also help augment pivotal deep learning models encompassing knowledge distillation and contrastive learning.</span></p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
<p>TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.</p> <p> </p> <p>If you use any of the data provided, please cite our paper:</p> <p> </p> <pre>@article{matzner2024topology, title={Topology Bench: systematic graph-based benchmarking for core optical networks}, author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina}, journal={Journal of Optical Communications and Networking}, volume={17}, number={1}, pages={7--27}, year={2024}, publisher={IEEE} }<br><br></pre>
Figure data for the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology"
<p>This file contains the data shown in Fig. 1, Fig 3, Fig. 4, Fig. 5 and Fig. 6 of the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology". Additional information on the data and processing procedure are available from the author upon reasonable request.</p>
Weighted Link Schedules in 100-node Fixed Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a fixed-topology placed networks. This data set contains samples for 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Random Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for sub 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Fixed Topology Wireless Networks
<p>This data repo contains the link schedules in a multi-hop wireless networks that aims to maximize the weighted throughput. Each instance is represented by a numpy data file that contains the necessary data fields to reconstruct the original problem instance.</p> <p> </p> <p>Note: 100_7_new.tar.xz should be in the repo https://zenodo.org/deposit/7671940.</p>
Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database
<p><strong>Introduction</strong></p> <p><strong>T</strong>opological characterization of the <strong>R</strong>etinal microvascular n<strong>E</strong>twork visualized by portable fu<strong>ND</strong>us camera (<strong>TREND 2</strong>) is a database of digital color eye fundus images created as an addition to TREND database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg).</p> <p>TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.</p> <p> </p> <p><strong>Purpose</strong></p> <p>1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera</p> <p>2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature</p> <p>3) to aid the development of biomarkers of accelerated aging</p> <p>4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases.</p> <p>All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.</p> <p> </p> <p><strong>Data</strong></p> <p>The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network.</p> <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td><strong>Inclusion Criteria</strong></td> <td><strong>Inclusion Criteria</strong></td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> </tr> <tr> <td> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> <p>- controlled hypertension (blood pressure<140/90 mmHg), and/or</p> <p>- controlled type 2 diabetes mellitus, and/or</p> <p>- Alzheimer's dementia</p> </td> </tr> <tr> <td><strong>Exclusion Criteria</strong></td> <td><strong>Exclusion Criteria</strong></td> </tr> <tr> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> </tr> </tbody> </table> <p><strong>Files:</strong></p> <p>1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format)</p> <p>2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format)</p> <p>3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format)</p> <p>4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format)</p> <p>5_ASSOCIATED DATA (xslx format)</p> <p>6_RETINAL PATHOLOGY (docx format)</p> <p> </p> <p> </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.