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9,152 results for “Networking”
Characterization of deep neural network features by decodability from human brain activity
<p>We present a dataset derived through the DNN feature decoding analyses (<a href="https://www.nature.com/articles/ncomms15037">Horikawa and Kamitani, 2017</a>), including true and decoded feature values of DNNs (AlexNet and VGG19) and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual DNN features were highly correlated across subjects, suggesting the systematic differences between the brain and DNNs. The unpreprocessed fMRI data is available from the OpenNeuro (<a href="https://openneuro.org/datasets/ds001246">https://openneuro.org/datasets/ds001246</a>). We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.</p>
Millimeter Wave Point to Multipoint for Affordable High Capacity Backhaul of Dense Cell Networks
<p>This dataset contains the data to reproduce the graphs in the article: Millimeter Wave Point to Multipoint for Affordable High Capacity Backhaul of Dense Cell Networks", 2018 IEEE Wireless Communications and Networking Conference (IEEE WCNC 2018).</p>
Dataset for: Multivascular networks and functional intravascular topologies within biocompatible hydrogels
<p>Dataset for:</p> <p>Multivascular networks and functional intravascular topologies within biocompatible hydrogels</p> <p>Bagrat Grigoryan1,∗, Samantha J. Paulsen1,∗, Daniel C. Corbett2,∗, Daniel W. Sazer1, Chelsea L. Fortin2, Alexander J. Zaita1, Paul T. Greenfield1, Nicholas J. Calafat1, John P. Gounley3, Anderson H. Ta1, Fredrik Johansson2, Amanda Randles3, Jessica E. Rosenkrantz4, Jesse D. Louis-Rosenberg4, Peter A. Galie5, Kelly R. Stevens2,†, Jordan S. Miller1,†</p> <p>1Department of Bioengineering, Rice University, Houston, TX 77005, USA 2Department of Bioengineering, University of Washington, Seattle, WA 98195, USA 3Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA 4Nervous System, Somerville, MA 02143, USA 5Department of Biomedical Engineering, Rowan University, Glassboro, NJ 08028, USA</p> <p>∗Equal contribution. †Corresponding authors. Email: ksteve@uw.edu (K.R.S.) and jmil@rice.edu (J.S.M.).</p> <p>Solid organs transport fluids through distinct vascular networks that are biophysically and biochemically entangled, creating complex 3D transport regimes that have remained difficult to produce and study. We establish intravascular and multivascular design freedoms with photopolymerizable hydrogels using food dye additives as biocompatible yet potent photoabsorbers for projection stereolithography. We demonstrate monolithic transparent hydrogels produced in minutes comprising efficient intravascular 3D fluid mixers and functional bicuspid valves. We further elaborate entangled vascular networks from space-filling mathematical topologies and explore the oxygenation and flow of human red blood cells during tidal ventilation and distension of a proximate airway. In addition, we deployed structured biodegradable hydrogel carriers in a rodent model of hepatic disease to highlight the potential translational utility of this materials innovation.</p>
Simulation of collaboration networks in software development
<p>Dataset resultant from Master Thesis 'Simulation of collaboration networks in software development' authored by José Miguel Gomes.</p>
pypsa-earth_tutorial_networks
<p>Unsolved and solved networks for the tutorial config of pypsa-earth.</p>
Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis
<p>Relevant data for the publication titled: Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis</p>
Climate network construction and analysis
<p>We provide the python codes for paper "Teleconnections among Tipping Elements in the Earth System".</p> <p> </p> <p>The fold named teleconnection_area contains the codes, which are used to calculate the cross-correlation between the nodes in the Amazon and outside.</p> <p>The fold named path contains the codes, which are used to find and plot the shortest path from the Amazon to the TP.</p> <p>The fold named resilience contains the codes, which are used to calculate and plot indicators of Early Warning Signal for the snow cover in the TP.</p>
Figure 5. The haplotype minimum spanning network using 612 in Phylogeny of the order Phoenicopteriformes and population genetics of the Caribbean flamingo (Phoenicopterus ruber: Aves)
Figure 5. The haplotype minimum spanning network using 612 bp of the cytochrome b gene to compare colonies of Caribbean flamingos from Cuba (N = 49), Bonaire (N = 38) and Galápagos (N = 35).
Associated code and data for "A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer (doi: 10.1016/j.csbj.2022.11.037)"
<p>This deposit contains the latest data, code, and analysis to recreate the results in the manuscript "A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer". Computational and Structural Biotechnology Journal 2023. <a href="https://www.researchgate.net/publication/365864472_A_network_medicine_approach_for_identifying_diagnostic_and_prognostic_biomarkers_and_exploring_drug_repurposing_in_human_cancer">DOI: 10.1016/j.csbj.2022.11.037</a>. </p> <p>If you use the code or data from this repository, please cite our publication.</p> <p> </p>
Improving out of network earthquake locations using prior seismicity for use in earthquake early warning
<p>relevant data and run-files related to manuscript: Improving out of network earthquake locations using prior seismicity for use in earthquake early warning</p>
Replication data for: Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty
<p>Simulated host-parasite communities in Llopis‐Belenguer, C., J. A. Balbuena, I. Blasco‐Costa, A. Karvonen, V. Sarabeev, and J. Jokela. 2022. Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty. Ecology</p> <ul> <li>Full communities</li> </ul> <p>01_full_communities.RDS</p> <ul> <li>Resampled communities affected by host sampling completeness. From 90% to 10% of host sampling completeness every 10% steps</li> </ul> <p>02_resampled_sampling_completeness_90.RDS, </p> <p>03_resampled_sampling_completeness_80.RDS, </p> <p>04_resampled_sampling_completeness_70.RDS, </p> <p>05_resampled_sampling_completeness_60.RDS, </p> <p>06_resampled_sampling_completeness_50.RDS, </p> <p>07_resampled_sampling_completeness_40.RDS, </p> <p>08_resampled_sampling_completeness_30.RDS, </p> <p>09_resampled_sampling_completeness_20.RDS, </p> <p>10_resampled_sampling_completeness_10.RDS</p> <ul> <li>Resampled communities affected by parasite taxonomic resolution at all levels of host sampling completeness. From 90% to 10% of parasite taxonomic resolution and from 100% to 10% of host sampling completeness every 10% steps</li> </ul> <p>11_resampled_taxonomic_resolution_90_sampling_completeness_100-10.RDS, </p> <p>12_resampled_taxonomic_resolution_80_sampling_completeness_100-10.RDS, </p> <p>13_resampled_taxonomic_resolution_70_sampling_completeness_100-10.RDS, </p> <p>14_resampled_taxonomic_resolution_60_sampling_completeness_100-10.RDS, </p> <p>15_resampled_taxonomic_resolution_50_sampling_completeness_100-10.RDS, </p> <p>16_resampled_taxonomic_resolution_40_sampling_completeness_100-10.RDS, </p> <p>17_resampled_taxonomic_resolution_30_sampling_completeness_100-10.RDS, </p> <p>18_resampled_taxonomic_resolution_20_sampling_completeness_100-10.RDS, </p> <p>19_resampled_taxonomic_resolution_10_sampling_completeness_100-10.RDS</p> <p> </p>
RInex files from HK GNSS network in observing simultaneous and consecutive occurrence of thunderstorm and geomagnetic storm
<p>These are the RInex files from HK GNSS network in observing simultaneous and consecutive occurrence of thunderstorm and geomagnetic storm</p>
Results from "The Field-Dependent Nature of PageRank Values in Citation Networks"
<p>This repo contains a gzipped archive of the resulting dataframes from the analyses discussed in our manuscript</p>
Pulse, Shunt and Storage: Hydrological Contraction Shapes Processing and Export of Particulate Organic Matter in River Networks
<p>This repository hosts the code to run the analysis presented in </p> <p>"Pulse, Shunt and Storage: Hydrological Contraction Shapes Processing and Export of Particulate Organic Matter in River Networks" <br> by Catalan et al., Ecosystems, 2022.<br> DOI: https://doi.org/10.1007/s10021-022-00802-4</p> <p>The code has been tested on Matlab R2019b.</p> <p>Run the Main.m code to produce the results of Figure 3 and 4 of the paper.<br> Each simulation takes about 8 hours on a standard Desktop computer.</p>
Temporal Network Benchmark Data
<p>We release a benchmark dataset for the evaluation and development of temporal network embedding methods that leverage such fundamental graph time-series representations. The benchmark consists of 26 temporal networks from a variety of different application domains. We also release the graphs in each of the different time-series representations as separate files that can be used for benchmarking purposes. This includes the $\tau$-graph time-series that decomposes the edge stream into a time-series of graphs based on an application time-scale such as 1 day, as well as the $\epsilon$-graph time-series that decomposes the edge stream into a time-series of graphs such that each graph has a constant number of edges. </p> <p>For a direct comparison between the $\tau$-graph and $\epsilon$-graph time series approaches, we require an equal number of graphs for each, that is, $|\mathcal{G}_{\tau}| = |\mathcal{G}_{\epsilon}|$. For this, we first fix $\tau$ to be an application time-scale (such as 1 hour, 1 day, and so on) and use this $\tau$ to derive a $\tau$-graph time-series $\mathcal{G}_{\tau}$. We then set the number of edges per snapshot in the $\epsilon$-graph time-series as $\epsilon = \ceil{\tfrac{|E|}{|\mathcal{G}_{\tau}|}}$, which results in an equal number of snapshots as desired.</p> <p>For each of the 26 temporal networks, we release a variety of temporal granulaties. For instance, for email-EU-core network, we have a directory called email-EU-core, inside that directory consists of the raw temporal network (edge stream) called email-EU-core.edges, which is a simple comma-delimited edge list where the first and second column are node ids, whereas the third is the timestamp of the edge. Furthermore, there are two other directories inside email-EU-core, namely, 1day and 1week, which indicates the temporal granulatiy, and inside either of those directories are two more directories called tau and epsilon, which contain the edge list files for each of the different graph time-series representations.</p>
Code and dataset for publication "Laser Wakefield Accelerator modelling with Variational Neural Networks"
<p>Data and code for reproducing figures in published work.</p> <p> </p> <p>High Power Laser Science and Engineering</p> <p><a href="https://doi.org/10.1017/hpl.2022.47">https://doi.org/10.1017/hpl.2022.47</a></p> <p>Code used various python packages including tensorflow.</p> <p>Conda environment was created with (on 6th Jan 2022)<br> conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge</p>
Onset of criticality in hyper-auxetic polymer networks
<p>Data from the figures presented in the article entitled <strong>Onset of criticality in hyper-auxetic polymer networks</strong>.</p>
Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
<p>This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO<sub>2</sub> Over the Arctic Ocean. <em>Journal of Geophysical Research: Atmospheres</em>. In Review.</p>
Key sulphur species predicted by photochemical models and their employed chemical networks
<p>The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 in Tsai et al. 2023 and the chemical networks used in each model.</p> <p> </p>
MHG4SNA: Middle high german texts annotated for social network analysis
<p><strong>Description</strong></p> <p>This corpus contains multiple middle high german texts with annotations for social network analysis. It contains annotations of: named entities and entity mentions (including partial coreference resolution), direct speech, narrator's comments. See below for further description.</p> <p>The annotated texts are part of my dissertation on social network analysis of arthurian romances. The research was developed in the context of the DH center <a href="https://www.creta.uni-stuttgart.de/">CRETA</a> at the University of Stuttgart.</p> <p> </p> <p><strong>Texts</strong></p> <ul> <li>Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.</li> <li>Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).</li> <li>Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.</li> <li>Wolfram von Eschenbach: 'Willehalm', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 421–640.</li> <li>'Das Rolandslied des Pfaffen Konrad', ed. by Carl Wesle, 3rd edition by Peter Wapnewski, Tübingen 1985 (Altdeutsche Textbibliothek 69).</li> </ul> <p>All texts are part of the MHDBDB (<a href="http://mhdbdb.sbg.ac.at/">Mittelhochdeutsche Begriffsdatenbank</a>).</p> <p> </p> <p><strong>Annotations</strong></p> <p>The texts contain annotations of different categories, as described in the following sections.</p> <p> 1.<em> Named Entities and Entity Mentions</em></p> <p>I annotated all namend entities and entity mentions that belong to the categories PER and LOC. PER stands for 'person' and refers to real persons as well as fictional characters. LOC stands for 'location' and includes real and fictional places.</p> <p>I annotated named entities (e.g. 'Parzival' as PER or 'Nantes' as LOC) as well as entity mentions referring to an instance of PER or LOC (e.g. 'the knight' for Parzival, or 'the city' for Nantes). I did not annotate pronouns. Entity references can contain multiple words, e.g. 'the lovely queen Ginover', and they can be nested, e.g. '[the son of [the king Gahmuret]]'.</p> <p>The annotations follow the guidelines created for multiple categories and disciplines in the context of CRETA. They are published <a href="https://www.creta.uni-stuttgart.de/cute/datenmaterial/annotationsrichtlinien-1-1/index.html">here</a>. </p> <p> 2.<em> Entity Grounding</em></p> <p>All annotated entity references are mapped to the entity instance that they refer to. E.g. the refences 'Parzival', 'Herzeloyde's son', 'the young man', 'the red knight' etc. all refer to the character instance 'Parzival'. The entity grounding takes into consideration the context of the entity mentions since one and the same expression can refer to different instances (in one context 'the king' refers to Arthur, in another context to Gahmuret)</p> <p> 3.<em> Direct Speech (DS)</em></p> <p>Passages of direct speech have been annotated by detecting quotation marks. They are tagged as 'DS'. There are a few cases of embedded direct speech (passages of direct speech containing another passage of direct speech); these cases are annotated as well.</p> <p> 4. <em>Narrator's comments (EK)</em></p> <p>As additional category I annotated passages that contain statements of the narrator, narrator's comments, extensive descriptions or digressions (e.g. an excursus to a specific topic). These passages are not part of the fictional world or lead to a pause in the timeline of events. The are annotated as 'EK' ('EK': passages that aren't part of the diegesis, 'EK2': passages that lead to a pause, e.g. comments or descriptions).</p> <p> 5. <em>Segmentation</em></p> <p>The texts are subdivided in passages of 30 verses. Since some text's editions ('Parzival', 'Willehalm') contain a formal segmentation in passages of 30 verses each, the same kind of segmentation has been transfered to the other texts. This means 'segment 1' contains the first 30 verses, 'segment 2' contains verses 31-60 and so on.</p> <p>According to the editions by Lachmann, 'Parzival' and 'Willehalm' are also subdivided in chapter-like books (Parzival: book 1 to 16, Willehalm: book 1 to 9). The other texts are similarly subdivided in chapter-like sections following common content-based divisions.</p> <p> </p> <p><strong>Social Network Analysis</strong></p> <p>The data can be used to explore and analyse the social network of the texts. SNA can be performed via gephi [4] using the gefx files.</p> <p>The social network is based on co-occurrences using a) the annotated and grounded entities, and b) the text segmentation in segments of 30 verses each. A relation between two or more entities is extracted whenever they co-occur in a segment.</p> <p> </p> <p><strong>Data downloads</strong></p> <p>The annotated texts can be downloaded in multiple formats: conll, csv, and gexf.</p> <p> 1. <em>Conll </em></p> <p>The files contain seven columns:</p> <ul> <li>(1) token,</li> <li>(2) POS-tag, tagged using a <a href="https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/pos-tag-mhg/">middle high german pos tagger</a>,</li> <li>(3) number of segment,</li> <li>(4) Entity reference annotation indicating the intance that the entity reference refers to. '-' if there is no entity reference,</li> <li>(5) EK: '1' in case there is an annotation of 'EK', '0' if not,</li> <li>(6) EK2: '1' in case there is an annotation of 'EK2', '0' if not,</li> <li>(7) DS: '1' if the token is tagged as direct speech, '0' if not.</li> </ul> <p> 2. <em>Csv</em></p> <p>The csv files contain all annotations of the category PER including entity grounding.</p> <p>The files contain the following columns:</p> <ul> <li>begin and end (start and end of the entity reference expression, character offset),</li> <li>doc_id (document id),</li> <li>buch (book number),</li> <li>quote (entity reference expression),</li> <li>coref (the entity instance that the expression refers to),</li> <li>overlap (indicates if there is an overlap, relevant for embedded entities),</li> <li>ek and ek2 (narrator's comment),</li> <li>ds (direct speech),</li> <li>space (annotations of the space where the story takes action, can be ignored here),</li> <li>segnr (number of segment),</li> <li>em (embedded),</li> <li>klasse (entity class),</li> <li>xrange (technical, relevant for annotation view).</li> </ul> <p> 3. <em>Gexf</em></p> <p>These files can be used to import the data to gephi. It is based on the annotation and grounding of entities (categorie PER). A relation between entities is based on co-occurrence (whenever two or more entities co-occur in a segment, they have a relation; with more relations, the intensitiy of their relation grows). The text segmentation is described above.</p> <p>Embedded entities are excluded. Entities mentioned in direct speech (DS) or in comments (EK) can optionally be selected or deselected. These optional filters are indicated in the name of the files.</p> <p>To visualize the graph dynamically, one can use the text segmentation as timeline.</p> <p> </p> <p>release v1.0.0: data publication in the context of my dissertation. </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.