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9,152 results for “Networking”
Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances
<p>Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures.</p> <p>For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.</p> <p> </p>
Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data
<p>Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks</p>
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
Identification of Thalweg and Ridge Networks as Landmarks for Terrain Partitioning
<p>Grid digital elevation models having resolution of 1 m or less are increasingly available to scientists and engineers interested in describing current state and evolution of Earth and space topography. Significant information loss is, however, clearly observed when existing terrain analysis methods are used in geophysical modeling, especially when coarse meshes are needed for computational efficiency. The present study shows how thalweg and ridge networks can be extracted automatically from any high-resolution grid digital elevation model without the need to alter the observed topographic data, and how these networks can be used as landmarks for terrain partitioning. The slopeline network extracted in grid digital elevation models is used to determine ridge points, related average rejunction lengths of slopelines extending from ridge points on opposite slopes, exorheic and endorheic basins. Exorheic and endorheic basins are connected through the spilling saddles from endorheic basins to form the thalweg network, and the related ridge network is identified. The obtained thalweg and ridge networks are characterized by using the known concept of drainage area and the new concept of spread area to provide physically meaningful landmarks for terrain partitioning at the desired level of detail. Although the developed methods are inspired by the observation of gravity-driven processes, they support any investigation in Earth and space science where thalweg and ridge networks are relevant topographic features. Potential impacts are exemplified by quantifications of preserved depressions over a mountain area and benefits from physically meaningful unstructured terrain partitioning in surface flow propagation over a complex floodplain.</p>
Pyrene-Based Macrocrosslinkers with Supramolecular Mechanochromism for Elastic Deformation Sensing in Hydrogel Networks
<p>Primary data used in the manuscript (NMR, MS, fluorescence, GPC) sorted after Figure and associated panel in manuscipt and supporting information.</p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network
<p><strong>Data Description</strong>:</p> <p>The USDA Long-Term Agroecosystem Research (LTAR) Network coordinates agricultural research across 18 research sites in the conterminous United States (CONUS). However, it is unclear how well these sites represent the totality of agricultural working lands within the CONUS. Therefore, we performed a quantitative analysis of the 18 sites, based on 15 climatic and edaphic characteristics, to produce maps of representativeness and constituency across the CONUS. Representativeness shows how well the combination of environmental drivers at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location.</p> <p>Files in collection (22):</p> <p>Collection contains 11 geospatial rasters and 11 PNGs visualizing them.</p> <p>TIF files:</p> <p>├── conus_ltar_constituency_workinglands.tif [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.tif [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.tif [Principal Component 1]<br> ├── conus_ltar_v5.pc2.tif [Principal Component 2]<br> ├── conus_ltar_v5.pc3.tif [Principal Component 3]<br> ├── conus_ltar_v5.pc4.tif [Principal Component 4]<br> ├── conus_ltar_v5.pc5.tif [Principal Component 5]<br> ├── conus_ltar_v5.pc6.tif [Principal Component 6]<br> ├── conus_ltar_v5.pc7.tif [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.tif [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.tif [Representativeness of combined LTAR + NEON + LTER networks]</p> <p>PNG files:</p> <p>├── conus_ltar_constituency_workinglands.png [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.png [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.png [Principal Component 1]<br> ├── conus_ltar_v5.pc2.png [Principal Component 2]<br> ├── conus_ltar_v5.pc3.png [Principal Component 3]<br> ├── conus_ltar_v5.pc4.png [Principal Component 4]<br> ├── conus_ltar_v5.pc5.png [Principal Component 5]<br> ├── conus_ltar_v5.pc6.png [Principal Component 6]<br> ├── conus_ltar_v5.pc7.png [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.png [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.png [Representativeness of combined LTAR + NEON + LTER networks]</p> <p><strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution, while the geospatial visualizations are provided in PNG format.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code class="language-bash">GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p><strong>Category labels for Constituency data</strong>:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Cat</th> <th scope="col">LTAR Siite</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Archbold-University of Florida</td> </tr> <tr> <td>2</td> <td>Central Mississippi River Basin</td> </tr> <tr> <td>3</td> <td>Central Plains Experimental Range</td> </tr> <tr> <td>4</td> <td>Eastern Corn Belt</td> </tr> <tr> <td>5</td> <td>Great Basin</td> </tr> <tr> <td>6</td> <td>Gulf Atlantic Coastal Plain</td> </tr> <tr> <td>7</td> <td>Jornada Experimental Range</td> </tr> <tr> <td>8</td> <td>Kellogg Biological Station</td> </tr> <tr> <td>9</td> <td>Lower Chesapeake Bay</td> </tr> <tr> <td>10</td> <td>Lower Mississippi River Basin</td> </tr> <tr> <td>11</td> <td>Northern Plains</td> </tr> <tr> <td>12</td> <td>Platte River High Plains Aquifer</td> </tr> <tr> <td>13</td> <td>R.J. Cook Agronomy Farm</td> </tr> <tr> <td>14</td> <td>Southern Plains</td> </tr> <tr> <td>15</td> <td>Texas Gulf</td> </tr> <tr> <td>16</td> <td>Upper Chesapeake Bay</td> </tr> <tr> <td>17</td> <td>Upper Mississippi River Basin</td> </tr> <tr> <td>18</td> <td>Walnut Gulch Experimental Watershed</td> </tr> </tbody> </table> <p><strong>Paper describing data and methods</strong>:</p> <p>Kumar, J., Coffin, A. W., Baffaut, C., Ponce-Campos, G. E., Witthaus, L., & Hargrove, W. W. (2023). Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks. In Environmental Management. Springer Science and Business Media LLC. https://doi.org/10.1007/s00267-023-01834-9</p> <p> </p>
Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"
<p>This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.</p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
Landslide Susceptibility and 3-day Antecedent Rainfall generated by Artificial Neural Networks
<p>[ENGLISH]</p> <p>In this dataset, you can find:</p> <p>- Landslide susceptibility indexes for the Serra Geral geomorphic unit, from 0 (low susceptibility) to 1 (high susceptibility). Files starting in map_susc</p> <p>- 3-day Antecedent Rainfall Thresholds for rainfall-induced landslides in the Serra Geral geomorphic unit. Files starting in map_3day</p> <p>The figure tiles_location.png shows the locations of each tile within the states of Rio Grande do Sul and Santa Catarina, Brazil. Background map: OpenStreetMap contributors (2022)</p> <p>This dataset was produced within the research conducted for the PhD Thesis of Luísa Vieira Lucchese. The link to the Thesis will be added here when it is available. Reference:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. PhD Thesis (Water Resources and Environmental Sanitation) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 2022.</p> <p> </p> <p>[PORTUGUÊS DO BRASIL]</p> <p>Neste conjunto de dados, você encontra:</p> <p>- Índices de suscetibilidade a deslizamentos de terra para a unidade geomorfológica da Serra Geral, de 0 (baixa suscetibilidade) até 1 (alta suscetibilidade). Os arquivos têm o prefixo map_susc</p> <p>- Precipitação antecedente de 3 dias para a ocorrência de deslizamentos de terra na unidade geomorfológica da Serra Geral. Os arquivos têm o prefixo map_3day</p> <p>A figura tiles_location.png mostra a localização de cada bloco dentro dos estados do Rio Grande do Sul e de Santa Catarina. Mapa de fundo: OpenStreetMap contributors (2022)</p> <p>Este conjunto de dados é produto da Tese de Doutorado de Luísa Vieira Lucchese. O link para a Tese será adicionado aqui, quando estiver disponível. Referência:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. Tese (Doutorado em Recursos Hídricos e Saneamento Ambiental) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande Sul, Porto Alegre, 2022.</p>
Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections - Supplementary videos
<p>We regard ROIs, regions of interest, from a human spleen specimen in single (four ROIs) and double (three ROIs) staining. The ROIs with the same number correspond to each other. Below we map references in manuscript (<strong>bold</strong>) to file names in this repository (<em>italics</em>).</p> <ul> <li>File <em>colour-deconvolution.png</em> – settings of colour deconvolution in Fiji for double staining.</li> <li>File <em>comments to videos.odt</em> – a commentary to S3[c,d] Video.</li> <li><strong>S1a,b Video to S3a,b Video</strong>: files <em>video_[1,2,3][a,b].mov</em> – sequence of section with single (a) and double (b) staining for ROI 1 to 3 in the main text.</li> <li><strong>S1c Video to S3c Video</strong>: files <em>video_[1,2,3]c.mov</em> – video of the reconstruction, single staining, special blood vessels highlighted.</li> <li><strong>S1d Video to S4d Video</strong>: files <em>video_[1,2,3]d.mov</em> – an overview video of the reconstruction, double staining.</li> <li><strong>S4 Video</strong>: file <em>video_4.mov</em> – quality control in virtual reality.</li> <li><strong>S1 Figure</strong>: a supplementary figure <em>fig_S1.tiff</em> and its caption <em>fig_S1_legend.odt</em></li> <li><strong>S2 Figure</strong>: a supplementary figure <em>fig_S2.tiff</em> and its caption <em>fig_S2_legend.odt</em></li> </ul> <p>This data corresponds to the publication "Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections" by B. S. Steiniger, C. Ulrich, M. Berthold, M. Guthe, and O. Lobachev, 2017.</p>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors
<p>This is the dataset and stata code for the paper "Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors” submitted to Plos One May 1st, 2024.</p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Accelerated Mechanophore Activation and Drug Release in Network Core-Structured Star Polymers Using High-Intensity Focused Ultrasound
<div>Data of the associated manuscript and supporting information sorted after Figures, Schemes, and Tables.</div>
Farmers' fields network of oilseed rape intercropped with service plant in Western Switzerland.
<p>These data are associated with the publication of Bousselin et al. (2024) in which the experiment and the protocols are explained into details.</p> <p><span>Bousselin, X., Lorin, M., Valantin-Morison, M. </span><em>et al.</em><span> Determinants of oilseed rape-service plant intercropping performance variability across a farmers’ fields network in Western Switzerland. </span><em>Agron. Sustain. Dev.</em><span> </span><strong>44</strong><span>, 40 (2024). https://doi.org/10.1007/s13593-024-00972-6</span></p>
Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus: optogenetical stimulation data
<p>This dataset contains 2-photon calcium imaging data from the paper 'Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus'. This is the calcium imaging data from CA1 pyramidal cells and interneurons, including both spontaneous activity and activity in response to optogenetic stimulation.</p> <p><strong>Data organization</strong></p> <p>This dataset contains all the data related to the all-optical part of the paper and was analyzed using the code from the <a href="https://gitlab.com/cossartlab/bocchio-vorobyev-et-al-2023/-/tree/main/Optogenetical%20stimulation?ref_type=heads">lab repository</a>. The original calcium imaging movies are excluded due to size limitations.</p> <p><strong>Further information</strong></p> <p>Please email vorobev[a t]phystech.edu if you need further information on the data or if you wish to access the raw calcium imaging movies (not uploaded here due to storage limitations).</p>
Conversational Networks For Automatic Online Moderation
<p><strong>Description. </strong>This repository contains several datasets of conversational networks, extracted from the chat messages exchanged by players of the <a href="https://www.spaceorigin.fr/">SpaceOrigin</a> MMORPG. Each graph represents a specific conversation, and belongs to one of two classes: <em>Abusive</em> (1) or <em>Non-abusive</em> (0). Vertices represent users, and edges represent the fact that the connected users exchanged message during the considered time period. Edges are weighted and directed: weights represent the intensity of the message exchanges, and directions represent who sent messages to whom.</p> <p>We provide two types of graphs: unsigned and signed. Unsigned graphs were extracted using the method described in paper [1], below. Version 1.0 of this dataset contain only a part of the conversations, subsampled to get balanced classes. Version 1.1 is extended to contain all available conversations, and there are much more <em>Non-abusive</em> than <em>Abusive</em> conversations. Signed graphs were extracted later, using the method described in publication [9] below. Each edge is described by an additional sign, that indicates the polarity of the messages exchanged by two users; friendly (positive) vs. hostile (negative). </p> <p>These datasets were used to train a classifier into automatically recognizing abusive messages. See the below papers for more details. The repository also contains some figures that appear in these papers.</p> <p><strong>Publications. </strong>The following papers used the unsigned version of the conversational networks. The extraction method is described in paper [1].</p> <ul> <li>[1] É. Papégnies, V. Labatut, R. Dufour & G. Linarès, “Conversational Networks for Automatic Online Moderation,” <em>IEEE Transactions on Computational Social Systems </em>6(1):38–55, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01999546">hal-01999546</a>⟩ DOI: <a href="https://doi.org/10.1109/TCSS.2018.2887240">10.1109/tcss.2018.2887240</a></li> <li>[2] É. Papegnies, R. Dufour, V. Labatut & G. Linarès. “Détection de messages abusifs au moyen de réseaux conversationnels,” in <em>8ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et informatiques</em> (MARAMI), 2017. ⟨<a href="https://hal.science/hal-01614279">hal-01614279</a>⟩</li> <li>[3] É. Papegnies, V. Labatut, R. Dufour, & G. Linares. “Graph-based Features for Automatic Online Abuse Detection,” in <em>International Conference on Statistical Language and Speech Processing</em> (SLSP), Springer, Lecture Notes in Computer Science 10583:70-81, 2017. ⟨<a href="https://hal.science/hal-01571639/">hal-01571639</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-68456-7_6">10.1007/978-3-319-68456-7_6</a></li> <li>[4] N. Cécillon. “Exploration de descripteurs de plongements de graphes pour la détection de messages abusifs,” MSc Thesis, <em>Université d'Avignon, </em>2019. ⟨<a href="https://dumas.ccsd.cnrs.fr/dumas-04073337">dumas-04073337</a>⟩</li> <li>[5] N. Cécillon, V. Labatut, R. Dufour, and G. Linarès, “Abusive Language Detection in Online Conversations by Combining Content- and Graph-based Features,” in <em>International Workshop on Modeling and Mining Socia-Media Driven Complex Networks</em>, Frontiers in Big Data 2:8, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02130205">hal-02130205</a>⟩ DOI: <a href="http://doi.org/10.3389/fdata.2019.00008">10.3389/fdata.2019.00008</a></li> <li>[6] N. Cécillon, V. Labatut, R. Dufour, & G. Linarès. “Tuning Graph2vec with Node Labels for Abuse Detection in Online Conversations,” in <em>11ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et</em> informatiques (MARAMI), 2020. <a href="https://hal.science/hal-02993571" target="_blank" rel="noopener">⟨hal-02993571⟩</a> <a href="http://ceur-ws.org/Vol-2750/paper8.pdf">Official Page</a></li> <li>[7] N. Cécillon, V. Labatut, R. Dufour & G. Linarès. “Graph embeddings for Abusive Language Detection,” <em>Springer Nature Computer Science</em> 2:37,<em> </em>2021<em>. </em>⟨<a href="https://hal.science/hal-03042171/">hal-03042171</a>⟩ DOI: <a href="http://doi.org/10.1007/s42979-020-00413-7">10.1007/s42979-020-00413-7</a></li> <li>[8] N. Cécillon, R. Dufour & V. Labatut. “Approche multimodale par plongements de texte et de graphes pour la détection de messages abusifs,” <em>Traitement Automatique des Langues</em><em> </em>62:13-38, 2021. ⟨<a href="https://hal.science/hal-03527016">hal-03527016</a>⟩ <a href="https://aclanthology.org/2021.tal-2.2/">Official Page</a></li> </ul> <p>The following publications use the signed version of the graphs. The modified extraction method is described in publication [9].</p> <ul> <li>[9] N. Cécillon. “Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection,” PhD Thesis, <em>Université d'Avignon, </em>2024. ⟨<a href="https://theses.hal.science/tel-04441308">tel-04441308</a>⟩</li> </ul> <p><strong>Funding. </strong>Part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation. </strong>If you use this dataset, please cite paper [1] for the unsigned networks:</p> <p><br><code>@Article{Papegnies2019,</code><br><code> author = {Papegnies, Étienne and Labatut, Vincent and Dufour, Richard and Linarès, Georges},</code><br><code> title = {Conversational Networks for Automatic Online Moderation},</code><br><code> journal = {IEEE Transactions on Computational Social Systems},</code><br><code> year = {2019},</code><br><code> volume = {6},</code><br><code> number = {1},</code><br><code> pages = {38-55},</code><br><code> doi = {10.1109/TCSS.2018.2887240},</code><br><code>}</code></p> <p>and [9] for the signed ones:</p> <p><br><code>@PhdThesis{Cecillon2024,</code><br><code> author = {Cécillon, Noé},</code><br><code> title = {Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection},</code><br><code> school = {Université d'Avignon},</code><br><code> year = {2024},</code><br><code> type = {PhD Thesis},</code><br><code> address = {Avignon, FR},</code><br><code> url = {https://theses.fr/2024AVIG0100},</code><br><code>}</code></p>
Node-layer duality in networked systems: multilayer brain networks dataset
<p>This dataset contains the processed data used in<a href="https://doi.org/10.1038/s41467-024-50176-5"> Presigny, Corsi and De Vico Fallani (2024)</a>. It is made available for replication purposes. The code to treat these data is available <a href="https://github.com/Presigny/multilayer_duality">here</a>.</p> <p>Multilayer brain networks are obtained from the experimental data published in <a>Guillon <em>et al. </em>(2017)</a>. 23 Alzheimer’s diseased (AD) patients and 27 healthy age-matched control (HC) subjects, participated in the study. For each subject, 6 minutes resting-state eyes-closed brain activity was recorded noninvasively using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX MEG system) at a sampling rate of 1000Hz. Signal artefacts were removed using different techniques including removed signal space separation, principal component analysis, and visual inspection. Finally, source-imaging was used to project the signals from the sensor to the source space consisting of N = 70 regions of interest (ROI) defined by the Lausanne cortical atlas parcellation (see file name_of_ROIs.txt for the order). Here, we used spectral bicoherence to estimate functional connectivity between ROIs and between frequencies of brain activity. Specifically, we considered M = 77 layers corresponding to frequencies in the 2 − 40 Hz range with a resolution of 0.5 Hz. Other parameters were non overlapping windows of 2s averaged according to the Welch method. The resulting networks are full-multilayer consisting of both intralayer and interlayer connections, including weighted links between replica nodes.</p>
Annotated Data in Spanish for Toxicity and Insults in Digital Social Networks
<p>This repository contains data sets and materials for a gold standard elaboration on toxicity and incivility in the digital sphere based on human coding to benchmark algorithmic classification tasks with transformers and LLMs. <strong>The labelling progress is 62%</strong>.</p> <p>We are labelling two samples of novel datasets of political digital interactions on Twitter (rebranded as X). The first set comprises almost 5 million data points from three Latin American protest events: (a) protests against the coronavirus and judicial reform measures in Argentina during August 2020; (b) protests against education budget cuts in Brazil in May 2019; and (c) the social outburst in Chile stemming from protests against the underground fare hike in October 2019. We are focusing on interactions in Spanish to elaborate a gold standard for digital interactions in this language, therefore, we prioritise Argentinian and Chilean data. The second set contains more than 31 million messages and more than 9 million interactions between 2010 and 2022, covering the election of members of the first Constitutional Convention in Chile, the drafting process and the referendum in which the proposal was rejected.</p> <p>This project is generously funded by the <strong>OpenAI Academic Programme</strong>, <strong>2024 FAE-UDP Research Grant</strong>, and partially by the <strong>St Hilda's College Muriel Wise Fund at the University of Oxford</strong>. The <a href="https://training-datalab.com/"><strong>Training Data Lab</strong></a> research group also logistically supports this project.</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.