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9,152 results for “Networking”
Plant metabolites modulate animal social networks and lifespan
<p><span>Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, <em>Athalia rosae</em>. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions <span>in sawfly groups where some or all individuals had prior access to clerodanoids</span>. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.</span></p>
Prebuilt Electricity Network for PyPSA-Eur based on OpenStreetMap Data
<p>This dataset contains a<strong> topologically connected representation of the European high-voltage grid (220 kV to 750 kV)</strong> <strong>constructed using OpenStreetMap data</strong>. Input data was retrieved using the Overpass turbo API (<a title="Overpass turbo" href="https://overpass-turbo.eu" target="_blank" rel="noopener">https://overpass-turbo.eu</a>). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of <strong>500 m</strong> are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID <em>way/83742802-1</em> can be viewed on OpenStreetMap using the query <a title="OpenStreetMap example (AC)" href="https://www.openstreetmap.org/way/83742802" target="_blank" rel="noopener">https://www.openstreetmap.org/way/83742802</a>. A DC line/cable with the ID <em>relation/15781671</em> can be accessed using the query <a title="OpenStreetMap example (DC)" href="https://www.openstreetmap.org/relation/15781671" target="_blank" rel="noopener">https://www.openstreetmap.org/relation/15781671</a></p> <p>A detailed explanation on the <strong>background, methodology, and validation </strong>can be found in the article published in <a href="https://www.nature.com/articles/s41597-025-04550-7"><strong>Nature Scientific Data</strong></a>:</p> <blockquote> <p><em>Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T.</em> Modelling the high-voltage grid using open data for Europe and beyond. <em>Sci Data</em> <strong>12</strong>, 277 (2025). <a href="https://doi.org/10.1038/s41597-025-04550-7" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04550-7</a></p> </blockquote> <p><strong>Countries</strong> included in the dataset:</p> <blockquote> <p>Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)</p> </blockquote> <p>The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves. </p> <p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> and provided dataset in PyPSA-Eur is released as free software under the MIT, <strong>different licenses and terms of use</strong> apply to the underlying input data.</p> <p><strong>Extract from OpenStreetMap Terms of Use</strong></p> <blockquote> <p>OpenStreetMap<sup><a href="https://www.openstreetmap.org/copyright#trademarks">®</a></sup> is <em>open data</em>, licensed under the <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a> (ODbL) by the <a href="https://osmfoundation.org/">OpenStreetMap Foundation</a> (OSMF).</p> <p>You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full <a href="https://opendatacommons.org/licenses/odbl/1.0/">legal code</a> explains your rights and responsibilities.</p> <p>Our documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0</a> license (CC BY-SA 2.0).</p> </blockquote> <p>This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.</p> <p><strong>Changelog from version 0.5 to 0.6:<br></strong></p> <ul> <li>Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.</li> <li>Interactive map.html now bundled with the dataset.</li> <li>Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).</li> </ul> <p><strong>Changelog from version 0.4 to 0.5:<br></strong></p> <ul> <li>Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.</li> <li>Clustering resolution improved from 5000 to 500 meters.</li> <li>Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).</li> <li>Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.</li> <li>There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).</li> <li>Wherever applicable, OSM IDs are preserved and used in the index of the network components.</li> </ul>
scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
<p>This repository contains the training data and source code to reproduce the results of our paper:<br>scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data</p> <p>More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).</p>
MedievalAvignon: Confront Networks of the City of Avignon During Papacy
<p><strong>Description. </strong>This repository contains several confront networks representing Avignon, as well as the many plots and statistics describing these networks. These files were produced by the R scripts written by Vincent Labatut, and available on the <a href="https://github.com/CompNet/MedievalAvignon">GitHub repository</a> associated to this dataset. Folder <code>paper_figures</code> contains the figures generated for paper [2].</p> <p>The input files of this processing are extracted from the historical and geographical database constituted by Margot Ferrand during her PhD [1], they are available in the GitHub repository. Note that the names of the networks are not exactly the same as in [1] and [2], because our terminology evolved through time: cf. file <code>method_names.txt</code> to get the various names. The network that best represents the urban space, according to the criteria selected in [1, 2], is <code>split_ext__flat_minus_311_filtered</code>.</p> <p><strong>Publications. </strong>The methods proposed to extract the graphs and produce the plots and statistics are abundantly discussed in manuscript [1], whereas a shorter and more synthetic description is available in article [2]:</p> <ol> <li>M. Ferrand. <em>Usages et représentations de l'espace urbain médiéval : Approche interdisciplinaire et exploration de données géo-historiques d’Avignon à la fin du Moyen Âge</em>, PhD. Thesis, Avignon University, 2022. <a href="https://www.theses.fr/2022AVIG1002" rel="nofollow">Web Page</a></li> <li>M. Ferrand and V. Labatut. <em>Approximating Spatial Distance Through Confront Networks: Application to the Segmentation of Medieval Avignon</em>. Journal of Complex Networks, 13(1):cnae046, 2025. DOI: <a href="http://doi.org/10.1093/comnet/cnae046">10.1093/comnet/cnae046</a> ⟨<a href="https://hal.science/hal-04786705">hal-04786705</a>⟩</li> </ol> <p><strong>Citation. </strong>If you use the original, raw data, please cite manuscript [1]. If you use the files available in this repository (in particular the network files), produced by our R scripts based on the raw data, please cite article [2]:</p> <p><code>@Article{Ferrand2025,</code><br><code> author = {Ferrand, Margot and Labatut, Vincent},</code><br><code> title = {Approximating Spatial Distance Through Confront Networks: Application to the Segmentation of Medieval {A}vignon},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2025},</code><br><code> volume = {13},</code><br><code> number = {1},</code><br><code> pages = {cnae046},</code><br><code> doi = {10.1093/comnet/cnae046},</code><br><code>}</code></p> <p><strong>Funding. </strong>This work was funded by the research federation Agorantic (FR 3621) through Margot Ferrand's PhD. fellowship, and through the <em>HistoGraph</em> research project.</p> <p> </p>
Deep neural networks and humans both benefit from compositional language structure
<p>This dataset holds the results generated in the paper:</p> <p>Deep neural networks and humans both benefit from compositional language structure</p> <p>by L. Galke, Y. Ram, and L. Raviv.</p>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
RNA-Protein Interaction Prediction Using Network-Guided Deep Learning
<p>RNA-protein interactions are critical to various life processes, including fundamental translation and gene regulation. Identifying these interactions is vital for understanding the mechanisms underlying life processes. Then, ZHMolGraph is an advanced pipeline that integrates graph neural network sampling strategy and unsupervised large language models to enhance binding predictions for novel RNAs and proteins.</p> <div> </div>
Isoprenoid Gene Network in Arabidopsis Thaliana
<p>To gain more insight into the cross-talk between both pathways at the transcriptional level, gene-expression patterns were monitored under various experimental conditions using 118 GeneChip (Affymetrix) microarrays. To construct a genetic regulatory network in Wille et al. (2004), the interventional dataset focusses on 40 genes, 16 of which were assigned to the cytosolic pathway, 19 to the plastidal pathway and five encode proteins located in the mitochondrion.</p> <p> </p> <p><strong>Task: </strong>The dataset can be used to study causal discovery algorithms.</p> <p> </p> <p><strong>Summary: </strong></p> <ul> <li><strong>Size of dataset</strong>: 118 x 41</li> <li><strong>Task:</strong> Causal Discovery Problem</li> <li><strong>Data Type:</strong> Continuous Data</li> <li><strong>Dataset Scope:</strong> Standalone Dataset</li> <li><strong>Ground Truth:</strong> Known Graph</li> <li><strong>Temporal Structure:</strong> Static Data</li> <li><strong>License:</strong> CC BY 2.0 (see https://genomebiology.biomedcentral.com/articles/10.1186/gb-2004-5-11-r92#MOESM1)</li> <li><strong>Missing Values:</strong> No Missing Values</li> </ul> <p> </p> <p><strong>Missingness Statement: </strong>There are no missing values.<strong><br></strong></p> <p> </p> <p><strong>Samples:</strong></p> <p>Experimental procedures involved seedlings, leaves and roots. For experiments involving seedlings or leaves, plants were grown in growth chambers at 70% humidity and daily cycles of 16 h light at 21◦C and 8 h darkness at 21◦C. Plant material from three independent experiments (replications not in parallel) for each experiment group respectively was pooled prior to RNA extraction. The 118 different experimental conditions are denoted by c1-c118:</p> <ul> <li><strong>c1-c2: </strong>Experiment with wild-type and era mutant seedlings. (growth stage 1.0; tissue: whole seedlings)</li> <li><strong>c3-c5:</strong> Arabidopsis tissue culture, leaf and seedling in a baseline experiment (growth stage: - ; tissue: tissue culture, seedling, adult leaf).</li> <li><strong>c6-c14: </strong>RNA was extracted from seedlings and adult leaves of wild-type and prenylation mutant plants grown under standard conditions (growth stage 1.0; tissue: whole seedlings and adult leaves).</li> <li><strong>c15-c22: </strong>RNA was extracted from wild-type and several transgenic seedlings (growth stage: 1.0; tissue: whole seedlings).</li> <li><strong>c23-c30:</strong> RNA was extracted from a root inducible system exposed to hormonal treatments. (growth stage: 1.0; tissue: lateral roots).</li> <li><strong>c31-c56: </strong>Arabidopsis seedlings were exposed to light and dark conditions in a time-course experiment (0, 10 min, 1h, 5h, 2d, 5d). (growth stage: 1.0; tissue: whole seedlings).</li> <li><strong>c70-c92:</strong> Experiment to assess the effect of inhibitors of the MVA pathway (lovastatin) and the MVA-independent pathway (fosmidomycin) on the expression of genes involved in isoprenoid biosynthesis. (growth stage: 1.0 and 3.90; tissues: whole seedlings and adult leaves).</li> <li><strong>c93-c118:</strong> Arabidopsis seedlings and adult leaves were exposed to ozone for several periods of time. (growth stage: 1.0 and 3.90; tissues: whole seedlings, cauline leaves and adult leaves).</li> </ul> <p> </p> <p><strong>Features: </strong></p> <ul> <li><strong>c[...]: </strong>This row indicates the experiment as descried above</li> <li> <p><strong>AACT1, AACT2, CMK, DPPS1, ...:</strong> Gene expressions level. The column names indicate the gene name.</p> </li> </ul> <div> <p> </p> <p><strong>Note: </strong>Although the gene UPPS2 was analyzed in Willie et al. (2004), it is not provided in its supporting material and in this dataset.</p> </div>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></p>
Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction - Datasets
<p>Datasets to NeurIPS 2021 accepted paper "Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction".</p> <p>Datasets are pytorch files containing a dictionary with training, validation and test sets. Train, validation and test sets are custom dataset classes which inherit from the standard torch dataset class. Corresponding code an be found at https://github.com/HSG-AIML/NeurIPS_2021-Weight_Space_Learning.</p> <p>Datasets 41, 42, 43 and 44 are our dataset format wrapped around the zoos from Unterthiner et al, 2020 (https://github.com/google-research/google-research/tree/master/dnn_predict_accuracy)<br> <br> Abstract:<br> Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still not fully understood. Therefore, we propose to use SSL to learn neural representations of the weights of populations of NNs. To that end, we introduce domain specific data augmentations and an adapted attention architecture. Our empirical evaluation demonstrates that self-supervised representation learning in this domain is able to recover diverse NN model characteristics. Further, we show that the proposed learned representations outperform prior work for predicting hyper-parameters, test accuracy, and generalization gap as well as transfer to out-of-distribution settings.</p>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
<p>This is the first release of Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016. The profile will be used by TERN data editorial system (SHaRED v4).</p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Don't put all your eggs in one leaf-roll: a network analysis of candy-striped spider leaf preferences for egg deposition
<p>"Enoplognatha leaf choice.R" - R script for all analyses and figures for the manuscript.</p> <p>"Enop_LeafENNR_EnopChoice.csv" - Interaction matrix showing the leaves selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_LeafTSENNR_EnopChoice.csv" - Interaction matrix showing the tropho-species selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant tropho-species community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_SNRENNR_EnopChoice.csv" - Interaction matrix showing the semi-natural habitats selected by each individual candy-striped spider.</p> <p>"Enop_SNRENNR_SNRs.csv" - Matrix equally representing the semi-natural habitats as the number of quadrats surveyed in each.</p> <p>"Leaf traits.csv" - The leaf trait data used to cluster leaves into tropho-species.</p> <p>"leaf.incidence.csv" - The number of candy-striped spiders that interacted with each plant species for assessment of interaction diversity and completeness.</p> <p>"Site Map.csv" - Latitude and longitude of the ten sampling sites used in this study.</p> <p>"TS plotting order.csv" - A file used to set the plotting order of tropho-species in a bipartite plot.</p>
Social Network Online Activity of 100+ Users Over Two Years
<p>This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data.</p> <p>The database is provided in SQLite3 format. The data format is the following:</p> <p><strong>'sessions' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> <p><strong>'map' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> <p>Device identifiers:</p> <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table> <p> </p> <p>This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.</p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Data for "Comparing ultrastable lasers at 7×10-17 fractional frequency instability through a 2220 km optical fibre network"
<p>Here we share the relevant data of the manuscript “Comparing ultrastable lasers at 7×10<sup>-17</sup> fractional frequency instability through a 2,220 km optical fibre network”.</p> <p>Raw data was acquired using multiple synchronised, dead-time free frequency counters in Lambda-mode [1]. The integration time for each data point was 1 s. The data provided here have been processed to reflect the fractional frequency difference between the ultrastable lasers at NPL and PTB, scaled to 1542 nm. Specifically,<br> <span class="math-tex">\(y=(\nu_{\text{NPL(ULE)}}\frac{777327}{1126090}-\frac{767233}{767235}\nu_{\text{PTB(Si)}})/194.4 \ \text{THz}\)</span></p> <p>where <span class="math-tex">\(y\)</span> is the value recorded in the data files, <span class="math-tex">\(\nu_{\text{NPL(ULE)}}\)</span> and <span class="math-tex">\(\nu_{\text{PTB(Si)}}\)</span> are the optical frequencies of the ultrastable lasers at NPL (referenced to a ULE cavity) and PTB (referenced to Si cavity), respectively. The numerators and the denominators of the scaling factors correspond to mode numbers of the optical frequency comb at NPL and PTB, respectively. The expression for <span class="math-tex">\(y\)</span> corresponds to the fractional transfer beat [2] between the NPL and PTB ultrastable lasers.</p> <p>The file</p> <ul> <li>“833000_s_874000_s_data_for_fig_2.txt”</li> </ul> <p> contains the timeseries data used to compute the modified Allan deviation reported in <strong>Fig. 2a</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The file</p> <ul> <li>“432000_s_912077_s_data_for_fig_3.txt”</li> </ul> <p>contains the timeseries data used in <strong>Fig. 3.</strong> The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. These data have additionally been high pass filtered with a cut off frequency of 1 mHz to decouple the short-term instability of the optical fibre link from the drift of the ultrastable lasers (with a characteristic time >1000 s), as described in the manuscript.</p> <p>The files</p> <ul> <li>“222000_s_232000_s_data_for_supp_fig_1.txt”,</li> <li>“270000_s_288000_s_data_for_supp_fig_1.txt”,</li> <li>“754000_s_765000_s_data_for_supp_fig_1.txt”,</li> <li>“832000_s_890000_s_data_for_supp_fig_1.txt”,</li> </ul> <p>contain the timeseries data used to compute the modified Allan deviation reported in <strong>Supplementary Fig. 1</strong>. The “0” values correspond to invalid data due to glitches in the operation of the optical fibre link. A linear drift of 40 mHz s<sup>-1</sup> has been removed in these data.</p> <p>The temporal starting point is displayed in seconds in the title of the files relative to 00:00 UTC of 2019/07/06.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Dawkins, S. T., McFerran, J. J. & Luiten, A. N. Considerations on the Measurement of the Stability of Oscillators with Frequency Counters. <em>IEEE Transactions on ultrasonics, ferroelectrics, and frequency control</em> <strong>54</strong>, 918-925 (2007).</p> <p>[2] Telle, H.R., Lipphardt, B. & Stenger, J. Kerr-lens, mode-locked lasers as transfer oscillators for optical frequency measurements. <em>Appl. Phys. B</em> <strong>74</strong>, 1-6 (2002).</p> <p> </p>
Webinar Nuberu: Reliable RAN Virtualization in Shared Platforms @ IMDEA Networks
<p><strong>VIEW IN OUR YOUTUBE CHANNEL:</strong> <a href="https://youtu.be/iAoyuzHuZP0">https://youtu.be/iAoyuzHuZP0</a></p> <p>RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station¿s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings.</p>
ScienceDex guides
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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.