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9,152 results for “Networking”
CNN Wild Park - Graph Neural Networks for Learning Equivariant Representations of Neural Networks
<p>This repository contains the <strong>CNN Wild Park</strong> dataset from the paper:</p> <blockquote> <p><strong>Graph Neural Networks for Learning Equivariant Representations of Neural Networks</strong><br><a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>*, <a href="https://bknyaz.github.io/">Boris Knyazev</a>, <a href="https://www.cyanogenoid.com/">Yan Zhang</a>, <a href="https://yunlu-chen.github.io/">Yunlu Chen</a>, <a href="https://gertjanburghouts.github.io/">Gertjan J. Burghouts</a>, <a href="https://egavves.com/">Efstratios Gavves</a>, <a href="https://www.ceessnoek.info/">Cees G. M. Snoek</a>, <a href="https://davzha.netlify.app/">David W. Zhang</a>*<br><em>ICLR 2024</em> (oral)<br><a href="https://arxiv.org/abs/2403.12143">https://arxiv.org/abs/2403.12143</a><br><a href="https://github.com/mkofinas/neural-graphs">https://github.com/mkofinas/neural-graphs</a><br>*Joint first and last authors</p> </blockquote> <p>We introduce a new dataset of CNNs, which we term <em>CNN Wild Park</em>.<br>The dataset consists of 117,241 checkpoints from 2,800 CNNs, trained for up to 1,000 epochs on CIFAR10.<br>The CNNs vary in the number of layers, kernel sizes, activation functions, and residual connections between arbitrary layers.</p> <p>More specifically, we construct the CNN Wild Park dataset by training 2,800 small CNNs with different architectures for 200 to 1,000 epochs on CIFAR10. We retain a checkpoint of its parameters every 10 steps and also record the test accuracy. The CNNs vary by:</p> <ul> <li>Number of layers L in [2, 3, 4, 5] (note that this does not count the input layer).</li> <li>Number of channels per layer c_l in [4, 8, 16, 32].</li> <li>Kernel size of each convolution k_l in [3, 5, 7].</li> <li>Activation functions at each layer are one of ReLU, GeLU, tanh, sigmoid, leaky ReLU, or the identity function.</li> <li>Skip connections between two layers with at least one layer in between. Each layer can have at most one incoming skip connection. We allow for skip connections even in the case when the number of channels differ, to increase the variety of architectures and ensure independence between different architectural choices. We enable this by adding the skip connection only to the min(c_n, c_m) nodes.</li> </ul> <p>We divide the dataset into train/val/test splits such that checkpoints from the same run are <strong>not</strong> contained in both the train and test splits. </p> <div> </div> <div> </div>
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>
Dataset for the publication: Tactile Convolutional Networks for Online Slip and Rotation Detection
<p>Raw data recorded for the publication "Tactile Convolutional Networks for Online Slip and Rotation Detection" in rosbag format. Each .bag file contains recordings of two tactile sensors sampled with 1kHz as "sensor_msgs/Image" and a third channel for data labeling in the format "sr_robot_msgs/UBI0All". Detailed informations about the preprocessing and labeling are in the publication.</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>
Network Data on Key Brussels Think Tanks
<p>This is anoynmized network on ties between experts and prominent Brussels think tanks dealing with economic policy, including gender, profession, nationality, PhD, and educational training. Data collated by Ramona Coman, Université libre de Bruxelles, for the ENLIGHTEN project (H2020 #649456).</p>
Enron Email Time-Series Network
<p>We use the <a href="https://www.kaggle.com/wcukierski/enron-email-dataset">Enron email dataset</a> to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.</p> <p>To build a graph <em>G = (V</em><em>, E</em><em>)</em>, we use email addresses as nodes <em>V</em>. Every node <em>v<sub>i</sub></em> has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge <em>e</em><em><sub><em>ij</em></sub></em> between two nodes <em>i</em> and <em>j</em> if there is at least one email exchange between the corresponding addresses.</p> <p>Column <em>'Count'</em> in <em>'edges.csv'</em> file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.</p> <p>The file <em>'nodes.csv'</em> contains a dictionary that is a compressed representation of time-series. The format of the dictionary is <em>Day->The Number Of Emails Sent By the Address During That Day.</em> The total number of days is 1448.</p> <p><em>'id-email.csv'</em> is a file containing the actual email addresses.</p>
Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: datasets collected using light microscopy and DNA metabarcoding
<p>This dataset contains all data required to reproduce the analyses conducted in Macgregor <em>et al. </em>(2018), using the R Notebook archived at doi: <a href="https://dx.doi.org/10.5281/zenodo.1322712">10.5281/zenodo.1322712</a>.</p> <p>Specifically, the dataset contains details of pollen transport detected on two matched samples, each containing 311 moths of 41 species, using two methods: a traditional light microscopy approach and a novel DNA metabarcoding approach. Both raw and manually-curated versions of each dataset are archived for full clarity. The dataset additionally contains all metadata required to fully interpret these data, including the RGB tables used to prepare Fig 4 in Macgregor <em>et al. </em>(2018).</p> <p>Macgregor <em>et al. </em>(2018) Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: a comparison using light microscopy and DNA metabarcoding. <em>Ecological Entomology</em>, doi: <a href="https://dx.doi.org/10.1111/een.12674">10.1111/een.12674</a>.</p>
Star Wars social network
<p><strong>Star Wars social network</strong></p> <p>This dataset contains the social network of Star Wars characters extracted from movie scripts. In short, two characters are connected if they speak together within the same scene. The data contain characters and links from episodes I to VII.</p> <p>How the data were created is described in my blog posts:</p> <ul> <li><a href="http://evelinag.com/blog/2015/12-15-star-wars-social-network/index.html">The Star Wars social network</a></li> <li><a href="http://evelinag.com/blog/2016/01-25-social-network-force-awakens/index.html">Star Wars social network: Force Awakens</a></li> </ul> <p>The associated code is available in the main Github repository <a href="https://github.com/evelinag/StarWars-social-network">evelinag/StarWars-social-network</a>.</p> <p>Contents of the files are the following:</p> <ul> <li> <p><code>starwars-episode-N-interactions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters speak within the same scene.</p> </li> <li> <p><code>starwars-episode-N-mentions.json</code> contains the social network extracted from Episode N, where the links between characters are defined by the times the characters are mentioned within the same scene.</p> </li> <li> <p><code>starwars-episode-N-interactions-allCharacters.json</code> is the <code>interactions</code> network with R2-D2 and Chewbacca added in using data from <code>mentions</code> network.</p> </li> <li> <p><code>starwars-full-...</code> contain the corresponding social networks for the whole set of 6 episodes.</p> </li> </ul> <p><strong>Description of networks</strong></p> <p>The json files representing the networks contain the following information:</p> <p><strong>Nodes</strong></p> <p>The nodes contain the following fields:</p> <ul> <li>name: Name of the character</li> <li>value: Number of scenes the character appeared in</li> <li>colour: Colour in the visualization</li> </ul> <p><strong>Links</strong></p> <p>Links represent connections between characters. The link information corresponds to:</p> <ul> <li>source: zero-based index of the character that is one end of the link, the order of nodes is the order in which they are listed in the “nodes” element</li> <li>target: zero-based index of the character that is the the other end of the link.</li> <li>value: Number of scenes where the “source character” and “target character” of the link appeared together. Please not that the network is <em>undirected</em>. Which character represents the source and the target is arbitrary, they correspond only to two ends of the link.</li> </ul>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
District heating network data for the city of Flensburg from 2014-2016
<p>The data package contains flow temperatures and the overall heat load for the district heating network of Flensburg, Germany for the years 2014-2016.</p>
Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins
<p><strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong></p> <p>The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.</p> <p>It is uploaded as tar.gz. and contains one directory.</p> <p>File names contain the PDB<sup>1</sup> identifier and the respective chain identifier. </p> <p>The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: <a href="https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz">https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz</a> This URL can be found with some explanation at <a href="http://www.rcsb.org/pdb/static.do?p=download/http/index.html">http://www.rcsb.org/pdb/static.do?p=download/http/index.html</a></p> <p>The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.</p> <p> </p> <p><strong>The files are tab-separated and contain the following columns:</strong></p> <ul> <li><strong>Pos</strong> Position in the sequence, starting from zero</li> <li><strong>AA</strong> Amino acid in that position</li> <li><strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank</li> <li><strong>dis</strong> if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)</li> <li><strong>GO:_______</strong> Sensitivity for the GO term</li> </ul> <p><strong>References</strong></p> <ol> <li>The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235</li> <li>Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).</li> </ol>
Metadata, Title Pages, and Network Graph of the Digitized Content of the Berlin State Library (146,000 items)
<p>The data set has been downloaded via the OAI-PMH endpoint of the Berlin State Library/Staatsbibliothek zu Berlin’s Digitized Collections (<a href="https://digital.staatsbibliothek-berlin.de/oai">https://digital.staatsbibliothek-berlin.de/oai</a>) on March 1<sup>st</sup> 2019 and converted into common tabular formats on the basis of the provided Dublin Core metadata. It contains 146,000 records.</p> <p>In addition to the bibliographic metadata, representative images of the works have been downloaded, resized to a 512 pixel maximum thumbnail image and saved in JPEG format. The image data is split into title pages and first pages. Title pages have been derived from structural metadata created by scan operators and librarians. If this information was not available, first pages of the media have been downloaded. In case of multi-volume media, title pages are not available.</p> <p>In total, 141,206 images title/first pages are available.</p> <p> </p> <p>Furthermore, the tabular data has been cleaned and extended with geo-spatial coordinates provided by the OpenStreetMap project (<a href="https://www.openstreetmap.org">https://www.openstreetmap.org</a>). The actual data processing steps are summarized in the next section. For the sake of transparency and reproducibility, the original data taken from the OAI-PMH endpoint is still present in the table.</p> <p> </p> <p>To conclude with, various graphs in GML file format are available that can be loaded directly into graph analysis tools such as Gephi (<a href="https://gephi.org/">https://gephi.org/</a>).</p> <p> </p> <p>The implementation of the data processing steps (incl. graph creation) are available as a Jupyter notebook provided at <a href="https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb">https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb</a>.</p> <p> </p> <p>Tabular Metadata</p> <p> </p> <p>The metadata is available in Excel (cleanedData.xlsx) and CSV (cleanedData.csv) file formats with equal content.</p> <p>The table contains the following columns. Italique columns have not been processed.</p> <p>· <em>title</em> The title of the medium</p> <p>· <em>creator</em> Its creator (family name, first name)</p> <p>· <em>subject</em> A collection’s name as provided by the library</p> <p>· <em>type</em> The type of medium</p> <p>· <em>format</em> A MIME type for full metadata download</p> <p>· <em>identifier</em> An additional identifier (most often the PPN)</p> <p>· <em>language</em> A 3-letter language code of the medium</p> <p>· <em>date</em> The date of creation/publication or a time span</p> <p>· <em>relation</em> A relation to a project or collection a medium has been digitized for.</p> <p>· <em>coverage</em> The location of publication or origin (ranging from cities to continents)</p> <p>· <em>publisher</em> The publisher of the medium.</p> <p>· <em>rights</em> Copyright information.</p> <p>· <em>PPN</em> The unique identifier that can be used to find more information about the current medium in all information systems of Berlin State Library/Staatsbibliothek zu Berlin.</p> <p>· spatialClean In case of multiple entries in coverage, only the first place of origin has been extracted. Additionally, characters such as question marks, brackets, or the like have been removed. The entries have been normalized regarding whitespaces and writing variants with the help of regular expressions.</p> <p>· dateClean As the original date may contain various format variants to indicate unclear creation dates (e.g., time spans or question marks), this field contains a mapping to a certain point in time.</p> <p>· spatialCluster The cluster ID determined with the help of the Jaro-Winkler distance on the spatialClean string. This step is needed because the spatialClean fields still contain a huge amount of orthographic variants and latinizations of geographic names.</p> <p>· spatialClusterName A verbal cluster name (controlled manually).</p> <p>· latitude The latitude provided by OpenStreetMap of the spatialClusterName if the location could be found.</p> <p>· longitude The longitude provided by OpenStreetMap of the spatialClusterName if the location could be found.</p> <p>· century A century derived from the date.</p> <p>· textCluster A text cluster ID on the basis of a k-means clustering relying on the title field with a vocabulary size of 125,000 using the tf*idf model and k=5,000.</p> <p>· creatorCluster A text cluster ID based on the creator field with k=20,000.</p> <p>· titleImage The path to the first/title page relative to the img/ subdirectory or None in case of a multi-volume work.</p> <p>Other Data</p> <p> </p> <p><em>graphs.zip</em></p> <p> </p> <p>Various pre-computed graphs.</p> <p><em> </em></p> <p><em>img.zip</em></p> <p> </p> <p>First and title pages in JPEG format.</p> <p> </p> <p><em>json.zip</em></p> <p> </p> <p>JSON files for each record in the following format:</p> <p> </p> <p>ppn "PPN57346250X"</p> <p>dateClean "1625"</p> <p>title "M. Georgii Gutkii, Gymnasii Berlinensis Rectoris Habitus Primorum Principiorum, Seu Intelligentia; Annexae Sunt Appendicis loco Disputationes super eodem habitu tum in Academia Wittebergensi, tum in Gymnasio Berlinensi ventilatae"</p> <p>creator "Gutke, Georg"</p> <p>spatialClusterName "Berlin"</p> <p>spatialClean "Berolini"</p> <p>spatialRaw "Berolini"</p> <p>mediatype "monograph"</p> <p>subject "Historische Drucke"</p> <p>publisher "Kallius"</p> <p>lat "52.5170365"</p> <p>lng "13.3888599"</p> <p>textCluster "45"</p> <p>creatorCluster "5040"</p> <p>titleImage "titlepages/PPN57346250X.jpg"</p>
Title, Author, Publisher, Place of Publication, and Language-related Network Graphs of the Berlin State Library Main Catalog
<p>The dataset contains graphs in GML, GraphML, and a simple JSON format.</p> <p>For each of the following languages:</p> <ol> <li>cze</li> <li>dan</li> <li>dut</li> <li>eng</li> <li>fre</li> <li>fry</li> <li>ger</li> <li>gre</li> <li>ice</li> <li>ita</li> <li>lat</li> <li>nor</li> <li>pol</li> <li>por</li> <li>rum</li> <li>rus</li> <li>slo</li> <li>spa</li> <li>swe</li> </ol> <p>two graphs are made available linking</p> <ul> <li>author, publisher, and place of publication</li> <li>author, publisher, place of publication, and title</li> </ul> <p>Additionaly, a third graph links authors and publishers to the language of publication (incl. year of the publication).</p> <p>The core statistics of each graph are outlined in <em>social_analysis_statistics.csv</em>. The smallest graph (fry, author_publisher_location) has 298 nodes and 264 edges, while the largest (ger, author_publisher_location_title) has 2,499,943 nodes and 3,950,900 edges.</p> <p>The language graphs spans all languages and has 1,706,273 nodes and 1,827,759 edges.</p> <p>All graphs have been created by a Python script available <a href="https://github.com/elektrobohemian/CulturalAnalytics/blob/master/SocialAnalysisStabikat.ipynb">here.</a></p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3
<p>R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.</p> <p>Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible.</p> <p>Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood.</p> <p>What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?</p> <p>INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.</p> <p>The <strong>SIMBED </strong>project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios.</p> <p>By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach. </p> <p>These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.</p> <p> </p> <p>The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on:</p> <ul> <li><strong><em>SubExp#1: </em></strong><em>Static point-to-point Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#1.1:</em></strong><em> Using w-iLab.2 (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#1.2:</em></strong><em> Using w-iLab.2 (low SNR scenarios)</em></li> <li><strong><em>SubExp#1.3:</em></strong><em> Using NITOS</em></li> <li><strong><em>SubExp#1.4:</em></strong><em> Using w-iLab.1 (datacenter room)</em></li> </ul> </li> <li><strong><em>SubExp#2: </em></strong><em>Static point-to-point Wi-Fi communications using fixed</em> rate</li> <li><strong><em>SubExp#3: </em></strong><em>Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)</em></li> <li><strong><em>SubExp#4: </em></strong><em>Static multiple access Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#4.1:</em></strong><em> Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#4.2:</em></strong><em> Using w-iLab.2 (bidirectional) (low SNR scenarios)</em></li> <li><strong><em>SubExp#4.3:</em></strong><em> Using NITOS (bidirectional)</em></li> <li><strong><em>SubExp#4.4:</em></strong><em> Using w-iLab.1 (bidirectional)</em></li> <li><strong><em>SubExp#4.5:</em></strong><em> Using NITOS (2 STAs)</em></li> <li><strong><em>SubExp#4.6:</em></strong><em> Using w-iLab.2 (2 STAs)</em></li> </ul> </li> <li><strong><em>SubExpExample</em></strong>: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results.</li> </ul> <p>Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the <strong>parsed</strong> experimental results, which contains</p> <p>This folder contains the details and parsed logs of the experiment, as follows:</p> <ul> <li><em>date_time</em><strong>.cfg </strong>– configuration details of the experiment</li> <li><em>date_time_NodeID<sup><a href="#_ftn1"><strong>[1]</strong></a></sup>_SenderID<sup><a href="#_ftn2"><strong>[2]</strong></a></sup>_ReceiverID<sup><a href="#_ftn3"><strong>[3]</strong></a></sup>_FlowType<sup><a href="#_ftn4"><strong>[4]</strong></a></sup>_Params<sup><a href="#_ftn5"><strong>[5]</strong></a></sup></em><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </li> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </li> <li><em>NodeID</em><strong>.</strong><strong>waypoints</strong> – coordinates of the static nodes</li> <li><em>date_time_MobileNodeID</em><strong>.</strong><strong>waypoints</strong> – waypoints of the mobile nodes (when applicable)</li> </ul> <p>The experiment’s folder also contains a folder for the simulations <strong>output</strong> with the simulations statistics files, for the multiple simulations approaches considered, as follows:</p> <ul> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em>.<strong>simstats </strong>– logs of the packets received (simulation)</li> </ul> <p> </p> <p><sub><a href="#_ftnref1">[1]</a> ID of the node Logging node</sub></p> <p><sub><a href="#_ftnref2">[2]</a> ID of the Sender node</sub></p> <p><sub><a href="#_ftnref3">[3]</a> ID of the Receiver node</sub></p> <p><sub><a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><sub><a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p>
Connectivity networks for Acropora corals on the GBR to investigate split spawning
<p>Connectivity networks for Acropora corals on the GBR to investigate split spawning.</p> <p>If using these outputs please cite the article:</p> <p>Hock K, Doropoulos C, Gorton R, Condie SA, Mumby PJ. (2019). <strong>Split spawning increases robustness of coral larval supply and inter-reef connectivity</strong>. Nature Communications <strong>10</strong>, 3463.</p> <p>Link to the paper:</p> <p>https://rdcu.be/bOW1x</p> <p> </p> <p> </p> <p> </p>
TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks
<p>The TwitCID collection consists of five Twitter datasets which were extracted from the 1 percent of tweets from Twitter API. </p> <p>The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords “iphone”, “gucci” and “galaxys” respectively. </p> <p>We publish these datasets on behalf of our academic institution – IRIT, France and for the sole purpose of non-commercial research under the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). In accordance with Twitter's Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached.</p> <p>If you would like to use this collection, please cite our paper: </p> <p>Hoang, T. B. N., Mothe, J., & Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 88-100). Springer, Cham.</p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Data files for In situ training of feedforward and recurrent convolutional memristor networks
<p>MATLAB data files for the manuscript "<em>In situ training of feedforward and recurrent convolutional memristor networks" </em>published on Nature Machine Intelligence, 2019.</p> <p>The MATLAB data file "exp_mnist.mat" consists of all experimental data on implementing the convolutional neural network with the 1-transistor 1-memristor array that is used for plotting the Figure 1 and 2 of the manuscript.</p> <p>The MATLAB data file "exp_mnistsequence.mat" consists of all experimental data on implementing the convolutional long short-term memory network on the 1-transistor 1-memristor array that is used for plotting the Figure 3 and 4 of the manuscript.</p> <p>The code that generated these data files are provided by the link within the manuscript. Alternatively, the code can be accessed via <a href="https://github.com/zhongruiwang/memristorCNN">https://github.com/zhongruiwang/memristorCNN</a>.</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.