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9,152 results for “Networking”
GNSS troposphere products from a network of low-cost GNSS receivers, Wroclaw, Poland, March-April 2021
<p>This dataset contains multi-GNSS troposphere products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties. These products were obtained using 3 processing strategies:</p> <p>1) real-time (for details see https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered);</p> <p>2) near real-time (NRT, for details see http://egvap.dmi.dk/);</p> <p>3) final (using CSRS online service, https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php).</p> <p>Products are stored as standard Matlab MAT files. Each file contains a set of table arrays (Matlab format). Each table array contains the selected set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>For convenience, the same information is stored in alternative data formats:</p> <p>1) for NRT and Final products: troposphere SINEX v1 (TRO / TRP)</p> <p>2) for real-time products: semicolon-delimited text files, with a self-explanatory header line; each file contains daily products for one station.</p>
Dynamic reconfiguration of macaque brain networks during natural vision
<p>Raw data acquired under awake imaging conditions in the macaque monkey during free-viewing on natural scenes. The movie presented is also shared which is based on 30 sec 0N and OFF periods. Time-series echo planar imaging (EPI) data for each subject (AL, DP, FL and VL). Data is named based on the subject.session.run. The format structure is on NIFTI. Anatomical files are also named according to the same nomenclature. EPI mask are also available for each in-session subject. </p>
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
Training data for neural network-based determination of nematic elastic constants
<p>Neural network training data packets (<strong><em>intensities_{i}.csv, K1K3_{i}.csv</em></strong>), each consisting of 1000 training data pairs, used in a machine learning-based method for determination of Frank elastic constants of nematic liquid crystals, experimental measurements of time-dependent light intensities (<strong><em>experimental_time</em></strong>_<strong><em>{i}.csv, experimental_intensity_{i}.csv</em></strong>), diode spectrum data (<strong><em>diode_lbd</em></strong><strong><em>.csv, diode_w.csv</em></strong>).</p> <p>These data sets are associated with the paper <a href="https://www.nature.com/articles/s41598-023-33134-x"><strong><em>[Zaplotnik et al. SciRep, 2023]</em></strong></a></p> <p>This is supplementary material for a Jupyter Notebook uploaded on <a href="https://zenodo.org/record/7368828">Zenodo</a>.</p>
[Dataset] Analysis of ego-networks of two CS-related Twitter accounts
<p><strong>Explanation/Overview:</strong></p> <p>This is the dataset for the analyses and results on Twitter Ego-Networks of two CS-related accounts (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>). The username have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R or any other network visualisation software (e.g., Gephi). The edges represent the follow relation and were retrieved using the Twitter API. All usernames except those of the two ego-accounts were anonymised by assigning a random number to each node. Due to the size of the network, we do not include any <code>.gexf </code>or <code>.gml </code>files in this upload, but rather resort to node and edge lists in the <code>.json</code> format</p> <p><strong>Relatedness:</strong></p> <p>The networks are the ego-networks for two related public accounts that are associated with citizen science (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>).</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, two files can be found.</p> <ul> <li><code>edges.json</code></li> </ul> <p>Represents the edge list, with the columns:</p> <table> <tbody> <tr> <td><code>source</code></td> <td><code>target</code></td> <td><code>wherefrom</code></td> <td><code>account</code></td> </tr> <tr> <td>EuCitSci</td> <td>23929</td> <td>friendslist</td> <td>EuCitSci</td> </tr> </tbody> </table> <p><code>Source</code> and <code>target</code> are the necessary columns for the network creation and in this example indicate that EuCitSci follows 23929. <code>wherefrom </code>indicates the origin of this relation in the crawling (i.e., whether it was retrieved using the friends or follower list) and <code>account </code>indicates the ego-account it belongs to. Thus, the edges can also be separated using this <code>account </code>attribute as they represent two distinct networks.</p> <ul> <li><code>nodes.json</code></li> </ul> <p>Represents the nodes in the networks. The following data fields are contained:</p> <table> <tbody> <tr> <td><code>username</code></td> <td><code>CS</code></td> <td><code>followers_count</code></td> <td><code>friends_count</code></td> <td>...</td> </tr> <tr> <td>1116</td> <td>CS</td> <td>689</td> <td>514</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>favourites_count</code></td> <td><code>listed_count</code></td> <td><code>statuses_count</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>2601</td> <td>18</td> <td>2141</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>degree</code></td> <td><code>in_degree</code></td> <td><code>out_degree</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>21</td> <td>5</td> <td>16</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>reciprocity</code></td> <td><code>account</code></td> </tr> <tr> <td>...</td> <td>0.48</td> <td>EuCitSci</td> </tr> </tbody> </table> <p> </p> <p><code>Username </code>represents the numerical and anonymised username, <code>CS</code> the community-membership. The different counts (e.g., <code>followers_count</code>) indicate the number of followers the user had at the time of the retrieval by the Twitter API. <code>degree</code> refers to the degree in the network (similarly the <code>in-</code> and <code>out_degree</code>), while <code>reciprocity</code> refers to the number of mutual edges in respect to the total number of edges per node (see <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html">here</a>). <code>Account</code> is similar as described above.</p> <p> </p> <p><strong>Grouping:</strong></p> <p>The data is grouped according the ego-account it is associated to, as can be read above (i.e., the <code>account</code> attribute).</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
The POPREBEL semantic social network data
<p>The <a href="https://populism-europe.com/poprebel/">POPREBEL project</a> explores the phenomenon of populism in Europe. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. It consists of coded interviews, realized between spring 2021 and spring 2022, to Internet users in Czechia, Germany and Poland, who used social media to gather information about the COVID-19 pandemic. The dataset is pseudonymized. POPREBEL is supported by the European Union's Horizon 2020 programme, grant n. 822682.</p> <ul> <li><a href="https://zenodo.org/record/7494327">Final ethnographic report.</a> Section 1.2 contains a detailed description of how and why data were collected.</li> <li><a href="https://wellbeing.edgeryders.eu">Funnel website</a> of the project.</li> <li><a href="https://hal.archives-ouvertes.fr/hal-02478720/document">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
ICELEARNING - Detection of ice core particles via deep neural networks
<p>This dataset refers to the ICELEARNING project - Detection of ice core particles via deep neural networks, by Maffezzoli N. et al., <em>The Cryosphere</em>, 10.5194/tc-17-539-2023, 2023.</p> <p>The main folder contains all TRAINING data. </p> <p>The TEST data are contained in the folder /test. </p> <p>Please refer to the <a href="https://github.com/nmaffe/icelearning">icelearning GitHub</a> repository for instructions. </p>
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
EDoS_Network_Slicing_K8S
<p>The dataset includes raw resource usage and service performance metrics collected from CNFs of vCDN slices deployed on a cloud-native platform (kubernetes). The The dataset serves to train ML models for mitigating EDoS attacks by detecting anomalous resource scaling operations caused by application-layer DDoS attacks. The raw resource usage and performance metrics data have been recorded from two vCDN slices over a period of 5 days. A time series for each resource usage and performance metric was recorded using a data sampling period of 300s. The training data were collected during the first 4 days of attack-free activity. The 5th day served to create the testing dataset which includes data for normal activity as well as anomalous activity caused by application-layer DDoS attacks (specifically Hulk and Slowloris attacks) executed on different periods of the day. A training dataset and a testing dataset, containing the raw multivariate time series data, are generated for each vCDN slice's CNF with a total of 5401 and 2701 samples, respectively. For each slice, we provide the anomaly labels, which correspond to the attack periods.</p>
Trajectory-Aware Rate Adaptation for Aerial Networks Simulation Results
<p><strong>Introduction</strong></p> <p>Even though the concept of ubiquitous wireless connectivity is becoming a reality, there are scenarios where wireless communications coverage is insufficient or does not exist. Considering natural and man-made disaster scenarios, communications infrastructures may be damaged and become unavailable. In temporary crowded events, the existing infrastructure may not have been designed to cope with the additional traffic demand, resulting in overload. In maritime scenarios, environmental monitoring activities using autonomous vehicles will take place in offshore zones, typically not in range of existing onshore communications infrastructures.</p> <p>Flying networks, composed of Unmanned Aerial Vehicles (UAV), are emerging as a flexible and cost-effective solution to provide on-demand wireless connectivity in such scenarios. UAVs have the possibility to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware, playing the role of mobile base stations, access points or relay nodes. A flying network may typically be composed of a fleet of UAVs, organized in a multi-tier topology with so-called Flying Edge Nodes (FENs) and Flying Gateways (FGWs) <a href="https://doi.org/10.1016/j.adhoc.2022.103000">[1]</a>. FENs can play the role of Flying Access Points that provide the access network to the users on the ground, or the role of Flying Sensor Nodes that can perform video surveillance missions. The FENs forward the traffic to the FGWs, that act as relay nodes and are responsible for forwarding the traffic to/from the backhaul (BKH) network and ultimately to/from the Internet.</p> <p>The flying network concept brings up new challenges. The flying nodes need to be properly positioned and their wireless link configuration dynamically adjusted in order to ensure the Quality of Service (QoS) expected by the end users. In addition, these scenarios are typically highly unpredictable due to the varying locations as well as the concentration/dispersion of end-users and their movements regarding direction and velocity - e.g., vehicles or pedestrians. Therefore, a static wireless link configuration and UAV positioning are not adequate. State of the art work has been mainly focused on the optimal positioning of the flying nodes, having most of the wireless link parameters statically configured with default values. The Rate Adaptation challenge is well-known in fixed or low mobility IEEE 802.11 networks, and Minstrel High Throughput (HT) <a href="https://lwn.net/Articles/376765">[2]</a> is the default Wi-Fi rate adaptation algorithm used in the Linux kernel since the IEEE 802.11n version. However, few works propose solutions designed to consider the characteristics of other communications environments, such as flying and vehicular networks <a href="https://doi.org/10.1007/s11276-020-02295-2">[3]</a>. To the best of our knowledge, solutions that use the node trajectory information to predict the wireless channel conditions and perform rate adaptation are yet to be developed.</p> <p>The main contribution of this paper is the Trajectory-Aware Rate Adaptation (TARA) algorithm. TARA takes advantage of knowing the trajectory of all nodes in the flying network to estimate future changes in the wireless link quality and perform rate adaptation accordingly. The network performance improvement achieved with TARA was evaluated using ns-3 <a href="https://doi.org/10.1007/978-3-642-12331-3_2">[4]</a>. The simulation results presented in this dataset show significant throughput gains when compared with conventional rate adaptation algorithms.</p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the TARA Paper, organized in different folders for each Rate Adaptation Algorithm, as well as the random seeds that were used to obtain such results:</p> <p><strong>Naming Convention:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> </strong> <ul> <li><strong>tara </strong>– Trajectory-Aware Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> </ul> <p><strong>Folder Content: </strong></p> <ul> <li><em>distances.csv - </em><strong>Distances between nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Distance between BKH and FGW</strong> (meters)</li> <li>Column 3 – <strong>Distance between FEN and FGW </strong>(meters)</li> </ul> </li> <li><em>positions.csv</em> <em>- </em><strong>Current 3D position of nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>BKH x </strong>(meters)</li> <li>Column 3 – <strong>BKH y </strong>(meters)</li> <li>Column 4 – <strong>BKH z </strong>(meters)</li> <li>Column 5 – <strong>FEN x </strong>(meters)</li> <li>Column 6 – <strong>FEN y </strong>(meters)</li> <li>Column 7 – <strong>FEN z </strong>(meters)</li> <li>Column 8 – <strong>FGW x </strong>(meters)</li> <li>Column 9 – <strong>FGW y </strong>(meters)</li> <li>Column 10 – <strong>FGW z </strong>(meters)</li> </ul> </li> <li><em>throughput.csv</em> - <strong>Link Specific Throughput, at MAC layer level</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Relay Link (BKH - FGW), Throughput measured in BKH </strong>(Mbit/second)</li> <li>Column 3 – <strong>Access Link (FEN - FGW), Throughput measured in FEN </strong>(Mbit/second)</li> <li>Column 4 – <strong>Relay Link (BKH - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> <li>Column 5 – <strong>Access Link (FEN - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> </ul> </li> </ul>
Data from: Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter” and includes recordings of improvisations by jazz duos over a network. The related paper is published in <em>Music Perception</em> and is accessible at <a href="https://doi.org/10.1525/mp.2024.42.1.48">doi:10.1525/mp.2024.42.1.48</a> </p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in Cambridge, United Kingdom between April-July 2022 as part of a doctoral research project. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in Cheston, Cross, and Harrison (2023), "Trade-offs in Coordination Strategies for Networked Jazz Performances". Please refer to this publication for full details on the data collection procedure. Our codebook is <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">hosted on GitHub</a> and, in conjunction with this dataset, can be used to reproduce the analysis contained in the article.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">this section of the code documentation website</a> for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html#reproduce-combined-audio-visual-stimuli">see the linked section of the code documentation website.</a></li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p>To reproduce the analysis from the paper, all files should be unzipped into the \data\raw directory of the code repository created after <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">cloning this from GitHub</a>. For more detail and instructions on installation, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">see the section of the code documentation website linked here</a>.</p> <p><strong>Usage:</strong></p> <p>These recordings of live, improvised duo performances are unattributed and anonymised as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by Cambridge Digital Humanities (<a href="https://www.cdh.cam.ac.uk/research/projects/newmusicsoftwareplatform/">project page</a>). All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please cite the paper it relates to:</p> <pre><code>@article{10.1525/mp.2024.42.1.48, author = {Cheston, Huw and Cross, Ian and Harrison, Peter M. C.}, title = "{Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter}", journal = {Music Perception}, volume = {42}, number = {1}, pages = {48-72}, year = {2024}, month = {09}, issn = {0730-7829}, doi = {10.1525/mp.2024.42.1.48}, url = {https://doi.org/10.1525/mp.2024.42.1.48}, eprint = {https://online.ucpress.edu/mp/article-pdf/42/1/48/833292/mp.2024.42.1.48.pdf}, }</code></pre> <p><strong>Contact:</strong></p> <p>Huw Cheston - <a href="http://twitter.com/huwcheston/">@huwcheston</a> - hwc31@cam.ac.uk</p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings
<p><strong>Abstract</strong></p> <p>The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of <a href="https://arxiv.org/abs/2302.07640">Bonafos et al. (2023)</a>. The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models.</p> <p> </p> <p><strong>Data acquisition</strong></p> <p>The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization.</p> <p> </p> <p><strong>Data source location</strong></p> <ul> <li>Institution: CNRS, Primate Facility</li> <li> <p>City/Town/Region: Rousset-sur-Arc</p> </li> <li> <p>Country: France</p> </li> <li> <p>Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905</p> </li> </ul> <p> </p> <p><strong>Value of the data</strong></p> <ul> <li> <p>This dataset is relatively unique in terms of the quantity of vocalizations available.</p> </li> <li> <p>This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing.</p> </li> <li> <p>The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example.</p> </li> </ul> <p> </p> <p><strong>Objective</strong></p> <p>This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169321">Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, & Fagot, 2017</a>; <a href="https://hal.science/hal-01649539">Kemp, Rey, Legou, Boë, Berthommier, Becker, & Fagot, 2017</a>). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in <a href="https://arxiv.org/abs/2302.07640">Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, & Rey (2023)</a>.</p> <p> </p> <p><strong>Data description</strong></p> <p>The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on <a href="https://gitlab.com/papers4375727/detection-and-classification-of-vocal-productions">gitlab</a>.</p> <p>In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model.</p> <p> </p> <p><strong>Experimental design, materials and methods</strong></p> <p>The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization.</p> <p>For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.</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.