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Dataset results
299 results for “Active networks”
SINS database - Node 8 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 11 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
Data from: Dynamic changes in chloride homeostasis coordinate midbrain inhibitory network activity during reward learning
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Data from: Multiplexed subspaces route neural activity across brain-wide networks
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Dataset The Lactate Receptor HCAR1 Modulates Neuronal Network Activity through the Activation of G(α) and G(βγ) Subunits
<p>This dataset is related to the study: </p> <p>"The Lactate Receptor HCAR1 Modulates Neuronal Network Activity through the Activation of G(α) and G(βγ) Subunits" by de Castro Abrantes H, Briquet M, Schmuziger C, Restivo L, Puyal J, Rosenberg N, Rocher AB, Offermanns S, Chatton JY. J Neurosci. 2019 Jun 5;39(23):4422-4433. doi: 10.1523/JNEUROSCI.2092-18.2019. Epub 2019 Mar 29. PubMed PMID: 30926749; PubMed Central PMCID: PMC6554634.</p>
Supplementary videos for "Active flow network generates molecular transport by packets: case of the endoplasmic reticulum"
<p>Videos showing simulated motion on the active flow network for different switching timescales. In particular, compare <span class="math-tex">\(\tau_{\text{switch}} = 3 \text{ s}\)</span> to <span class="math-tex">\(\tau_{\text{switch}} = 30 \text{ ms}\)</span>. The red bubbles are proportional to the number of particles present in a node. Initially, all particles are placed in a central source node.</p>
Data from: Ethanol abolishes vigilance-dependent astroglia network activation in mice by inhibiting norepinephrine release
<p>This collection of data sets was obtained during a study of the effect of acute ethanol intoxication on vigilance-dependent astroglia network activation in mice. The main findings have been that ethanol inhibits astroglia activation by inhibiting vigilance-dependent norepinephrine release. This leads to a failure of activation of alpha<sub>1A</sub>-adrenergic receptors on astroglia. Further, this study has revealed that ethanol inhibition of cerebellar Bergmann glia Ca<sup>2+</sup> activation does not account for ataxic motor behavior, but may rather contribute to cognitive deficits.</p>
The FORTH-TRACE dataset for human activity recognition of simple activities and postural transitions using a Body Area Network
<p>The dataset is collected from 15 participants wearing 5 Shimmer wearable sensor nodes on the locations listed in Table 1. The participants performed a series of 16 activities (7 basic and 9 postural transitions), listed in Table 2.</p> <p>The captured signals are the following:</p> <ul> <li>3-axis accelerometer</li> <li>3-axis gyroscope</li> <li>3-axis magnetometer</li> </ul> <p>The sampling rate of the devices is set to 51.2 Hz.</p> <p>DATASET FILES</p> <p>The dataset contains the following files:</p> <ul> <li>partX/partXdev1.csv</li> <li>partX/partXdev2.csv</li> <li>partX/partXdev3.csv</li> <li>partX/partXdev4.csv</li> <li>partX/partXdev5.csv</li> </ul> <p>Where X corresponds to the participant ID, and numbers 1-5 to the device IDs indicated in Table 1.</p> <p>Each .csv file has the following format:</p> <ul> <li>Column1: Device ID</li> <li>Column2: accelerometer x</li> <li>Column3: accelerometer y</li> <li>Column4: accelerometer z</li> <li>Column5: gyroscope x</li> <li>Column6: gyroscope y</li> <li>Column7: gyroscope z</li> <li>Column8: magnetometer x</li> <li>Column9: magnetometer y</li> <li>Column10: magnetometer z</li> <li>Column11: Timestamp</li> <li>Column12: Activity Label</li> </ul> <p>Table 1: LOCATIONS</p> <ol> <li>Left Wrist</li> <li>Right Wrist</li> <li>Torso</li> <li>Right Thigh</li> <li>Left Ankle</li> </ol> <p>Table 2: ACTIVITY LABELS</p> <p>(Arrows (->) indicate transitions between activities)</p> <ol> <li>stand</li> <li>sit</li> <li>sit and talk</li> <li>walk</li> <li>walk and talk</li> <li>climb stairs (up/down)</li> <li>climb stairs (up/down) and talk</li> <li>stand -> sit</li> <li>sit -> stand</li> <li>stand -> sit and talk</li> <li>sit and talk -> stand</li> <li>stand -> walk</li> <li>walk -> stand</li> <li>stand -> climb stairs (up/down), stand -> climb stairs (up/down) and talk</li> <li>climb stairs (up/down) -> walk</li> <li>climb stairs (up/down) and talk -> walk and talk</li> </ol>
Social network shrinking is explained by active and passive effects but not increasing selectivity with age in wild macaques
<p>Evidence of social disengagement, network narrowing, and social selectivity with advancing age in several non-human animals challenges our understanding of the causes of social ageing. Natural animal populations are needed to test whether social ageing and selectivity occur under natural predation and extrinsic mortality pressures, and longitudinal studies are particularly valuable to disentangle the contribution of within-individual ageing from the demographic processes that shape social ageing at the population level. Data on wild Assamese macaques (<em>Macaca assamensis</em>) were collected between 2013 and 2020 at the Phu Khieo Wildlife Sanctuary, Thailand. We investigated the social behaviour of 61 adult females observed for 13,270 hours to test several mechanistic hypotheses of social ageing and evaluated the consistency between patterns from mixed-longitudinal and within-individual analyses. With advancing age, females reduced the size of their social network, which could not be explained by an overall increase in the time spent alone, but by an age-related decline in mostly active, but also passive, behaviour, best demonstrated by within-individual analyses. A selective tendency to approach preferred partners was maintained into old age but did not increase. Our results contribute to our understanding of the driver of social ageing in natural animal populations and suggest that social disengagement and selectivity follow independent trajectories during ageing.</p>
Dataset: Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1
<p>This dataset contains initial molecular configurations and an example script used with the pDynamo3 library to obtain the results published in the paper "Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1" by Guilherme M. Arantes (USP, Brazil).</p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</p>
Long short-term memory (LSTM) recurrent neural network for muscle activity detection
<p><strong>Background: </strong>The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning<br> from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological<br> patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors<br> is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to<br> detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle<br> activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.<br> </p> <p><strong>Methods: </strong>First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied<br> through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches,<br> i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in<br> clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise<br> Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR<br> values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological<br> and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic<br> patients, and 6 neurological patients) were included in the analysis.</p> <p><strong>Results</strong>: The proposed algorithm overcomes the main limitations of the other tested approaches and it works<br> directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate<br> that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard<br> similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated<br> and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for<br> signals featuring a low to medium SNR.</p> <p><strong>Conclusions</strong>: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The<br> validation carried out both on simulated and real signals, considering normal as well as pathological motor function<br> during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/<br> distinction of muscle activity from background noise in sEMG signals.</p> <p> </p>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Activity at the DAEMON booth in the European Conference on Networks and Communications
<p>@h2020daemon booth and 3 Demos at European Conference on Networks and Communications (@EuCNC) 2022 @Telefonica_En @tudelft @InformaticaUMA @IMDEA_SOFTWARE @UC3M @nec_sws @i2CAT @ADLINK_Tech @IMEC @BellLabs @SrsSystems @wings_ict @zettascaletech <a href="https://www.youtube.com/hashtag/h2020daemon">#h2020daemon</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a> @EU_H2020 @5GPPP</p>
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks
<p>Data related to publication:</p> <p>Heider et al.: Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks.</p> <p>The files include:</p> <p>1) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2018) of the compounds <strong>1</strong>, <strong>3a</strong>, <strong>3b</strong>, <strong>3c</strong>, <strong>3d</strong>, <strong>3e</strong>, <strong>3f</strong>, <strong>3g</strong>, <strong>3h</strong>, <strong>3j</strong>, <strong>3k</strong>, <strong>3l</strong>, <strong>3m</strong>, <strong>6f</strong> and <strong>6g</strong> [.mae and .sdf files].</p> <p>2) Movies of the MD simulations of compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> (6GN1) and movies (*_2) of <strong>3a</strong>, <strong>3j</strong>, <strong>6b</strong>, <strong>6g</strong> (4PTC) [.mpg files] .</p> <p>3) Raw Desmond trajectory files of the MD simulations of the compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> [out.cms and the full trj files].</p> <p> </p> <p><br> </p>
Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network
<p>Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network".</p> <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>
ScienceDex guides
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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.