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299 results for “Active networks”
Test dataset for "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks"
<p>Data corresponding to test dataset used in Aberra AS, Lopez A, Grill WM, Peterchev AV. (2022). "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks". bioRxiv. Dataset includes:</p> <ul> <li><em>simnibs/ -</em> SimNIBS mesh and E-field solution file used in test dataset (posterior-anterior TMS of M1 in <em>ernie</em> example mesh, meshed with mri2mesh pipeline)</li> <li><em>layer_data/ - </em>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li> <li><em>nrn_sim_data/ - </em>Thresholds from NEURON simulations for all 25 model neurons included in the study, each at 4,999-5,000 positions and 12 azimuthal orientations ("ground truth" for CNN) </li> <li><em>cell_data/</em> - Coordinates and morphology information for all model neurons</li> <li><em>weights/</em> - Trained 3D convolutional neural networks for estimating neuron model-specific TMS thresholds given input E-field distributions on a 3D grid (see code/manuscript for dimensions)</li> <li><em>est_data/ </em>- Output of trained CNNs on all E-field data for test dataset <em> </em></li> </ul> <p> </p>
Data from: Thermal springs and active fault network of the central Colca River basin, Western Cordillera, Peru, published in Journal of Volcanology and Geothermal Research
<p>We used hydrogeochemical analysis of 35 water samples from springs and geysers, together with isotopic (δ<sup>18</sup>O and δD) analysis, chemical and mineral studies of precipitates collected in the field around these outflows, and field observations to study the thermal system of the Colca River basin in S Peru. We aimed to determine the geochemistry of thermal waters, identify fluid sources and their origin, estimate reservoir temperature, and discuss the regional tectonic and volcanic framework. Our findings presented in Tyc et al. (2022; https://doi.org/10.1016/j.jvolgeores.2022.107513) corroborate a heterogeneous and complex geothermal system in the central region of the Colca River basin. This system exhibits contrasting hydrogeochemical and physical characteristics, variable isotope compositions, distinct reservoir temperatures, and associated precipitates near thermal springs. The control of water chemistry in this area is closely linked to the activity of the Ampato-Sabancaya magmatic chamber and the presence of tectonic structures, which enable intricate interactions between meteoric waters, magmatic fluids, and gases.</p> <p>Here, we present datasets used in the article (Tyc et al., 2022; https://doi.org/10.1016/j.jvolgeores.2022.107513), including:</p> <p>- Physicochemical characteristics of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 1)</p> <p>- Chemical and isotopic composition of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 2) and those monitored by INGEMMET in years 2013-2018 (Table 3)</p> <p>- Chosen molecular ratios discussed in Tyc et al., 2022 (Table 4)</p> <p>- Calculated reservoir temperature with the use of different Na/K geothermometers (Table 5)</p> <p>- Mineral phases in efflorescences precipitating at the water sampling sites (Table 6).</p> <p>Thirty-five sets of water samples were collected in the field in September 2012 and August–September 2017 using polyethylene bottles of high density (Table 1). Consequently, these were analyzed in the Water Analysis Laboratory at the University of Silesia in Katowice (Poland; Table 2). Water temperatures, pH, and electrical conductivity were measured in the field using portable pH meter CP-315 and conductivity meter CC-315, both with temperature sensors, with an accuracy of ±0.1 °C, ±0.01 pH, and ± 0.1% (up to 19.999 mS/cm) or ± 0.25% (above 20.00 mS/cm), respectively. Discharge of springs was estimated if possible (Table 1). Both cations and anions were analyzed by ion chromatography using Methron 850 Professional Ion Chromatograph with separate Metrosept C4–150 and A-supp 7–250 columns for cations and anions, respectively (Tables 2 and 4). Analysis of water analyses collected by INGEMMET in years 2013-2018 was performed at the INGEMMET Chemical Laboratory in Lima with the use of ion chromatography (Dionex ICS 5000) for the determination of anions and inductively coupled plasma optical emission spectrometry (ICP-OES) – VARIAN for cations (Table 3). Isotopic analyses (δ<sup>2</sup>H, δ<sup>18</sup>O) of 17 water samples collected in 2017 were performed at the Stable Isotope Laboratory Institute of Geological Sciences Polish Academy of Sciences (Table 2). The δ<sup>2</sup>H values of studied H<sub>2</sub>O were measured using the H-Device peripheral coupled to MAT 253 IRMS (Thermo Scientific) in a dual inlet system. For the determination of δ<sup>18</sup>O in H<sub>2</sub>O, an equilibration technique was used. The analysis used the GasBench II peripheral device (Thermo Scientific) coupled to MAT 253 IRMS with a continuous He flow. The AquaChem 4.0.284 software was used to evaluate the water samples' geochemical properties and calculate reservoir temperature for thermal waters (Table 5). Precipitates found at the water sampling sites were collected separately into plastic bags with strings and sealed boxes. These samples were subsequently analyzed at the Institute of Earth Sciences, University of Silesia in Katowice. The qualitative chemical composition and mineral characteristics were examined using a Philips XL 30 ESEM/TMP scanning electron microscope coupled with an energy-dispersive spectrometer (EDS; EDAX type Sapphire). The phase composition of the precipitates was determined through X-ray diffraction (XRD) using a Philips PW 3710 diffractometer. The XRD data were analyzed and interpreted using the X'Pert HIGHScore Plus software (Table 6).</p>
Data repository of the paper "Quantum-noise-limited optical neural networks operating at a few quanta per activation"
<p>This data repository includes the requisite data and code for deriving the primary results from the paper, "Quantum-noise-limited optical neural networks operating at a few quanta per activation". The repository is structured to provide everything needed to reproduce the figures included in the main manuscript, along with the source code for training the neural network models and the collected experimental data mentioned in the paper.</p> <p>The code in this repository is primarily intended for reproducing the results discussed in the paper. Those interested in developing their own applications may refer to our Github repository: https://github.com/mcmahon-lab/Single-Photon-Detection-Neural-Networks.</p> <p><strong>Where to Start</strong></p> <p>The directory 'main_figures' includes Jupyter notebooks to generate each panel in Figure 3 and Figure 4 in the main text, using the data from the directory 'results', which can be generated by notebooks in the directory 'test'. </p> <p>The simulations, experiments, and figure generation were all conducted in Python. As certain parts of the code require specific versions of Python packages, the necessary packages are listed in the 'requirements.txt' file.</p> <p>For more information, please refer to 'README.txt'.</p>
Exploring the anti-glioma mechanism of the active components of Cortex Periplocae based on network pharmacology and iTRAQ proteomics in vitro
<p>Hierarchical clustering analysis of candidate proteins was illustrated in heat map, which showed obvious differences between CP-induced cells and controls (Fig. 6). Each column is a sample and each rowindicates a differentially expressed protein. Log values (log<sub>2</sub>expression) of significantly differentially expressed proteins in different samples are displayed in heat maps in different colors. GreenandRedrepresent up-regulation and down-regulation, respectively. The gray part represents no quantitative information of the proteins. T1, T2 and T3 means the CP-treated U251 cells group and C2, C2 and C3 means control group (n=3).</p>
Evaluation of the Scleroderma Patient-centered Intervention Network COVID-19 Home-isolation Activities Together Program
ClinicalTrials.gov study NCT04335279. IPD Sharing: YES. Countries: 1. Publications: 2.
The Effect of Pramipexole on Metabolic Network Activity Compared With Levodopa in Early Parkinson's Disease
ClinicalTrials.gov study NCT01470859. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Data from: Ethanol abolishes vigilance-dependent astroglia network activation in mice by inhibiting norepinephrine release
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Supplementary data from: Bimodal retrograde signaling disrupts a suppressor network and activates a key transcriptional activator to direct stress responses
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Data from: Presynaptic GABAB receptors functionally uncouple somatostatin interneurons from the active hippocampal network
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Social network shrinking is explained by active and passive effects but not increasing selectivity with age in wild macaques
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Adult-born granule cells modulate CA2 network activity during retrieval of developmental memories of the mother
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Invasion of freshwater ecosystems is promoted by network connectivity to hotspots of human activity
<p><b><span>Aim:</span></b> Hotspots of human activity are focal points for ecosystem disturbance and non-native introduction, from which invading populations disperse and spread. As such, connectivity to locations used by humans may influence the likelihood of invasion. Moreover, connectivity in freshwater ecosystems may follow the hydrological network. Here we tested whether multiple forms of connectivity to human recreational activities promotes biological invasion of freshwater ecosystems.</p> <p><b><span>Location:</span></b><span> England, UK.</span></p> <p><b><span>Time period:</span></b><span> 1990-2018.</span></p> <p><b><span>Major taxa studied:</span></b> 126 non-native freshwater birds, crustaceans, fish, molluscs and plants.</p> <p><b><span>Methods:</span></b> Machine learning was used to predict spatial gradients in human recreation and two high risk activities for invasion (fishing and water sports). Connectivity indices were developed for each activity, in which human influence decayed from activity hotspots according to Euclidean distance (spatial connectivity) or hydrological network distance (downstream, upstream and along-channel connectivity). Generalised linear mixed models identified the connectivity type most associated to invasive species richness of each group, while controlling for other anthropogenic and environmental drivers.</p> <p><b><span>Results:</span></b> Connectivity to humans generally had stronger positive effects on invasion than all other drivers except recording effort. Recreation had stronger influence than urban land cover, and for most groups high risk activities had stronger effects than general recreation. Downstream human connectivity was most important for invasion by most of the groups, potentially reflecting predominantly hydrological dispersal. An exception was birds, for which spatial connectivity was most important, possibly because of overland dispersal capacity.</p> <p><b><span>Main conclusions:</span></b> These findings support the hypothesis that freshwater invasion is partly determined by an interaction between human activity and species dispersal in the hydrological network. By comparing alternative connectivity types for different human activities, our approach could enable robust inference of specific pathways and spread mechanisms associated with particular taxa. This would provide evidence to support better prioritisation of surveillance and management for invasive non-native species.</p>
Application specific network traffic with specified activities
<p>This dataset contains network traffic of 12 different containerized, isolated applications. It includes headers of network traffic data in the initial two minutes of execution of the application.<br> We have specified each network traffic with an activity that the application was involved in during traffic capturing.<br> </p> <p>------------------------------------<br> Application name, No. of samples, Engaged activity while capturing<br> ------------------------------------<br> Curl, 60, downloading<br> Firefox, 47, browsing<br> mpg123, 99, audio streaming<br> mplayer, 82, audio streaming<br> mpv, 48, video streaming<br> Slack, 58, no chatting (just opening the app and menus)<br> streamlink, 57, video streaming<br> Trello, 46, just opening the app (just opening the app and menus)<br> Vlc, 102, audio streaming<br> w3m, 60, downloading<br> wget, 20, file downloading<br> wgetaudio, 20, file downloading(audio)<br> wgetvideo, 20, file downloading(video)<br> ydm (mplayer + youtube-dl),61, video streaming</p>
Data from: The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida
Network analysis has proved to be a valuable tool for studying the behavioural patterns of complex social animals. Often such studies either do not distinguish between different behavioural states of the organisms or simply focus attention on a single behavioural state to the exclusion of all others. In either of these approaches it is impossible to ascertain how the behavioural patterns of individuals depend on the type of activity they are engaged in. Here we report on a network-based analysis of the behavioural associations in a population of bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida. We consider three distinct behavioural states—socializing, travelling and foraging—and analyse the association networks corresponding to each activity. Moreover, in constructing the different activity networks we do not simply record a spatial association between two individuals as being either present or absent, but rather quantify the degree of any association, thus allowing us to construct weighted networks describing each activity. The results of these weighted activity networks indicate that networks can reveal detailed patterns of bottlenose dolphins at the population level; dolphins socialize in large groups with preferential associations; travel in small groups with preferential associates; and spread out to forage in very small, weakly connected groups. There is some overlap in the socialize and travel networks but little overlap between the forage and other networks. This indicates that the social bonds maintained in other activities are less important as they forage on dispersed, solitary prey. The overall network, not sorted by activity, does not accurately represent any of these patterns.
Data from: Predicting forest insect flight activity: a Bayesian network approach
Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.
Source data for "In-degree centrality in a social network is linked to coordinated neural activity"
<p>The following includes the source data for the manuscript titled "In-degree centrality in a social network is linked to coordinated neural activity". A second version of the source data ("Source Data_updated_011222.xlsx") includes the source data for the figures in the supplementary materials.</p>
CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks - Dataset
<p>This contains the raw and processed data for the paper "CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks" by H. Wolf, M. Szell and A. R. Vierø.</p>
Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements
<p>This data set represents the output of our paper "Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements". It comprises png images of 400x800 pixels. Half of the image represents a section of CORONA imagery where a site (files named Site_.png) or no site (files named NonSite_.png) is present. The other half of the image contains the activation map produced by various explainability techniques.</p>
Networks of Swiss water governance issues. Studying fit between media attention and organizational activity
<p>Anonymized data and R code needed to replicate the analysis presented in the study "Networks of Swiss water governance issues. Studying fit between media attention and organizational activity" to be published in Society & Natural Resources.</p> <p>The study looks at how relations between Swiss water governance issues are portrayed in the media as compared to the way organizations involved in water governance reflect these relations in their activity.</p> <p>This is a paper output of the SNF funded project "Overlapping subsystems". Access to the complete, non-anonymized dataset is restricted.</p> <p>Study doi: tbd</p>
Latent space active learning with message passing neural network: the case of HfO_{2}
<div>A dataset containing the structures obatined from the active learning process and their calculated energies and forces. See more details in our papr "Latent space active learning with message passing neural network: the case of HfO_{2}".</div>
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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.