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1,721 results for “network data”
Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot
<p><span>To evaluate the synchronization of bivariate time series has been a hot topic and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network (OPTN) into the crossplot,</span> <span>a new method for measuring the synchronisation of bivariate time series is proposed.</span> <span>After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy (CPTE) of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model</span> <span>and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitable for short time series. Finally, EEG data from auditory evoked potential EEG-Biometric dataset are investigated, and some useful and interesting results are obtained.</span></p>
Data for: Prediction in cultured cortical neural networks
<p>Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing, and is believed to be involved in motor and cognitive control and decision making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short and long-term memory. To answer these questions we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). <br> <br>Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction. </p>
Data Set "Protein network centralities as descriptor for QM region construction in QM/MM simulations of enzymes"
<p>This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains:</p> <p>- PDB files of the starting structures</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the substrates and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p> <p>- Python script for WISP and centrality analysis</p>
Data and code for "Competitive hierarchies in bryozoan assemblages mitigate network instability by keeping short and long feedback loops weak"
<p>This repository contains all scripts and data files to reproduce the analysis of the manuscript "Competitive hierarchies in bryozoan assemblages mitigate network instability by keeping short and long feedback loops weak"</p> <p><strong>Abstract</strong></p> <p>Competitive hierarchies in diverse ecological communities have long been thought to lead to instability and prevent coexistence. However, system stability has never been tested and the relation between hierarchy and instability has never been explained in complex competition networks parameterised with data from direct observation. Here we test model stability of 30 multispecies bryozoan assemblages, using estimates of energy loss from observed interference competition to parameterise both the inter- and intraspecific interactions in the competition networks. We find that all competition networks are unstable. However, instability is mitigated considerably by asymmetries in the energy loss rates brought about by hierarchies of strong and weak competitors. This asymmetric organisation results in asymmetries in the interaction strengths, which reduces instability by keeping the weight of short (positive) and longer (positive and negative) feedback loops low. Our results support the idea that interference competition leads to instability and exclusion but demonstrate that this is not because of, but despite, competitive hierarchy.</p> <p><strong>Data</strong></p> <p>Our data set contains records of overgrowth competition in 30 high-latitude bryozoan assemblages. Rocks were collected by hand from shallow subtidal coastal locations at Rothera Island, West Antarctic Peninsula, Signy Island in the maritime Antarctic and Spitsbergen in the Arctic. For each assemblage, the data set contains one .csv file with abundance per species and one .csv file containing the species-contact-matrix. All bryozoans were identified to species and counted, giving abundance data in colonies per species. Then, all pairwise contests between colonies were classified as win, draw or loss and the results were compiled in the species-contact-matrices. For details, see the methods section of the paper.</p> <p><strong>Analysis </strong></p> <p>The analysis is subdivided into the following sections:</p> <ul> <li>0 <strong>Random matrices</strong>: Stability of random matrices with symmetric and asymmetric interactions.</li> <li>1 <strong>Preparation</strong>: Define functions to calculate asymmetry measures and set plotting parameters</li> <li>2 <strong>Read and process raw data</strong>: Converts raw data to Jacobian matrices</li> <li>3 <strong>Analysis of empirical matrices</strong>: Calculates stability, asymmetry measures, loop weights of empirical matrices.</li> <li>4 <strong>Analysis of randomised matrices:</strong> Randomises empirical matrices and analyses the effect on stability, asymmetry measures and loop weights.</li> <li>5<strong> Sensitivity</strong>: Effect of model assumptions (cost-values / replacement of missing values) on the results.</li> </ul> <p>Details on how to reproduce the full analysis, including all figures and tables in the manuscript can be found in the ReadMe file.</p>
Data for: Honey bees (Apis mellifera) modify plant-pollinator network structure, but do not alter wild species' interactions
<p>Honey bees (<em>Apis mellifera</em>) are widely used for honey production and crop pollination, raising concern for wild pollinators, as honey bees may compete with wild pollinators for floral resources. The first sign of competition, before changes appear in wild pollinator abundance or diversity, may be changes to wild pollinator interactions with plants. Such changes for a community can be measured by looking at changes to metrics of resource use overlap in plant-pollinator interaction networks. Studies of honey bee effects on plant-pollinator networks have usually not distinguished whether honey bees alter wild pollinator interactions, or if they merely alter total network structure by adding their own interactions. To test this question, we experimentally introduced honey bees to a Canadian grassland and measured plant-pollinator interactions at varying distances from the introduced hives. We found that honey bees increased the network metrics of pollinator and plant functional complementarity and decreased interaction evenness. However, in networks constructed from just wild pollinator interactions, honey bee abundance did not affect any of the metrics calculated. Thus, all network structural changes to the full network (including honey bee interactions) were due only to honey bee-plant interactions, and not to honey bees causing changes in wild pollinator-plant interactions. Given widespread and increasing use of honey bees, it is important to establish whether they affect wild pollinator communities. Our results suggest that honey bees did not alter wild pollinator foraging patterns in this system, even in a year that was drier than the 20-year average.</p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</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>
Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields
<p>Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields.</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>
Data of "Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks"
<p>The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.48550/arXiv.2304.11209">https://doi.org/10.48550/arXiv.2304.11209</a>.</p>
Understanding the impact of host networking elements on traffic bursts: Raw measurement data
<p>This record contains the raw trace files gathered by the Valinor network traffic burst measurement framework in Redis dump (rdb) format. Please refer to the artifact repository for instructions on how to parse and use the datasets:</p> <p><a href="https://github.com/hopnets/valinor-rawdata">hopnets/valinor-rawdata: Raw Redis datasets containing the measurement results of Valinor NSDI '23 paper (github.com)</a></p>
Supplemental data for characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks
<p>This is the supplemental data for the manuscript titled <em>Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks</em> submitted to <em>Nano Select</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1.</p> <p><strong>Dataset description:</strong></p> <p>Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder.</p> <ul> <li><em>particle_detection_training.ipynb:</em> This notebook can be used for network training.</li> <li><em>particle_detection_evaluation.ipynb:</em> This notebook is for evaluation of a trained network with simulated test images.</li> <li><em>particle_detection_evaluation_experiment.ipynb:</em> This notebook is for evaluation of a trained network with experimental test images.</li> <li><em>particle_detection_measurement_experiment.ipynb:</em> This notebook is for application of a trained network to experimental data.</li> </ul> <p>In addition, a script titled <em>particle_detection_functions.py</em> is provided which contains functions required by the notebooks. Details can be found within the comments.</p> <p>The zip archive <em>training_data.zip</em> contains the training data. The subfolder <em>HAADF</em> contains the images (sorted as training, validation and test images), the subfolder <em>json </em>contains the annotation (sorted as training, validation and test images). Each file within the <em>json</em> folder provides for each image the following information:</p> <ul> <li>aggregat_no: image id, the number of the corresponding image file</li> <li>particle_position_x: list of particle position x-coordinates in nm</li> <li>particle_position_y: list of particle position y-coordinates in nm</li> <li>particle_position_z: list of particle position z-coordinates in nm</li> <li>particle_radius: list of volume equivalent particle radii in nm</li> <li>particle_type: list of material types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></li> <li>particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</li> <li>rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</li> <li>deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</li> <li>cluster_index: list of cluster indices for each particle</li> <li>initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</li> <li>fractal_dimension: the intended fractal dimension of the aggregate</li> <li>fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</li> <li>fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</li> <li>fractal_prefactor: fractal prefactor</li> <li>mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</li> <li>mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</li> <li>particle_1_rho: density of TiO<sub>2</sub> used for the calculations</li> <li>particle_1_size_mean: mean TiO<sub>2</sub> radius</li> <li>particle_1_size_min: smallest TiO<sub>2</sub> radius</li> <li>particle_1_size_max: largest TiO<sub>2</sub> radius</li> <li>particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</li> <li>particle_1_clustersize: average TiO<sub>2</sub> cluster size</li> <li>particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</li> <li>particle_2_rho: density of WO<sub>3 </sub>used for the calculations</li> <li>particle_2_size_mean: mean WO<sub>3</sub> radius</li> <li>particle_2_size_min: smallest WO<sub>3</sub> radius</li> <li>particle_2_size_max: largest WO<sub>3</sub> radius</li> <li>particle_2_size_std: standard deviation of WO<sub>3</sub> radii</li> <li>particle_2_clustersize: average WO<sub>3</sub> cluster size</li> <li>particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</li> <li>number_of_primary_particles: number of particles within the aggregate</li> <li>gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</li> <li>gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</li> <li>mean_coordination: mean total coordination number (particle contacts)</li> <li>mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</li> <li>mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</li> <li>radius_equiv: list of area equivalent particle radii (in projection)</li> <li>k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</li> <li>polygons: list of polygons that surround the particle (COCO annotation)</li> <li>bboxes: list of particle bounding boxes</li> <li>aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</li> <li>n_pix: number of pixel per image in horizontal and vertical direction (squared images)</li> <li>pixel_size: pixel size in nm</li> <li>image_size: image size in nm</li> <li>add_poisson_noise: 1 if poisson noise was added, 0 otherwise</li> <li>frame_time: simulated frame time (required for poisson noise)</li> <li>dwell_time: dwell time per pixel (required for poisson noise)</li> <li>beam_current: beam current (required for poisson noise)</li> <li>electrons_per_pixel: number of electrons per pixel</li> <li>dose: electron dose in electrons per Å<sup>2</sup></li> <li>add_scan_noise: 1 if scan noise was added, 0 otherwise</li> <li>beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</li> <li>scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)</li> <li>add_focus_dependence: 1 if a focus effect is included, 0 otherwise</li> <li>data_format: data format of the images, e.g. uint8</li> </ul> <p>There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>The zip archive <em>experiment_test_data.zip</em> includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder <em>HAADF</em> includes thirteen images. The subfolder <em>json</em> includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in <em>json/COCO/annotation_COCO.json</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript.</p> <p>The zip archive <em>particle_detection.zip</em> includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file <em>particle_detection/logs/fit/20230622-222721/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>particle_detection/logs/fit/20230622-222721/config_file.py</em>.</p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
236-Bus Low Voltage Distribution Network Data
<p>The data describes a realistic low-voltage distribution network with 236 buses, 235 underground cables, 96 residential customers supplied by a transformer with 1000 kVA and 10 kV/420V.</p><p>The network characteristics, such as cable resistances, reactances, and thermal limits, are available.</p><p>Demand, EV demand, and photovoltaic generation profiles are presented for 8640 total 15-minute periods.</p>
Inferring microbial co-occurrence network from amplicon data: a systematic evaluation
<p>Supporting data for the manuscript "<em>Inferring microbial co-occurrence network from amplicon data: a systematic evaluation</em>".</p>
Data from: Median-Joining Networks and Bayesian phylogenies often do not tell the same story
<p>Inferring phylogenies among intraspecific individuals often yields unresolved relationships (i.e., polytomies). Consequently, methods that compute distance-based abstract networks, like Median-Joining Networks (MJNs), are thought to be more appropriate tools for reconstructing such relationships than traditional trees. Median-Joining Networks visualize all routes of relationships in the form of cycles, if needed, when traditional approaches cannot resolve them. However, the MJN method is a distance-based phenetic approach that does not involve character transformations and makes no reference to ancestor-descendant relationships. Although philosophical and theoretical arguments challenging the implication that MJNs reflect phylogenetic signal in the traditional sense have been presented elsewhere, an empirical comparison with a character-based approach is needed given the increasing popularity of MJN analysis in evolutionary biology. Here, we use the conservative Approximately Unbiased (AU) test to compare 85 cases of branching patterns of cycle-free MJNs and Bayesian Inference (BI) phylogenies using datasets from 55 empirical studies. By rooting the MJN analyses to provide directionality, we report substantial disagreement between computed MJNs and posterior distributions on BI phylogenies. The branching patterns in MJNs and BI phylogenies show significantly different relationships in 37.6% of cases. Among the relationships that do not significantly differ, 96.2% show alternative sets of relationships. Our results indicate that the two methods provide different measures of relatedness in a phylogenetic sense. Finally, our analyses also support previous observations of the statistical hypothesis testing by reconfirming the over-conservativeness of the Shimodaira-Hasegawa test versus the AU test.</p>
Data for: Does capital account liberalization drive the systemic importance of mainland China stock market? A network perspective
<p>Even though Chinese government has launched a series of capital account liberalization schemes to promote the systemic importance of mainland China stock market, the mainland China market still remains relatively marginal in the global stock markets, that is, China’s capital account liberalization does not seem to work as expected. This paper measures the systemic importance of mainland China stock market based on dynamic tail correlation networks, and further analyzes the impact of China’s capital account liberalization on it. Empirical results manifest that, capital account liberalization indeed contributes to improving the systemic importance of mainland China stock market. Besides, the systemic importance is also significantly and positively affected by economic policy uncertainty and direct investment. More specially, investor sentiment in mainland China stock market has the apparent impact on systemic importance only when extreme events occur.</p>
Data for Vasari Social Network
<p>Files to run scripts in <a href="https://github.com/ISE-FIZKarlsruhe/vasari_network">https://github.com/ISE-FIZKarlsruhe/vasari_network</a> for generating a social network weighted with Pointwise Mutual Information (PMI) from <em>The Lives of The Artists</em> (1568) by Giorgio Vasari.</p> <p>The 10 .csv files in <em>volumes/</em> contain the pages extracted from <em>The Lives of The Artists </em>edition on Project Gutenberg (<a href="https://onlinebooks.library.upenn.edu/webbin/metabook?id=livespainters">here</a>)</p>
DLC networks from: Application of a novel deep learning based 3D videography workflow to bat flight data
<p>Studying the detailed biomechanics of flying animals relies on producing accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this is achieved through manual digitization of animal videos, a labor-intensive task that grows more so with increasing frame rates and numbers of cameras. In this study, we present a workflow that combines deep learning-powered automatic digitization with intelligent filtering and correction of mislabeled points using 3D information. We tested our workflow using a particularly challenging scenario – bat flight. First, we documented bats flying steadily in a wind tunnel. We compared the results from manually digitizing bats with markers applied to anatomical landmarks against using our automatic workflow on the same bats without markers. In our second test case, we compared manual digitization against our automated workflow for bats exhibiting complex maneuvers in a large flight arena. We found that the variation between the 3D coordinates from our workflow and those from manual digitization was less than a millimeter larger than the variation between 3D coordinates resulting from two different human digitizers. The reduced reliance on manual digitization stemming from this work has the potential to significantly increase the scalability of studies into the detailed biomechanics of animal flight.</p>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
<p>Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterized soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighboring evergreen broadleaf forest. Carbon (C) mineralization combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+−N), nitrate nitrogen (NO3−−N), and available phosphorus (AP). Structural equation modeling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralization.</p> <p>Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralization. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralization in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests.</p>
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.