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opencc-zeroFeb 2022View details →

Code and data from: Familiarity, dominance, sex and season shape common waxbill social networks.

In gregarious animals, social network positions of individuals may influence their life-history and fitness. Although association patterns and the position of individuals in social networks can be shaped by phenotypic differences and by past interactions, few studies have quantified their relative importance. We evaluated how phenotypic differences and familiarity influence social preferences and the position of individuals within the social network. We monitored wild-caught common waxbills (Estrilda astrild) with radio-frequency identifiers in a large mesocosm during the non-breeding and breeding seasons of two consecutive years. We found that social networks were similar, and that the centrality of individuals was repeatable, across seasons and years, indicating a stable social phenotype. Nonetheless, there were seasonal changes in social structure: waxbills associated more strongly with opposite-sex individuals in breeding seasons, while in non-breeding seasons they instead assorted according to similarities in social dominance. We also observed stronger assortment between birds that were introduced to the mesocosm at the same time, indicating long-lasting bonds among familiar individuals. Waxbills that had been introduced to the mesocosm more recently occupied more central network positions, especially during breeding seasons, perhaps indicating that these birds had less socially-differentiated associations with flock members. Finally, individual differences in color ornamentation and behavioral assays of personality, inhibitory control and stress were not related to network centrality or association patterns. Together, these results suggest that, in gregarious species like the common waxbill, social networks may be more strongly shaped by long-lasting associations with familiar individuals than by phenotypic differences among group members.

opencc-zeroFeb 2022View details →

Differences in network structure and connectivity of four (protected) Palearctic-Afrotropical flyways

Aim – Waterbirds that travel seasonally between Europe and Africa use wetlands along four major Palearctic-Afrotropical flyways. However, it is unknown to what extent the overall connectivity of these flyways may be threatened by ongoing habitat loss and degradation. Here, we contrasted the wetland connectivity along these four flyways, applying graph-theoretic connectivity metrics on an intercontinental scale. We also explored for which flyway connectivity is most at risk. We then identified the most important wetlands by their contribution to connectivity in each flyway. Location – Western Palearctic, Afrotropics Methods – Based on high-resolution wetland maps, we calculated directional probabilistic connectivity metrics. Estimates of overall connectivity of each flyway were obtained, as well as the relative importance of wetlands, for birds with different migration strategies: short-distance hoppers and long-distance jumpers. Results – The East-Atlantic flyway and Eastern Mediterranean flyway had higher overall functional connectivity than the two central routes, reflecting the larger barrier represented by the Mediterranean Sea and Sahara Desert. Fewer than 5% of all wetlands supported more than 70% of the total connectivity of the network in each flyway, regardless of the considered migration strategy. These wetlands were either large, strategically positioned, or both. Removing non-protected wetlands from the analysis showed that the connectivity of some flyways could be jeopardised and that the East-Atlantic and Eastern Mediterranean flyway may be most vulnerable to additional habitat loss. Main conclusions – Our results illustrate (1) the major contribution of unprotected wetlands to flyway connectivity, (2) the importance of integrating migration ecology into site-based connectivity analyses, and (3) the utility of graph-based connectivity metrics to inform conservation prioritisation under present and future scenarios.

opencc-by-4.0Feb 2022View details →

Dissociable Multi-scale Patterns of Development in Personalized Brain Networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results demonstrate that the development of functional brain networks align with and refine a hierarchy linked to cognition

opencc-by-4.0Jul 2021View details →

4D-Var data assimilation experiment of the Lorenz 96 model using an adjoint model of a neural network surrogate model

These data are the output of the 4D-Var data assimilation experiment of the Lorenz96 model using an adjoint model of a neural network surrogate model. The details are described in Nishizawa (2022).

opencc-by-4.0Feb 2022View details →

Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks

We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.

An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents

Classical biological control (CBC) can be used to decrease the density of invasive species to below an acceptable ecological and economic threshold. Natural enemies specific to the invasive species are selected from its native range and released into the invaded range. This approach has drawbacks, despite the performance of specificity tests to ensure its safety, because the fundamental host range defined under controlled conditions does not represent the actual host range in natura, and these tests omit indirect interactions within community. We focus on Sonchus oleraceus (Asteraceae), a weed species originating from Western Palearctic that is invasive in many countries and notably in Australia. We explore how analyses of interaction network within its native range can be used to 1) inventory herbivores associated to the target plant, 2) characterize their ecological host ranges, and 3) guide the selection of candidate biocontrol agents considering interactions with species from higher trophic levels. Arthropods were collected from plant community sympatric to S. oleraceus, in three bioclimatic regions, and interactions were inferred by a combination of molecular and morphological approaches. The networks reconstructed were structured in several trophic levels from basal species (plant community), to intermediate and top species (herbivorous arthropods and their natural enemies). The subnetwork centered on S. oleraceus related interactions contained 116 taxa and 213 interactions. We identified 47 herbivores feeding on S. oleraceus, 15 of which were specific to the target species. Some discrepancies with respect to published findings or conventional specificity tests suggested possible insufficient sampling effort for the recording of interactions or the existence of cryptic species. Among potential candidate agents, 6 exhibited interactions with natural enemies. Synthesis and applications: Adopting a network approach as prerequisite step of the classical biological control program can provide a rapid screening of potential agents to be tested in priority. Once ecological host range defined, we suggest that priority should be given to agent used by a minimum species, and, when they exist, to agents that possess enemies from the most distant taxonomical group from those occurring in the range of introduction.

Connectome of memristive nanowire networks through graph theory - Dataset

This is the dataset of "Connectome of memristive nanowire networks through graph theory"

Strateole-2 data set associated to the publication "A seismic network in the stratosphere"

NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021). One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT). These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.

CycleGAN network for photoacoustic histology

An unsupervised deep learning algorithm based on cycle-consistent generative adversarial networks (CycleGAN ) converted the UV-PAM images into H&E-like pseudocolor histologic images, allowing the pathologists to readily identify the cancerous features following existing pattern-recognition parameters. Unlike supervised deep learning methods such as generational adversarial networks (GAN), the unsupervised deep learning method based on CycleGAN does not require coupled pairs of stained and unstained images. It avoids the need for well-aligned UV-PAM and H&E-stained images for neural network training, which can be challenging to acquire due to artifacts caused by sample preparation-induced morphology changes.

Data sets used in "Neural network processing of holographic images"

Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml

Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows: In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.

Dataset for: Short flowers for long tongues: functional specialization in a nocturnal pollination network of an asclepiad in long-tongued hawkmoths (Biotropica)

Since Darwin, very long and narrow floral tubes have been known to represent the main floral morphological feature for specialized long-tongued hawkmoth pollination. However, specialization may be driven by other contrivances instead of floral tube morphology. Asclepiads are plants with a complex floral morphology where primary hawkmoth pollination had never been described. We detailed here the intricate pollination mechanism of the South American asclepiad Schubertia grandiflora, where functional specialization on long-tongued hawkmoth pollinators occurs despite the short floral tube of this species. We studied two plant populations in the Brazilian Cerrado and recorded floral visitors using different approaches, such as light-trapped hawkmoths for pollen analysis, direct field observations, and IR motion-activated cameras. Finally, using a community-level approach we applied an ecological network analysis to identify the realized pollinator niche of S. grandiflora among the available niches in the pollinator community. Throughout a period of 17 years, long-tongued hawkmoths were consistently recorded as the main floral visitors and the only effective pollinators of S. grandiflora. Flowers rely on highly modified corona and gynostegium, and enlarged nectar chambers, to drive visitors and pollination mechanism. Despite relatively short-tube, network analysis placed S. grandiflora in the module including exclusively long-tongued hawkmoth pollinators and the most phenotypically specialized sphingophilous plants in the community. These results represent the first example of functional specialization in long-tongued hawkmoths in an asclepiad species. However, this specialization is uncoupled from the long floral tubes historically associated with the sphingophily syndrome.

TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities

Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8

Datasets for Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection

These are the datasets about the experimental group (N=20, where N is the sample size, that is, 20 participants in total) and control group (N=20, where N is the sample size, that is, 20 participants in total) for our published paper entitled "Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection", DOI: 10.1109/JBHI.2021.3134024.  1. What do these datasets include? There are 40 participants' data in 40 folders, respectively. Each folder includes EEG data, Questionnaires, and answers as well as other non-EEG biometrics and memory test result of the cognitive task. 1.1 EEG data All EEG data is either *.easy file or *.info file. The *.easy file is the raw EEG data. The *.info file is the information (e.g., EEG montage) paired with those raw EEG data. The *.info file can be simply readable by any Notepad software. The *.easy file can be readable by Neuroelectrics's plugin for EEGLAB (https://www.neuroelectrics.com/wiki/index.php/EEGLAB). Note, most of the participants' EEG data is a single easy file, but due to technical problems, few participants' EEG data consists of some separated easy files. Please use the function 'merge dataset' in the EEGLAB to merge them, and then use the following markers to extract segments of interest. All received EEG markers are integer numbers. Specifically, the markers are as follows: For control group: a) The 'target' stimuli in the cognitive task: 16 b) The 'distractor' stimuli in cognitive task:32 c) The end of Baseline_1(that is, before the first cognitive task): 37 d) The end of Baseline_2(that is, before the round 1 neutral task): 47 e) The end of Baseline_3(that is, before the round 2 neutral task): 57 f) The end of Baseline_4(that is, before the second cognitive task): 67 g) The end of FMS reporting during the round 1 neutral task: 100 h) The end of FMS reporting during the round 2 neutral task: 101 For experimental group: a) The 'target' stimuli in the cognitive task: 16 b) The 'distractor' stimuli in cognitive task:32 c) The end of Baseline_1(that is, before the first cognitive task): 37 d) The end of Baseline_2(that is, before the tunnel travel task): 47 e) The end of Baseline_3(that is, before the rollercoaster task): 57 f) The end of Baseline_4(that is, before the second cognitive task): 67 g) The end of FMS reporting during the Tunnel travel task: 100 h) The end of FMS reporting during the Rollercoaster task: 101 1.2 Questionnaires 1.2.1 SSQ All SSQ questionnaires are named as xxx_QuestionnaireResult.csv, where xxx stands for the timestamps. 1.2.2 FMS For control group: All FMS questionnaires are named as xxx_Nature_FMS_backup.csv or xxx_Bio-data and FMS Nature.csv, where xxx stands for the timestamps and 'Nature' refers to the vection-free neutral task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read. For experimental group: All FMS questionnaires are named as xxx_Gabor_FMS_backup.csv or xxx_Rollercoaster_FMS_backup.csv or xxx_Bio-data and FMS GaborRacer.csv or xxx_Bio-data and FMS Rollercoaster.csv, where xxx stands for the timestamps and Gabor or GaborRacer refer to the tunnel travel task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read. 1.3 Non-EEG biometrics All non-EEG biometrics are named as xxx_Bio-data 'Task name'.csv, where 'Task name' refers to 'VA' (that is the cognitive task), or 'Nature' (for the control group) or 'GaborRacer' (for the experimental group) or 'Rollercoaster' (for the experimental group). The column information is as follows: Column A: time Column Q: PPG raw data (the left index finger) Column R: Fingertip temperature (the left middle finger) Column S: Forehead temperature Column T: VR hermetic space temperature The data in other columns are not used in this study. 1.4 The memory test result of the cognitive task Memory test result: xxx_CheckMemoryResult.csv, where xxx stands for the timestamps. Column A: time Column B: memory test result (Correct/Wrong) Column C: the number of the target (green sunfish) Column D: the reported number of the target in the memory test

Fused Image dataset for convolutional neural Network-based crack Detection (FIND)

The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types. The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance. If you share or use this dataset, please cite [4] and [5] in any relevant documentation.  In addition, an image dataset for crack classification has also been published at [6]. References: [1]          Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. <a href="https://doi.org/10.1061/(asce)cp.1943-5487.0000873">https://doi.org/10.1061/(asce)cp.1943-5487.0000873</a> [2]          Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. <a href="https://doi.org/10.1016/j.autcon.2021.103605">https://doi.org/10.1016/j.autcon.2021.103605</a> [3]          Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. <a href="https://doi.org/10.1177/1475921720948434">https://doi.org/10.1177/1475921720948434</a>   [4]           Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. <a href="https://doi.org/10.1016/j.autcon.2022.104678">https://doi.org/10.1016/j.autcon.2022.104678</a> [5]          (This dataset) Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6383044">https://doi.org/10.5281/zenodo.6383044</a> [6]          Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. <a href="https://doi.org/10.17603/ds2-bzv3-nc78">https://doi.org/10.17603/ds2-bzv3-nc78</a>

Schematic Multimodal Network In the Ohio River Basin

The topological connectivity of the components of Ohio Basin, including the waterway segments, highways, and railways serving the OD demand of commodities

Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling

This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal. This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy. Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.

ICA magnetic susceptibility networks

Magnetic susceptibility networks derived from independent component analysis from a cohort of 170 healthy individuals aged 9 to 81 years ("dorsal-striatum_mni152.nii.gz", "brainstem_mni152.nii.gz", "gpi_mni152.nii.gz", "thalamus_mni152.nii.gz", "cerebellar_mni152.nii.gz"), and from a cohort of 118 patients with MS and 115 healthy controls ("ms-caudate_mni152.nii.gz", "ms-mesencephalon_mni152.nii.gz", "ms-pulvinar_mni152.nii.gz"). These have been non-linearly transformed to MNI152 space (Desikan et al, 2006).   Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968-980. doi:10.1016/j.neuroimage.2006.01.021

Protein vibrational frequencies dataset: Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks

Dataset for machine learning model, based on graph neural network, to predict protein natural frequencies using Graph Neural Networks.  Code: https://github.com/lamm-mit/ProteinMechanicsGNN Paper:  Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks Kai Guo and Markus J. Buehler Digital Discovery, 2022, DOI: 10.1039/D1DD00007A