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325 results for “network structure”
Differences in network structure and connectivity of four (protected) Palearctic-Afrotropical flyways
<p class="MsoNormal"><em><span>Aim – </span></em><span>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. </span></p> <p class="MsoNormal"><em><span>Location – </span></em><span>Western Palearctic, Afrotropics</span></p> <p class="MsoNormal"><em><span>Methods – </span></em><span>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.</span></p> <p class="MsoNormal"><em><span>Results – </span></em><span>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. </span></p> <p class="MsoNormal"><em><span>Main conclusions – </span></em><span>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.</span></p>
Expanding the Concept of Comprehensive Area Ratio Parameter to the South-Central States: Towards Simplifying the Structural Evaluation of Flexible Pavements at the Network Level
<p>The surface deflection bowl data collected through falling weight deflectometer (FWD) test is utilized by highway agencies in assessing the performance of the flexible pavement. However, a robust method to evaluate pavement sections utilizing FWD data from all the sensors is seldom developed. There is always a need for DOTs and highway agencies to have a simplified procedure, which can be directly implemented in agencies' databases. This study focuses on expanding and validating the concept of previously developed area ratio parameters towards the pavement section of South-Central States (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) in effectively analyzing the pavement performances. Simulation-based deflections are utilized to develop enhanced deflection-based parameters and to reduce the need for extensive FWD testing in the field. Ninety-seven pavement sections in these states are considered to implement and validate simplified procedures that will be readily available to various transportation agencies to evaluate their pavement conditions at the network level. Due to this purpose, a pavement ranking chart is proposed for the five South-Central states, which categorizes the pavement section into very good, good, fair, and poor pavement sections. Eventually, load-induced effects concerning developed parameters are effectively analyzed to predict the remaining service life of the flexible pavement structures. The developed methodologies will be helpful for DOTs and highway agencies to carry out the rehabilitation and maintenance work in time and estimate the budget required in these procedures.</p>
Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network"
<p>This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". In this manuscript, we propose an efficient deep learning method using a Convolutional Neural Network (CNN) to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. The CNN architecture is beneficial for the flexible incorporation of empirical geological knowledge when trained with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an expressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. </p>
Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network
<p><span>Co-flowering communities are usually characterized by high plant generalization but knowledge of the underlying factors leading to high levels of generalization and pollinator sharing, and how these may contribute to network structure is still limited. </span>Flowering phenology and floral trait similarity are considered among the most important factors determining plant generalization and pollinator sharing. However, these have been evaluated independently even though they can act in concert with each other. Moreover, the importance of flowering phenology and floral similarity, via their effects on plant generalization, in the structure of plant–pollinator networks have been scarcely studied. Here, we aim to evaluate the effect of flowering phenology and floral similarity in mediating the degree of pollinator sharing and plant generalization in two coastal communities and uncover their importance as drivers of plant–pollinator network structure.</p> <p>We recorded flower production per species, as well as the identity and frequency of floral visitors along the entire flowering season. We estimated the degree of flowering overlap, the degree of floral similarity (using floral traits associated with size and color), and the degree of pollinator sharing among plant species within both communities.</p> <p>Structural equation models (SEM) showed a positive effect of flowering overlap on pollinator sharing and plant generalization. Pollinator sharing and plant generalization positively affected network nestedness. Furthermore, SEM showed a direct positive effect of flowering overlap on network modularity. The SEM analyses also revealed a significant interaction effect of floral similarity and flowering overlap on pollinator sharing, with consequences for network nestedness in one community.</p> <p><span>Our results highlight the importance of integrating multiple axes of differentiation such as flowering phenology and floral similarity into our understanding of the drivers of plant–pollinator network structure.</span></p>
Research data: "Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe"
<p>This data set belongs to the paper Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe in the International Journal of Production Economics (DOI). The BatPac model, as well as, the model for the economic assessment of the recycling route are not included. The needed data can be found in the file (name). Further, the BatPaC model can be gather from the website of Argonne National Laboratory and the assessment tool for the recycling routes via this DOI: 10.5281/zenodo.6500946.</p> <p> </p> <p>This work is part of the research project Recycling 4.0 (EFRE | ZW 6-85018080), which is funded by the European Regional Development Fund and managed by the development bank for the German federal state of Lower Saxony (NBank).</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>
A study of the spatial correlation network structure of urban innovation in Guangdong
<p>Based on the modified gravity model, a spatial correlation network of innovation was constructed among cities in Guangdong, China. Social network analysis was employed to explore their evolution characteristics during 2009–2017. The results indicate that the innovation output of prefecture-level cities in Guangdong Province shows both spatial correlations and differences. Their network shows lower density, higher efficiency, and rigid stratification properties. Based on small cluster analysis, these cities are classified into four blocks, the members of which changed. In 2017, four well-defined subgroups formed, which are "bidirectional spillover plate", "main spillover plate", "net beneficial plate", and "agent plate". With this network, the geographical characteristics of the innovation capabilities and differences among the cities in Guangdong, as well as the different positions and roles of each city in the associated network, can be properly understood. Consequently, the transmission mechanisms and development strategies of innovation in Guangdong Province can be better explored.</p>
Structure, function, and control of the musculoskeletal network - Data
<p>Supplementary data for: Structure, function, and control of the musculoskeletal network</p> <p>Table S8: The assigned homunculus categories and data driven community assignments of muscles.</p> <p>Table S9: The hypergraph of muscles and bones from the Hosford muscle tables used in the main text.</p> <p>Table S10: The hypergraph of muscles and bones from Grant's atlas used in the supplementary text.</p> <p> </p> <p>Data for Figures:</p> <p>2e</p> <p>3a, 3b</p> <p>4b, 4c, 4d</p> <p>S4a-h</p> <p>S5</p> <p>S6a, S6b</p> <p>S7a, S7b</p> <p>S8</p> <p>S9</p> <p>S10</p> <p>S11</p> <p>S12</p> <p> </p>
Forest structure and heterogeneity increase diversity and alter the composition of host-parasitoid networks
<p>Antagonistic host-parasitoid interactions can be quantified using bipartite and meta networks, which have the potential to reveal how habitat structural elements relate to this important ecosystem function. Here, we analysed the host-parasitoid interactions of cavity-nesting bees and wasps, as well as their abundance, diversity, and species richness with forest structural elements from 127 forest research plots in southwestern Germany. We found that parasitoid abundance, diversity, and species richness all increase with host abundance, a potential mediator between parasitoids and forest structure. Both parasitoid abundance and diversity increased with stand structural complexity, possibly mediated by the abundance of hosts. Additionally, parasitoid abundance increased with increasing standing deadwood and herb cover. The bipartite networks of host-parasitoid interactions showed higher connectance with increasing standing deadwood, herb cover, and host abundance. Analyses of interactions within the host-parasitoid metanetwork revealed that increasing host abundance and decreasing canopy cover diversify the suites of interactions present at the plot level. These results demonstrate that forest structural elements can improve the stability and resilience of host-parasitoid networks by promoting parasitoids and diversifying interactions in ecological networks.</p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</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 (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts 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 for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</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 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· 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).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, 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>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </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 and STEM tomography reconstructions. 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>
Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach
<p><span>This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.</span></p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
The global structure of marine cleaning mutualistic networks
<p># Global-Cleaning-Networks</p> <p>The data sets available here are parts of the following article:</p> <p>Quimbayo JP, Cantor M, Dias MS, Grutter AS, Gingins S, Becker JHA, Floeter SR. (2018) The global structure of marine cleaning mutualism networks. Global Ecology and Biogeography. DOI: 10.1111/geb.12780.</p> <p><br> Description</p> <p>We combined field and literature data to test if recurrent patterns in mutualistic networks—nestedness,<br> modularity—describe the distributions of marine cleaning interactions. Nested network structures suggest <br> some cleaner species interact with many clients while the others clean fewer, predictable subsets of these clients; <br> modular network structures suggest cleaners and clients interact with defined, densely-connected subsets of species. </p> <p>All datasets are binary matrices indicating cleaning interaction between cleaner species (columns) <br> and client species (rows) in 28 marine habitats across of 11 marine biogeographical provinces defined for reef fish fauna.<br> These were the Caribbean, the Southwestern, Central, North and Eastern Atlantic, the Western Indian,<br> the Central Indo-Pacific and the Southwestern, Central, Northeastern and Tropical Eastern Pacific.</p> <p>Each element of these matrices equal 1 when the cleaner species i interacts with the client species j, <br> and 0 otherwise. A cleaning event is defined as the observation of a cleaner removing ectoparasites, <br> diseased tissue, and/or mucus from the body surface, gills or buccal cavity of the clients.</p> <p>Data are provided in the form of comma separated values files (csv), one for each of the 28 localities.<br> Please refer to Table S1 in the electronic supplementary material of this article for further details</p> <p><br> If you use any dataset please cite the article above and the original reference for the data <br> set as described below.</p> <p>CARIBBEAN PROVINCE</p> <p>File: Barbados.cvs<br> Reference: Whiteman EA, Côté IM. 2002 <br> Cleaning activity of two Caribbean cleaning gobies: intra- and interspecific comparisons. <br> J. Fish Biol. 60, 1443–1458. (doi:10.1006/jfbi.2002.1947)</p> <p>File: Bonaire.cvs<br> Reference: Wicksten MK. 1998 <br> Behavior of cleaners and their client fishes at Bonaire Netherlands Antilles. <br> J. Nat. Hist. 32, 13–30. </p> <p>File: Curacao.cvs<br> Reference: Titus, B.M., Vondriska, C. & Daly, M. 2017 & This study<br> Comparative behavioural observations demonstrate the “cleaner” shrimp <em>Periclimenes </em><em>yucatanicus</em> engages in true <br> symbiotic cleaning interactions. <br> Royal Society Open Science, 4, 170078.</p> <p>File: Tobago.cvs<br> Reference: Dunkley, K., Cable J. & Perkins S.E. 2018 & This study <br> The selective cleaning behavior of juvenile blue-headed wrasse (<em>Thalassoma bifasciatum</em>) in the Caribbean. <br> Behavioural Processes. 147: 5–12.</p> <p>File: StCroix.cvs<br> Reference: Johnson WS, Ruben P. 1988 <br> Cleaning behavior of <em>Bodiunus </em><em>rufus</em><em>, Thalassomu </em><em>bifasciatum</em><em>, Gobiosoma </em><em>evelynae</em><em>, and Periclimenes </em><em>pedersoni</em> along a depth gradient at Salt River Submarine Canyon, St . Croix. Environ. Biol. Fishes 23, 225–232.</p> <p>SOUTHWESTERN ATLANTIC</p> <p>File: Abrolhos.cvs<br> Reference: Sazima C. 2002 <br> Atividade de limpeza de duas espécies sintópicas de peixes limpadores e <br> diversidade de seus clientes em Abrolhos, Bahia. </p> <p>File: Noronha.cvs<br> Reference: Francini-Filho RB, Sazima I. 2007 & This study<br> A comparative study of cleaning activity of two reef fishes at Fernando de Noronha Archipelago, Tropical West Atlantic. <br> Environ. Biol. Fishes 83, 213–220. (doi:10.1007/s10641-007-9322-6)</p> <p>File: Rocas.cvs<br> Reference: Quimbayo JP, Nunes LT, Ozekoski R, Floeter SR, Morais RA, Fontoura L, Bonaldo RM, Ferreira CEL, Sazima I. 2017<br> Cleaning interactions at the only atoll in the South Atlantic. <br> Environ. Biol. Fishes 100, 865–875. (doi:10.1007/s10641-017-0612-3)</p> <p>File: SantaCatarina.cvs<br> Reference:Quimbayo, J.P., Schlickmann, O.C., Floeter, S.R. & Sazima I 2018. <br> Cleaning interactions at the southern limit of tropical reef fishes in the Western Atlantic. <br> Environmental Biology of Fishes. doi: 10.1007/s10641-018-0768-5.</p> <p>File: StPaulsRocks.cvs<br> Reference: This study; Quimbayo JP, Cantor M, Dias MS, Grutter AS, Gingins S, Becker JHA, Floeter SR.<br> The global structure of marine cleaning mutualism</p> <p>File: Trindade.cvs<br> Reference: This study; Quimbayo JP, Cantor M, Dias MS, Grutter AS, Gingins S, Becker JHA, Floeter SR.<br> The global structure of marine cleaning mutualism</p> <p>CENTRAL ATLANTIC</p> <p>File: Ascension.cvs<br> Reference: Morais RA, Brown J, Ferreira CEL, Floeter SR, Quimbayo JP, Rocha LA, Sazima I. 2017 <br> Mob rulers and part-time cleaners: two reef fish associations at the isolated Ascension Island. <br> J. Mar. Biol. Assoc. United Kingdom 97, 799–811. (doi:10.1017/S0025315416001041).</p> <p>NORTH ATLANTIC</p> <p>File: Banyuls.cvs <br> Reference: Zander CD, Sötje I. 2002 <br> Seasonal and geographical differences in cleaner fish activity in the Mediterranean Sea. <br> Helgol. Mar. Reser 55, 232–241. (doi:10.1007/s101520100084)</p> <p>File: Azores.cvs<br> Reference: Narvaez P, Furtado M, Neto A, Moniz I, Azevedo J, Soares M. 2015 <br> Temperate facultative cleaner wrasses selectively remove ectoparasites from their client-fish in the Azores. <br> Mar. Ecol. Prog. Ser. 540, 217–226. (doi:10.3354/meps11522)</p> <p>EASTERN ATLANTIC</p> <p>File: Canarias.cvs<br> Reference: Van Tassell JL, Brito A, Bortone SA. 1994 <br> Cleaning Behavior among marine fishes and invertebrates in the Canary Islands. <br> Cybium 18, 117–127. </p> <p>File: CapeVerde.cvs<br> Reference: Quimbayo JP, Floeter SR, Noguchi R, Rangel CA, Gasparini JL, Sampaio CLS, Ferreira CEL, Rocha LA. 2012<br> Cleaning mutualism in Santa Luzia (Cape Verde Archipelago) and São Tomé Islands, Tropical Eastern Atlantic. Mar. <br> Biodivers. Rec. 5, e118. (doi:10.1017/S175526721200108X)</p> <p>File: Principe.cvs<br> Reference: This study; Quimbayo JP, Cantor M, Dias MS, Grutter AS, Gingins S, Becker JHA, Floeter SR.<br> The global structure of marine cleaning mutualism</p> <p><br> File: SaoTome.cvs<br> Reference: Quimbayo JP, Floeter SR, Noguchi R, Rangel CA, Gasparini JL, Sampaio CLS, Ferreira CEL, Rocha LA. 2012<br> Cleaning mutualism in Santa Luzia (Cape Verde Archipelago) and São Tomé Islands, Tropical Eastern Atlantic. <br> Mar. Biodivers. Rec. 5, e118. (doi:10.1017/S175526721200108X)</p> <p>WESTERN INDIAN<br> File: RedSea.cvs<br> Reference: Barbu, L., Guinand, C., Bergmüller, R., Alvarez, N. & Bshary, R. 2011 & This study<br> Cleaning wrasse species vary with respect to dependency on the mutualism and behavioural adaptations in interactions. <br> Animal Behaviour, 82, 1067–1074.</p> <p>CENTRAL INDO-PACIFIC</p> <p>File: KimbeBay.cvs<br> Reference 1: Becker, J.H.A. 2006 <br> Interactions between cleaner shrimp and their client fishes on coral reefs. <br> Ph.D. thesis, The University of Queensland, St Lucia, Queensland, Australia, 154 pp. </p> <p>Reference 2: Grutter A.S. & Feeney W.E. 2016 <br> Equivalent cleaning in a juvenile facultative and obligate cleaning wrasse: an insight into the evolution of cleaning in labrids? <br> Coral Reefs. 35: 991–997. doi:10.1007/s00338-016-1460-x</p> <p>File: LizardIsland.cvs<br> Reference 1: Becker, J.H.A. & Grutter, A.S. 2004 <br> Cleaner shrimp do clean. <br> Coral Reefs, 23, 515–520.</p> <p>Reference 2: Grutter, A.S. & Poulin, R. 1998 <br> Intraspecific and interspecific relationships between host size and the abundance of parasitic larval gnathiid isopods<br> on coral reef fishes. <br> Marine Ecology Progress Series, 164, 263–271.</p> <p>SOUTHWESTERN PACIFIC</p> <p>File: AlthorpeIsland.cvs<br> Reference: Shepherd, S.A., Teale, J. & Muirhead, D. 2005<br> Cleaning symbiosis among inshore fishes at Althorpe Island, South Australia and elsewhere. <br> Transactions of the Royal Society of South Australia, 129, 193–201.</p> <p>File: NewZealand.cvs<br> Reference: Ayling, A. M. & Grace, R. V. 1971<br> Cleaning symbiosis among New Zealand fishes. <br> New Zealand Journal of Marine and Freshwater Research, 5, 205–218.</p> <p>NORTHEASTERN PACIFIC</p> <p>File: LaJolla.cvs<br> Reference: Hobson, E.S. 1971<br> Cleaning symbiosis among California inshore fishes. <br> Fishery Bulletin, 69, 491–523.</p> <p>TROPICAL EASTERN PACIFIC</p> <p>File: Galápagos.cvs<br> Reference: This study; Quimbayo JP, Cantor M, Dias MS, Grutter AS, Gingins S, Becker JHA, Floeter SR.<br> The global structure of marine cleaning mutualism</p> <p>File: Gorgona.cvs<br> Reference 1: Rodríguez-Moreno M. 2005<br> Interacciones y patrones diarios de actividad de limpieza en peces de un arrecife coralino de la Isla Gorgona. <br> Departamento de Biologia, BSc thesis, Universidad del Valle, Cali-Colombia.</p> <p>Reference 2: Quimbayo, J.P., & Zapata, F.A. 2018 <br> Cleaning interactions by gobies on a Tropical Eastern Pacific coral reef. <br> Journal of Fish Biology. doi:10.1111/jfb.13573.</p> <p>File: Malpelo.cvs<br> Quimbayo, J.P., Dias, M.S., Schlickmann, O.C. & Mendes, T.C. 2017 <br> Fish cleaning interactions on a remote island from the Tropical Eastern Pacific.<br> Marine Biodiversity, 47, 603–608.</p> <p><br> All R codes for reproducing analyses are available from the authors on request. </p> <p>Any additional queries should be directed to the corresponding author <br> Juan P. Quimbayo (quimbayo.j.p@gmail.com)</p> <p>The release of this data does not exempt those who reuse the data from <br> following community norms for scholarly communication, in particular from citation<br> of this paper and the original data authors as detailed above.</p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Simplified Approach for Structural Evaluation of Flexible Pavements at the Network Level
<p>Corresponding data set for Tran-SET Project No. 17PUTA02. Abstract of the final report is stated below for reference:</p> <p>"Currently, there are few available simple procedures to identify structurally weak pavement sections utilizing Falling Weight Deflectometer (FWD) data at the network level (e.g., city, state or province). A simple method is required to determine the structural condition of pavement sections that can be directly implemented and automated in current pavement databases. The objective of this research study is to develop a simple analysis method to determine the structural condition of pavement sections utilizing the currently available non-destructive testing (NDT) deflection measurement devices at the network level that can be directly implemented and automated in the database of a typical transportation agency. In addition, the study had conducted an advanced mechanistic analysis to mimic the FWD deflection bowl obtained from the field. The developed structural condition parameters can be easily implemented in pavement management systems (PMS). This will aid Departments of Transportation (DOTs) and local highway agencies to make more informed decisions about the most suitable maintenance and rehabilitation strategies. Those parameters were also utilized to predict the remaining fatigue lives of the studied pavement sections."</p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking
<p>Comparative analysis of SIRIUS to evaluate our method, Biotransformation-based Annotation Method (BAM). This dataset includes all scripts, data, and results relevant to this analysis. BAM can be found on GitHub (https://github.com/HassounLab/BAM). </p> <p> </p> <p> </p>
Data from: Structural and defensive roles of angiosperm leaf venation network reticulation across an Andes-Amazon elevation gradient
1.The network of minor veins of angiosperm leaves may include loops (reticulation). Variation in network architecture has been hypothesized to have hydraulic and also structural and defensive functions. 2.We measured venation network trait space in eight dimensions for 136 biomass-dominant angiosperm tree species along a 3,300 m elevation gradient in southeastern Peru. We then examined the relative importance of multiple ecological, and evolutionary predictors of reticulation. 3.Variation in minor venation network reticulation was constrained to three axes. These axes described branching vs. reconnecting veins, elongated vs. compact areoles, and high vs. low density veins. Variation in the first two axes was predicted by traits related to mechanical strength and secondary compounds, and in the third axis by site temperature. 4.Synthesis. Defensive and structural factors primarily explain variation in multiple axes of reticulation, with a smaller role for climate-linked hydraulic factors. These results suggest that venation network reticulation may be determined more by species interactions than by hydraulic functions.
data for Newbury et al Short term fitness effects of bipartite interactions shape network structure of mutualistic and antagonistic communities
<p>speciescountdata.csv contains colony couts and plasmid detection from the main experiment.</p> <p>evonet.csv conatins colony counts of bacteria with and without plasmid pkjk5 from commuities where donors were o/ p ancestral/evolved. </p> <p>AOPV.csv contains optical density data for species a,o,p and v. column 1 is time in hours. Then columns alternate between a, o, p, s, v, a+,o+,p+,s+,v+,s+,v+ (where + denotes plasmid carriage) until column 49. After which the same patten continues, but these were grown with tetracycline. s did not grow at all in the 96-well plate this data was taken from. This is likely due to experimental error, so an additional well plate was used just for s (S.scv).</p> <p>S.csv contains optical density data for species s. column 1 is time in hours. then there are 6 colulmns of s and 6 columns of s+ until column 49. After this the same pattern continues but bacteria were grown with tetracycline.</p>
Habitat loss shapes the structure and species roles in tropical plant-frugivore networks
<p>Habitat loss is a global threat to biodiversity with pervasive effects on species and populations. These impacts may generate cascading effects on ecological processes propagating across ecological networks. Thus, understanding how habitat loss affects ecological networks is fundamental for conservation. We used a database of 25 plant-frugivore networks distributed across the whole Brazilian Atlantic Forest to understand how landscape-scale habitat loss shapes network structure, robustness, species role and traits related to seed dispersal. We compared whether these network properties have linear or non-linear relationships and used centrality metrics and indirect effects to evaluate if habitat loss change the role of species in plant-frugivore networks. We found linear and non-linear relationships with negative effects of habitat loss on the network structure. As a consequence of shifts in species richness and number of links, the number of interactions and the proportion of possible interactions observed (connectance) were negatively associated with habitat loss. In contrast, nestedness increased with habitat loss. Network robustness, mean bill width and mean seed size were not significantly related to habitat loss. In addition to changes in interaction patterns at network level, habitat loss also favors changes in interaction among species, shifting the species playing central roles in network organization or contributing to indirect effects in the networks. In forested landscapes, obligate frugivores are the main central species in the network, and the ones potentially contributing to indirect effects, while in deforested landscapes these roles are fulfilled by occasional frugivores. Thus, our results emphasize the widespread effect of habitat loss on plant-frugivore systems, adding evidence that its pervasive effects on biodiversity also proliferate on mutualistic interactions with negative consequences for seed dispersal that potentially go beyond the direct pairs of interacting species. </p>
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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.