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9,152 results for “Networking”
Constructing a social-behavioral association network to study management impact on waterbird community ecology using digital video recording cameras
<p>Studying social behavior and species associations in ecological communities is challenging because it is difficult to observe the interactions in the field. Animal behavior is especially difficult to observe when selection of habitat and activities are linked to energy costs of long-distance movement. Migrating communities tend to be resource specific and prefer environments that offer more suitability for coexisting in a shared space and time. Given the recent advances in digital technologies, digital video recording systems are gaining popularity in wildlife research and management. We used digital video recording cameras to study social interactions and species-habitat linkages for wintering waterbird communities in shared habitats. Examining over 8,640 hours of video footages, we built tetrapartite social behavioral association network of wintering waterbirds over habitat (n=5) selection events in sites with distinct management regimes. We analyzed these networks to identify hub species and species role in activity persistence, and to explore the effects of hydrological regime on these network characteristics. Although the differences in network attributes were not significant at treatment level (<i>p</i> = 0.297) in terms of network composition and keystone species composition, our results indicated that network attributes were significantly different (<i>p </i>= 0.000, <i>r<sup>2</sup></i><sup> </sup>= 0.278) at habitat level. There were evidences suggesting that the habitat quality was better at the managed sites, where the formed networks had more species, more network nodes and edges, higher edge density, and stronger intra- and inter-species interactions. In addition, we also calculated the species interaction preference scores (SIPS) and behavioral interaction preference scores (BIPS) of each network. The results showed that species synchronize activities in shared space for temporal niche partitioning in order to avoid or minimize any potential competition for shared space. Our social network analysis (SNA) approach is likely to provide a practical use for ecosystem management and biodiversity conservation.</p>
Neural network for forecasting egg abundance of the cabbage looper on three cole crops
<p>Neural network for predicting eggs of <em>Trichoplusia ni</em> in plants of brassica varieties using weather data (mean temperature, rainfall, and relative humidity from 15 prior), host plant (broccoli, cabbage, and cauliflower), and days after transplanting as input variables.</p>
Relative species abundance successfully predicts nestedness and interaction frequency of monthly pollination networks in an alpine meadow
<p>Plant-pollinator networks have been repeatedly reported as cumulative ones that are described with >1 years observations. However, such cumulative networks are composed of pairwise interactions recorded at different periods, and thus may not be able to reflect the reality of species interactions in nature (e.g., early-flowering plants typically do not compete for shared pollinators with late-flowering plants, but they are assumed to do so in accumulated networks). Here, we examine the monthly sampling structure of an alpine plant-pollinator bipartite network over a two-year period to determine whether relative species abundance and species traits better explain the network structure of monthly networks than yearly ones. Although community composition and species abundance varied from one month to another, the monthly networks (as well as the yearly networks described with annual pooled data) had a highly nested structure, in which specialists directly interact with generalist partners. Moreover, relative species abundance predicted the nestedness in both the monthly and yearly networks and accounted for a statistically significant percentage of the variation (i.e., 20%-44%) in the pairwise interactions of monthly networks, but not yearly networks. The combination of relative species abundance and species traits (but not species traits only) showed a similar prediction power in terms of both network nestedness and pairwise interaction frequencies. Considering the previously recognized structural pattern and associated mechanisms of plant-pollinator networks, we propose that relative species abundance may be an important factor influencing both nestedness and interaction frequency of pollination networks.</p>
Benchmarks for paper "Checking validity of the min-plus operations involved in the analysis of a real-time embedded network"
<p>Benchmarks for the paper "Checking validity of the min-plus operations involved in the analysis of a real-time embedded network"</p>
Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"
<p>Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"</p>
Quantifying the spatial homogeneity of urban road networks via graph neural networks
<p>Publication: Quantifying the spatial homogeneity of urban road networks via graph neural networks, Nature Machine Intelligence, 2022.</p> <p>Publication DOI: 10.1038/s42256-022-00462-y</p> <p>Please refer to https://github.com/jiang719/road-network-predictability.</p>
FoS Co-occurrence networks related to 103 FoS, and Direct ancestors to emerging topics between 2001~2010
<p>FoS Co-occurrence networks related to 311 Level 0 and 1 FoS, and Direct ancestors to emerging topics between 2000~2010.</p> <p> </p> <p>graphs.7z contains python graph binary files, and Golden dataset.7z contains python list binary files.</p>
"Temporal Network Dataset of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Temporal Network Dataset of OSS Programming Language Ecosystems" to the Mining Software Repositories 2022 conference. Contains the dataset, extracted metrics, and all scripts used for the construction and the analysis done in the paper.</p> <p> </p> <p>Anonymised for the double-blind review process.</p>
Adjacency matrix (.csv) of wood industry project network
<p>Adjacency matrix (.csv) of wood industry project network exported from R code.</p>
Pre-trained neural network model for pruning example code
<p>Pre-trained neural network models for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuResearch/automatic_pruning".</p>
current_network
<p>Network I trained, I need to test with kipoi</p>
Convolutional-neural-network-based reflection-waveform inversion (major revision)
<p>This dataset is used to plot the synthetic results shown in paper: "Convolutional-neural-network-based reflection-waveform inversion"</p>
Source data for "In-degree centrality in a social network is linked to coordinated neural activity"
<p>The following includes the source data for the manuscript titled "In-degree centrality in a social network is linked to coordinated neural activity". A second version of the source data ("Source Data_updated_011222.xlsx") includes the source data for the figures in the supplementary materials.</p>
Topographic deep neural networks predict the functional organization of the primate ventral visual pathway
<p>Recording of presentation at the Neuroscience 2021 annual meeting (held virtually). The abstract follows:</p> <p> </p> <p>The primate ventral visual pathway is organized into functional maps, including pinwheel-like arrangements of orientation-tuned neurons in primary visual cortex (V1) and patches of category-selective neurons in higher visual cortex. While deep convolutional neural networks (DCNNs) trained for object recognition accurately predict neural representations throughout the ventral pathway, they have no spatial layout for features at a given retinotopic location and are thus unable to predict the rich topographic organization of visual cortex. Here, we close this gap by first assigning each DCNN unit a position in a 2D cortical sheet, then training the network to minimize a cost function with two components: one encouraging accurate object recognition, and another favoring correlated responses among nearby units in each model layer (Figure 1A, 1B). </p> <p>We find that training with this composite spatial loss produces brain-like topographic maps in both early and later model layers (Figure 1B). Early layers contain smooth orientation preference maps with pinwheels, clusters of units preferring the same spatial frequency, and color-preference domains resembling V1 “blobs”. In a later layer of the same model, we observe clusters of category-selective units, e.g., face patches, whose spatial organization largely matches that found in primate higher visual cortex. Our model thus leverages local response correlations, which have been linked to theories of wire-length minimization, to accurately predict neuron responses and functional organization throughout the ventral visual pathway. In support of the wire-length minimization hypothesis, we find that our topographic DCNN would require shorter connections than a standard DCNN to support connections between similarly-tuned neurons within early (38% reduction) and later (31% reduction) model layers (Figure 1D). These results suggest that the functional organization of visual cortex can be explained by two constraints: the need to perform object recognition and pressure for local populations of neurons to have correlated responses.</p>
Original waveform of HAVESArray portable seismic network in Hainan Island and surrounding areas
<p>The waveform in this folder was recorded by the National Institute of Natural Hazards (NINH) (cut by catalog from 2018 and 2020). Waveform data were intercepted from the 20s before and 20s after the theoretical arrival-time of Pn. It is only used for scientific research.</p>
Ecosystem restoration and belowground multifunctionality: a network view
<p><span><span>Ecological restoration is essential to reverse land degradation worldwide. Most studies have assessed the restoration of ecosystem functions individually, as opposed to a holistic view. Here we developed a network-based ecosystem multifunctionality (EMF) framework to identify key functions in evaluating EMF restoration. Through synthesizing 293 restoration studies (2900 observations) following cropland abandonment, we found that individual soil functions played different roles in determining the restoration of belowground EMF. Soil carbon, total nitrogen and phosphatase were key functions to predict the recovery of belowground EMF. On average, abandoned cropland recovered about 19% of EMF during 18 years. The restoration of EMF became larger with longer recovery time and higher humidity index, but lower with increasing soil depth and initial soil carbon. Overall, this study presents a network-based EMF framework, effectively helping to evaluate the success of ecosystem restoration and identify the key functions.</span></span></p>
A validation sample (input, label, and prediction) for the network: "SeisLoca_Unet"
<p>This file is a validation sample including the input, label, and prediction. The data of variable "pred" can be obtained by running the prediction file: "prediction.py". Please remember to modified the data directory.</p>
Supporting Information for "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"
<p><strong>Introduction</strong></p> <p>The supporting information for this paper consists of a movie version of Figure 2 in the main paper, comparing the high frequency (HF) amateur radio observations to differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements. In this revised version of the repository, the TEC data has been reprocessed such that all GNSS data with elevation angles down to 10 degrees were kept when applying the Savitzky–Golay (SG) filter. After the SG filter was applied, data with elevation angles below 30 degrees were discarded. This was done to remove artifacts observed in the original movies.</p> <p> </p> <p><strong>Movie S1 (20171103 Ham and TEC LSTID.mp4)</strong></p> <p>(Top Panel) Time series showing the TX-RX distance for 14 MHz amateur radio spots in 2 min by 25 km bins from 1200 UT 3 Nov 2017 - 0000 UT 4 Nov 2017. (Bottom Panel) differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements over the Continental United States corresponding to the times indicated by the moving white vertical line in the top panel.</p> <p> </p> <p><strong>Movie S2 (20171103 Ham and TEC LSTID – With Arrow.mp4)</strong></p> <p>Same as movie S1, but with a fiducial black arrow indicating an estimated LSTID horizontal wavelength of 1681 km and propagation azimuth of 163°.</p>
OA eBook Usage Data Exchange Network Business Model Canvas
<p>This initial business model canvas was created to inform technical and governance roadmap discussions pertaining to the data exchange elements of the Developing a Data Trust for Open Access Ebook Usage project.</p>
Species and environmental datasets from Sierra Nevada, CA (USA) streams in lake-stream networks
<p>A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks have dominated research describing biodiversity change along longitudinal gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to specific stream ecosystems and biomes, and these frameworks have placed less emphasis on general mechanisms driving biodiversity patterns. Rethinking biodiversity patterns and processes in stream ecosystems with a focus on the overarching mechanisms common across ecosystems will provide a more holistic understanding of why biodiversity patterns vary along river networks. In this study, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: dispersal, speciation, niche selection, and ecological drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test established stream ecology frameworks and compared them to the TEC framework. Local diversity increased and β-diversity decreased moving downstream from the headwaters, consistent with the <em>river continuum concept</em> and the <em>small but mighty</em> framework of mountain stream biodiversity. Local diversity was also structured by distance below upstream lakes, where diversity increased with distance below upstream lakes, in support of the <em>serial discontinuity</em> <em>concept</em>. Despite some support for the biodiversity patterns predicted from the stream ecology frameworks, no single framework was fully supported, suggesting "context dependence". By framing our results under the TEC, we found species diversity was structured by niche selection, where local diversity was highest in environmentally favorable sites. Local diversity was also highest in sites with small community sizes, countering predicted effects of ecological drift. Moreover, higher β-diversity in the headwaters was influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites exhibit higher community variation. Taken together our results suggest that combining system specific ecological frameworks with the TEC provides a powerful approach for inferring the mechanisms driving biodiversity patterns and provides a path toward generalization of biodiversity research across ecosystems.</p>
ScienceDex guides
Understand access before you commit
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.