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481 results for “network modeling”
Climate change modelling indicates extensive range contractions for a scarce southern African endemic and minimal protected area network within its future climatically suitable range
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Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
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Data from: Mechanisms of reciprocity and diversity in social networks: a modelling and comparative approach
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Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk
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Data from: Modelling the current and future biodiversity distribution in the Chilean Mediterranean Hotspot. The role of protected areas network in a warmer future
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Data from: Predicting species occurrences with habitat network models
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Data from: A stochastic generative model for citation networks among academic papers
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Modeling plate and spring reverberation using a DSP-informed deep neural network
<p>Accompanying audio samples for the paper:</p> <p>Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Modeling plate and spring reverberation using a DSP-informed deep neural network” in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.</p> <p>Dry and wet bass and guitar recordings.</p> <p>Bass and Guitar dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html</p> <p>Plate Reverb - Bass - recordings are taken from the IDMT-SMT-Audio-Effects dataset. Plate settings are the following:</p> <ul> <li><strong>Smaertelectronix ambience</strong>: ’Gating Amount - 0’, ’Gating Attack" - 10 ms’, ’Gating Release - 10 ms’, ’Decay Time - 2225 ms’, ’Decay Diffusion - 50%’, ’Decay Hold - off’, ’Shape Size - 16%’, ’Shape Predelay - 0 ms’, ’Shape Width - 100%’, ’Shape Quality - 100%’, ’Shape Variation - 0’, ’EQ Bass Frequency - 43 Hz’, ’EQ Bass Gain - −7.8 dB’, ’EQ Treble Frequency - 5044 Hz’, ’EQ Treble Gain - −3.7 dB’, ’Damping Bass Frequency - 158 Hz’, ’Damping Bass Amount - 87%’, ’Damping Treble Frequency - 8127 Hz’, ’Damping Treble Amount - 32%’, ’Dry - −Inf’, ’Wet - 0dB’.</li> </ul> <p>Spring Reverb - Bass and Guitar - recorded from the spring reverb tank<strong>: Accutronics </strong><strong>4</strong><strong>EB</strong><strong>2</strong><strong>C</strong><strong>1</strong><strong>B</strong>: ’Dry Mix - 0%’, ’Wet Mix - 100%’</p> <p>Plate<em> </em>reverb samples correspond to a VST audio plug-in, while spring<em> </em>reverb samples are recorded using an analog reverb tank which is based on 2 springs placed in parallel.</p> <p>The recordings are downsampled to 16 kHz. Also, since the plate reverb samples have a fade-out applied in the last 0.5 seconds of the recordings, we process the spring reverb samples accordingly.</p>
Integrated Performance Evaluation of Extended Queueing Network Models with Line
<p>Numerical examples in the paper "Integrated Performance Evaluation of Extended Queueing Network Models with Line" by G. Casale accepted for publication in the Winter Simulation Conference (WSC) 2020.</p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
<p>This repository contains the cornerplots of relevant parameters for the 23 protoplanetary disks modeled in the manuscript "Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses" (Ribas et al. 2020).</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The aim of the project is to analyse the inventory and functioning of scientific terms and special lexemes in textbooks for secondary schools of the Russian Federation with the help of modern methods of natural language processing and deep learning. The number of terms from different fields of knowledge that a pupil should learn during secondary school studies has never been evaluated. According to the preliminary evaluations made on the basis of the Model Basic Curriculum for General and Secondary Education in 2015 only the subject "Russian language" presupposes that a pupil finishing the 11th grade of secondary school should be able to understand, recognise and use about 1000 terms and terminological combinations. Thus, taking into account the number of school subjects, the total number of special vocabulary units studied in general education schools is measured in thousands. At the same time, the comparative characteristics of the inventory and functioning of terms in textbooks for different school subjects are not studied and remain unknown. The correlation between the terminological density of the text in school textbooks for different subjects and the place occupied by these subjects in the curriculum is not clear. The traditional way of compiling lists of scientific terms is simply by gleaning them from special texts and writing down manually. If this method is reliable in terms of intellectualisation of selection principles, it cannot be applied to large data sets and does not reflect either the frequency of use of terms, or the specificity of their syntagmatic connections, or the systemic relationship between terms. The current project is aimed at filling this gap by means of 1) creating a full-text corpus of school textbooks for 5–11 classes included in the Federal List compiled by the Ministry of Education, 2) automatic extraction, stratification, and mapping of terms with the help of distribution semantics algorithms, 3) creation and training of a deep neural network capable of predicting the subject, level of education and educational topic given a group of vector representations of terms as input. The results of the research can be of fundamental interest in the perspective of terminology science development and also have practical applications in the creation of different types of educational literature.</p> <p><em>Funding: The reported study was funded by RFBR, project number 19-29-14032</em></p>
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
<p>The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (<a href="https://github.com/swagnercarena/ovejero">https://github.com/swagnercarena/ovejero</a>).</p>
Data from: Modelling social care provision in an agent-based framework with kinship networks
Current demographic trends in the UK include a fast-growing elderly population and dropping birth rates, and demand for social care amongst the aged is rising. The UK depends on informal social care -- family members or friends providing care -- for some 50% of care provision. However, lower birth rates and a graying population mean that care availability is becoming a significant problem, causing concern amongst policy-makers that substantial public investment in formal care will be required in decades to come. In this paper we present an agent-based simulation of care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need, and geographical location. Results demonstrate that the model can produce similar patterns of care need and availability as is observed in the real world, despite the model containing minimal empirical data. We propose that our model better captures the complexities of social care provision than other methods, due to the socioeconomic details present and the use of kinship networks to distribute care amongst family members.
Data from: A model to identify urban traffic congestion hotspots in complex networks
The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real- traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.
Data from: Trait-based modeling of multi-host pathogen transmission: plant-pollinator networks
Epidemiological models for multi-host pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities, that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution, but have not accounted for within-species trait variation or between-species trait overlap. Here, we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop SIS models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks, and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and co-specialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions, in any community.
Data from: The response of migratory populations to phenological change: a Migratory Flow Network modelling approach
1. Declines in migratory species have been linked to anthropogenic climate change through phenological mismatch, which arises due to asynchronies between the timing of life-history events (such as migration) and the phenology of available resources. Long-distance migratory species may be particularly vulnerable to phenological change in their breeding ranges, since the timing of migration departure is based on environmental cues at distant non-breeding sites. 2. Migrants may, however, be able to adjust migration speed en route to the breeding grounds and thus ability of migrants to update their timing of migration may depend critically on stopover frequency during migration; however, understanding how migratory strategy influences population dynamics is hindered by a lack of predictive models explicitly linking habitat quality to demography and movement patterns throughout the migratory cycle. 3. Here, we present a novel modelling framework, the Migratory Flow Network (MFN), in which the seasonally varying attractiveness of breeding, winter, and stopover regions drives the direction and timing of migration based on a simple general flux law. 4. We use the MFN to investigate how populations respond to shifts in breeding site phenology based on their frequency of stopover and ability to detect and adapt to these changes. 5. With perfect knowledge of advancing phenology, 'jump' migrants (low frequency stopover) require more adaptation for populations to recover than 'hop' and 'skip' (high or medium frequency stopover) migrants. If adaptation depends on proximity, hop and skip migrants' populations can recover but jump migrants cannot adjust and decline severely. 6. These results highlight the importance of understanding migratory strategies and maintaining high-quality stopover habitat to buffer migratory populations from climate-induced mismatch. 7. We discuss how MFNs could be applied to diverse migratory taxa, and highlight the potential of MFNs as a tool for exploring how migrants respond to other environmental changes such as habitat loss.
Data from: Spatially structured statistical network models for landscape genetics
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape-genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non-binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape-genetic data. We further explore a set of spatial-statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape-genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
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.