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491 results for “population modelling”
R code and data for running models in "Rapid Growth of the Swainson's Hawk Population in California since 2005"
<p>By 1979 Swainson's Hawks (<em>Buteo swainsoni)</em> had declined to as low as 375 breeding pairs throughout their summer range in California. Shortly thereafter the species was listed as threatened in the state. To evaluate the hawk's population trend since then, we analyzed data from 1,038 locations surveyed throughout California in either 2005, 2006, 2016, or 2018. We estimated a total statewide population of 18,810 breeding pairs (95CI: 11,353–37,228) in 2018, and found that alfalfa (<em>Medicago sativa</em>, lucerne) cultivation, agricultural crop diversity, and the occurrence of non-agricultural trees for nesting were positively associated with hawk density. We also concluded that California's Swainson's Hawk summering population grew rapidly between 2005 and 2018 at a rate of 13.9% per year (95CI: 7.8–19.2%). Despite strong evidence that the species has rebounded overall in California, Swainson's Hawks remain largely extirpated from Southern California where they were historically common. Further, we note that the increase in Swainson's Hawks has been coincident with expanded orchard and vineyard cultivation which is not considered suitable for nesting. Therefore, we recommend more frequent, improved surveys to monitor the stability of the species' potential recovery and to better understand the causes. Our results are consistent with increasing raptor populations in North America and Europe that contrast with overall global declines, especially in the tropics.</p>
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Preprocessed Datasets
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains the preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the raw datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
Model Zoo: A Dataset of Diverse Populations of Neural Network Models - USPS
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from USPS. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "usps_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>
The appendix for dynamic model of respiratory infectious disease transmission by population mobility based on city network
<p>First, a scale-free city network was established, and the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset, and hospitalisation, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterised after summarising the information. Finally, the reliability of the model was verified.</p>
Fig. 3. A 50 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 3. A 50% majority rule consensus tree from MSC-bootstrap analysis of the "All individuals" dataset. Color labels as in figures 1 and 2.
Fig. 2 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 2. Maximum likelihood tree of the "All individuals" concatenated dataset generated with IQ-TREE.Topology with branch lengths in substitutions/site shown in bottom left. Black dots indicate deeper nodes with bootstrap and SH-like aLRT both under 90, otherwise deeper nodes are above 90. Support values for shallow nodes not shown.
Fig. 1 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 1. Collection locations in western North America. Colors for S. MCCooki are based on phylogenetic group assignment (see Fig. 2). Female (left) and male (right) S. MaxiMa from Davis, CA shown in inset.
Fig. 5 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 5. Cluster analysis of unlinked SNPs using VAE and carapace length of (a) male and (b) female individuals from Davis, CA. Individuals with carapace length (a)>8.5 mm (male) and (b)>10.0 mm (female) are indicated as S. MaxiMa.
Fig. 4 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 4. Cluster analysis of unlinked SNPs using VAE for "Western group" individuals. Individuals are assigned to clades from the concatenated analysis (see Fig. 1).
Fig. 8 in Phylogenomic Variation at the Population-Species Interface and Assessment of Gigantism in a Model Wolf Spider Genus (Lycosidae, Schizocosa)
Fig. 8. Waveforms from substrate-borne vibrations produced by males in response to female pheromone cues. Phylogeny is based on maximum likelihood analysis of the concatenated dataset generated with IQ-TREE (see Fig. 2).
Dataset for "Gravity model explained by the radiation model on a population landscape"
<p>This dataset contains simulated data used in the paper "Gravity model explained by the radiation model on a population landscape".</p>
Dataset belonging to SNF project: Use of physiologically based pharmacokinetic modelling to simulate dosing requirements of long-acting intramuscular antiretroviral drugs in special populations and to manage drug-drug interactions
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Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"
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Evaluating the effects of nest management on a recovering raptor using integrated population modeling
<p>Data and code from Cappello et al. 2024 Ecosphere</p> <p>Evaluating the effects of nest management on a recovering raptor using integrated population modeling.</p> <p>Abstract: Evaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle (Haliaeetus leucocephalus) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species’ population trend and an evaluation of ongoing nest-level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36-year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 77 females in 1993 to 180 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM-PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.</p>
Dataset for "Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions" in the Annals of Applied Statistics
<p>This is the dataset to be used for the paper in the Annals of Applied Statistics titled:</p> <p><strong>"Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions"</strong></p> <p>Download, unzip and place in the ./<strong>paper-data</strong> directory in the R package repository <a href="https://github.com/sangwon-hyun/flowmix">https://github.com/sangwon-hyun/flowmix</a>. Then, run the code in <strong>./paper-code</strong> to produce the figures and tables.</p>
Supplementary material 6 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Stochastic population growth for alternative seed bank scenarios (graphic results, population dynamics) :
Supplementary material 8 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
r-cost curves for different seed survival scenarios and reference data sets (graphic results, population dynamics) :
An evaluation of pool-sequencing transcriptome-based exon capture for population genomics of non-model species.
<p>This archive is associated with the article “An evaluation of pool-sequencing transcriptome-based exon capture for population genomics of non-model species.”. Authors: Emeline Deleury, Thomas Guillemaud, Aurelie Blin & Eric Lombaert.</p> <p>The archive contains :<br> - The sequences of the 5,717 Harmonia axyridis randomly selected CDS (5717-targeted-CDS-sequences.gff3, sequence in FASTA format at the end of the file)<br> - For the subset of 3,161 targeted CDS that have a genomic match over their entire length, the positions of exons on transcripts (3161-targeted-CDS-EXON-POSITIONS.csv)</p>
Fig. 6 a–d. Ecological niche models for H. fumariifolia populations. a in Refugia and geographic barriers of populations of the desert poppy, Hunnemannia fumariifolia (Papaveraceae)
Fig. 6 a–d. Ecological niche models for H. fumariifolia populations. a Prediction of suitable habitat in the current environment. b Prediction projected onto past climatic layers (LGM; CCSM). c Prediction under
Cellular population data (Agent-based model CRC)
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.