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38 results for “opportunistic data”
Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains data, scripts, and code. It is organized into two main directories: <em>Materials and Methods,</em> and <em>Results</em>.</p> <h2>Materials and Methods</h2> <p>The raw data can be accessed in the <em>Materials_and_Methods/Data directory</em>. The processed data used for modeling is available in <em>Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData</em>. All scripts for data processing are located in <em>Materials_and_Methods/Processing_scripts</em>. The <em>Plots_and_Figures </em>directory<em> </em>includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is <em>Main_script.R</em>.</p> <h2>Results</h2> <p>The <em>Results</em> directory contains three subdirectories and the script <em>Models_Outputs.R</em>. The <em>Results/Fitted_models</em> directory includes all .RData files with fitted models for each species. The script <em>Models_Outputs.R </em>generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the <em>Results/Plots </em>folder, while <em>Results/RData</em> contains intermediate .RData files created by <em>Models_Outputs.R</em> to manage computational costs.</p>
A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble
<p>Supporting information for manuscript titled "A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble"</p><p>Datasets S1. Early to Middle Miocene NetCDF files: E2MMIO280.nc, E2MMIO400.nc, E2MMIO560.nc, E2MMIO850.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S2 Middle to Late Miocene NetCDF files: M2LMIO280.nc, M2LMIO400.nc, M2LMIO560.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S3 Preindustrial NetCDF files: PI contains MioMIP1 climate variables used to make manuscript figures.</p><p>Dataset S4 CSV file MioMIP_MAP_compilation contains newly revised miocene reconstructed mean annual precipitation from proxies. </p>
Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?
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Data from: Evidence for structure and variable recombination rates among Dutch populations of the opportunistic human pathogen Aspergillus fumigatus
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Data from: Tracing the effects of eutrophication on molluscan communities in sediment cores: outbreaks of an opportunistic species coincide with reduced bioturbation and high frequency of hypoxia in the Adriatic Sea
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Data from: Broad thermal tolerance is negatively correlated with virulence in an opportunistic bacterial pathogen
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Data from: Rich resource environment of fish farms facilitates phenotypic variation and virulence in an opportunistic fish pathogen
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Data from: Growth and nitrogen uptake characteristics reveal outbreak mechanism of the opportunistic macroalga Gracilaria tenuistipitata
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Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
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Data from: Opportunistic records reveal Mediterranean reptiles’ scale‐dependent responses to anthropogenic land use
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The impact of data quality filtering of opportunistic citizen science data on species distribution model performance: dataset used for Maxent modelling.
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Data from: DNA metabarcoding unveils multi‐scale trophic variation in a widespread coastal opportunist
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Data from: Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat
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Data from: Use of long-term opportunistic surveys to estimate trends in abundance of hibernating Townsend's big-eared bats
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Data from: Fitness trade-offs explain low levels of persister cells in the opportunistic pathogen Pseudomonas aeruginosa
Microbial populations often contain a fraction of slow-growing persister cells that withstand antibiotics and other stress factors. Current theoretical models predict that persistence levels should reflect a stable state in which the survival advantage of persisters under adverse conditions is balanced with the direct growth cost impaired under favourable growth conditions, caused by the nonreplication of persister cells. Based on this direct growth cost alone, however, it remains challenging to explain the observed low levels of persistence (<<1%) seen in the populations of many species. Here, we present data from the opportunistic human pathogen Pseudomonas aeruginosa that can explain this discrepancy by revealing various previously unknown costs of persistence. In particular, we show that in the absence of antibiotic stress, increased persistence is traded off against a lengthened lag phase as well as a reduced survival ability during stationary phase. We argue that these pleiotropic costs contribute to the very low proportions of persister cells observed among natural P. aeruginosa isolates (3 × 10−8–3 × 10−4) and that they can explain why strains with higher proportions of persister cells lose out very quickly in competition assays under favourable growth conditions, despite a negligible difference in maximal growth rate. We discuss how incorporating these trade-offs could lead to models that can better explain the evolution of persistence in nature and facilitate the rational design of alternative therapeutic strategies for treating infectious diseases.
Data from: Coinfection outcome in an opportunistic pathogen depends on the inter-strain interactions
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Data from: Unravelling species boundaries in the Aspergillus viridinutans complex (section Fumigati): opportunistic human and animal pathogens capable of interspecific hybridization
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Data from: Fitness trade-offs explain low levels of persister cells in the opportunistic pathogen Pseudomonas aeruginosa
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International Brain Laboratory public data
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OpenNeuro
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