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172 results for “R script”
Data and R script for: Shoaling behaviour in response to turbidity in three-spined sticklebacks
<p class="MsoNormal"><span>Many fresh and coastal waters are becoming increasingly turbid because of human activities, which may disrupt the visually-mediated behaviours of aquatic organisms. Shoaling fish typically depend on vision to maintain collective behaviour, which has a range of benefits including protection from predators, enhanced foraging efficiency, and access to mates. Previous studies of the effects of turbidity on shoaling behaviour have focussed on changes to nearest neighbour distance and average group-level behaviours. Here, we investigated whether and how experimental shoals of three-spined sticklebacks (<em><span>Gasterosteus aculeatus</span></em>) in clear (<10 <span>Nephelometric Turbidity Units (NTU))</span> and turbid (~35 NTU<span>) </span>conditions differed in five local-level behaviours of individuals (nearest and furthest neighbour distance, heading difference with nearest neighbour, bearing angle to nearest neighbour, and swimming speed). These variables are important for the emergent group-level properties of shoaling behaviour. We found an indirect effect of turbidity on nearest-neighbour distances driven by a reduction in swimming speed, and a direct effect of turbidity which increased variability in furthest neighbour distances. In contrast, the alignment and relative position of individuals was not significantly altered in turbid compared to clear conditions. Overall, our results suggest that the shoals were usually robust to adverse effects of turbidity on collective behaviour, but group cohesion was occasionally lost during periods of instability.</span></p>
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
Data & R Scripts - Jönander et al. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.
<p>Data and R scripts associated with:</p> <p>Jönander, C., Backhaus, T., Dahllöf, I. (2022) Single substance and mixture toxicity of dibutyl-phthalate and sodium dodecyl sulphate to marine zooplankton. Ecotoxicol. Environ. Saf.</p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Data and R-scripts from: Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions
<p>This contains data and R-scripts used in: </p> <ul> <li>Bård-Jørgen Bårdsen and Jan Ove Bustnes (2022). Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions. Journal of Avian Biology.</li> </ul> <p>This study assessed the population viability of a population of the lesser black-backed gull (<em>Larus fuscus fuscus</em>) using data collected during 2005-2020 from a nature reserve in Northern Norway. The study merged results from statistical analyses of empirical data with a Leslie model. Here, we provide the underlying data, and the R-scripts used to analyse the data and run the model. The data set include information about reproduction at several stages (laying, hatching and fledgling), nest predation, and individual capture histories (used to estimate apparent survival; see <a href="https://doi.org/10.1111/jav.02953">Bårdsen and Bustnes 2022</a>).</p>
Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)
<p>This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022</p>
Data and R script for 'Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
Data and R scripts for the paper "New Evidence for a Directed Forgetting Effect in Source Memory and a Role of Source Feature Intrinsicality in the Item-Method"
<p>Data and R scripts from Experiment 1 and 2 of the paper "New Evidence for a Directed Forgetting Effect in Source Memory and a Role of Source Feature Intrinsicality in the Item-Method".</p>
Questionnaire, R Scripts and Response Data Set of the Survey on Functionally Similar Code Clones
<p>In 2017, we conducted an open online survey regarding functionally similar code clones with practitioners. We make the used questionnaire, the data from the response to the questionnaire and our used R script for the analysis openly available.</p>
Simulating the dispersal of Monochamus galloprovinciallis : R script of the dispersal model and video of the simulation
<p>This folder contains the R script to simulate the dispersal of Monochamus galloprovincialis from an individual-based model and the resulting video. This study was conducted in the frame of the FP7 project called "REPHRAME" and a working group of ANSES (French Agency for Food, Environmental and Occupational Health & Safety).</p> <p>This material complements the following publication:</p> <p>Robinet C, David G, Jactel H (2019) Modeling the distances traveled by flying insects based on the combination of flight mill and mark-release-recapture experiments. Ecological Modelling, 402: 85-92.<br> https://doi.org/10.1016/j.ecolmodel.2019.04.006</p>
Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'
<p>One raw data file and one R script that reproduces all analyses and figures reported in the paper '<strong>Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study</strong>' by Nettle et al. </p>
Data and R-script to analyse timing of northward migration of Sanderlings (Calidris alba) through Europe
<p>The data file contains data of observations of individual Sanderlings (<em>Calidris alba</em>) from specific wintering areas observed during migration in different latitude sectors through Europe. This is a subset of the raw data file which also includes observations at the wintering grounds. Each individual is indicated with a unique number in column “Individual”. The column “Winter Area” indicates the winter location of each individual in sectors of 5 degrees latitude (as depicted in Fig. 1 in the manuscript), with 7 indicating Scotland, 8 England, 9 France, 10 North Iberia, 11 Portugal, 13 Canary Islands, 15 Mauritania, 18 Ghana and 23 Namibia, as indicated in the column “Winter_Country”. “Date_DOY” indicates the day of year on which current observation was made. “Year” indicates the year during which an observation was made, “lat” is the latitude and “lon” the longitude of each observation in decimal degrees. “Lat_Sector” specifies in which of the sectors of 6 latitudinal degrees in Europe (see Fig. S1) the observation was made. “Sex” indicates the sex, based on molecular methods of an individual where 0=unknown, 1=female, 2=male. “Age”is the age of an individual, where 50=juvenile (i.e. less than 1 year old), 99=adult (i.e more than 1 year old) and 0 indicates that the age was unknown. “migration” indicates whether an observed individual is conisdered to be on migration (“yes” i.e. seen at least 2 latitudinal degrees north of its average winter location) or not (“no”). Further details can be found in the methods section in the manuscript.</p> <p>The R-script uses this dataset to analyse the variation in timing of migration through Europe for individual Sanderlings from different non-breeding areas. Comments and explanations can also be found in the script and in the methods section of the manuscript.</p> <p> </p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Time-to-fatigue data for five Cypriniformes fish species and R script for data analysis
<p>The Excel file contains data from fixed velocity fatigue experiments for five small-sized Cypriniformes fish species. The recorded data includes common and scientific names of fish species, date and time of test trial, test flume length [cm], flow velocity treatment [cm/s], time-to-fatigue [sec], test water temperature [°C], fish mass [g], fish fork length [cm], fish width [cm], and fish height [cm]. The readme text file explains the column names used in the Excel file. The Rscript file contains the code used to analyse the data.</p>
Physiological data and R script for running physiology combined model for Drosophila suzukii
<p>This is the dataset that accompanies an article entitled "The use of insect life tables in optimizing invasive pest distributional models" that would be published in Ecography. The dataset include two R script that used to generate physical model and the physiology combined model respectively. Our paper shows that the physiology combined model show good performance when applying ecological niche model in risk assessment. We addressed this by determining whether incorporating physiological data from life table analyses of an invasive insect, Drosophila suzukii, improved predictions of ecological niche models. The dataset also include the physiology data D. suzukii that we assembled for running our physiology combined model.</p>
Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories (dataset and R script)
<p>The present digital archive is the outcome of the paper: <strong>Palmisano, A., Bevan, A., Kabelindde, A., Roberts, N., and Shennan, S., 2021. <a href="https://doi.org/10.1007/s10963-021-09159-3">Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories</a>. <em>Journal of World Prehistory, 34 (3)</em>, </strong>381-432<strong>.</strong></p> <p>The dataset included here provides a collection of <strong>4,010</strong> radiocarbon dates from <strong>947</strong> archaeological sites for a period spanning between 11,000 and 1500 BP. In addition, the digital archive related to this paper provides reproducible analyses in the form of one script written in R statistical computing language.</p> <p>List of versions:</p> <ul> <li><strong>1.0.</strong> 4 August 2021 - First public release of the dataset on Zenodo. </li> </ul>
Data and scripts for: track2KBA: An R package for identifying important sites for biodiversity from tracking data
<p>Data derivates and analysis scripts (in R) used for the companion paper for the R package track2KBA.</p>
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)
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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.