Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies. Methods The dataset and code stored here is for Case Studies 1 and 2 in the paper. Datsets were generated using simulations in R. Here we provide 1) the R code used for the simulations; 2) the simulation outputs (as .RDS files); and 3) the R code to analyse simulation outputs and generate the tables and figures in the paper.

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.

SSP (simulation-based sampling protocol) is an R package that uses simulations of ecological data and dissimilarity-based multivariate standard error (MultSE) as an estimator of precision to evaluate the adequacy of different sampling efforts for studies that will test hypothesis using permutational multivariate analysis of variance. The procedure consists in simulating several extensive data matrixes that mimic some of the relevant ecological features of the community of interest using a pilot data set. For each simulated data, several sampling efforts are repeatedly executed and MultSE calculated. The mean value, 0.025 and 0.975 quantiles of MultSE for each sampling effort across all simulated data are then estimated and standardized regarding the lowest sampling effort. The optimal sampling effort is identified as that in which the increase in sampling effort does not improve the highest MultSE beyond a threshold value (e.g. 2.5 %). The performance of SSP was validated using real data. In all three cases, the simulated data mimicked the real data and allowed to evaluate the relationship MultSE – n beyond the sampling size of the pilot studies. SSP can be used to estimate sample size in a wide variety of situations, ranging from simple (e.g. single site) to more complex (e.g. several sites for different habitats) experimental designs. The latter constitutes an important advantage in the context of multi-scale studies in ecology. An online version of SSP is available for users without an R background.

The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

The GOES-R PLT Fly’s Eye GLM Simulator (FEGS) dataset consists of lightning flash, lightning pulse, and radiance data collected by the FEGS flown aboard a NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). These data files are available in ASCII format with browse imagery available in PNG format.

Example computer code (R script) and associated data to run the Greater Glider simulation example in the manuscript.

Contains an explainer document, data documentation, raw data, and R files.

The shiny app is available at: https://aaroncaldwell.us/project/shinypowerapp/ and https://aaroncaldwell.us/project/shinyexactapp/

A complete manual using Superpower is available at: https://aaroncaldwell.us/SuperpowerBook/

Researchers often use an analysis of variance (ANOVA) when reporting results of study. In order for an ANOVA to be informative researchers need to ensure their study is adequately powered. Yet, power analyses for factorial ANOVA designs are often challenging. First, current software solutions do not enable power analyses for complex designs with many within-subject factors. Second, power analyses often need partial eta-squared or Cohen's f as input, but these effect sizes do not generalize to different experimental designs. We have created R functions and an online Shiny app that performs simulations for ANOVA designs. Our functions allow for up to three within- or between-subject factors, with an unlimited number of levels. Predicted effects are entered by specifying means, standard deviations, and correlations (for within-subject factors). The simulation provides power calculations for all ANOVA main effects and interactions. In addition, power calculations are provided for pairwise comparisons and there are range of options to correct for multiple comparisons. The simulation plots p-value distributions for all tests. This tutorial will demonstrate how to perform power analysis for ANOVA designs. The simulations illustrate important factors that determine the statistical power of factorial ANOVA designs. The code and Shiny app will enable researchers without extensive programming experience to perform power analyses for a wide range of ANOVA designs.

This release contains the data artifacts of the paper A Reference Architecture for Datacenter Scheduling presented at Supercomputing 2018

For the paper, experiments have been run on the following traces:

• Askalon (W-Eng) - askalon_workload_ee
• Chronos (W-Ind) - chronos_exp_noscaler_ca

Each of the directories for the traces have the following structure:

• /setup.txt
  This text file describes the trace used for the experiment in addition to the amount of times the experiment was repeated and the amount of warm-up experiments.
• /setup.json
  This JSON file describes the topology of the datacenter used in the experiments. Each item represents the identifiers of the resource (here, CPU type) to use in the machine. The available CPU types are (1) Intel i7 (4 cores, 4100 MHz) and (2) Intel i5 (2 cores, 3500 MHz).
• /trace
  This directory contains the trace used in the simulation. The trace is stored in the Grid Workload Format. See the Grid Workload Archive for more information.
• /data/experiments.csv
  A CSV file containing information of all simulations that have been run on the OpenDC platform for this experiment.
• /data/job_metrics.csv
  A CSV file containing metrics (NSL, JMS, etc.) for each job that ran during the simulations.
• /data/stage_measurements.csv
  A CSV file containing timing measurements for the scheduling stages that ran during the simulations.
• /data/task_metrics.csv
  A CSV file containing metrics for each task that ran during the simulations.

• /data/tasks.csv
  A CSV file containing information about the tasks (submit time, runtime, etc.) that ran during the simulations as extracted from the traces.

  Additionally, we describe the format of each data file in the associated metadata file.

Hardware

The hardware used for running the experiments is a MacBook Pro with a 2,9 GHz Intel Core i7 processor and 16 GB 2133 MHz LPDDR3 internal memory.

Reproduction

This section describes the instructions for reproducing the paper results using a provided Docker image. Please make sure you have Docker installed and running.

For reproduction, you will run the following experiments:

• askalon_workload_ee
  This is the large experiment of the paper and will take approximately 4 hours to complete similar hardware.
• chronos_exp_noscaler_ca
  This is the smaller experiment of the paper and will take approximately 5 minutes to complete on similar hardware.

The Docker image atlargeresearch/sc18-experiment-runner can be used for running the experiments. A volume can be attached to the directory /home/gradle/simulator/data to capture the results of the experiments.

Make sure you have, in your current working directory, the following files:

• /setup.json
  This JSON file describes the topology of the datacenter and can be found in this archive at askalon_workload_ee/setup.json.
• /askalon_workload_ee.gwf
  This file contains the trace for the Askalon workload. This file can be found in the archive at askalon_workload_ee/trace/askalon_workload_ee.gwf.
• /chronos_exp_noscaler_ca.gwf
  This file contains the trace for the Chronos workload. This file can be found in the archive at chronos_exp_noscaler_ca/trace/chronos_exp_noscaler_ca.gwf.

Then, you can start the Askalon experiments as follows:

$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/askalon_workload_ee.gwf

The experiment runner can be configured with the following options

• -r, --repeat
  The amount of times to repeat an experiment for each scheduler.
• -w, --warm-up
  The amount of times to warm-up the simulator for each scheduler.
• -p, --parallelism
  The number of experiments to run in parallel.
• --schedulers
  The list of schedulers to test, separated by spaces. The following schedulers are available: SRTF-BESTFIT, SRTF-FIRSTFIT, SRTF-WORSTFIT, FIFO-BESTFIT, FIFO-FIRSTFIT, FIFO-WORSTFIT, RANDOM-BESTFIT, RANDOM-FIRSTFIT, RANDOM-WORSTFIT.

After the Askalon experiments have been finished, you can start the Chronos experiments. Make sure you have a copy of the result files in your directory as the result files will be overwritten.

$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/chronos_exp_noscaler_ca.gwf

tree_constant_07-15-12R file for running the 'clade-constant' simulations as described in the paper.ntaxa_constant_07-01-12R script for the 'size-constant' simulations described in the paper.100extant_07-28-12R script file for simulations of extant only taxa.ratesVsResolved_anag_08-18-12R script for simulations with varying sampling and differentiation rates under a pure-anagenesis model.propDur_r0.01_09-15-12R script simulating clades under various models of differentiation and estimating the proportion of taxa with an observable, sampled duration.modeComparison_propDur_workspace_09-16-12An R workspace file containing all simulation data needed for plotting the figures where multiple models of differentiation are compared (Fig. 3-6, 8). Can be read into R with function load().ratesVresolved_08-16-12An R workspace file containing all simulation data needed for plotting the results of the simulations comparing sampling and differentiation rates (i.e. Fig. 7). Can be read into R with func...

Simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].

Two *.zip files are provided:

data.zip - contains the datasets of train/test simulations produced using the Sheet Model algorithm [1, 2] models.zip - contains the GNN model weights (.pkl) + relevant training information and model parameters (.yml and *.txt)

Source Code

The source code used to produce the data, train, and test the models can be found at: https://github.com/diogodcarvalho/gns-sheet-model

References

[1] D. D. Carvalho, D. R. Ferreira, L. O. Silva, "Learning the dynamics of a one-dimensional plasma model with graph neural networks", arXiv:2310.17646 (2023)

[2] J. Dawson, "One‐Dimensional Plasma Model", The Physics of Fluids 5.4 (1962): 445-459.

Example codes to run 8 hydrological modelling R packages. Each script enables application of the models on a simple hydrology example.

This dataset contains the following ".PDF", ".R", and ".RData" files: (1) A PDF file "Description of the SimuBP function.PDF"; (2) R scripts for Algorithm 1 (SimuBP), Algorithm 2, and Algorithm 3; (3) R scripts for Simulations S1a, S1b, S1c, S2a, S2b, S2c, and S3a; (4) An R script "pLD.R" used in Simulation S1c. (5) Results generated in Simulations S1a, S1b, S1c, S2a, S2b, and S3a.

Data and R scripts for counterfactual simulations of representation in the US House and UK House of Commons. Article abstract: We show how to use multilevel modeling and post-stratification to estimate legislative outcomes under counterfactual representation schemes that e.g. boost the representation of women or translate votes into seats differently. We apply this technique to two research questions: (1) Would the U.S. Congress be less polarized if state delegations were formed according to the principle of party proportional representation? (2) Would there have been stronger support for legalizing same-sex marriage in the U.K. House of Commons if Parliament more closely reflected the population in gender and age?

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).

This material complements the following publication:

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. https://doi.org/10.1016/j.ecolmodel.2019.04.006

R code for creating the introduced visualizations and simulating the demonstration data.

The data is a programming code written in Java which has been used to generate results for an article accepted for publication at the robotic conference DARS 2016. The code will be made available and become public at the end of the conference in November 2016. The paper will have a doi to the repository.

Simulation output from CMAQ runs for Uinta Basin. This dataset is associated with the following publication: Matichuk, R., G. Tonnesen, D. Luecken, R. Gilliam, S. Napelenok, K. Baker, D. Schwede, B. Murphy, D. Helmig, S. Lyman, and S. Roselle. Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin.. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 122(24): 13545-13572, (2017).

This dataset includes the source computer code and supporting data files for the predator-prey simulation model (parameterized for summer flounder, Paralichthys dentatus) developed to investigate bottom-up effects defined to be temporal pulses in prey abundance on predator growth, production, and fisheries management. The model is age-structured and spatially explicit to accommodate ontogenetic dietary changes and seasonal migrations, respectively. Three general prey groups were modeled and assumed to be small crustaceans, forage fish, and larger fish prey. The code was written in R by Andre Buchheister.

The dataset includes:
Source computer code (core simulation): PredPreySim.r
Source computer code (graphing results): PredPreySim_Graphing.r
Data file (stock recruitment): Stock_Recruitment.csv
Metadata file (simulation model parameter descriptions): Parameter_Categories.csv
Data file (summer flounder growth): LW_Age.csv

See http://www.vims.edu/research/departments/fisheries/programs/multispecies_fisheries_research/index.php for more information about growth data.

Related Publications:
Buchheister, A., M.J. Wilberg, T.J. Miller, and R.J. Latour. In press. Simulating bottom-up effects on predator productivity and consequences for the rebuilding timeline of a depleted population. Ecological Modelling.

Replication data to reproduce the results presented in J. Schneider & S. Kiemle, K. Heck, Y. Rothfuss, I. Braud, R. Helmig, J. Vanderborght (2024) Analysis of Experimental and Simulation Data of Evaporation-Driven Isotopic Fractionation in Unsaturated Porous Media. (Under review) Vadose Zone. The replication data contains numerical data sets generated via the numerical simulator tools DuMuX and SiSPAT-isotope and experimental data published by Rothfuss (2015). Further, this data set provides python scripts and a MatLAB script to reproduce the displayed figures in the linked publication.