The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.

Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling).

The .Rmd file contains all the code scripts to analyze the data as explained in the method section and plot figures, generate slope values, and report significance levels

This repository holds data and scripts used for the analyses presented in a manuscript entitled "Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA" submitted to JGR Biogeosciences.

Data for the scaling of metabolic rate, rates of growth, and annual reproduction for animals. Data and R scripts associated with "Metabolic scaling is the product of life history optimization"

The attached files were used to estimate the population size of golden-cheeked warblers in 2018 and develop a habitat model for the breeding range in Texas.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

This child item describes R code used to determine public supply consumptive use estimates. Consumptive use was estimated by scaling an assumed fraction of deliveries used for outdoor irrigation by spatially explicit estimates of evaporative demand using estimated domestic and commercial, industrial, and institutional deliveries from the public supply delivery machine learning model child item. This method scales public supply water service area outdoor water use by the relationship between service area gross reference evapotranspiration provided by GridMET and annual continental U.S. (CONUS) growing season maximum evapotranspiration. This relationship to climate at the CONUS scale could result in over- or under-estimation of consumptive use at public supply service areas where local variations differ from national variations in climate. This method also assumes that 50% of deliveries for total domestic and commercial, industrial, and institutional deliveries is used for outdoor purposes. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following file: PS_ConsumptiveUse.zip - a zip file containing input datasets, scripts, and output datasets

In the data you can find the age and gender as demographics for 439 participants among Syrian Refugees living in Gaziantep province of Turkey. then you can find M1, M2, M3,....M21 for each item of SCAS-R Social adaptation scale.

Repository of data, code, and analysis for manuscript titled "Scaling COVID-19 rates with population size in the United States".

Data and R code for Continental-scale crop production as a function of the soil microbiome

AbstractMetabolic scaling, whereby larger individuals use less energy per unit mass than smaller ones, may apply to the combined metabolic rate of group-living organisms as group size increases. Spiders that form groups in high disturbance environments can serve to test the hypothesis that economies of scale benefit social groups. Using solitary and group-living spiders, we tested the hypothesis that spiders exhibit negative allometry between body or colony mass and the standing mass of their webs and whether, and how, such a relationship may contribute to group-living benefits in a cooperative spider. Given the diverse architecture of spider webs—orb, tangle, and sheet-and-tangle, and associated differences in silk content, we first assessed how standing web mass scales with spider mass as a function of web architecture and whether investment in silk differs among web types. As group-living spiders are predominantly found in clades that build the presumably costlier sheet-and-tangle webs, we then asked whether cost-sharing through cooperative web maintenance contributes to a positive energy budget in a social species. We found that larger spiders had a relatively smaller investment in silk per unit mass than smaller ones, but more complex sheet-and-tangle webs contained orders of magnitude more silk than simpler orb or tangle ones. In the group-living species, standing web mass per unit spider mass continued to decline as colony size increased with a similar slope as for unitary spiders. When web maintenance activities were considered, colonies also experienced reduced mass-specific energy expenditure with increasing colony size. Activity savings contributed to a net positive energy balance for medium and large colonies after inputs from the cooperative capture of large prey were accounted for. Economies of scale have been previously demonstrated in animal societies characterized by reproductive and worker castes, but not in relatively egalitarian societies as those of social spiders. Our findings illustrate the universality of scaling laws and how economies of scale may contribute to setting limits on social behaviour and hunting strategies. Usage notesData are split into two folders. Folder entitled "raw_data" includes all raw field and laboratory data (.csv files), plus an accompanying metadata file. The folder entitled "downstream_data" includes .csv files created during the cleaning and analysis process, and includes an accompanying metadata file. The folder entitled "code" includes all of the R scripts used for analysis. SpiderCommunity_WebScaling.R runs the analyses of economies of scale in the web-building spider community at Jatun Sacha. Anelosimus_total_outputs.R processes energetic outputs from web-building, Anelosimus_total_inputs.R processes energetic inputs from prey capture, and "Anelosimus_BudgetAnalysis_Figures.R runs the linear, quadratic and cubic spline analyses and generates figures used in the paper.

The data and codes that are used to illustrate the application of two-scale spatial component analysis in evaluating the regionalization results.

The R script and data are available for download:
https://metadata.bonares.de/smartEditor/rest/upload/ID_7050_2020_05_13_Beule_Karlovsky.zip

R script and data for the reproduction of the paper entitled "Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities" by Lukas Beule and Petr Karlovsky.

Comparison of scaling with ranked subsampling (SRS) with rarefying for the normalization of species count data in ecology. The example provided is a library obtained from next generation sequencing of a soil bacterial community. Different alpha diversity measures, community composition, and relative abundance of taxonomic units are compared.

AbstractWhile a large family of unfolding models for Likert-scale response data have been developed for decades, very few applications of these models have been witnessed in practice. There may be several reasons why these have not appeared more widely in published research, however one obvious limitation appears to be the absence of suitable software for model estimation. In this article, the authors demonstrate how the mirt package can be adopted to estimate parameters from various unidimensional and multidimensional unfolding models. To concretely demonstrate the concepts and recommendations, a tutorial and examples of R syntax are provided for practical guidelines. Finally, the performance of mirt is evaluated via parameter-recovery simulation studies to demonstrate its potential effectiveness. The authors argue that, armed with the mirt package, applying unfolding models to Likert-scale data is now not only possible but can be estimated to real-datasets with little difficulty.

Scaling with ranked subsampling (SRS) is an algorithm for the normalization of species count data in ecology. So far, SRS has successfully been applied to microbial community data. "SRS is now available on CRAN: https://CRAN.R-project.org/package=SRS" An implementation of SRS in R is available for download: https://metadata.bonares.de/smartEditor/rest/upload/ID_7049_2020_05_13_SRS_function_v1_0_R.zip

SRS consists of two steps. In the first step, the counts for all OTUs (operational taxonomic untis) are divided by a scaling factor chosen in such a way that the sum of the scaled counts (Cscaled with integer or non-integer values) equals Cmin. In the second step, the non-integer count values are converted into integers by an algorithm that we dub ranked subsampling. The scaled count Cscaled for each OTU is split into the integer-part Cint by truncating the digits after the decimal separator (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cint). Since ΣCint ≤ Cmin, additional ∆C = Cmin - ΣCint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches ∆C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking ∆C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement.

1.) Applying phylogeny-based analyses of trait evolution and diversification in the fossil record generally involves transforming an unscaled cladogram into a phylogeny scaled to geologic time. Current methods produce single time-scaled phylogenies with artificial zero-length branches and no indication of the uncertainty in the temporal relationships. 2.) Here I present a stochastic algorithm for time-scaling phylogenies of fossil taxa by randomly sampling node ages from a constrained distribution, with the ultimate goal of producing large samples of time-scaled phylogenies for a given dataset as the basis for phylogeny-based analyses. I describe how this stochastic approach can be extended to consider potential ancestral relationships and resolve polytomies. 3) The stochastic selection of node ages in this algorithm is weighted by the probability density of the total inferable unobserved evolutionary history at single divergence events in a tree, a distribution dependent on rates of br...

Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks).

Created using showyourwork from this GitHub repo.

Scaling Relationship Evolution R ScriptsAnnotated R scripts to model the evolution of scaling relationships using an evolutionary algorithm.Dryer et al Scripts.R

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum.

BIO-R is a set of R programs that do biodiversity analysis of molecular data, in order to calculate heterozygosity, diversity among and within groups, shannon index, number of effective allele, percent of polymorphic loci, Rogers distance, Nei distance, cluster analysis and multidimensional scaling 2D plot and 3D plot. You can included external groups for colored the dendogram or MDS plots, and additionally you can obtain a Core Subset using molecular data and also phenotypic, geographical and any source of data. BIO-R was designed because to do biodiversity analysis easily.