RSOS Submission Code and DataData files used in analysis of Pedruski et al. "Predicting the outcome of competition when fitness inequality is variable", and script files (code) necessary to produce these data files and associated figures.

In four studies (total N = 534), we examined the moderating impact of Interoceptive Accuracy (i.e., IAcc, as measured with the heartbeat counting task) and Interoceptive Sensibility (IS, assessed via questionnaire) on negative affect, following social exclusion or after receiving negative feedback. Results from an integrative data analysis combining the four studies confirmed that the manipulations were successful at inducing negative affect. However, no significant interaction between mood induction (control versus negative affect induction) and interoception on mood measures was observed, and this was true both for objective (i.e., IAcc) and subjective (i.e., IS) measures of interoception. Hence, previous conclusions on the moderating impact of interoception in the relationship between mood induction and self-reported mood were neither replicated nor generalized to this larger sample. We discuss these findings in light of theories of emotion regulation as well as recent concerns raised about the validity of the heartbeat counting task.

The dataset include biomass data, interaction coefficients, omega, asymmetry index for all possible communities, and exclusion probability for each species, as well as includes all relevant data and R code for replication of the study results. The meanings of the variable names, please refer to the “Annotation explanation”

speciestree20 species tree simulated under Yule model using MesquitemsPerl script to simulate coalescent genealogies for given species treeseqgene_paramtershort R script to generate random mutation rates (as the theta for simulating dna sequences ) from a log normal distributionseqgen parametersthe result from seqgene_paramter.R-- the mutation rates used in simulating sequencesseqge.parameterseq-genPerl script to simulate sequences given genealogies and mutation rate (as theta)infomissinga Perl script to filter out sequences with mutations at enzyme cutting sitescoveragea Perl script for filtering out sequences with no read (coverage draw from a poisson distribution)clustera Perl script for generating post-sequencing missing datalociinda perl script for summarizing the number of individuals for each locus (output in a txt file)countsa Perl script for counting the number loci at different tolerance levels (output a txt file)phylipranda Perl script for generating phylip formatted sequenc...

Values of parameters for sequence exclusion criteria.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

R scripts for all the published statistical analyses included in the paper. (RMD)