Information on published codes and datasets is provided below, with direct links to their respective repositories
EvoPhylo (R package)
An R package: Pre- and Postprocessing of Morphological Data from Relaxed Clock Bayesian Phylogenetics (version 0.3).
Co-develop with Noah Greifer and Stephanie Pierce for its first release, and with Joëlle Barido-Sottani for its upcoming expansion. EvoPhylo performs automated morphological character partitioning for phylogenetic analyses and analyze macroevolutionary parameter outputs from clock (time-calibrated) Bayesian inference analyses.
Constructing new phenotypic (i.e., morphological) or molecular datasets from scratch for phylogenetic and evolutionary analyses is a time consuming and painstaking process. However, these are the necessary foundations upon which all questions in evolutionary and comparative biology depend on. In my career, I have created guidelines for phenotypic dataset construction, brand new phenotypic datasets, as well as revised datasets for various groups of tetrapods, mostly on reptiles. I have also created molecular datasets using published sequence data, besides ongoing projects to extract and sequence whole genomic data. Some of these are detailed below with links to their repositories.
Diapsida-Lepidosauria (morphological + nDNA/mDNA)
There have been several edits and expansions of this dataset by myself and others. Below I make reference to select (significant) expansions of this dataset that strictly follow the guidelines and empirical approach towards its construction (e.g., personal collection of phenotypic data).
Simões et al., 2017 (Cladistics): Character revisions and data re-scoring to the datasets of Conrad et al., 2008 and Gauthier et al., 2012: Dataset and analytical protocols available as Supplementary Information
Early Tetrapodomorpha (morphological)
Simões et al., 2021 (Nature Ecology and Evolution): Character revisions and additional taxa to the dataset from Cloutier et al., 2020 (Nature): Dataset and analytical protocols available in Harvard Dataverse