Open Software and Data

HerbShare

HerbShare is a platform to make spectral data from herbarium specimens more accessible, usable, and impactful. Through HerbShare, we aim to facilitate discovery, accelerate research, and unlock the hidden potential of these vast plant collections—transforming them into dynamic laboratories that can inform our understanding of biodiversity, environmental change, and the future of plant science.

CWT

CWT is an R package focused on the fast application of 1D - Continuous Wavelet Transformation (CWT) on time series with special attention to signals from spectroscopy. The current code has been enhanced using C++ language through Rcpp and RcppArmadillo packages, and it run in parallel over samples using OpenMP.

FAB

FAB is an R package focused on the processing and visualization of data from Forest and Biodiversity Experiments. Its creation is based on key calculations such as mortality or Net Biodiversity Effects. This package was developed in conjunction with by Dr. Jeannine Cavender-Bares (Harvard University). This package was developed to support current and future goals associated with Cavender-Bares et al. 2024.

SpecTraits

SpecTraits is a user-friendly platform that consolidates tools for deriving leaf traits from spectral data. The goal of this platform is to make leaf spectroscopy more accessible to researchers and plant scientists who use leaves for phenotyping (e.g., ecologists, agronomists, and evolutionary biologists), enabling them to fully leverage the potential of spectroscopy to advance the understanding of plant evolution and function.

rTLS

rTLS is an R package that compiles a series of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging). This started as a personal hobby, but due to its current use, I decided to formalize their routines.

This package was carried out also by Dr. Arturo Sanchez (University of Alberta) and Ronny Hernandez. The development of rTLS was focused on four major processes applied on point clouds: i) filtering, ii) voxelization and their visualization, iii) point neighborhood features, and iv) data manipulation (e.g., rotation, distance matrices).

Take a look at the last stage of development at github as well as the CRAN repository. A published paper that uses some of the routines from this package can be found in Guzmán et al. 2020.