pyMCR is an open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of constraints, regressors, and error functions to minimize. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.

MCSimMod is a flexible and efficient R package that can be used for implementation and application of physiologically based pharmacokinetic (PBPK) and other ordinary differential equation (ODE) models.

A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data. These include:* Reweighting to arbitrary chemical potential* Segmenting ``lnPi`` (to identify unique phases)* Containers for interacting with several values of ``lnPi`` in a vectorized way.* Calculating thermodynamic properties from these containers* Calculating limits of stability, and phase equilibrium

A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data. These include:* Reweighting to arbitrary chemical potential* Segmenting ``lnPi`` (to identify unique phases)* Containers for interacting with several values of ``lnPi`` in a vectorized way.* Calculating thermodynamic properties from these containers* Calculating limits of stability, and phase equilibrium

RxTerms is a drug interface terminology derived from RxNorm for prescription writing or medication history recording (e.g. in e-prescribing systems, PHRs). RxTerms is derived only from the non-proprietary content of RxNorm, which is developed and maintained by the U.S. National Library of Medicine. No fee or license is required to use the RxTerms data. Technical documentation at https://wwwcf.nlm.nih.gov/umlslicense/rxtermApp/rxTermFileStructure.cfm

The MSL ChemCam LIBS RDR data set contains calibrated spectra and higher level products derived from raw data collected by the ChemCam Laser Induced Breakdown Spectrometer on the Mars Science Laboratory rover. Standard derived products include summed calibrated spectra (RDR), Intermediate Clean Calibrated Spectra (CCS), and Multivariate Prediction of Oxide Composition (MOC) tables. Special products, which may be generated as needed and as resources permit, are Univariate Prediction of Elemental Composition (UEC) tables, Univariate Prediction of Oxide Composition (UOC) tables, Multivariate Prediction of Elemental Composition (MEC) tables, and Sammon's Map (RSM) tables.

The dataset contains chromatographic retention times, CAS numbers and name of chemical compounds identified/tentatively identified in pyrolysates of tire road wear samples. Also attached are pyrolysis-GC-MS chromatograms or pyrograms obtained for the samples. This dataset is associated with the following publication: Paterson, K., B. Beckingham, G. Momplaisir, and K. Varner. Adapting Methods for Isolation and Enumeration of Microplastics to Quantify Tire Road Wear Particles with Confirmation by Pyrolysis GC–MS. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 1769–1779, (2025).