RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis
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Updated
Apr 4, 2025 - Jupyter Notebook
RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis
A Python utility for the processing and quantification of chromatography data
Deep (Transfer) Learning for Peptide Retention Time Prediction
Awesome papers and codes list of small molecule mass spectrometry-related machine learning methods
Python code to identify and calculate decomposition of materials using Raman spectroscopy
Data hub and data tool repository related to the NIST PFAS Program.
Mass spectral libraries search tool (MSL-ST), used to enhance organic compounds' identification
Deep Learning for Mass Spectra Quality Assessment
[Analytical Chemistry] Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations
A Python package to plot fractional composition diagrams and pH-log c diagrams
Universal API to work with chromatography data using Peaksel
Helper Functions For Dealing With GCMS and LCMS data from IonAnalytics
An open-source Matlab toolbox for multivariate statistical analysis and data mining
The scripts uploaded in this repository were developed for the automated processing of fluorescence microscopy images of Nile Red stained microplastics
📊 User-friendly mass spectrometry and chromatography data analysis app with native UI, graphing, quantification, MS/MS and data export capabilities
Perform titration curves of any acid-base pair.
Software for the optimization of stability constants from potentiometric titration data.
Isotopes and their intensities of a molecule. Used for mass spectrometry to compare the measured spectra to the theoretical.
A project designed to ease GC hydrocarbon quantification using multiple GC systems with complex product streams.
PoliBrush is a freely distributed software designed for teaching exploratory multivariate analysis in the frame of color RGB and spectral imaging. PoliBrush implements principal component analysis (PCA) as its core method. A detailed user guide is provided in the following tutorial paper: https://doi.org/10.1016/j.chemolab.2023.104918
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