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The Sashimi project hosts the Trans-Proteomic Pipeline (TPP), a mature suite of tools for mass-spec (MS, MS/MS) based proteomics: statistical validation, quantitation, visualization, and converters from raw MS data to the open mzML/mzXML formats.
Fit X-ray Photoelectron Spectroscopy (XPS) and XAS data
⚠️ KherveFitting has moved. New releases will no longer be published here. Download the latest version from https://khervetools.com or directly from GitHub: https://github.com/KherveFitting/KherveFitting/releases/latest. This SourceForge page is kept for archival/older versions only.
YouTube Channel: https://www.youtube.com/@xpsexamples-imperialcolleg6571
The Continuum Solvation Nonpolar Surface Area (CS-NPSA) package is a general purpose program suite for defining and computing nonpolar surface areas based on continuum solvation models.
It is based on the classification of individual surface elements representing the solvent accessible surface used for the description of the polarized charge density elements in the CS models.
This is a .exe program (originally python) to balance chemical equations. Just enter the reactants and the products, and this tool will automatically add the coefficients.
IFEFFIT is a library and set of interactive programs for the analysis of x-ray
absorption fine-structure (XAFS) data. IFEFFIT combines state-of-the-art
analysis algorithms with graphical display of XAFS data, and general data
manipulation. It can be
easy to use sepctrography software. allow to display spectrography curves, and to overlay severals curves. you can create firste or second derivatives from the curves, or place marks to identify peaks.
finals drawings can be saved as png or pdf files.
pyPulsar is a program for simulation and fitting of solid-state NMR spectra. It provides scripting facilities using Python.
(This project was not developed for a long time: It will be soon moved in a new project currently developed)
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ABE is a small, fast and convenient program for visualizing and modeling experimental bioassay data. The data can be modeled using either polynomials or a more specific four-parameter model based upon the standard, sigmoidal dose-response curve.