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Accumulation of Spectra  v.1.1

Analyze nuclear magnetic resonance signals with this tool. Accumulation of Spectra can display how the SNR of NMR (nuclear magnetic resonance) signals can be increased by accumulating many measurements. In data acquisition experimenters are often

Mnova  v.6.1.1

Mnova is a software suite designed for the handling of Analytical Chemistry data in a multiplatform (Windows, Mac, Linux) environment.





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TARQUIN for Linux  v.4.2.10

TARQUIN is an analysis tool for automatically determining the quantities of molecules present in NMR spectroscopic data.

TARQUIN  v.4.2.10

TARQUIN is an analysis tool for automatically determining the quantities of molecules present in NMR spectroscopic data.

INMR  v.3.1.1

Have you been longing for an NMR software as stylish as the iMac, as intuitive as the iPod, as resourceful as iLife and as cheap as iWork?

Burrow-owl  v.1.5.1

Burrow-owl is a software package for visualizing multidimensional nuclear magnetic resonance (NMR) spectra, with an emphasis on spectra used in macromolecular structure determination.

CnsFace  v.1.0

cnsFace is a GUI for the Crystallography and NMR System.

Janocchio  v.1.0

Janocchio is an application or applet for viewing 3D molecular structures and calculating NMR couplings and NOEs

JVisualizer  v.1.0

jVisualizer is a graphical tool to visualize and analyse first-order coppling patterns found in NMR-spectroscopy; additional you can type in chemical shifts from an experiment to calculate coupling constants

Kradview  v.0.5.3

kradview is a viewer of images obtained for some different sources: X-ray, NMR and all DICOM-compatible imaging devices.

MatNMR  v.3.9.94

MatNMR is a highly flexible toolbox for processing 1D and 2D NMR/EPR spectra in MATLAB and creating high-quality 1D, 2D or 3D plots.

The Unscrambler

The Unscrambler helps you discover patterns and relationships in your data that traditional statistical methods and visual inspection cannot reveal. The Unscrambler`s Multivariate Curve Resolution (MCR), Derivatives, Principal Components Analysis (PCA),

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