# Before getting started¶

## Prepping the 2D dataset for inversion¶

The following is a list of some requirements and recommendations to help prepare the 2D dataset for inversion.

### Common recommendations/requirements¶

 Dataset shear The inversion method assumes that the 2D dataset is sheared, such that one of the dimensions corresponds to a pure anisotropic spectrum. The anisotropic cross-sections are centered at 0 Hz. Required: Apply a shear transformation before proceeding. Calculate the noise standard deviation Use the noise region of your spectrum to calculate the standard deviation of the noise. You will require this value when implementing cross-validation.

### Spinning Sideband correlation dataset specific recommendations¶

 Data-repeat operation A data-repeat operation on the time-domain signal corresponding to the sideband dimension makes the spinning sidebands look like a stick spectrum after a Fourier transformation, a visual, which most NMR spectroscopists are familiar from the 1D magic-angle spinning spectrum. Like a zero-fill operation, a spinning sideband data-repeat operation is purely cosmetic and adds no information. In terms of computation, however, a data-repeated spinning-sideband spectrum will take longer to solve. Strongly recommended: Avoid data-repeat operation.

### Magic angle flipping dataset specific recommendations¶

 Isotropic shift correction along the anisotropic dimension Ordinarily, after shear, a MAF spectrum is a 2D isotropic vs. pure anisotropic frequency correlation spectrum. In certain conditions, this is not true. In a MAF experiment, the sample holder (rotor) physically swaps between two angles ($$90^\circ \leftrightarrow 54.735^\circ$$). It is possible to have a slightly different external magnetic fields at the two angles, in which case, there is an isotropic component along the anisotropic dimension, which is not removed by the shear transformation. Required: Correct for the isotropic offset along the anisotropic dimension by adding an appropriate coordinates-offset. Zero-fill operation Zero filling the time domain dataset is purely cosmetic. It makes the spectrum look visually appealing, but adds no information, that is, a zero-filled dataset contains the same information as a non-zero filled dataset. In terms of computation, however, a zero-filled spectrum will take longer to solve. Recommendation: If zero-filled, try to keep the total number of points along the anisotropic dimension in the range of 120 - 150 points. Sinc wiggles artifacts Kernel correction for spectrum with sinc wiggle artifacts is coming soon.