I'm using some of National Instruments' DAQs for some measurements, but their calibration is slightly off. To work around this I am hoping to factor in any imperfections into the measurements such that they can be removed.
The objective is to find the transfer function of a series of passive networks. Each component in the network has been measured so the model should fit the measurement almost exactly.
The first thing I have tried is to model the output, spec'd at >10 G // 100p. Using a known resistance of 2 Meg I found a more accurate value for the capacitance at 75p.
simulate this circuit – Schematic created using CircuitLab
This reduced the differences I was finding in high frequencies as the load I am using is around 1 Meg.
There is also a phase difference at high frequencies. This I tried to calibrate out by measuring the input/output relationship without any circuit in between, and deconvolving this from the measurements.
Unfortunately there is still a discrepancy at low frequencies, which looks like an additional capacitance in series with the circuit. This can be seen in my results: 
Grey marks the unknown low-frequency effect, orange marks the overcompensation of the phase issue. The frequency axis is 2 Hz- 20 kHz.
What other things must be compensated for in a measurement scenario?
Answer
1) Low frequency discrepancy
When producing FFTs in MATLAB it is critical to get the frequency vector correct. In my original example I had been creating a frequency vector using:
f = linspace(1,fs,Ns)
where fs is the sampling frequency and Ns is the number of samples. I thought this was correct as when indexing in MATLAB you always being at 1. However with an FFT the first bin is DC, i.e. 0 Hz. The low frequency discrepancy was caused by a bad frequency vector. For anyone seeing similar issues, I found the best way to create this vector is with:
f = (0:Ns-1)*(fs/Ns)
2) High frequency phase issues
After further investigation I found that the phase issue is sampling frequency related. Illustrating the phase vs. sampling frequency showed that by increasing the sampling frequency, the phase accuracy improves.
Fig. 1 Phase vs. Sampling rate of the NI 6251 DAQ with input connected directly to the output
The original problem saw over-compensation, which has now been reduced as there is less of a problem to compensate.
3) Input capacitance
As a side note, I haven't found any reason for a series capacitance anywhere in the DAQ as I first assumed, and presume there is not a compensation capacitor in the input stage (although it seemed like a logical conclusion given the question). For those interested I am including a capture of the transfer function from 1 Hz-20 kHz of a 1 M series resistance between input and output, showing the effects of the parallel input capacitance. I have then modelled this with an RC circuit transfer function, and fit the curve using an optimisation algorithm to select the best capacitance. The resistor was measured with a multimeter with a resolution of 0.001, and an accuracy of +-0.8% + 3 kohm, so that defines the limit of accuracy of the measured capacitance.
Fig. 2: Comparison between modelled and measured transfer functions of a 1 Meg series resistance and the unwanted input capacitance. The measurement was made at fs=1 MHz to minimise phase inaccuracy.
There is a pretty good fit, meaning the capacitance can be compensated for in any further measurements.
Thank you to both Tony Stewart and Daniel Turizo for their help in reaching this answer.
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