There are results, that should not be formatted with fixed decimal precision, especially when they are spread over a wide scales.

In Senaite LIMS you cannot explicitely configure significant digits for analysis results at this time.

But, you can use the feature `Calculate Precision from Uncertainties`

.

The idea behind is, that senaite rounds the result to the decimal, which is indicated by the absolute uncertainty value of the actual result. This can be configured as a relative percent value.

So you get the following context:

- Uncertainty value 10% will lead to 2 significant figures
- Uncertainty value 1% will lead to 3 significant figures
- Uncertainty value 0.1% will lead to 4 significant figures
- etc.

For example, create an analysis service with those attributes:

- Precision as number of decimals: 5 (don’t know if necessary)
- Uncertainty:
- Range Min: 0
- Range Max: 10000000
- Value:
**1%**(means 3 significant figures!)

- Calculate Precision from Uncertainties: Yes

After using this service in analysis requests/samples this is the effect in different situations after submission of results:

- entered result 0.012345678 formats to 0.0123
- entered result 0.12345678 formats to 0.123
- entered result 1.2345678 formats to 1.23
- entered result 12.345678 formats to 12.3
- entered result 123.45678 formats to 123

So far so good, but I’m afraid this does not work correctly onto the integer part of values:

- entered result 1234.5678 formats to 1235, should be 1230
- entered result 12345.678 formats to 12346, should be 12300

Nevertheless this is a kind of workaround, that works for me. I don’t know whether this interferes with other result processing, e.g. decimals, detection limits,…

IMHO in measurement and analysis this is more important than a fixed decimal precision.

I would appreciate any suggestions and ideas.

Also see example worksheet: