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
For example, create an analysis service with those attributes:
- Precision as number of decimals: 5 (don’t know if necessary)
- 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: