Stiffness and standardization are one of the main characteristics of scientific research papers.

Data analysis should not be "arrogant". When judging the data, don't take the object with attribute A, and use B as the reference standard. Units of measurement shall conform to national standards or relevant industry norms.

Pay attention to the choice of effective figures. It is not that the more digits after the decimal point, the more accurate it is, but that we should combine the methods and means of obtaining data. For example, when observing with an instrument with a precision of 5%, the data should be written as "19" instead of "19.2".

You can't simply copy the values quoted by the instrument. The data above the upper limit of detection or below the lower limit of detection should be ">: detection limit".

The label should be detailed. For example, the sampling location map should have parameters such as scale, orientation, coordinates, legend and description. When using professional symbols and codes to represent objects in diagrams, we should pay attention to the corresponding text descriptions.

Don't invent technical terms. When new terms are really needed, they should be fully explained.