The International System of Units
The International System of Units (SI) is the most used measurement system and frequently appears in the field of science. It uses the metric system based around the number 10. The system consists of base unts and a set of prefixes. Scientists are likely to need the names, symbols and prefixes when writing up scientific observations and describing the set up in the methods.
Table 1: SI base units
|amount of substance
Table 2: SI derived units
|equivalent dose (of ionising radiation)
Table 3: English names and prefixes for the factors they represent
|trillion (short scale)
|billion (short scale)
|billionth (short scale)
|trillionth (short scale)
You can find a larger list of derived units and factors here: https://en.wikipedia.org/wiki/International_System_of_Units
Below is another example methods section to read through for style and language.
We used 150 years (1870–2019) of gridded, monthly reconstructed historical SST data sources to evaluate centennial changes in global occurrences of extreme marine heat events: the Hadley Centre Sea Ice and SST dataset (HadISSTv1.1)  and the Characteristics of the Global SST data (COBESSTv2)  (Table A in S1 File). These independent and complementary global SST products were reconstructed from instrument records and the historical network of in situ measurements and have been widely deployed as ground-truthed SST fields [35, 36].
Our statistical definitions frame how we characterized the frequency and extent of extreme heat events across space and time. For each month, in each 1° × 1° grid, we defined the extreme marine heat as a monthly average SST value that exceeds the 98th percentile SST value observed over 1870–1919 (corresponds to the period of second industrial revolution), or hottest temperature observed in the earliest 50-year period of record (Fig A in S1 File) [37–39]. Such percentile based thresholds can be derived from climatological data and are robust to drivers or variabilities associated with individual extreme events . Our particular percentile based threshold also easily relates to the standard deviation (σ) which offers an alternative expression of dataset anomaly–the 98th percentile is when σ = 2.05. Though they are limited in describing daily and hourly extremes, we selected monthly SST products in order to evaluate the properties of extreme marine heat events at a 1° × 1° scale within the longest historical context possible– 150 years. At daily scales, species may respond to stressful abnormal temperatures by changing distributions but could suffer greater thermal-induced stress if an extreme heat event persists beyond one month. In addition, extreme heat events at shorter time scales are more likely to be smaller in scale than our analyzed spatial units (e.g., EEZs, large marine ecosystems). Furthermore, statistical analysis of monthly-resolved temperature variations can offer centennial-scale proxies of the frequency of extreme heat event properties .
Next, to highlight any different information presented in our approach, we compared the global variability of the LEHI to a more traditional SST anomaly metric. For the year 2019, we computed both spatial outputs from the same 1870–1919 climatology and the same spatial scale (1° × 1°) to ensure that any differences are from the methodology alone. SST anomalies are widely used and an important impact parameter in climate extreme studies [10, 11, 21]. The difference in distributions of two climate indices derived from the same baseline period offers an alternative assessment of climate stress from the conventional anomaly-from-mean signals.
We conducted all data wrangling and analyses in the R programming environment , and provided our data and scripts in open access repositories (https://bit.ly/2QjhYld) and through the Open Science Framework (https://osf.io/mj8u7/).
This extract is from: Tanaka KR, Van Houtan KS (2022) The recent normalization of historical marine heat extremes. PLOS Clim 1(2): e0000007. https://doi.org/10.1371/journal.pclm.0000007
The personal form
The first thing to note in this passage, is that the authors have written this passage using personal pronouns describing that ‘We’ did the experiment. This is clearly the convention in this field of climate science.
This means that the authors put themselves first in many sentences followed by the action then took (the verb) and then what they did it to (the subject). For example:
We used 150 years (1870–2019) of … data sources.
We conducted all data … analyses in the…
…we compared the global variability…
…we computed both spatial outputs from…
… provided our data and scripts…
Phrases for methods
This extract also uses several useful phrases for presenting your methods:
SST data sources to evaluate centennial changes in global occurrences of…: what is being evaluated is the main focus of this study. Evaluate means to assess something.
Our statistical definitions frame how we characterized…: the definitions are essential to how the data are represented and understood.
we defined the extreme marine heat as a monthly average…: this sentence as more detail to the definition of how the data can be interpreted.
Such percentile based thresholds can be derived from: derived from tells readers where something came from or where an idea or measurement came from.
Our particular percentile based threshold also easily relates to…: this phrase ‘relates to’ means the context that it can be viewed in or how it fits with something else.
we selected…: describes what was chosen.
The passage also includes several linking words for continuing the description in the next sentence:
In addition, extreme…
Next, to highlight any….
These phrases can all be used to introduce a new idea or item or the following part of the process.
Further study for this week
Try using the advice that comes with the Journal this week to write up a materials and methods section from a recent piece of research you have been involved with. Now you can try today’s short quiz.