The introduction typically begins with an overview of the context (e.g., domain, application, process, etc.) in which the research fits. The role of this section is twofold. First, it allows to naturally lead the reader to the specific research problem. Second, it serves to motivate the research by describing the importance of its context. For example, the presentation of context could be as follows:

The citation [1] could typically correspond to a general article or literature review in the domain of applied spectroscopy.

Once the context is established, it is necessary to identify the problem more precisely or the specific questions to be addressed by the research presented in the article. For example, there may be a limitation with an existing approach, a new or different application, an open question and so on. Once again, we must emphasize the importance of the problem addressed by the research, so it is not seen as simplistic or irrelevant. Here is an example of a research problem:

In this case, reference [2] is used to justify the importance of the research problem.

The next part is to describe the proposed answer to the problem or research question. The objective is not to describe the approach in detail (this is done in the Methodology section) but rather to present an outline, focusing on its advantages and innovative aspects.

For example:

It is common to end the introduction with an outline of the rest of the article, as in the following example :

The methodology is very often the longest and most complex section of an article. It usually contains several sub-sections and it is not uncommon to even have sub-sub-sections. It is therefore important to structure the presentation so that the reader can follow and understand each of its parts without having to go back and read another section.

For the convenience of the reader, it is necessary to choose meaningful titles for subsections. A good title should give a clear idea about the contents of the sub-section, using as few words as possible.

For example, the following title describes the contents of the section, but is too long :

A shorter and informative title would be :

Scientific papers often use mathematical symbols to identify different variables, constants, parameters, etc. The use of such symbols can greatly simplify the writing, but can also confuse the reader, especially if they are not familiar with the notation used.

To facilitate reading, make sure to properly present the notation at the beginning of the methodology. This corresponds to :

When the notation contains a large number of symbols, it may be synthesized in the form of a table. For example:

Before describing in detail the methodology, it may be necessary to present the theoretical framework that underpins this method. The latter is composed of general principles recognized in the field, used or adapted in its own way to fit the present work. The objective of the theoretical framework is twofold:

For example, the theoretical framework could contain a recognized technique which is based on the approach proposed in the article:

Once the notation and the theorical framework presented, we can then describe our own method of research. Dependending on the nature of the research, this description can contain the following elements: The main steps of the methodology (e.g., analysis, design, implementation, etc).

The following tips should be considered in the description of the method:

As the number of research papers is constantly increasing, the validation of the experimental work becomes increasingly important. Consequently, even if an article offers original contributions and good results, it may still be refused if the experimental protocol is incomplete or inadequate.

Good experimental protocol should thus have the following two properties :

After reading the title and abstract of an article, many readers directly consult its tables and figures to decide if it is worth reading or not. These tables and figures should be visually interesting and easy to understand without having to refer to the text. Here are some other rules to follow when developing the tables and figures:

Results should always be interpreted objectively, without extrapolating or seeking unsupported conclusions. Thus, readers should normally be able to reach these conclusions by themselves, if they were presented the results objectively.

Conclusions that seems supported a priori by the results may, however, be wrong if they do not have the statistical significance required. This error is often found in the comparison of different approaches, where a better approach is declared if the average performance over a number of tests is greater than other approaches. However, if the number of tests is very small or the standard deviation of the results is very large, these results may be largely due to chance. To avoid such errors, it is advisable to use a statistical hypothesis test. To perform such a test, we must first formulate an hypothesis (called the null hypothesis) related to the question we are trying to answer. For example:

This assumption corresponds to a statistic that is estimated from a sample. In the previous example, the statistics would be the difference between average performances (assumed to be zero if the methods are equivalent), and the sample corresponds to the the tests on which the methods are compared. Then, we verify that the estimated value falls within a certain confidence interval, typically corresponding to 95 % of the probability (i.e., p-value of 0.05). If this value is outside the range, we can reject the hypothesis.

The test in the previous example, known as a paired t-test, concludes with some confidence that the performance of the two methods are not equivalent. However, this test does not allow us to determine which of the two methods is best. To do this, use a one-tailed test which considers only the probability that the estimated value lies on a certain side of the expected value (zero in the example).

It is generally not enough to just analyze the results with statistical tests; the results require interpretation as well. For example, a statistical test could indicate that on average, the calculation method A gives better results (closer to reality) than calculation method B, with a confidence level of 95%. However, the test gives no interpretation of the result, that is to say, no information on why method A is better than method B. It is therefore advisable to propose plausible explanations (based on the methodological details of approaches A and B, and the characteristics of the data they treat) for results. For example:

This interpretation is particularly relevant when discussing “negative” results (i.e. contrary to the hypothesis) and is particularly useful for the advancement of research. It identifies situations where the proposed approach does not work and allows the proposal of new research hypotheses.

The conclusion often begins with a brief overview of the objectives and contributions of the research. Although these elements have already been stated in the introduction, they may have been forgotten during the reading of the article. Moreover, having read the article, the reader is now able to better understand and appreciate these objectives and contributions.

This part is normally very short (2 or 3 sentences). For example:

The next part allows one to answer the research questions or evaluate the objectives, highlighting the key findings of the article and its main conclusion. This section should address the following questions:

Here is an example for this part:

It is customary to finish the conclusion by suggesting possible future work that would address new issues raised by the research, or address additional questions. If the work described in the paper is part of a research project with several stages, one can also mention what is the next step to be achieved.

For example: