Presenting your data

Data presentation is the foundation of our collective scientific knowledge, as readers’ understanding of a dataset is generally limited to what the authors present in their publications. Figures are critically important because they often show the data that support key findings (Weissgerber 2015).

Unfortunately, authors generally use simple graphs to present summary statistics, instead of providing detailed information about the distribution of the data or showing the full data. In addition, digital images are not to be considered as just nice illustrations, but are underlying data and should be treated as such (Cromey 2013). As figures are the main method of giving insight into the results of your work, researchers should strive to present their data faithfully and transparently.

ALLEA Code:

Researchers share their results in an open, honest, transparent, and accurate manner, and respect confidentiality of data or findings when legitimately required to do so.
Researchers report their results and methods, including the use of external services or AI and automated tools, in a way that is compatible with the accepted norms of the discipline and facilitates verification or replication, where applicable.

Reporting bias

Researchers not able to reproduce or replicate previous results are often not inclined to further pursue their investigation nor to publish the findings. Nevertheless, the resulting reporting bias might influence the correctness of the scientific literature (publication bias), with potentially canonization of false facts (Nissen et al 2016). In addition, not having the full picture might distort the conclusions that can be drawn from meta-analyses and systematic reviews and as such may lead to a biased view which in turn might impact policy decisions.

Who is involved?

Junior Researcher - Phd student

In most cases analysis and presentation of the data will be in the hands of the researchers that have collected the data.

Senior Researcher

Supervisors and promotors are responsible for checking the integrity of the collected data and making sure that the analysis, presentation and conclusions of the research are faithful to the obtained data.

Publishers should have clear journal policies in place with regards to the presentation of data into figures. In addition, these guidelines should not only be available but also be enforced.

Peer Reviewer - Editor

Peer reviewers should be sufficiently critical regarding the figures, look out for potential pitfalls and propose alternative presentation methods.

When to think about this?

Thinking about data presentation is most relevant during the research phase with regards to correct data management and data analysis, during the publication process when preparing the figures and reviewing the data, and during post-publication, for example in relation to questions related to the validity of the presented data.

Illustrating research with graphs

Although it has become standard practice to illustrate continuous data using bar graphs, thereby presenting the data using summary statistics as averages and deviations, researchers are advised not to use this approach and instead look for other ways to present the data, including its distribution, especially when the results are based on a low number of observations.

When generating graphs, you should ask yourself whether the presentation accurately presents the research findings and does not in any way mask particularities that could affect the way the data is perceived. The data should be convincing by itself, not because of the presentation!

Some good practices when presenting (continuous) data:

Try to provide as much data as possible when presenting data allowing others to interpret the distribution of the data.
Avoid bar graphs for continuous data, especially when working with low n-values. Examples of alternative graph presentation are dot and/or box plots (examples can be found in the Weissgerber 2019 paper).
Show outliers, indicate whether or not these have been included in the statistical analysis and explain why.
In addition to listing the different statistical tests used in the methods section, please also indicate the test used for each particular dataset.

The figure below illustrates that different data distributions can lead to the same bar graph. Having access to the full data may, however, suggest different conclusions from the summary statistics (Weissgerber 2015).

Weissgerber TL, Milic NM, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation.

This illustrates the importance of having access to the full data set, as this will show potential outliers or unequal distribution of the data, which might in turn affect the statistical analysis and/or the perception of the data.

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Reporting results

Responsibilities within the publication process

In order for research to be robust and trustworthy, it is key that correct scientific behavior is not only implemented during the research phase, but also when reporting the results to the scientific community and society in general. Overall, transparency is key. Although research starts with an idea that can culminate into a paper, it is important to note that one should not only look at the scientists performing the work to make sure that everything is correct and that the work is subsequently used in a correct way. In fact, there are other actors in this ecosystem, such as the journals, publishers and other methods that give a platform to the research. In addition, it is also crucial that both the scientific community and society in general are sufficiently critical when embracing results. As such, responsible conduct of research is a shared responsibility.

Who is involved?

(Co-) Author

The above of course does not reduce the responsibility of authors as they are the ones responsible for making sure the reporting is accurate, timely and transparent. Good academic practices will encompass correct behaviour when granting authorship, how to properly cite previous work, how to choose a proper platform to report your work and how to deal with negative results.

The most common way to disseminate research findings is still to publish the work in a peer reviewed scientific journal. Journals exist in many forms, sometimes with a broad spectrum of topics, while others are tailored to specific disciplines or topics. Regardless of their focus, journals have the important task of performing a quality check of the research to make sure that low quality research is not published and that research findings are only communicated after they have been verified by peers. This process is called peer review. Journals have to develop internal quality criteria and organise a review process that is adequate for the task. For this review process, journals rely on reviewers to be critical and to identify where a study falls short.

It is important to note that the responsibility of the author(s) and the journal/publisher, does not end once the work has been published. In fact, both authors and journals/publishers have to take responsibility whenever post-publication issues arise, if necessary by correcting or even retracting the work in a transparent way. The Committee on Publication Ethics provides a number of core practices together with a variety of useful flowcharts that can assist readers of the work and journal editors with determining a strategy in case they encounter questionable research practices

Reader(s)

Finally, it is of utmost importance to acknowledge the role of the readers of the work. Those who read publications will determine the importance of the findings, for example by citing the work. It is therefore important that this group is critical of the work and does not misuse it for its own benefit. Responsible readers look further than the title and abstract of the work, and dive into the paper to fully grasp the content. Reading is to understand whether the conclusions of the work are justified, and how they might be applied.

Gender-sensitive reporting of research

Sex and Gender Equity in Research Guidelines (SAGER) encourage a more systematic approach to the reporting of sex and gender in research across disciplines. They apply to all research with humans, animals or any material originating from humans and animals (e.g. organs, cells, tissues), as well as other disciplines whose results will be applied to humans such as , e.g. engineering.

General principles:

Authors should use the terms ‘sex’ and ‘gender’ carefully in order to avoid confusing both terms
Where the subjects of research comprise organisms capable of differentiation by sex, the research should be designed and conducted in a way that can reveal sex-related differences in the results, even if these were not initially expected.
Where subjects can also be differentiated by gender (shaped by social and cultural circumstances), the research should be conducted similarly at this additional level of distinction.

Recommendations per section of the article:

Title and abstract: if only one sex is included in the study, or if the results of the study are to be applied to only one sex or gender, the title and the abstract should specify the sex of animals or any cells, tissues and other material derived from these and the sex and gender of human participants.
Introduction: authors should report, where relevant, whether sex and/or gender differences may be expected.
Methods: authors should report how sex and gender were taken into account in the design of the study, whether they ensured adequate representation of males and females, and justify the reasons for any exclusion of males or females.
Results: where appropriate, data should be routinely presented disaggregated by sex and gender. Sex- and gender-based analyses should be reported regardless of positive or negative outcome. In clinical trials, data on withdrawals and dropouts should also be reported disaggregated by sex.
Discussion: The potential implications of sex and gender on the study results and analysis should be discussed. If a sex and gender analysis was not conducted, the rationale should be given. Authors should further discuss the implications of the lack of such analysis on the interpretation of the results.

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