Content

1. Scientific Communication. Science generates products that must be properly delivered. This part of the course helps students realize that developing skills to effectively communicate scientific results is as important as generating them. Since English is the official language of science, all activities in this part of the module will be conducted in English. Essentially, this part of the module consists of:

• Presentations: speaking to an audience about your research is an obligation, but also a privilege and a great opportunity to meet and be known. Making slides as simple as possible, using body language to your advantage, making eye contact with the audience, and respecting time limits are some of the strategies that will be discussed and practiced.
• Poster presentations: an effective scientific poster presentation is much more than simply designing a poster, and a design is much more than just combining figures and fitting text in between. It is also very important to know how to present the poster in 2-3 minutes, paying attention to non-verbal language and considering the audience.
• Paper writing: what to publish, where, and how. We will emphasize writing an abstract since abstracts are one of the most challenging parts of scientific writing. Moreover, most potential readers will only spend a few seconds reading your abstract in scientific databases. If it doesn't catch their attention, you have failed.
• Other topics of interest: discussions will cover the ethics of science, the publication process, and science communication to a general audience.
• Artificial intelligence: students will be introduced to AI tools for scientific communication and will discuss the advantages and disadvantages of using them.

2. Statistical Analysis of Experimental Data. Statistics is essential in experimental sciences throughout the entire process: in design, to ensure experiments adequately address the questions posed; in data collection, to ensure quality and avoid biases; and in analysis, to obtain impartial and reproducible conclusions. It is key to modeling the inherent variability in biology and identifying significant relationships. The theoretical classes in this part of the module will be conducted in English and Catalan. The fundamental objective of this part of the module is to train students in the analysis and interpretation of experimental data. Therefore, basic competencies will be provided to design, execute, and analyze research projects, apply appropriate statistical techniques, interpret results, and draw appropriate conclusions. Thus, the content will be:

• Introduction to statistics: utilities and limits.
• Population, sample, sampling, and hypothesis formulation.
• Types of variables and effect measurement.
• Descriptive statistics and probability.
• Diagnostic tests and bivariate statistical significance.
• Effect estimation, confidence intervals, concordance, correlation, and regression.
• ANOVA and introduction to multivariate analysis.
• Common designs, sample size calculation, and experimental planning.
• Interpretation of results: statistical significance versus practical relevance.

The first hour of this part will be dedicated to data processing considering gender.