Analyse

Get them most out of your data by optimising your data analysis! The following article aims at giving you an overview over important aspects to consider when analysing behavioural experiments.

If you are planning to conduct experiments in UUBF's facilities, please read through our important information.

Caption "Analyse". Icon depicting the outline of a person with a thinking bubble containing an oscillogram.

Analyse experiments - Gain insights

1. Make sure your raw data is saved

Before you start your analysis, create a copy of your raw data that will not be worked with during the analysis. That way, you will always have an unaltered version of your raw data at hand to verify your analysis.

The same applies to experimental journals if you are planning to make manual annotations in these during the analysis process. In that case, ensure that the original journal is unaltered and an additional, annotated copy is created.

2. Preprocessing of your data

This step can involve a large number of different individual measures depending on your particular experiment. It may involve:

Acquisition of your experiment in a tracking software
Manual scoring of behavioural experiments
Merging different data sources (for example time protocols of interventions with video recordings)
Bringing the raw data of different sources into the right format for your analysis
Annotating raw data with subject information
Ensuring blinding also during the analysis

3. Statistical analysis

The majority of studies will involve some type of statistical analysis. This should be defined during your planning stage to ensure that meaningful conclusions can be drawn from your experiments.

The details of your statistics will largely vary depending on your experimental design and hypothesis. If you need specific help with your statistical analysis, feel free to write an email to UUBF.

4. Document your analysis and findings

While performing your statistical analysis, document the conclusions you draw from each test and any steps proceeding or following from them. By doing this, you make reporting and controlling your analysis process and results easier.

Depending on the statistical software you use, you have several options for documenting your analysis process:

Document your thoughts and process in your lab journal. Make sure to create a trace between your documentation and the actual analysis.
If you are using R for your analysis, you can create reports containing information regarding your analysis and scripts. This is performed with RMarkdown or LaTeX and the packages Sweave and Knitr.

5. Additional experiments and repetitions

Depending on the results, you might need to perform additional experiments or adjust the experimental setup to gain additional insights. Furthermore, you might need to increase the number of animals used if the assumptions underlying the sample size calculation were not completely accurate. In several cases, this will warrant going back to the design and perform stages of your research to make the necessary alterations.