Data science

Statistical analysis

Statistical analysis is key for gaining insight and meaning out of data. We provide statistical support ranging from exploratory analyses to statistical modeling and hypothesis testing. We select and apply the most relevant statistical methods tailored to your research questions, help you interpret results and communicate statistical findings to your target audience.

We generally use and encourage the use of R statistical software, advocating for reproducible research by providing reproducible statistical workflows and open source code. Other statistical software can be employed if preferred.

Machine learning

The increasing complexity and quantity of data in environmental research fields often demands the use of advanced analytical approaches. We provide machine learning software development, building and optimizing machine learning modeling algorithms tailored to your research requirements, e.g.:

Data visualization

We help you communicate your research findings through high quality graphics, maps, or even interactive tools. Easily interpreted and clear visualization of your results, tailored to your audience, will increase the impact of your research.

Data management

Data cleaning and preprocessing

Results can only be as good and reliable as the data behind it. We assess and optimize the quality of your data, addressing incorrect, missing, duplicate or potentially erroneous data. The format of the data can also be adjusted to specific analysis requirements. This is most common for machine learning applications. Some of our work includes open data for

Data management and data management plans

Good data management will make your research life easier throughout your various projects. We provide support in data and code documentation, developing relational and modern databases, version control, and the use of repositories. Data management plans are also increasingly required for research proposals and funding acquisition. We help you develop a roadmap for data management throughout your project, and plan a strategy for data storage and accessibility for after a project has ended.