Open, Reproducible and Transparent Science

Objective:

The course aims to equip students with the essential background knowledge and the practical skills in open and reproducible science with a focus on ecology.

Content:

The course offers hands-on training on methods and tools to make ecological research more open, reproducible, and transparent. The students will be introduced to the data life cycling including data collecting, processing, storing, publishing, sharing, communicate, and reusing following best practice FAIR data principles and data standards such as Darwin Core Standards. The students will learn methods and tools for data cleaning, reproducible workflows and Artificial Intelligence (AI) for supported programming

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• Has an overview over open science, FAIR data principles, and the life cylce of data.
• Has a working understanding of the methodologies and tools for data management and reproducible workflows.
• Is familiar with a diversity of approaches in open and reproducible science

Skills

The student

• Can collect, process, store, publish, share, communicate, and reuse ecological data according to best practice and FAIR data principles.
• Can use relevant methods and tools to make research more open, reproducible, and transparent.
• Can evalute if research is open and reproducible.

General competence

The student

• Can manage research data accoring to best practice standards.
• Can effectively use methods and tools to make research more open and reproducible.
• Can discuss advantages and issues with open and reproducible science