Week 1: Introduction to Open Science
Introduction to the Open Science movement. What are the objectives, main concepts, and benefits of Open Science? This includes:
- Discussing the traditional subscription-based journal system with regards to Open Access publishing
- Practical Open Science benefits for researchers
- Policies of important funding organizations
- Programs on Open Data and Open Access publishing and
- Case studies on successful application of Open Science by researchers from different backgrounds.
Week 2: Research Data Management
Introduction to effective and secure research data management, including:
- Learning the disciplinary standards of FAIR data sharing
- Evaluating the strengths and weaknesses of different data storage and backup options
- Organizing, documenting, and adding metadata to research data to optimize the visibility of your data
- Data archiving, access, sharing and re-use with the use of data repositories
- Understanding the different copyright licenses designed to deal with open data
- Dealing with confidential data, company restrictions, and third-party agreements through case studies
- Evaluating a data management plan
- Explaining how Open Data can be applied in your field of research
Week 3: Publishing Open Access
Here you will discuss the main differences between the open access and subscription-based publication model in science, and the main misconceptions about open access publishing. In this week we also introduce the creative commons licenses used by open access journals and self-archiving policies. You will examine how you can maximize the accessibility of publications in subscription-based journals, you will present your opinion of the open access publishing model and assess the 'openness' of the main scientific journals in your field of research.
Week 4: Choose topic(s) depending on your interests:
Increasing your research visibility
Here you discuss and formulate your communication strategy, describing and choosing your social media channels for reaching certain target groups. Finally, conclusions of this course will be discussed and we will reflect on what has been taught in the previous weeks.
Making your research software FAIR
Research relies ever more on software. Software is used to model and simulate, but it is also almost impossible to deal with the ever growing volume of research data without software. Software is used to read, record, collect and generate data; clean, filter and analyse data; present and visualise data.
This module looks at research software: software that is specific to a research field or research project.
In this module you will learn the role of publicly accessible software version control repositories, the importance of choosing the right software licence, how to make your software citable and tips on how to make it easier for other researchers to reuse your software.
Virtual Research Environments
Introduction to Virtual Research Environments (VREs) and multidisciplinary collaboration through VREs. Here you will find and describe a particular Virtual Research Environment, and analyze motivations for sharing and using research data through Virtual Research Environments.