Open Science training
Phase 3 - Processing and analysing data
- Open and FAIR Research Data - by FOSTER
- Make Your Research Data F.A.I.R. - by CESSDA
- Assessing the FAIRness of Data - by FOSTER
- FAIR Data Management Gaps and Solutions - by EOSC Pillar and DICE
- Data Protection and Ethics - by FOSTER
- How to anonymise qualitative and quantitative data - by UK Data Service
- OpenSource Software and Workflows - by FOSTER
- OpenRefine - by OpenRefine
This course explains the difference between open data and FAIR data. Upon completion of this course, you will be able to:
- understand the principles of making research data open
- understand the FAIR principles
- recognize the difference between FAIR data and open data.
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: An interactive mix of text and quiz
Course period: Ongoing
Learn tips on how to make your data FAIR – that is, Findable, Accessible, Interoperable and Reusable.
Researchers, learn how to make data Findable, Accessible, Interoperable, and Reusable (FAIR), how to assess the FAIRness of research data and which tools to use to make data more FAIR.
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: Video (06:09mins)
Course period: Ongoing
Learn about FAIR data and how to assess the FAIRness of research data.
You have likely heard people using the term ‘FAIR’ data a lot recently and might wonder what exactly is meant by this term. FAIR data are those that are Findable, Accessible, Interoperable and Reusable. Sounds simple enough, but what do each of these terms mean in a practical sense and how can you tell if your own research data is FAIR? This short course will:
- introduce you to the key terms and explain what they mean in a practical sense
- tell you how data management planning can help to make data FAIR from the very start of research projects
- show you how you can use freely available tools to help assess the FAIRness of data
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: An interactive mix of text and quiz
Course period: Ongoing
Learn from other researchers about the benefits and difficulties of reusing research data, and how ensuring for data FAIRness can help to overcome such challenges.
The webinar is open to all, but the following are highly encouraged to attend, particularly those interested in making their research activities more FAIR:
- researchers, both attached to institutions and independent citizen scientists
- repository managers in academic or research institutions
- research community managers
- actors in the EOSC community that are interested in the practicalities of handling and producing research data
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: Video (65:09mins)
Course period: Ongoing
Learn about the basics of data protection and consent: how to protect, store and anonymise your research data.
This course covers data protection in particular and ethics more generally. It will help you understand the basic principles of data protection and introduces techniques for implementing data protection in your research processes. Upon completing this course, you will know:
- what personal data are and how you can protect them
- what to consider when developing consent forms
- how to store your data securely
- how to anonymise your data
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: An interactive mix of text, video and quiz
Course period: Ongoing
Learn about the process of data anonymisation as well as the ethical and legal standards associated with the protection of research participants’ identities.
The past two years has involved a huge change in expectations for researchers in how they manage and share participants’ information. There are new legal obligations, such as the new GDPR regulations, as well as a greater emphasis in sharing data after the completion of a research project. The process of anonymisation is an essential part to protect the identities of research participants while complying with these ethical and legal standards. Before sharing, archiving, or publishing data, you should ensure that all identifying and disclosive information is managed appropriately and redacted when necessary.
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: Video (73:20mins)
Course period: Ongoing
An introduction to Open Source Software and open workflows, including benefits to data sharing, reproducibility and reuse.
This course introduces Open Source Software (OSS) management and workflow as an emerging but critical component of Open Science. The course explains the role of software sharing and sustainability in reproducibility, trust and longevity, and provides different perspectives around the sharing and reuse of computational code and methods, namely the software producer, the software reuser, and the non-coder with an interest either in reproducing research findings or in following experimental processes. You'll learn about useful resources and tools for sharing and exposing your code and workflows. Upon completing this course, you will:
- understand the roles that open source software and open workflows play in supporting Open Science
- know how Open Science can support reproducibility
- be aware of issues to consider at different stages of the research lifecycle
- know about useful tools and resources to help you get started with using OSS and open workflows
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: An interactive mix of text, video and quiz.
Course period: Ongoing
Learn how to use OpenRefine (previously Google Refine) to clean up messy data, transform it from one format into another, and how to extend it with different webservices.
OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. OpenRefine always keeps your data private on your own computer until YOU want to share or collaborate. Your private data never leaves your computer unless you want it to. (It works by running a small server on your computer and you use your web browser to interact with it)
OpenRefine is available in more than 15 languages. OpenRefine is part of Code for Science & Society.
Language: English
Level: Master’s, PhD, early-stage researcher
Format/length: An interactive mix of text and video
Course period: Ongoing
