Data Stewardship – Module 1: MOOC
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The online Data Stewardship course provides an introduction to research data management. It has been created within the framework of the international, EU funded project DocEnhance project by a working group of research data management specialists at UiT The Arctic University of Norway.
The main target group for this course is PhD candidates at universities, research institutes and university colleges. It is primarily designed to function as a preparatory task for interactive seminars with peers, as well as collaborations with partners from the non-academic sector. It may equally well function as an independent teaching and learning resource, and the course is therefore open for anyone who wishes to improve their competences and skills in research data management.
The course can be taken at any point of time or place.
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Fórum
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In this section, you will learn about why research data management (RDM) is important for science and society, and why good research data management is beneficial for you as a junior researcher. You will also get an introduction to some basic RDM concepts that are essential to fully benefit from the reminder of this course.
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In empirical sciences, it is expected that you base your arguments on the analysis of data and not on anecdotal evidence, personal beliefs or guesses (and certainly not on alternative facts). Trained PhD candidates know this, so this section will not dwell on the importance of data in research, but rather on the research data landscape.
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Before embarking on a new project or study, it's advisable to locate any relevant research, in order to make sure that you are making use of the latest information. Under this section you will learn more about how you can find and cite relevant data and use it in your own research.
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In this section you will learn how to best structure your dataset into files and folders, and what you should keep in mind when naming your files and folders. You will also learn how to document a dataset, and why good documentation is vital for the quality and reusability of an archived dataset.
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Power outage, hardware or software problems, USB keys gone missing. We have all at some point experienced just how frustrating that can be. In this session you'll learn more about how to avoid loss of data and how the nature of the data you are working with has implications for how they can be stored.
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Cleaning, analysing and visualising your data may be crucial to understand and communicate the information that your data holds. In this section you will learn the basics of good practice in data cleaning, analysis and visualisation, and also how to choose useful tools.
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Archiving research data is a crucial part of the research process. It's not just about fulfilling data sharing requirements, but also about sharing your findings and making the results of your research publicly available. In this section you will learn about the basics of data archiving/sharing; why it is important, and what considerations you should take into account when you are in the process of archiving/sharing research data.
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In this section you will learn to plan your project well. The Data Management Plan (DMP) is a project management tool revolving around everything that has to do with your data. The key is to chunk down all the steps ahead of you, to help you through your PhD project step by step.
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In this section, you can test your knowledge in our exam assignment. If you get 80%, or 24 correct answers, you will receive a certificate that shows that you have completed and passed Module 1 of the DocEnhance Data Stewardship course.
Before you do the exam, we give you a brief recap of the course.
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