Clean datasets have similar properties and look the same, while “dirty” datasets are messy in their own ways. Knowing what clean data looks like and how to clean data is an important skill in assisting researchers in making their data FAIR ( findable, accessible, interoperable, and reusable).
In this webinar, you will learn to identify the components of a clean and tidy dataset and describe the steps needed to process a “dirty” dataset. With these components identified, you will be able to tidy your own data and provide guidance to researchers.
You’ll see, in action, common data issues solved by carrying out data transformation and pivoting operations. You’ll also learn the steps needed to break down observational units into separate tables (“normalize” data) so they can be efficiently stored in databases.
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Laatst geüpdatet: 21 januari 2022 door j.lind
Clean & Tidy Data: Making Data Usable (Online)
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Evenement type
Clean datasets have similar properties and look the same, while “dirty” datasets are messy in their own ways. Knowing what clean data looks like and how to clean data is an important skill in assisting researchers in making their data FAIR ( findable, accessible, interoperable, and reusable).
In this webinar, you will learn to identify the components of a clean and tidy dataset and describe the steps needed to process a “dirty” dataset. With these components identified, you will be able to tidy your own data and provide guidance to researchers.
You’ll see, in action, common data issues solved by carrying out data transformation and pivoting operations. You’ll also learn the steps needed to break down observational units into separate tables (“normalize” data) so they can be efficiently stored in databases.
More information.
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