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Data documentation and metadata play an important role in making data FAIR, which both provide critical information about data necessary to understand, interpret and effectively use the data. While data documentation is any human-readable documents that describe the data in a research project in a variety of forms, metadata is the structured machine-understandable information about the data in a controlled format.
Data documentation can be written at project or file level and in a variety of forms, e.g. data dictionaries, codebooks, vocabularies and readme-files, etc.
A good project level documentation describes the general parameters of the research project, which should answer the following questions:
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A file level documentation describes the contents of an individual data file, which could include the following elements if applicable:
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In this video, we talk about the importance of documentation to keep data understandable and reusable. What are the different types of documentation levels and what should be documented at each of these levels?
UGent Data Stewards. (2020, December 2). Knowledge clip: Data Documentation [Video]. YouTube. https://youtu.be/7Ogbkx74Ym8
Metadata is the "data about data" which provides structured information about data in a controlled format that is readable by machine. To maximize reusability of data, you are encouraged to use a discipline-specific metadata standard to describe your research data whenever possible. You can make use of the following tools to find out the metadata standards commonly used in your field:
In this knowledge clip, we talk about metadata. Why do we need it and what different types of metadata exist? And how is metadata created and where can be stored? We also have a look at metadata in data repositories and provide a brief explanation about metadata standards.
Source: UGent Data Stewards. (2020, October 15). Knowledge clip: Metadata [Video]. YouTube. https://youtu.be/DW2T_cnqKPU
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