Documentation and metadata refer to the detailed information that describes and contextualizes data, ensuring its usability and comprehension.
Data documentation provides all the information required to discover, interpret, understand, access, and reuse the data. It includes protocols, methodologies, and data collection processes, providing a comprehensive understanding of the research. In contrast, metadata consists of structured information such as titles, dates, authors, and keywords that facilitate data discovery and retrieval. Together, they enhance data transparency, reproducibility, and sharing, enabling researchers to effectively manage, interpret, and utilize data throughout the research lifecycle.
Metadata
Metadata provides context and information about the data set, including details such as the creator, date of creation, file format, and any relevant methodology. Proper and comprehensive metadata ensures that data can be understood, interpreted, and reused by others in the future. Generally, it includes descriptive, structural, and administrative information.
| Type | Description | Examples |
| Descriptive Metadata | Provides information about the content and context of the data. It helps users understand what the data is about, who created it, and when it was created |
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| Structural Metadata | Describes the organization and relationships within a data set. It provides information on how data is structured and how different components of the data relate to each other |
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| Administrative Metadata | Provides information needed to manage a data set. This type of metadata ensures that data is properly maintained and accessible over time. |
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Some funders require the metadata standards to be detailed in the Data Management Plan (DMP), while others stipulate that associated data from funded projects must be shared openly and described with comprehensive metadata that follows best practices of the respective discipline. Researchers should carefully observe the metadata requirements specified by their funders.
Below are some useful resources for you to explore metadata schema in your research areas:
For enquiries, please contact the Library's Research Data Management Services of the Research Support and Scholarly Communication Section at lbrdms@cityu.edu.hk
Documentation of metadata
Data Dictionary
A data dictionary is a detailed description of the data within a dataset. The data dictionary helps users understand the variables, their definitions, data types, and any specific coding or classification used.
An example: Survey responses
| Variable Name | Description | Data Type |
| Respondent ID | Unique identifier for each respondent | Integer |
| Age | Age of the respondent | String |
| Gender | Gender of the respondent | String |
| Score | Customer satisfaction score | Integer |
Readme file
Similar to a data dictionary, a README file is a text document that provides an overview of the dataset, including essential information about its purpose, structure, and usage. It typically includes details on how to navigate the dataset, any pre-processing steps, and instructions for using the data.
Useful guidelines of creating a Readme file
For enquiries, please contact the Library's Research Data Management Services of the Research Support and Scholarly Communication Section at lbrdms@cityu.edu.hk
