Metadata helps researchers understand the content, context, and structure of the dataset. It provides details about variables, units of measurement, data sources, and data collection methods.
As interdisciplinary research becomes more common, metadata becomes even more critical when datasets from various sources may be combined and analyzed together. It helps researchers from different fields understand and use data from diverse disciplines.
Metadata can include information about data ownership, licensing, and ethical considerations. Researchers can determine if they have the right to use and share the data based on metadata disclosures and allows them to cite your dataset accurately, giving you credit for your work and contributions to the field.
Documenting your data at the very beginning of your research project and incorporating changes as your project progresses ensures accuracy and completeness. Doing so will make the process much easier, as constructing metadata at the end of the project, will be painful and important details may have been lost or forgotten.
To help others understand your metadata and thus make your data comprehensible and usable, it is good practice to include a README.txt file and/or Data Dictionary with your data. More information about these documents is detailed below. In addition, Stanford University has some excellent information about Metadata and the site is referenced below, as well.
We will continue to add to guidance and tools for creating metadata as it becomes available.
- recommended by a data policy from a journal, journal publisher, or funder.
- actively maintained by a representative of the resource.
- active and ready for use.
Common Data Elements
The README File
- Keywords: Terms or phrases that describe the subject, domain, and/or content of the data.
- Persistent Identifiers (PIDs): Unique identifiers, such as: ORCID ids, DOI (Digital Object Identifier), etc.
- Naming Conventions: Standards used to organize and identify folders and files and for version control.
- Data Ownership: Details regarding the creator, ownership/source(s), and rights associated with the data.
- Data Content/Quality: Information on data validation, anomalies, accuracy, precision, and completeness.
- Time Intervals: Information about the time resolution and frequency of data collection or timestamps indicating when data was collected or recorded.
The Data Dictionary
- Data Element Name: This is the name of the data element.
- Definition/Description: Describes the data element, its purpose and its context. e.g., weight in kilos, height in cm
- Data Type: This defines the type of data that can be stored in a field. E.g., text or numeric, date format
- Values and Anomalies: Variables used for a particular data element and deviations from standards, norms, or expected results.
- Data Structure/Groups: A group of data elements that describe a unit in the system and/or relationships between data elements.
From Stanford University: Creating Metadata for Scientific Research
Creating metadata manually can be a confusing and time-consuming task. Stanford University offers information about the process, including existing tools to assist researchers in automating the creation of Metadata.
We will update this page as we gain more knowledge on this topic.