Welcome to the Unisa Research Data Management LibGuide. This Guide will provide information on processes, procedures and policy with regards to research data management (RDM), as well as access to resources and tools that can support researchers in managing their data.
It will also serve as a space to pilot platforms for archiving the research data
Please send any suggestions and comments to the compiler of this guide.
Metadata may be described as data about data: they are the data that characterise or identify a file.
As soon as you are going to publish or archive your data, you will be asked to provide these data. Metadata normally contains information that describes the data and provides context to the data.
Read more at: Metadata in plain language
A number of metadata tools are available for the creation of metadata. Some of these tools help you to select controlled vocabularies to include in your documentation (see block below which covers documentation) while other combine that functionality with a fully supported metadata schema.
Example:
Dublin Core Metadata Generator
Please also see the DCC for a list of more metadata tools.
There are a number of metadata standards which address the needs of particular user communities. See more at What are Metadata Standards (DCC)
Examples
Good metadata is key for research data access and re-use, figuring out precisely what metadata to capture and how to capture it is a complex task. Fortunately, many academic disciplines have supported initiatives to formalise the metadata specifications the community deems to be required for data re-use.
This section provides links to information about disciplinary metadata standards, including profiles, tools to implement the standards, and use cases of data repositories currently implementing them. For those disciplines that have not yet settled on a metadata standard, and for those repositories that work with data across disciplines, the General Research Data section links to information about broader metadata standards that have been adapted to suit the needs of research data.
Attribution: DCC-Disciplinary Metadata
Please click on the infographic below for metadata standards for BIOLOGY
Please click on the infographic for metadatastands for the EARTH SCIENCES
Attribution: DCC-Disciplinary Metadata
Please click on the infographic for metastandards for the PHYSICAL SCIENCES
Attribution: DCC-Disciplinary Metadata
Please click on the infographics for metadata standards for the SOCIAL SCIENCES & HUMANITIES
Attribution: DCC-Disciplinary Metadata
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Please click on the infographic for metadata those disciplines that have not yet settled on a metadata standard, and for those repositories that work with data across disciplines.
The General Research Data section links to information about broader metadata standards that have been adapted to suit the needs of research data
Attribution: DCC-Disciplinary Metadata
Assigning metadata means describing a dataset in such a way that it can be read by computers, e.g. to facilitate the search function on the website of a data repository. Besides this, the dataset is accompanied by documentation, i.e. information meant to be read by humans.
The key distinction between metadata and documentation is that metadata, in the standard sense of "data about data," formally describes various key attributes of each data element or collection of elements, while documentation makes reference to data in the context of their use in specific systems, applications, settings. Documentation also includes ancillary materials (e.g., field notes) from which metadata can be derived.
Source: Digital Preservation Coalition (DCP) - Metadata and documentation
