In 2016, a community of scholars, librarians, archivists, publishers and research funders called Force11 developed the FAIR guiding principles to aid in the discovery and reuse of research data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. Data that meet these principles are more optimal for reuse and discoverability, and in turn, increase your research’s exposure. Here’s how your data becomes FAIR when it’s on ZivaHub | Open Data UCT:
Every public item on ZivaHub is assigned a doi, which is a persistent identifier for your data.
Every item published on ZivaHub is marked for indexing in Google Scholar.
ZivaHub has a set of standard metadata fields (Title, Author, Description, etc.), but we can also set up subject-specific metadata fields to enhance the documentation of your data.
All data on ZivaHub are openly available: no need to create an account to view/download.
Items can be deposited under embargo or confidentially, but metadata is always accessible.
Data can be pushed into and pulled out of ZivaHub using Figshare’s open API.
Data on ZivaHub are documented using the ‘Fields of Research’ metadata schema.
All data published on ZivaHub are assigned a license, documenting the author’s requirements for reuse. The data can be ‘as open as possible, and as closed as necessary.’
Standard and custom metadata fields ensure your data are as thoroughly documented for reuse as possible. Furthermore, the DLS department at UCT Libraries reviews each deposit, ensuring that your metadata is of a high standard.
Ethics: Why you should seek consent | Anonymisation | GDPR | POPIA
FAIR: FAIR Principles | Increasing your research's exposure using the FAIR data principles
File formats: Recommended file formats | Formatting your Research Outputs | Projects versus Collections
General: Figtionary | FAQs | Best practice for managing your outputs on Figshare | YouTube support videos
Licences: Information on licenses | ZivaHub licensing options | Choose a License: Choose an open-source license
README files: DCC’s list of disciplinary metadata standards | 4TU | Cornell University
Open Collaboration: https://opensciencemooc.eu/
Open Data: Open Data in a Big Data World | 'The Turing Way' - A handbook for reproducible data science