Open data is data that is:
See the Library Guide Research data management for more information.
Who benefits from open data? Everyone! Open data supports:
The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015, and have since received worldwide recognition by various organisations, including FORCE11, National Institutes of Health (NIH), and the European Commission, as a useful framework for thinking about sharing data in a way that will enable maximum use and reuse. They are a way of thinking about getting the most out of your research data, and its place in the wider researcher community.
Can your data be found if someone is looking for it? Does it have a DOI or a Handle? Does it have rich metadata? Is it discoverable through a research portal or a repository?
Does your data utilise a standardised protocol? Your data does not necessarily have to be "open" - there are sometimes good reasons why data cannot be made open, i.e. privacy concerns, national security, or commercial interests - but if it is not there should be clarity and transparency around the conditions governing access and reuse.
To be interoperable the data will need to use community agreed formats, language, and vocabularies. Will someone who finds your data be able to meaningfully reuse it, and build or reproduce your work? The metadata you use will also need to use community agreed standards and vocabularies, and contain links to related information using identifiers.
Reusable data should maintain its initial richness. For example, it should not be diminished for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable licence and provenance information on how the data was formed. It should also have discipline-specific data and metadata standards to give it rich contextual information that will allow for reuse.
The CARE Principles for Indigenous Data Governance guide appropriate use and reuse of Indigenous data. This set of principles indicates the significant and crucial role of data in advancing Indigenous innovation and self-determination.
Data ecosystems should be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.
Indigenous Peoples' rights and interests in Indigenous data must be recognised and their authority to control such data should be empowered. Indigenous data governance enables Indigenous Peoples to determine how they are represented within data.
Those working with Indigenous data have a responsibility to share how this data is used to support Indigenous Peoples' self-determination and collective benefit.
Indigenous Peoples' rights and wellbeing should be the primary concern at all stages of the data life cycle.
To be made open and FAIR, data should be deposited in a data repository. This is a service that exists to preserve and provide access to research data, and is a future-proofed vehicle for ensuring that data remain accessible and usable over the long-term. Deposit in a data repository is preferable to sharing data as supplementary files alongside a published article, or via cloud-based file storage services, or maintaining data in private storage and sharing on request only.
A data repository should not be confused with cloud-based services that provide file storage and sharing, such as Google Drive or the Open Science Framework. A data repository performs a number of specific functions:
It may be most strategic for you to publish your data in a discipline-specific data repository, where it will be found by other researchers in your field. You may already be aware of prominent data repositories in your area of study. Your colleagues or your supervisor might also be able to point you towards suitable repositories. Another method of finding suitable discipline-specific repositories is by consulting a registry of data repositories such as:
There are also several general repositories where you can create a free account and deposit research data from any discipline. These include:
You may also want to promote your data by publishing a data paper in a data journal. Data papers provide an opportunity for you to describe your dataset in detail and have your work peer-reviewed. Here are some methods of finding data journals:
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