Some examples of research data include:
Research data does not include references, literature reviews, or the end product of the research undertaken such as a published article.
The Australian National Data Service (ANDS) explains more about: What is research data?
Grant and funding bodies require research data to be managed through its lifecycle. You may need to provide information about the data or the data itself, for example, some journals require it or you may want to patent an invention.
Creating a research data management plan at the start of the research project is the simplest way to save time in the collection, description, analysis, and reuse of the data. Effective management and documentation of research data means you can verify your research results, replicate the research, and provide access to data.
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.