Frequently asked questions
Research data management is the term used to describe the management and curation of research data both throughout the research project and beyond. It usually includes the following elements:
- Advanced planning prior to the start of a research project.
- Implementation of good data management practices during the project.
- A long-term strategy for data preservation and sharing at the end of the project.
Research data management helps to:
- increase transparency of the research process
- enable validation of published results
- ensure that the data remain accurate and reliable
- reduce the risk of data loss and corruption
- facilitate data sharing and re-use
Research data includes all of the information that has been created and/or collected for research. This may include:
- spreadsheets
- documents
- questionnaires
- transcripts
- surveys
- photographs
- audio or video recordings
- simulations
It is important to remember that data can also be physical or print as well as digital, but irrespective of its format, all research data needs to be effectively managed throughout the research lifecycle.
Metadata, or data about data, is structured information that describes research data and is both machine and human readable. Metadata provides context to research data ensuring that it is findable, understandable, and usable. Examples of metadata could be descriptive metadata, such as title and author, or administrative metadata, such as access rights.
A data management plan (DMP) describes how the research data will be managed throughout the research lifecycle. This includes what research data will be created and/or collected, how it will be managed during the project, and how it will be shared and preserved at the end of the project. It should typically also describe any potential legal or ethical issues that need to be addressed. A DMP is a dynamic document, and should be updated as the project progresses and it should be reviewed regularly as your data needs change.
By planning ahead, a DMP can offer help to identify possible tasks and problems that need to be planned for in advance of the project. This will ultimately save time and resources in the long run. Writing and maintaining a DMP ensures compliance with research funders and the University of Exeter policy, which requires all research projects to have a DMP.
ORE (Open Research Exeter) is the University's institutional repository, and as such it is a showcase for the research outputs of the University. All research outputs (eg, journal articles, conference proceedings, datasets, theses etc.) can be deposited into ORE for long-term preservation, and all items in ORE are highly discoverable.
A DOI or Digital Object Identifier is a persistent identifier that is commonly used in academia. A DOI is an identifier, and not a location, so that even if the website hosting the dataset changes, the DOI does not.
All datasets deposited into ORE are allocated a unique DOI, that can be use to link-to and cite the dataset, and should be included in the data access statement.
ORCID maintains an open registry of unique researcher identifiers and a transparent method of linking research outputs and activities to these identifiers. An ORCID iD is a unique 16-digit persistent researcher identifier that distinguishes you from every other researcher, and your ORCID iD will belong to you throughout your scholarly career. Further details about ORCID iDs can be found on the Research Toolkit. ORCID
A data access statement, also known as a data availability statement, is a short statement included in research publications describing where and how any underlying research data may be accessed, ideally including a link to the data using a persistent identifier. The data access statement should also explain the terms on which the data are available, and if the data are not publicly available, then the data access statement should explain why this is the case.
For data that are publicly available in ORE, a typical data access statement would be "The research data supporting this publication are openly available from the University of Exeter's institutional repository at: [insert DOI]"
Datasets associated with journal articles should be deposited into ORE once the corresponding article has been accepted for publication. Once the dataset has been approved into ORE, it will be allocated a DOI. This DOI should be included in a data access statement that should be added to the article during the proofs-checking stage of production.
Datasets that are not associated with a research publication can be deposited anytime.
Like journal articles, datasets should be deposited into ORE via Symplectic. Further details on depositing datasets via Symplectic can be found in the Depositing A Dataset Into ORE Guide. Note that in Symplectic, you will need to create a separate record for the journal article and the dataset.
If your dataset is ≥2GB in size, you will not be able to deposit it via Symplectic and will need to use the ORE Deposit Tool. Guidance on using this tool can be found in the ORE Deposit Tool User Guide
Please contact the Research Data Management team if you require any assistance with depositing your data into ORE.
No. If you have deposited your data in an external repository, you do not need to deposit it into ORE. However, you should still create a record of it in Symplectic and include a link to the data hosted elsewhere. See the Registering A Dataset In Symplectic Guide for more information on this process.
Although GitHub is extremely useful and widely used for sharing software, it does not meet the requirements for long-term preservation. Therefore, using GitHub to manage your code during the project is fine, but to preserve and share your code, you should use a data repository. The Zenodo repository provides a GitHub plug-in that enables you to share your code, allowing you to published specific versions, while also assigning a DOI. Full guidance can be found on the GitHub guides.
Yes, all post-graduate research students must ensure that their research data are preserved for the long-term by depositing into ORE.
Datasets associated with journal articles should be deposited into ORE once the corresponding article has been accepted for publication. Once the dataset has been approved into ORE, it will be allocated a DOI. This DOI should be included in a data access statement that should be added to the article during the proofs-checking stage of production.
Data associated with your thesis should also be deposited into ORE. After all corrections required by your examiners have been implemented, and once you have been recommended for award by your Board of Examiners, you should deposit your thesis data to ORE. Once the dataset has been approved into ORE, it will be allocated a DOI that should then be included in the final version of your thesis that should also be submitted to ORE.