Open research data is a practice that can be used by all disciplines.
Research data (aka research materials or research assets) is the evidence that underpins the answer to a research question and that can be used to validate findings.
Researchers of all disciplines use research data and research data can have many different forms (e.g. archival documents, audio files, images, transcripts, field notes, text corpus, statistics, models, spreadsheets, log files).
It is a good idea for you to open your research data.
- Boost the credibility of your research. Open data enables replication and validation of your research, which in turn boosts its credibility and robustness.
- Enhance the visibility of your work. Increase the discoverability of your research by linking your article and its related datasets.
- Increase the impact of your research. Those who make use of your data and cite it in their own research will help to increase your impact within your field and beyond it.
- Enable benefits for wider society. Open data improves not just public access to and involvement in research, but also public understanding of research and the value it provides.
- Support learning in your field. Open data supports learning and enables a deeper, richer understanding of the research topic – this is particularly useful in teaching, as students are able to interrogate raw research data for themselves.
- Improve transparency and reproducibility of research. Choosing open data accelerates the pace of research by reducing unnecessary experiments and enabling faster discovery. This streamlining of the research workflow reduces inefficiencies and supports reproducibility and transparency.
Do you have unanswered questions on opening your research data?
It can be the case that researchers are not against the principle of opening their research data but are hesitant with unanswered questions around competitive advantage, issues with re-use, sensitivity and confidentiality or IPR protection.
Another reality is that opening research data takes effort and it is a learning process. This can lead to perceived barriers in the way of making progress.
The Open Research Resources webpage hosts a PowerPoint 'Opening your research data is easier than you think' which seeks to address researcher concerns around open data and offer guidance for moving your open data journey forward.
How do you ensure that your research data is as open as possible?
The best practice recommendation for research data is to be as open and FAIR as possible, while accounting for ethical, commercial and privacy constraints with sensitive data or proprietary data. FAIR Data Principles represent a community-developed set of guidelines and best practices to ensure that data or any digital object are Findable, Accessible, Interoperable and Re-usable. The FAIR principles can seem science-focused but they are equally relevant to arts, humanities and social sciences research.
There are a number of things that you can do to ensure that your research data is as open and FAIR as possible. The best way to start is to:
- Upload to and share your data via a repository.
- Describe your data with as much detail as possible.
- Apply a Persistent Identifier e.g. DOI.
- Include a Data Access Statement in all publications. Guidance on strengthening your data access statement is available on the Ulster University Research Data Management Support webpage.
- Consider what can and will be shared under which conditions. Find out how you can adopt an open research approach if your research is sensitive and/or personal.
- Obtain participant consent and perform risk management. How you ask for consent has a great impact on the accessibility of your research data. Use informed consent to faciliate appropriate sharing of your research data.
- Use open standardised and common formats.
- Have consistent vocabulary.
- Think carefully about how you format and name your files.
- Apply common metadata standards. A metadata standard is a schema developed by a particular user community to enable the best possible description of a resource type for their needs.
- Consider permitted use.
- Apply an appropriate open licence.
- Add sufficient documentations and provenance information. A crucial part of making data user-friendly with long-lasting usability, is to ensure they can be understood and correctly used by other researchers. Documentation should provide the context for how your data was created, analyzed and stored.
How do you know which data repository you should use?
Any repository you use must be a certified or trusted digital repository: one which will provide reliable, long-term access to managed digital resources.
When you are choosing a data repository there are a number of checks you should make:
- Does my funder specify a repository that I should use? Some funders mandate that award holders submit research data in a specified repository e.g. NERC (NERC Data Centres), ESRC funds the UK Data Service and requires ESRC grant holders to submit their data here, Wellcome Trust maintains a list of approved data repositories.
- If my data is underpinning a journal publication are there publisher requirements on which repository I can choose? Some journals maintain a list of approved repositories they will accept, or have specific policies regarding data archiving you will need to comply with.
- Are there disciplinary specific repositories in my field? As a general rule your first choice should be a relevant disciplinary repository where there is one available. This repository is more likely to have the best metadata schema for your datasets and importantly, it will be where other researchers in your field expect to find data.
In the absence of a suitable external repository, Ulster academic and research staff, and PhD researchers, can use Ulster’s PURE Research Portal. Guidance on uploading datasets to PURE is available on the PURE Support Resources webpage.
If you are using any external repository (funder, disciplinary or generalist) to open your research data, we need to know about it! Send pure-support@ulster.ac.uk a DOI for any dataset you have published in an external repository, and the PURE support team will create a metadata record in PURE for you.
Would you like to hear of experiences of other researchers in sharing their research data?
The UK Reproducibility Network have produced a video of members of the network discussing their experiences of data sharing. Watch the video to see other researchers reflect on how they started doing open data, their motivations to share data, concerns about data sharing and addressing practicalities.
Open Licenses and Sharing your Research Data
Licenses are essential to enable data reuse. You need a licence to clearly state what others can do with your work, whether they must cite it, and how they can share derivative work.
We have developed a section specifically focused on what you need to know about licensing and copyright for open research. This section includes guides for choosing an Open Data Licence and applying an Open Licence to your research data.
Find further support on Research Data Management
Ulster's Research Data Management (RDM) website introduces research data management and highlights good practice for each stage of the research data lifecycle. You will find a wide range of supportive information on the RDM webpages including information on:
- Funder Requirements for research data. Most research funders (research councils, charities and foundations) have introduced policies on Research Data Management. The general expectation is that data from publicly funded research projects should be made openly available with as few restrictions as possible. Ulster's Research Data Management (RDM) website provides information and links to the data policies of some of Ulster's key funders.
- Guidance for creating a Data Management Plan. A Data Management Plan (DMP) is a written document which describes the data that will be collected or generated during a research project and sets out a detailed plan for how the data will be managed throughout the project and what will happen to it after the project completes. If you think critically and carefully through a Data Management Plan it becomes a quality assurance statement for your research data in the long term. Signposts to DMP templates and good practice pointers are available within RDM Planning before commencing a funding application.
