Researchers devote a large amount of physical and intellectual effort to collect, manage, collate, and analyse their data before publishing their results. Many of these datasets have significant value beyond the usage for the original research, and sharing the data can be seen as beneficial in a number of ways:
Research integrity and reproducibility
Publishing research data and citing its location in published research papers allows other to replicate, validate or build upon your results thus improving the scientific record by encouraging scientific enquiry and debate. Openly sharing research data also encourages the improvement and validation of research methods and minimises the need for data re-collection.
Journal publisher requirements
A growing number of journal publishers require data that underpin research findings to be published in open access repositories when manuscripts are submitted.
Others who re-use your data and cite it in their own research help to raise interest in your research and increase your impact within your field and beyond. “Open” data leads to increased citations of the data itself, and of associated papers.
Data sharing may lead to new collaborations between data users and data creators. Sharing data can often lead to improvements such as corrections in the documentation, or combination or comparison of datasets leading to new information.
Data created for one research purpose may be re-invented or re-interpreted for future unrelated research and into contexts not currently envisaged. Data sharing and re-use across borders and disciplines can also promote innovation by potential new data users.
Preservation for your own future use
Some research data will be unique and cannot be replaced if destroyed or lost. By preparing your data for sharing with others, you will benefit by being able to identify, retrieve, and understand the data yourself after you have lost familiarity with it, perhaps several years hence. Sharing via a repository will mean that the repository will look after and preserve your data into the future, even after technology becomes obsolete.
Not all of the data you have collected will be suitable or appropriate for sharing. The following need to be considered:
Enabling others to re-use your data will mean planning for this from the start of your research project. You will need to think critically of how research data can be shared, what might limit or prohibit data sharing (e.g. consent forms, confidentiality concerns), and whether any steps can be taken to remove such limitations. Refer to our Checklist which will point you to key matters to consider in managing and sharing your data.
Deposit your data with a data repository / archive
There are many services available, depending on your research area. Each repository will have different policies and they may charge you to deposit your data. If you are planning to use one of these repositories it is important that you include such charges in your funding bid. In addition to depositing your data you will also need to include your supporting documentation and metadata in order to help others make sense of your data.
You will also need to use a data license to clearly indicate how you expect the data to be used.
Ideally your data should be desposited in a disciplinary specific repository to facilitate discoverability and preservation of the data.
If no disciplinary repository exists for your discipline consider depositing data in a multidisciplinary repository:
Irish repositories include:
“The intent of a data paper is to offer descriptive information on the related dataset(s) focusing on data collection, distinguishing features, access and potential reuse rather than on data processing and analysis.”
Newman Paul; Corke Peter (2009). "Data papers — peer reviewed publication of high quality data sets". International Journal of Robotics Research. 28 (5): 587–587. doi:10.1177/0278364909104283.
Below are some examples of Data Journals, however there are many more:
By SangyaPundir CC BY-SA 4.0 https://commons.wikimedia.org/wiki/File%3AFAIR_data_principles.jpg
The FAIR Data Principles are a set of community developed guiding principles for making data Findable, Accessible, Interoperable, and Re-usable. Below we list resources that will help you make your data FAIR.
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