There can be very good reasons for sharing research data:
Funders will require that you do so.
Sharing data reduces redundancy and risk of data loss: others may find uses for your data beyond your original intention. The datasets they produce may become useful to you at some point in the future
Data sharing increases efficiency in the research process (once collected, the same cluster of data does not need to be collected again)
Access to the data means the research process can be replicated and validated
Data is a potential output and another string to your bow as a researcher. Data can be cited in the same way as publications. There is an increasing popularity of data papers which describe the datasets, rationale, and methodology without offering analysis or conclusion. These can also be cited in the same way as academic articles.
Data sharing fosters collaboration between researchers, giving rise to new projects and new discoveries
By sharing datasets, a researcher and an institution can acquire a reputation for producing good quality datasets. However, researchers can have legitimate concerns about sharing data. Many consider it a personal resource which fuels their publications. A researcher may not trust others to use the data appropriately, there can be a lack of time to develop metadata around the dataset and there can be concerns about privacy and anonymity, especially in relation to human subjects. But researchers have always shared their data and more and more are doing so.
Remember data needs to be accompanied by explanatory documentation – called ‘metadata’ - to be meaningful. Metadata provides context and the provenance of the data. It will provide an explanation of the methodology and instructions on how to replicate the process. This will be easily done if the data has been managed with a plan throughout the research process. When it comes to sharing your data, a Readme File is essential. Please see Guidelines for Creating a Readme File here..
This work is licensed under CC BY-NC-SA 4.0
