They might be! Your data can be valuable not only to researchers within your field but also to researchers from other disciplines who can apply new methods to analyse it or merge it with other data to reach new insights. It is difficult to predict which data will be essential for future research.
This approach not only complicates access to data for users but also burdens the authors who has to manage the requests individually. In addition, there is a risk that the researcher might lose their data due to hardware failure or human error, or if a longer period has passed since the publication of the article, the researcher might not remember which file was used for the data analysis.
It is important to manage your data carefully throughout the research project. Making data management a regular part of your research practice significantly reduces the time and financial demands of preparing your data for publication. Data management and sharing costs are usually considered eligible expenses in grant applications.
To minimise the risk of misinterpretation, make sure that your data are accurately described. The documentation should clearly explain what different variables represent, when, on what population and how the data were created, and should provide other information that helps users interpret your data.
Research data are typically shared along with the publication they are related to. If you plan to conduct further research on the same data or if it is an extensive dataset, consider preregistering your research project or publishing the data with a time embargo (i.e., the data become public after a predefined time period). Keep in mind that you have been working with your data for some time and undoubtedly understand them better than anyone else who might want to use them. Last but not least, if you publish your data independently, they can also be cited.
The principle “as open as possible, as closed as necessary” applies to data sharing. If you wish to patent your research or commercially exploit the data, you can delay sharing, keep the data closed, or employ multiple licensing. For instance, you could provide your data freely for research purposes and for commercial use at a fee.
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