Storage deals with how you can safely store your research data while you are working on it. Archiving deals with storing data after your project is over, for scientific integrity. In some cases, you will want to share your data publicly for reuse after your project is over. This is not common for students and if it applies to you, this should be discussed with your supervisor in detail.
Safe storage and sharing is important, whether you’re working with personal data or not, because it prevents loss of data and data leaks. The following options are considered safe to use and recommended by Radboud University:
If your storage solution has no built-in sharing solution, you can use SURFfilesender. Enable file encryption in case it is absolutely necessary to share personal data through SURFfilesender. Sharing personal data should be avoided in general.
Do NOT use:
Why should you archive?
Once your research project is completed, it is important to archive your data for the sake of scientific integrity, making it verifiable for others (e.g., for your supervisor or during audits). These datasets are not made public or shared with other researchers, but are merely accessible to people such as your supervisor.
Once you have archived your data, remove all other copies of the data you have stored elsewhere, for example on your local drive on a personal computer.
What should be archived?
You should archive all data that are relevant and necessary for an outsider to be able to reproduce your analysis and conclusions. If you have any doubts about what to archive, you should discuss this with your supervisor.
It is good practice to include documentation with your data. This documentation explains your data and makes sure that your dataset is still understandable in a few years from now. The files that should ideally be included in a dataset are:
NB: Personal data which you did not need for your conclusions should be deleted as soon as possible (e.g., administrative data such as email addresses used to contact participants) and should not be archived in order to protect your participants’ privacy.
Where to archive?
Check with your supervisor or teacher to see where you should archive your data.
NB: Data from RadboudUMC cannot be archived in RIS for Students nor in a workgroup folder.
Sharing data publicly is not standard procedure for student projects. If, however, your research gets published and/or your supervisor and you think that your data could be valuable to others for reuse, you should discuss the following points with your supervisor.
A dataset that you share for reuse must not contain any personal data (unless this is required for a journal publication and you got specific consent from participants to do so and if your local ethics committee approved this). Thus, a dataset that you share publicly will often be different from the dataset you archived for scientific integrity. For example, you usually have to archive raw data for scientific integrity. However, you are often not allowed to share these data publicly. For example, when collecting audio recordings of participants, you will archive these for scientific integrity. However, if you want to share your data publicly for reuse, you will very likely only be able to share an anonymous transcript of the audio and not the audio files themselves.
Dataset title: Good arguments or a charming narrator? Exploring a text’s persuasiveness through eye-tracking
Student: Sanne Huisman (s123456)
First supervisor: dr. Lisa Begeleider
Second reader: dr. Ton Lezer
This dataset contains all relevant data files for the thesis Good arguments or a charming narrator? Exploring a text’s persuasiveness through eye-tracking, written by Sanne Huisman to obtain the degree of Bachelor of Arts and conclude the bachelor’s programme International Business Communication at Radboud University. This research was conducted at the CLS Lab in the spring of 2019 and supervised by dr. Lisa Begeleider and dr. Ton Lezer.
The goal of this thesis was to explore the persuasiveness of a text as a function of the quality of the presented arguments as well as the likability of the person making these arguments. While persuasiveness is often measured with questionnaires, we explored whether persuasiveness is also reflected in eye movements. A total of 78 participants took part in this study.
This dataset contains a total of 8 files as well as two zip folders:
McCroskey, J. C., & Teven, J. J. (1999). Goodwill: A reexamination of the construct and its measurement. Communications Monographs, 66(1), 90-103. doi: https://doi.org/10.1080/03637759909376464